This PDF file contains the front matter associated with SPIE Proceedings Volume 13402, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Building change detection (CD) based on deep learning is an important means of remote sensing building change detection. However, this method couples feature extraction and feature fusion comparison into a whole, which weakens the feature extraction ability for remotely sensed data under different time phases, resulting in a large number of errors and omissions in building CD. To address these problems, we propose a two-stage task mechanism network (TSTM-Net) for building CD. This method divides the networks into two stages of tasks. In the first stage, the bottom-level features from different temporal phases are directly connected for feature fusion and comparison, guiding the learning of features across different temporal phases and obtaining initial features. In the second stage, features of different scales in different temporal phases are compared and learned through multiscale residual fusion to achieve feature optimization and building change information mining in different temporal phases and complete the building change information detection. In addition, to better complete network training, we introduced Bayesian weighted loss functions in the two-stage task mechanism to automatically adjust loss function weights for different tasks. Through comparative analysis with different methods, such as Changer and MDFFNet, in the WHU-CD and GZ-CD datasets, we see that compared to other methods, our method can successfully detect more complete building change information and reduce omissions of small-sized building changes.

Landslide susceptibility maps (LSM) assess the geospatial distribution of potential landslide hazards by analyzing the intrinsic and extrinsic factors affecting landslides, and provide decision support for urban construction planning and landslide hazard prevention. In order to obtain a more accurate LSM, we combine the information model (I) and random forest (RF) methods. First, we combined 41,965 landslide data records and eight types of basic environmental factors (digital elevation model (DEM), slope, aspect, lithology, fault distance, normalized difference vegetation index (NDVI), annual precipitation, and land cover type) in southern China, and calculated the informativeness values (IV) of each factor by the I model, and then used these IV as the input variables of the RF. Subsequently, we divided the landslides and randomly selected non-landslide samples as training set and test set, predict landslide susceptibility using the trained model and make graphs. Finally, evaluate the I-RF model by using indicators such as accuracy (ACC) and area under the curve (AUC). The results show that: 1. The ACC of the I-RF model is 0.803 and the AUC is 0.894, which is a significant improvement over the I model alone; 2. The LSM obtained by the I-RF model show that the high and very high susceptibility zones account for 21.92% and 3.77% of the area, respectively, but contain 56.01% and 26.80% of the landslide hazards.

In this study, the meteorological fields for 2001, 2010, and 2022 were simulated using the WRF (Weather Research and Forecasting) model, based on land use data from Chengdu and its neighboring satellite cities. The purpose is to uncover the effects of urbanization on extreme high-temperature weather and the urban heat island phenomenon. It was found that urbanization notably elevates temperatures in Chengdu’s central and new urban areas, with a significant increase in 2022, whereas areas like Longquan Mountain, unaffected by urbanization, experienced minimal temperature changes. The urban heat island effect varies throughout the day, emerging in the morning, peaking at noon, and remaining evident at night due to the thermal inertia of urban structures. The transformation in urban land cover and usage patterns directly influences the distribution of urban temperatures. The study emphasizes the importance of green space conservation, building material selection, and the optimization of urban layouts in urban planning to alleviate the heat island effect. This study contributes scientific insights into climate change amid global urbanization and proposes strategies for urban planning and climate adaptation.

In order to solve the problems of rich information of ground objects, complex environment, unclear target segmentation and incorrect target classification caused by inconsistent size and uneven light in the shooting process, a remote sensing image segmentation algorithm based on improved TransUnet was proposed. Firstly, the empty convolutional space pyramid pooling is integrated into the feature coding downsampling stage, so that the network can better capture the feature information of different scales, Secondly, Depthwise Separable Convolution is added in the feature decoding and upsampling stages of each layer to reduce the number of model parameters and increase the spatial dimension information and depth dimension information. Finally, the loss function is optimized to make the model obtain better accuracy on the dataset than before. According to the experimental results, the mIoU value and F1-socre value of AD-TransUnet reach 52.3% and 64.1% on the LoveDA dataset, respectively, which are 1.9% and 2.1% higher than those of TransUnet. Then, The results show that the AD-TransUnet model achieves semantic segmentation of remote sensing images with better accuracy.

Under the backdrop of global warming, extreme precipitation events, especially severe rainstorms, are becoming increasingly frequent and severe, making them one of the most significant meteorological disasters worldwide. In this study, we numerically simulated a historical heavy precipitation process in North China during the summer of 2023 using the WRF model. The simulation used NCEP's (National Centers for Environmental Prediction) GFS(the Global Forecast System) datas as the initial field and compared the effectiveness of three different cumulus convection parameterization schemes: the Tiedtke Scheme, Kain-Fritsch Scheme, and Betts-Miller-Janjic Scheme. By contrasting the simulation outcomes with ERA5 (the Fifth Generation of EC-MWF Atmospheric Reanalyses of the Global Climate) reanalysis remote sensing datas, we assessed the influence of these schemes on total precipitation, radar reflectivity, and the wind field at 850 hPa. The Tiedtke scheme, when used within the D01 domain, simulates a uniform and smooth precipitation distribution with concentrated high-intensity areas. Expanding to nested domains (D01, D02, D03) increases the strength and distribution of precipitation centers. This scheme generally predicts a flatter spatial distribution compared to the ERA5 data. In contrast, the Kain-Fritsch scheme shows stronger spatial variability and more localized intense precipitation, particularly in areas of strong convection, with scattered but intense precipitation centers in the D01 domain that become more pronounced in nested domains. The Betts-Miller-Janjic scheme produces precipitation patterns with intensities between those of Tiedtke and Kain-Fritsch, with some concentrated and some scattered precipitation centers. Its performance shows minimal changes across different domain configurations, and it demonstrates significant differences in precipitation in drier areas when compared to ERA5 data.

Wind turbines and high-voltage transmission towers, as critical energy facilities, pose certain challenges in detection under extreme weather conditions. To enhance the accuracy of target identification for these two facilities, this study designed an experiment using the YOLOv5-CBAM model, where a mixed dataset of wind turbines and high-voltage transmission towers was trained under this model to create a joint model for combined recognition. Additionally, single models were trained using individual datasets for wind turbines and high-voltage transmission towers to perform independent identifications, the experimental results indicate a clear advantage in accuracy for the independent identification method. Specifically, in the joint model, the accuracy rates for wind turbines and high-voltage transmission towers were 92.1% and 74.1%, respectively, while in the single models, these rates increased to 99% and 92.3%. This improvement is significant for enhancing the performance of automatic monitoring of energy facilities in complex environments.

The signal processing technology of Compressive Sensing (CS) overturns the limitation of the traditional sensing method that only high sampling rate can achieve high resolution. The compressive sensing method saves internal storage and resources of information processing due to the small amount of data processed. Applying CS to spaceborne Synthetic Aperture Radar (SAR) system can effectively reduce the pressure of hardware platform. Sparse representation of SAR image is a prerequisite for CS application in remote sensing SAR imagery, which is analyzed quantitatively under different orthogonal bases and representation approach. The effect analysis is measured by the evaluation indexes such as peak signal-to-noise ratio, structural similarity and edge retention index. The simulation results show that the effect of sparse representation can meet the quality and structure requirements of SAR images in most cases. The conclusion verifies that SAR images satisfy CS requirement for sparsity, which supports the further study of CS reconstruction application in spaceborne SAR image.

Vegetation, as the most sensitive component of the natural geographical environment, can reveal the degree of pollution in the ecological environment of mining areas. Long-term antimony mining has led to severe environmental degradation in Lengshuijiang, Hunan Province. In this study, multi-year Landsat satellite images were utilized, based on the pixel binary model, to estimate vegetation coverage using the Normalized Difference Vegetation Index (NDVI). Through temporal and spatial comparative analysis, the evolving characteristics of the impact of antimony mining on the ecological environment were revealed. Results indicate that vegetation coverage gradually decreases with the increase in pollution level, namely: vegetation coverage in heavily polluted areas < moderately polluted areas < lightly polluted areas < clean areas. Moreover, from a temporal perspective, overall vegetation coverage was poor during 2006-2009, especially in heavily polluted areas, which experienced a very low level of vegetation coverage due to the pollution from mining activities. After 2009, with the implementation of mining restoration projects, overall vegetation coverage gradually returned to the level of 1990. This outcome aligns with previous field studies but at a reduced cost. This method offers valuable insights for monitoring pollution, assessing ecological restoration effectiveness, land management, and future planning in Lengshuijiang and similar mining regions.

The increasingly abundant satellite resources provide a foundation for the application of remote sensing big data in more spatial scales and business scenarios. However, the application of remote sensing big data in provincial-level natural resource management still faces significant challenges. Especially, different scenarios have different requirements for the acquisition, aggregation, processing, and governance of remote sensing data, and it is necessary to comprehensively consider factors such as requirements, scenario, and multi-source data. This paper takes Hainan Province in China as an example, and focuses on the construction of remote sensing big data in Hainan. At the same time, combining with the analysis of its own business requirements in natural resource management, it analyzes the key application scenarios such as natural resource investigation and dynamic monitoring, cultivated land protection and dynamic inspection, national spatial planning and supervision implementation, ecological environment protection, and sea and island management, uses different resolution combination modes with matching different scales from the whole province, land areas, key areas, land-sea interface, to key objects, designs a scenario driven provincial-level remote sensing application business mode, discusses its work flow and system architecture, proposes a satellite remote sensing shared service platform, and implements a "one-stop" service for provincial-level natural resource management based on the RSBD key technologies for local governance, such as data coordinated acquisition and optimization utilization, dynamic perception remote sensing monitoring, comprehensive data analysis, and data sharing. Finally, it also presents the achievements of the scenario-driven remote sensing big data sharing and service platform in the application of natural resource management in Hainan Province, and discusses the key directions of subsequent researches in this paper.

Urbanization leads to the encroachment on ecological land in urban fringe areas, resulting in a decline in ecosystem quality. This study combines the Ecosystem Quality Index (EQI) and land cover change data to analyze the spatial changes in ecosystem quality in Beijing from 2013 to 2022 and its response to changes in land cover types. The study revealed several key findings: 1) The EQI in Beijing increased from 74.72 to 77.89, with the most rapid growth rates observed in ecological conservation areas and central urban districts, at 4.75% and 4.8%, respectively; 2) The increase in forest area within Beijing's ecological conservation areas has offset the negative impact on ecosystem quality caused by the expansion of impervious surfaces; 3) To further enhance ecosystem quality in Beijing, it is essential to strengthen ecological and environmental management in the plains area following construction. The findings indicate that ecological protection measures in Beijing have effectively mitigated the adverse effects of urbanization, providing valuable insights for territorial spatial planning and ecological environmental protection in other cities.

Capturing motion vehicle information from satellite videos is crucial for real-time traffic monitoring and emergency response. However, vehicles in satellite videos are small in size, lack detailed textural features and are easily obscured by complex backgrounds. Traditional frame differencing and background subtraction methods often lead to a high number of false positives and false negatives, while deep learning methods struggle to meet real-time processing requirements. To strike a balance between detection performance and processing efficiency, a real-time vehicle detection method combining hole features extracted from frame differencing and motion direction estimation is proposed. Initially, a multiframe image accumulation (MIA) strategy is employed to enhance the visibility of vehicle targets and suppress background noise. Subsequently, the hole features of moving vehicles are extracted by differencing adjacent accumulated images, leading to the creation of a hole model for coarse detection of moving vehicles. Then, by estimating the motion direction of vehicles and enforcing direction consistency constraints, hole matching of neighboring vehicles in motion is achieved to improve the detection accuracy. Finally, a novel region extraction algorithm that integrates target hole features and motion direction information is designed to effectively suppress false positives generated by background noise. This method exhibits superior detection performance on the VISO benchmark dataset, achieving a recognition accuracy of 89.5% while meeting real-time processing requirements with an average processing time of only 0.04 seconds per frame, ensuring both detection performance and processing efficiency.

Peanut is considered a crucial economic crop, and accurately identifying its planting areas is regarded as vital for ensuring food and oil security. This study utilizes ultra-high spatial resolution unmanned aerial vehicle (UAV) imagery and the random forest (RF) method to classify multispectral imagery. Three types of features are combined to identify peanut crop and verify accuracy. Results indicate that using only multispectral features yields unsatisfactory accuracy. Introducing texture features significantly improves classification accuracy, particularly for spectral features alone. However, accurately identifying crop pixels at field edges remains challenging due to noise influence. When UAV image spectral and texture features are employed for classification, the principal component analysis (PCA) method, combined with post-classification patch removal, proves to be the best strategy, achieving an overall classification accuracy of 98.17% and a Kappa coefficient of 0.97. The comprehensive peanut recognition accuracy reaches 90.39%.

As an important product and component of social development, buildings are closely related to various activities of human society. In this study, LEDNet network is used to extract buildings in order to solve the problems of imprecision edge extraction and misclassification. The encoder-decoder structure is adopted in this method. The encoder uses ResNet as the backbone network, channel split and shuffle operations are introduced into each residual block, and the attention pyramid network APN is adopted in the decoder. While reducing the complexity of the whole network and greatly reducing the computing cost, it can also maintain high segmentation accuracy. The evaluation indexes F1 and IoU on the WHU building dataset reached 93.54% and 87.86% respectively, and all evaluation indexes were higher than the other four comparison algorithms, which verified the advantages of the new method.

With the increasing number of planetary orbiter images, how to quickly select optimal images from the redundant data for planetary mapping tasks has become a critical issue. This paper proposes a planetary orbiter image selection method based on multi-constraints. In this method, orbiter image metadata and various constraints in photogrammetry mapping are combined, and a subset of images that satisfies mapping requirements from a large amount of redundant orbiter data is obtained through three parts: metadata-based selection in the preprocessing, images selection before feature matching, and stereo image pairs selection before dense matching. The experimental results indicate that the mean elevation deviation between the Digital Elevation Model (DEM) generated by the selected data subset and by the raw data is less than 1 meter, the DEM has 100% coverage ratio in the region of interest, and the data redundancy ratio is reduced by more than 500%. This method improves mapping efficiency while ensuring mapping accuracy.

Numerous earthquake rescue experiences indicate that obtaining the accessible roads in time after the destructive earthquakes is the cornerstone of the emergency rescue. Traditional methods, such as on-site surveys and manual interpretation of optical data, are time-consuming and don’t meet the requirement of emergency rescue. The rapid development of computer technology has promoted the application of the deep learning in the road extraction. In this study, we first used the high-spatial resolution optical image of Beijing to produce the road labels containing the road surface, road edges and road centerline. Then a network model connecting two CNNs in series is constructed. This network model is designed to extract road surface and road edges at same time, then the road centerline is calculated using the skeletonization method. This model achieved a Precision of 0.86 and a Recall of 0.91 on road surface segmentation; a ODS of 0.82 and an OIS of 0.84 on road edges and centerline extraction. Therefore, it means that building a pre-trained road extraction model that learning the local road characteristics can quickly and accurately extract the spatial distribution of accessible roads from optical images of areas attacked by the earthquake, and provide data support and technical reserves for the emergency rescue planning and rescue forces deployment.

Solar energy, an inexhaustible and environmentally friendly resource, has significant potential to reduce carbon emissions. This study aims to provide a comprehensive and accurate assessment of the potential for solar photovoltaic (PV) power generation in Ningxia, to provide a basis for targeted policy interventions. This study first evaluates the environmental factors of existing PV power plants in Ningxia to determine the best locations for PV systems. Then, the theoretical PV power generation potential of Ningxia is quantified based on physical modeling. Furthermore, the research delves into a comprehensive analysis of annual and seasonal variations in PV power generation from 2020 to 2023 in Ningxia. The results indicate that west-central Ningxia exhibits highly favorable conditions for photovoltaic (PV) construction owing to its advantageous topographic and climatic attributes. Analysis of inter-annual and seasonal fluctuations in PV power generation reveals a significant sensitivity to environmental variables, with solar radiation identified as the primary driver influencing PV output. These observations underline the critical role of solar irradiance in influencing the variability of PV power generation in the region. In conclusion, this research contributes valuable insights into the temporal dynamics and determinants of PV power generation potential in Ningxia, laying a foundation for strategic planning and sustainable energy development initiatives in the region and beyond.

Segmentation of scenes in remote sensing images represents a crucial and demanding area in the processing of remote sensing image data, merging the fields of computer vision and natural language processing. Traditional image segmentation methods often struggle with the complex spatial scales of remote sensing images and the issue of fuzzy boundaries in segmentation targets, resulting in suboptimal outcomes. To address these challenges, we introduce the Multimodal Information Interaction Network (IMNet), a novel method specifically designed to meet the unique needs of scene segmentation in remote sensing imagery. First, we design an Intra-Modal Information Mining Module (IMM) to effectively handle fine-grained details at multiple scales. Additionally, to better capture global information, we introduce a mechanism to compute double cross-attention between two parallel axial attentions. Our experimental results indicate that our method attains a mean Intersection over Union (mIOU) of 80.46% on our dataset, outperforming current state-of-the-art models and establishing a new standard in the performance of remote sensing image scene segmentation.

In the field of engineering surveying, it is necessary to measure discrete points on circular objects in order to obtain the coordinates of their centers. However, field measurements are often limited by environmental conditions, which result in uneven sampling of points and, consequently, deviations between the results of classical least-squares circle fitting algorithms and the actual situation. This paper improves upon the classical least-squares circle fitting algorithm by proposing an algebraic circle fitting algorithm. By combining simulation and statistical analysis, we analyze the fitting results under different distribution conditions for the collected data. Simulation results show that compared with the classical least squares circle fitting algorithm, the fitting accuracy (RMS) of the method in this paper is improved by 85.25% when sampling along small arcs. This indicates that the algorithm can perform local measurements in limited environments and accurately fit the circle center data, which will have good application prospects in fields such as engineering surveying.

As climate change and human activities exert their influence, significant changes have occurred in the phenology of vegetation on the Qinghai-Tibet Plateau. Identifying and studying the changes in vegetation phenology is an important foundational work for researching phenology and further investigating driving factors. Therefore, this study utilizes NDVI to invert the phenology of vegetation using three algorithms: dynamic threshold method, logistic function, and double logistic function. The phenology information is then identified and analyzed. Finally, Theil-Sen + Mann-Kendall algorithm is employed to analyze the spatiotemporal variation trends of phenology.

Individual tree detection is crucial for ecological analysis in forestry, which plays a key role in effectively constructing land use planning and protecting biodiversity. In this paper, Hartford County, Connecticut, USA, is selected as the research area. Mask-RCNN is used to extract individual trees in the study area, and then the local maximum method is applied for extracting individual trees in high-density areas. The study shows that multi-source remote sensing data can effectively improve the accuracy of individual tree extraction, and a total of 13,274,355 trees are detected in Hartford County. The aim of this study is to provide a more effective method of tree monitoring to help governments prioritize green space management on a super-urban scale.

Under the influence of climate change, it is crucial for humanity to address the challenges to agricultural production, particularly the disruptions in the supply of agricultural products. This study focuses on the state of Wisconsin and uses corn as the subject of research. We utilize the Near-Infrared Reflectance of vegetation (NIRv) index derived from the time series of Sentinel-2 and Landsat 8 images from 2017 to 2023 to assess the phenological diversity of corn. Additionally, we evaluate the stability of crop yield as an indicator of the agroecosystem's resilience to the impacts of climate change. The influence of corn phenological diversity on yield stability is quantified using the Random Forest Regression model. The results indicate that higher crop phenological diversity can mitigate the climatic stresses encountered during the crop growth process.

Utilizing deep learning for fire point detection is currently one of the most popular approaches in satellite remote sensing. However, even with appropriate models, the variable quality of fire point training data can significantly impact detection accuracy. Regarding this issue, the study introduces a sampling method for fire point data that combines VIIRS fire point product J1 with high spatial resolution Sentinel-2 data. It enhances the OTSU algorithm using Sentinel-2 data features to identify burned areas and performs spatio-temporal matching with fire point product J1, classifying and sampling through post-validation methods. The sampling method was validated and evaluated using fire point verification data obtained through on-site manual verification and visual interpretation. The results demonstrate that the proposed method can more accurately and efficiently sample true and false fire point data, generate a large number of high-quality samples, and provide data support for deep learning fire point detection models.

Temperature change is a complex atmospheric phenomenon. Even the ERA5-Land atmospheric reanalysis temperature dataset with the highest accuracy currently has errors with the actual observed temperature. This study captures the nonlinear relationship between ERA5-Land temperature and various geographic and climate data such as population density, land use type, DEM, air pressure, and relative humidity by constructing a LightGBM model. The experimental results show that compared with ERA5-Land, the constructed model has a Mean Absolute Error is reduced by 5% and Root Mean Squared Error is reduced by 13%, Mean Squared Error is reduced by 24%, Pearson correlation coefficient increased by 2%, R-squared is increased by 3%. A machine learning model with strong versatility and accurate prediction has been obtained, and the correction of daily temperature data in the Yellow River Basin from 2015 to 2019 has been realized. After the model training is completed, SHAP is used to analyze the impact of various feature data on the prediction of temperature by the machine learning model, sort them according to the degree of impact, and analyze the interaction between various feature values to study how different features affect the model prediction of temperature. The results show that in the process of model prediction, except for the ERA5-Land temperature data used for correction, the greatest impact on the model prediction is air pressure and DEM.

Timely and accurately acquiring spatial distribution information for winter wheat is crucial for formulating agricultural policies. However, the information extraction for traditional spatial distribution for winter wheat relies primarily on manual field surveys. This method is labour-intensive and subjective and less accurate. With that remote sensing technology continues to develop, new avenues for extracting distribution information for winter wheat have emerged. Nevertheless, previous studies often did not fully leverage the advantages of remote sensing data from multiple sources, resulting in lower extraction accuracy. To address this, this study focuses on Henan Province, where Sentinel-2 optical imagery and Sentinel-1 radar imagery. Then support vector machine and random forest algorithms were used to construct winter wheat extraction model. The results indicate that the extraction accuracy of Sentinel-2 optical imaging surpasses Sentinel-1 radar imaging, with an overall accuracy of 82.04%, the producer's accuracy for winter wheat reaches 86.25%, the accuracy of user is 85.19%, the Kappa coefficient is 0.770. Further integration within the Sentinel-2 optical imagery with Sentinel-1 radar imagery significantly enhances extraction's accuracy. The overall accuracy improves to 90.19%, the accuracy for producers increases to 96.25%, user's accuracy rises to 95.06%, and the Kappa coefficient increases to 0.874. Additionally, by comparing different machine learning algorithms, it is observed that the algorithm random forest consistently outperforms the algorithm support vector machine in extraction accuracy regardless of the dataset. In this way, this study provides valuable guidance for obtaining information on the spatial distribution of winter wheat in Henan Province.

Due to the shape, size and diversity of water bodies in SAR remote sensing images as well as the complexity of the scene, the problem of loss of fine water bodies often occurs in the water body segmentation task, which leads to the low accuracy of water body segmentation. Therefore, in this paper, we improve on the DeeplabV3+ network. The network first uses the lightweight MobileNetV2 backbone network to reduce the number of parameters and computation of the net-work and to increase the model training speed. Secondly, in the encoder phase, the dense empty space pyramid pooling (DenseASPP) module is used to obtain denser feature pyramids and larger sensory fields, and bar pooling branches are added to enable the network to capture long-distance dependencies more efficiently and to improve the segmentation accuracy of the network for water bodies. Finally, in the decoder stage, the Attention Gate module is introduced before the fusion of low-level features and high-level features to suppress irrelevant information in the image and highlight im-portant features of the target. The improved network is named DSA-DeepLabV3+ network. The results of experiments performed on the home-made dataset show that the segmentation accuracy of DSA-DeepLabV3+ is better than DeepLabV3+ and its comparison methods.

OSM road network data are mostly uploaded by non-professional volunteers, and the data quality is unknown, which hinders its wide application. Therefore, it is of great significance to evaluate OSM road network data objectively and accurately. Connectivity is an important attribute in road network topology, but there is no index to evaluate this attribute in the quality evaluation of road network data. Therefore, this paper proposes 'relative connectivity' as the quality evaluation index of road network data, and establishes a new multi-level road network data quality evaluation system based on the existing indicators, aiming to evaluate the quality of OSM road network data more comprehensively. In view of the shortcomings of most of the previous OSM road network data quality evaluation only considering the single-level and single-dimensional evaluation objects, the FAHP-CRITIC comprehensive evaluation model is proposed to combine the weights of the indicators to make the evaluation results more comprehensive and objective. Taking the OSM road network data of Taiwan Island as an example, this paper selects Map World as the reference data set for experimental verification. The results show that the quality of OSM road network data in more than 80% of Taiwan is better than that of Map World road network data. Therefore, Taiwan OSM road network data can be used as one of the updated data sources of basic geographic information.

Major nationwide monitoring projects usually involve the integration and analysis of a large number of satellite images. When counting the application area of large-scale remote sensing images covering the whole country, it is usually easy to take into account the overlapping coverage, invalid value coverage, and cloud coverage area, which makes the statistical results have redundancy problems. To address this problem, the paper proposes an optimal image screening model and a statistical method for remote sensing image application area based on graphical metadata. For the overlapping coverage regions. The optimal remote sensing image is selected through three factors: image spatial resolution, date difference between image photting time and a unified time point, and the proportion of effective coverage area of the image. The graphic metadata of polygon vector structure is used to record the spatial range and attribute information of each selected remote sensing image block, and based on which the actual application area of large-scale remote sensing images is calculated and counted. Experimental results showed that this method, through redundancy deduction, could accurately calculate the application area of remote sensing images at various spatial resolution levels and plotting time periods nationwide, which would provide data reference for overall planning and scientific decision-making of remote sensing image acquisition.

Salt marsh vegetation performs a vital role in wetland ecosystems. Accurate information on the distribution of salt marsh vegetation plays a crucial role in the sustainable management of wetland ecosystems. Extracting salt marsh vegetation with remote sensing is urgent and requisite for scientific control and management of wetland ecosystem. This study addressed to develop a method for extracting salt marsh vegetation based on multidimensional features from combined Landsat-8 and Sentinel-2 image stacks. Specifically, we explored the potential of using individual satellite and the integration of various sensors in extracting salt marsh vegetation. Multidimensional features obtained from the satellite images were used to evaluate the importance of different dimensional features. The results mainly indicated that multidimensional features synthetic image stacks achieved the best accuracy (overall accuracy (OA = 92.4%, kappa coefficient = 0.88). Methods with spatial feature incorporation show better performance in extracting sparse salt marsh vegetation, with the accuracy improved by 4.87% (OA = 87.23%, kappa coefficient = 0.78) comparing to the method with only spectral features (OA= 82.36%, kappa coefficient = 0.69). The 10m salt marsh vegetation map in 2015~2023 generated by the method enhanced both the accuracy and cost-effectiveness of salt marsh vegetation monitoring.

In this paper, the distribution of tidal gully ecosystems in the Yellow River Estuary, from 2017 to 2023 was analyzed, and the distribution, change, stability and correlation between tidal gullies and salt marsh vegetation and ecological restoration projects were analyzed. (1) The total length of tidal ditch in 2021 is 793.64km, which is slightly higher than that of 701.33km in 2017, and the total length of tidal ditch in 2023 is 919.86km, which is a significant upward trend compared with 2021. (2) The overall curvature in 2017, 2021 and 2023 is 1.19, 1.18 and 1.16, respectively, and the overall curvature is slightly curved and the degree of bending gradually decreases. In 2017, 2021, and 2023, the tidal channel density was 1.91km•km^-2, 2.16km•km^-2, and 2.50km•km^-2, respectively. (3) From 2017 to 2023, the instability of tidal gullies increased, with 47% of the main tidal gullies belonging to stable tidal trenches, 49% belonging to unstable tidal trenches, and 4% of tidal gullies being severely unstable tidal trenches. (4) The density and length of the tidal channel had the same development trend with the area of Spartina alterniflora. From 2021 to 2023, the disappearance of 13 main tidal gullies, the severe instability of 2 main tidal gullies, and the disappearance of a large number of small tidal gullies in areas II and III may be related to anthropogenic restoration activities. The increase in the instability of some of the main tidal ditches in the northeast and southern ecological restoration areas of the Yellow River Estuary, and the increase in tidal gullies in areas II and III may be related to natural factors such as tides.

The development of agriculture and animal husbandry, as well as the establishment of permanent shelters, enabled prehistoric humans to permanently settle on the harsh natural environment of the Qinghai Tibet Plateau. While macro-level studies on the development of agriculture and animal husbandry to promote human settlement on the plateau and the relationship between settlement distribution and the environment are relatively numerous and mature, studies on the establishment of permanent prehistoric human shelters in micro-environments in relation to the adaptations and impacts on the natural environment are relatively rare. In view of the fact that the ancient human site selection mainly considers the micro-environment, this paper extracts the remote sensing archaeological weak information of the Tawendariha site of the Nomhom culture and its surrounding environment based on the spatial data of DEM, worldview and geophysics, and analyzes it in comparison with the excavated Dalitaliha site of the same culture, in order to understand the formation mechanism of the prehistoric settlement building pattern on the Qinghai-Tibet Plateau from different perspectives, and to deeply understand the relationship between the prehistoric settlement pattern and the natural environment. It is found that the prehistoric people of the Nomhom culture relied mainly on water resources when establishing permanent shelters, and the special shape of the buildings was to prevent flooding.

In order to meet the monitoring needs of dynamic changes in housing under the background of rapid urbanization, a housing change detection algorithm that integrates deep learning and multi-scale features is proposed based on multi-source satellite remote sensing data. The study utilized an improved deep residual U-Net model, combined with Landsat 8 OLI, Sentinel-2 MSI, and Sentinel-1 SAR data, to achieve high-sensitivity detection of small-scale and subtle changes.

The Zhengzhou reach of the Yellow River is a typical meandering section with frequent changes in river morphology. In this paper, using satellite remote sensing technology and Landsat 8 satellite imagery data, the morphological changes of the Yellow River in Zhengzhou (Taohuayu - Huayuankou) in the lower reaches from 2013 to 2022 were quantitatively studied and analyzed. The overall water surface area of the study segment changes significantly (61.8 square kilometers), while the length remains stable. The variations in the area and length of the river segment exhibit a pattern of phased changes. Regarding the main meanders, the first and third meanders showed a trend of initial strengthening followed by weakening, while the second meander showed an upward trend. The intensity of the river's morphological changes followed a pattern of stability, followed by a period of drastic alteration, and then a return to stability. Overall, the tendency for the river segment in the study area to meander has diminished over the study period.

Remote sensing has proven to be an effective approach for acquiring information on regional and global vegetation canopy chlorophyll content (CCC). Existing remote sensing methods for estimating CCC often rely on vegetation indices constructed from different spectral bands. However, Sun-Induced Chlorophyll Fluorescence (SIF) emitted by chlorophyll is more sensitive to changes in chlorophyll content. This study explores the potential of SIF indices, specifically those obtained from the 771nm, 757nm, and 740nm bands of OCO-2 satellite products, to estimate vegetation canopy chlorophyll content. Correlations between SIF indices at each wavelength and CCC were investigated, and empirical models were established using both simple linear regression and multiple linear regression methods. The results indicate that the SIF indices (SIF₇₄₀, SIF₇₅₇, SIF₇₇₁) have high correlations with CCC values, with determination coefficients for the univariate regression equations being 0.5124, 0.5565, and 0.5684, respectively. The determination coefficient for the multivariate regression model considering all three SIF indices and CCC is 0.6549. Validation analysis using 185 independent samples, which were not part of the training set, confirms the high predictive accuracy of the multivariate diagnostic model based on SIF₇₄₀, SIF₇₅₇, and SIF₇₇₁ for CCC. This suggests that the inversion of vegetation canopy chlorophyll content can be well achieved based on SIF indices.

With the emergence of blind spot reconstruction networks in hyperspectral anomaly detection, a series of new challenges have arisen. The most significant issues include local anomaly identity mapping and inaccurate detection of large anomaly targets in deep learning networks. Therefore, we propose a busy point reconstruction network for hyperspectral anomaly detection based on multi-scale mask convolution. We creatively design a multi-scale mask convolution module that uses an inner window mask to isolate the center block and edge background for reconstruction. To further enhance space-spectral information interactivity, we introduce a joint space-spectral module. Additionally, to increase the network's focus on global low-rank backgrounds, we incorporate a block low-rank penalty to guide global low-rank attention. Finally, by considering the scale difference in detection performance between different mask convolutions, we develop a dynamic learnable fusion module that performs adaptive fusion of two mask convolution results for optimal detection performance on two real data sets.

Constructing ecological security pattern (ESP) is a crucial approach to guarantee the healthy operation of ecosystems and regional ecological security. Taking Chongqing for example, we conducted an evaluation of Chongqing's ecological significance. Subsequently, areas of high ecological importance and nature-protected were regarded as ecological sources. A resistance surface of representative mountain animals was constructed by screening 6 factors in terms of natural environmental conditions and the intensity of human activity interference. By calculating the cost of species movement between different ecological sources, the ecological corridors were determined to construct a comprehensive regional ESP. The results show that (1) The ecological sources area of Chongqing spans 30,751.11km², the proportion of area is 37.32%, predominantly enriched within the mountainous forested lands in the northeast and southeast of Chongqing; (2) A total of 138 ecological nodes and 73 ecological corridors were built, comprising 58 land corridors and 14 river corridors; (3) The ESP of Chongqing exhibits the characteristics of "three belts, four barriers, multiple corridors, and multiple nodes." The results can give decision support for improving ecological functions in Chongqing.

Aiming at the problem of complex construction scene with complicated change situations and the difficulty of identifying and extracting effective change information, based on multi-temporal LiDAR point cloud data, an octree structure optimization-based change detection method for construction scene is proposed. First, the octree index is constructed for the construction point cloud data of a large scene; then, the object surface continuity and internal connectivity constraints are added to find the continuous change point cloud with planar features, and the change entity detection for the construction scene is designed; finally, the experimental validation is carried out based on the designed change detection method using the collected multi-temporal construction scene point cloud data. The results show that the change detection algorithm optimized by octree structure can accomplish the change detection requirements of multi-temporal construction scenes.

Slope monitoring is the basis of slope management and an important means of ensuring slope safety. Deformation monitoring of slopes around hydropower station sites not only provides effective means for the safe and stable operation of hydropower stations, but also reduces the risk to the lives of local residents. In this paper, for the actual needs of slope monitoring in a hydropower station in Zhejiang Province, the deformation monitoring of the slope is implemented based on GNSS, achieving fully automatic monitoring and reducing the safety risks of manual inspection and monitoring. The slope health monitoring system is used to continuously, real-time, and online evaluate the stability and safety of the slope. Combined with the data monitored by the on-site equipment, the applicability of GNSS monitoring technology in the monitoring of complex slopes is analyzed, providing a reference for the monitoring of the same type of slopes.

This study focuses on a Tibetan Buddhist temple as the research subject and employs UAV oblique photography and SLAM technology for mapping and preserving ancient buildings. By acquiring oblique photography data of the historical structures, along with indoor and outdoor point cloud data, we integrate UAV oblique photography with ground laser point cloud fusion modeling to rectify missing image point clouds in the oblique photography model, resulting in a high-precision three-dimensional oblique model. Through precise measurement of feature points and side lengths of the ancient buildings, which are compared with the accuracy of the three-dimensional model achieved at centimeter-level precision, this study significantly contributes to the conservation of ancient architecture. The primary objective is to explore innovative technical approaches for proactive protection, architectural mapping, and restoration.

An improved aerosol retrieval method based on the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target algorithm was introduced to retrieve the aerosol properties over the Pearl River Delta (PRD) region. Empirical relationships between visible and shortwave infrared bands were built based on the regression analysis of MODIS surface reflectance products at a spatial resolution of 500m over the PRD region, so as to separate surface contributions from satellite-measured signals. In addition, local representative aerosol types for the PRD region were derived based on the statistics of ground-based Aerosol Robotic Network observations. The validation result showed that the newly derived 500m aerosol optical depth (AOD) product had a slight performance advantage over MODIS 10km and 3km aerosol products. It also had significantly better spatial coverage than the two MODIS aerosol products. The high spatial resolution aerosol products brought new information about distributions of aerosol sources, and they were much more suitable for the aerosol studies over the urban areas than the MODIS aerosol products at a coarse resolution.

Hyperspectral LiDAR (HSL) is a new type of active detection equipment, which can combine high-resolution spectral data and 3D spatial information into one system. However, the process of collecting data with HSL is susceptible to the effects of incident angle, which in turn affects the ability to monitor chlorophyll, requiring correction measures. In this paper, we analyzed the theoretical basis of using multiple band spectral ratios to overcome the influence of incident angles and proposed four types of multi-band spectral ratio indices for chlorophyll inversion. Next, HSL was used to collect leaf sample data at different angles and their SPAD values, the index forms were optimized with kernel functions, and index construction was completed based on correlation coefficients and variation coefficients. Finally, SPAD inversion was conducted at multiple angles using Partial Least Squares Regression (PLSR) as the prediction model. The results show that the average R² of inversion with the new spectral index in the public dataset (ANGERS) and HSL data are 0.908 and 0.916, RMSE are 6.041 and 3.318, which is better than conventional spectral indices. It indicates that the new multi-band spectral ratio index can correct the incident angle and improve the accuracy of chlorophyll inversion of HSL data.

Sound speed correction is a critical component of multibeam bathymetry. To address the issue of low system computational efficiency caused by the high number of sampling points in deep-sea sound speed profiles, a simplification method based on wavelet transform is proposed. Initially, the measured sound speed profiles are subjected to wavelet denoising and low-frequency coefficients obtained from wavelet decomposition are then used to construct the contour of the denoised sound speed profiles. Next, extreme values are extracted to simplify the denoised sound speed profiles. Finally, the constant gradient ray tracing method is utilized to quantitatively analyze the accuracy of ray tracing for the simplified sound speed profiles. The results indicate that the simplification rate of the sound speed profiles obtained through this method exceeds 98%, with the depth errors remaining within the limits prescribed by the IHO Standards for Hydrographic Surveys. This method effectively enhances the computational efficiency of the multibeam system and has practical application value.

Multi beam bathymetry is a water depth measurement technology developed based on single beam bathymetry, utilizing the propagation characteristics of sound waves. Its coverage range is wider than that of single beam bathymetry. In order to improve the measurement efficiency of multi beam bathymetric vessels and obtain survey line optimization schemes suitable for different sea conditions, a specific sea area model was established. Firstly, a spatial coordinate system was established to calculate the transformation model of the equivalent slope spatial slope problem, and data with good regularity was obtained. Through this method, the linear variation pattern of the slope surface was discovered, and a linear model of the measuring line was established. In addition, by analyzing the impact of boundary depth changes caused by slopes on coverage, constraints for optimizing coverage and path length were established, and the optimal solution scheme for the survey line was obtained. By applying this solution, depth vessels can more accurately obtain seabed terrain data and improve measurement efficiency. This study has broad application prospects and important practical value in different types of seabed topography.

Cloud detection is a fundamental first step in the preprocessing of optical remote sensing images and a pivotal element for subsequent analytical tasks. High-resolution remote sensing images often require cropping into smaller patches to facilitate effective cloud detection. However, these segmented patches often lack rich features and exhibit high similarity between classes, which complicates accurate classification, particularly in intricate scenarios such as those involving both clouds and snow. To tackle this challenge, we introduce a novel cloud detection approach that employs boundary position attention, based on the CloudS26 cloud-snow dataset. This technique utilizes deformable convolution to generate boundary-focused attention, proficiently delineating the interfaces between clouds and snow in coexistent environments. Our approach has demonstrated robust detection capabilities in both the CloudS26 and CSWV datasets, showcasing its effectiveness in handling complex meteorological phenomena.

This system adopts the technology of digital earth and UE engine, which separates the digital twin technology base from the secondary development and deduction rules of specific scenarios, in order to achieve fast and convenient design of specific disaster scenarios, user-defined deduction and evaluation rules. For the simultaneous online interactive drilling of various posts at the port, the system can present real-time simulation results in three-dimensional form, statistically display relevant information of damage and response effect, and evaluate the cooperative and coordinated mechanism of various posts in the disaster plan. This system can discuss and evaluate the coordinated emergency plans of various departments more truthfully, scientifically, and reliably, which helps to provide immersive emergency training for the personnel of various positions in the port, and has reference value for departmental coordinated disaster emergency deduction in other regions.

Sugarcane is a major sugar crop which support for the half world sugar provide with widely growth in Guangxi, Guangdong, Yunnan, and Hainan of China. Remote sensing technology provide an effective way which is a large area coverage in short periods for sugarcane classification which is developed from MODIS data (250m) to submeter satellite data to more precise result. Deep learning with a high performance was used in recent years which needs high-quality samples. The number of samples with wildly distribution is one of important factors for the training result which needs heavy workflow of labeling by manual work. In this research, a residual neural network, PSENet is proposed to high accuracy of sugarcane classification compared with the U-Net Model.

The Alxa Block, located on the northeastern edge of the Tibetan Plateau, is largely covered by deserts. In this study, gravity and magnetotelluric (MT) data measured within the range of (38°N-40°N, 102°E-105°E), as well as three-dimensional joint inversion technology, were used to generate a geophysical dataset for the study area. Three-dimensional density and resistivity models were created. By analyzing overlays and RGB composites of the density and resistivity models at different depths, we have deepened our understanding of deformation mechanisms at the front edge of the Tibetan Plateau and the western margin of the Ordos block. This study has important scientific significance in expanding the resource potential of the region and reassessing the background and mechanisms of basin formation.

Coal mining will lead to geological disasters such as surface subsidence and ground fissures, especially the goaf after mining. Therefore, it is particularly important to monitor the deformation of the coal mining area. In this study, we used the small baseline subset InSAR technology to measure the deformation of three major coal mining areas near Hunchun city to detect the spatiotemporal distribution of surface deformation in two periods from 2018 to 2021. In addition, by combining interferograms from multiple monitoring periods, several historical high-deformation areas were mapped. The research shows that from 2018 to 2021, significant mining-induced subsidence occurred in the southern and northeastern parts of the Baliancheng Coal Mine and the eastern part of the Yingan Coal Mine. The eastern part of the Yingan Coal Mine experienced the maximum cumulative LOS deformation, reaching up to -348mm. Additionally, railways and highways pass through the severe subsidence areas in the central part of the Yingan Coal Mine and the northeastern part of the Baliancheng Coal Mine, respectively, which will adversely affect their safe operation.

PM2.5 is a critical physical parameter for characterizing air pollution. Monitoring the spatiotemporal patterns of urban PM2.5 is crucial for environmental health and sustainable development. This study utilizes measured PM2.5 data, MODIS remote sensing data, land use and other data to study the monitoring and simulation of PM2.5 concentration distribution in Chongqing's main urban area using GIS spatial analysis and the Land Use Regression (LUR) model. The results indicate: 1) The 2020 LUR model included variables FL-4000, DEM, and PE5000, with an R² of 0.789 and an RMSE of 11.84; the 2013 model should include FL-5000, RS5000, DEM, and PE5000, with an R² of 0.688 and an RMSE of 14.84. 2) The spatial distribution of pollutants is closely related to DEM and the location of green spaces, with greenery mitigating pollution and contributions from traffic emissions being smaller than those from industrial emissions. 3) Compared to 2013, the 2020 PM2.5 concentration significantly decreased, with average concentrations dropping by 13.23%. The spatial distribution of annual average PM2.5 concentrations was generally higher in the central-western part and lower in the southeastern part, aligning with the natural geographic conditions of Chongqing's main urban area as well as its level of socioeconomic development and urbanization.

Terrestrial laser scanning (TLS) is widely utilized for acquiring point cloud data, with ground filtering playing a pivotal role in data processing. Variations in scanning angles and station setup conditions often lead to a broader distribution range of non-ground points compared to ground points within TLS data. However, prevailing ground filtering methods commonly assume a uniform distribution of ground points across the entire scanning scene, which may not hold true in TLS contexts. To tackle this issue, this study proposes an iterative analysis approach for ground filtering based on relative density, focusing on the scanning lines of the point cloud. The methodology involves restoring the angular resolution and scanline information of the disordered ground laser point cloud. Subsequently, a scanline-by-scanline relative density analysis is performed to identify both the ground candidate point set and the non-ground point set. Once this criterion is met, the iteration concludes, and the remaining candidate points are designated as the final ground point set. Experimental validation of the proposed method is conducted using three datasets, accompanied by comparisons with existing approaches. The results demonstrate the efficacy of the method in constraining the range of ground points, leading to a more precise filtering effect, particularly in transitional zones between ground and non-ground surfaces, such as wall footings, curbs, and poles. These findings emphasise the suitability of the method for ground filtering and mapping applications in TLS data.

In the forefront of contemporary urban infrastructure management and maintenance, monitoring the health status of pipeline systems is of paramount importance. However, conventional approaches to Ground Penetrating Radar (GPR) image data analysis heavily rely on the subjective judgment of expert personnel, making the process both time-consuming and prone to individual biases, thereby limiting its potential for large-scale application. This paper sets out to explore the innovative application of the YOLOv5 model in pipeline radar imaging, harnessing the power of deep learning algorithms to enable rapid and accurate identification of critical information such as underground pipeline defects and foreign object intrusions. Ultimately, the detection algorithm of this model achieves precision and recall rates of 91.8% and 89.3%, respectively, in identifying underground pipelines. These metrics not only meet but also exceed the practical requirements for engineering applications, highlighting the efficacy and robustness of the proposed YOLOv5-based approach in enhancing the efficiency and accuracy of pipeline inspection tasks.

Carbon dioxide in the atmosphere, the most abundant greenhouse gas, is recognized as one of the main causes of climate change. A large amount of missing data is contained in XCO₂ datasets obtained from carbon observing satellites due to unfavorable atmospheric conditions and satellite orbits, making it inadequate to reflect the heterogeneity of XCO₂ in space. Our understanding of carbon dynamics is limited by the sparsity of terrestrial measurements and the lack of fine-scale XCO₂ modeling datasets. A downscaled machine learning model was developed to fill the gaps in the retrieval of data from the Orbiting Carbon Observatory 2 satellite retrievals across Northeast China during 2018–2023 (cross-validated R²=0.88, RMSE=1.49ppm). The results showed that the annual mean value of XCO2 in the northeast region for the last 6 years was (407.83±67.31) ppm, and the XCO₂ content increases annually with 2.68ppm/year. The highest concentrations of XCO₂ were found in the southern region and around large cities. The seasonal distribution pattern of “high in spring and winter, low in summer” was observed to be obvious. This work confirms the necessity for downscaled modeling of XCO₂ from satellites, which can enhance our comprehension of carbon cycles.

3D laser scanning technology is a high-precision, high-efficiency surveying and mapping tool that in recent years, has been widely used in the field of surveying and mapping technology but also realized from the single-point surveying and mapping to surface mapping of the revolutionary breakthrough; at the same time, the collected data information from the spatial coordinates of the data to expands the surface of the object, color and texture information. This paper mainly discusses the current situation and development trend of 3D laser scanning technology in the field of surveying and mapping, introduces the principle and characteristics of 3D laser scanning technology, compares the advantages and disadvantages with traditional surveying and mapping means, and analyzes its application in terrain mapping, deformation monitoring, cultural relic protection, underground engineering, geologic hazards, etc., concludes that this technology is a new type of high-precision, high-efficiency technology that has great development prospects in the future.

Accurate classification of urban tree species is essential for the improvement of urban environment and rational distribution of urban space. However, urban tree species are distributed and diverse, and it is challenging to classify them accurately. We extracted spectral, textural and vertical structure information based on UAV hyperspectral and LiDAR data, and used a random forest classifier to classify 14 tree species in complex urban areas. The results showed that the accuracy of tree species classification was significantly improved by the addition of LiDAR data. The fusion of spectral, textural and vertical structural features was the most effective combined features, with an overall accuracy (OA) of 83.48% and a Kappa of 0.81, in which the addition of vertical structural features increased the OA by 7.77% and the Kappa by 0.09. This study provides an effective reference for accurately obtaining information on tree species in complex urban environments.

PM2.5, which refers to particles with an aerodynamic diameter of 2.5 micrometers or less. The spatial distribution of PM2.5 within China exhibits inherent heterogeneity on a national scale, yet within certain regional confines, a semblance of homogeneity can be observed. The mitigation of the detrimental impacts of PM2.5 necessitates the formulation of geographically tailored regulatory measures by policymakers, predicated upon a comprehensive clustering analysis of PM2.5 concentration data across the nation. The execution of such an analysis, however, is not without its challenges, given the complex and often noisy nature of the data. In this scholarly work, a robust clustering framework is proposed, grounded in a novel depth-based concept capable of effectively managing outliers in both magnitude and form, while also integrating spatial dimensions. The application of this framework to a dataset of PM2.5 concentrations has delineated ten distinct regions within China, each characterized by unique PM2.5 distribution profiles. The homogeneity within these clusters has been substantiated, and the demarcation of cluster boundaries is discernible. Such delineation facilitates the development of localized emission reduction policies by policymakers and fosters the establishment of regional collaborative frameworks aimed at the management of air pollution in China.

This study mainly focuses on the experimental analysis of the application of quantum lidar technology in air pollutant monitoring. Through the discussion of the principle of lidar meteorological detection and quantum lidar, the practical foundation is laid, and it is found that the latitude and longitude of each hot spot corresponding to the scanning profile of lidar can be obtained by calculation, and then the area of hot spot area can be calculated. Moreover, all the analysis results of a scanning lidar can be spliced into a circular pseudo-color map according to the absolute angle of each section and superimposed on the vector map, which is convenient for managers to observe the distribution of pollution intuitively. At the same time, through the experiment of lidar detection, it is found that when atmospheric pollutants appear, the original lidar echo signal will fluctuate obviously, and the polluted particles will make the laser scatter strongly, which will lead to the rapid increase of the intensity of the atmospheric backscattered echo signal, and the lidar detection can be realized by generating strong mutation signals.

The Mars atmospheric entry is the first phase in the entry, descent, and landing (EDL) process for Mars exploration. There is a high demand for the proper guidance law for atmospheric entry under the effects of uncertainties in the entry state condition and model parameters. This paper presents a two-stage numerical predictor-corrector method based on the constant bank angle to achieve precise guidance of Mars atmospheric entry with less trajectory segmentation and a more concise guidance concept. According to the flight characteristics of the Mars vehicle during the entry phase, the entry trajectory is segmented into two stages for numerical predictor-corrector guidance. In the first stage, only one guidance cycle is involved, which can reduce energy consumption. In the second stage, more guidance cycles are exploited to reduce trajectory error. The Monte Carlo simulation experiment results show that the proposed guidance algorithm has relatively steady performance under uncertainties such as entry state, lift-to-drag ratio, ballistic coefficient, and atmospheric density.

Image segmentation technology based on Convolutional Neural Network (CNN) to process cloud images taken by satellite remote sensing has become a valuable research field. However, in previous practice, the shallow network of the traditional UNet model has been found to be inadequate for dealing with satellite remote sensing data that lacks contextual information. Additionally, it suffers from information bottlenecks if the hopping connections are not optimised. This paper designs and improves the UNet architecture as Computational-Efficient Dilated Attention UNet (CEDA-UNet). Specifically, we initially proposed a method to introduce the attention mechanism into the decoder, and subsequently employed the dilated convolution method to replace the conventional convolution. The model can be competent for image segmentation tasks of extracting contextual information. In a dataset named 'Understanding Clouds from Satellite Images' comprising satellite remote sensing images, cloud patterns are manually annotated by expert meteorologists. This dataset allows for the evaluation of the cloud segmentation detection effect of the CEDA-UNet model in comparison to five other classic models, including Attention UNet, U2-Net, UNet, UNet3+ and VNet.

Forest resources have important environmental and economic values. Although many models have been developed to analyze the dynamic changes of forest cover, significant challenges remain in explaining the relationships between geographically non-contiguous regions with similar forest cover changes. To address this issue, we propose Gate Spatiotemporal Network (GateSTNet), a new model for predicting multi-region, multi-feature, and multi-network datasets. The GateSTNet comprises the gate GCAN (Graph Convolutional Attention Network) information focus module and the ConvOut spatiotemporal predict module. The network effectively combines the capabilities of graph convolutional networks (GCNs) for processing non-Euclidean spatial data and convolutional LSTM (ConvLSTM) networks for spatiotemporal data prediction. Experimental results show that GateSTNet performs well in both prediction accuracy and efficiency. Compared with existing models, including LSTM, CNN-LSTM, and ConvLSTM, GateSTNet exhibits excellent prediction accuracy and stability. This enhanced performance is attributed to the network's ability to effectively integrate spatial and temporal information, making it a powerful tool for predicting forest cover dynamics and informing environmental management and policy decisions.

This article proposes a method for constructing a finite element calculation model of tank structures with measured defects based on improved IDW algorithm and three-dimensional laser technology. Taking a petroleum tank that has been in service for nearly ten years as an example, the specific implementation process of this method is explained. The research results show that the improved IDW algorithm results can meet the actual situation of the tank structure. Based on finite element analysis software, further modal analysis and structural strength analysis under experimental water load are conducted on tank structures with geometric defects. The analysis results show that geometric defects greatly reduce the natural frequency of the structure, weakening the dynamic stiffness of the structure; the calculated stress at the location of geometric defects is much higher than the structural stress of an ideal tank, and the impact of geometric defects on the tank structure cannot be ignored.

Camera calibration is one of the key technologies to realize precision measurement in photogrammetry. Traditional camera calibration method has the challenges of low calibration efficiency and accuracy under the conditions of complex background and uneven illumination. In this paper, a compound planar target comprising four ArUco markers and a circular grid is designed for camera calibration in complex scenes. First, the fast detection of planar targets is achieved using ArUco markers, then the robust orientation of planar targets is achieved by sorting circular markers. Finally, the ArUco markers are utilized for precise camera calibration by affine transformation to correct the eccentricity errors. The experimental results show that the proposed technique improves the efficiency and accuracy of camera calibration, with an almost doubling of detection performance and a 30% improvement in calibration accuracy, respectively.

Conventional high-speed videogrammetry based on artificial marker points has lower applicability in the application of structural response measurement on rough and non-flat surfaces, such as rocky slopes, etc. In this paper, a high-speed videogrammetric method based on speckle patterns and digital image correlation (DIC) technique is proposed to monitor the deformation of the slope accurately. The method is mainly divided into the following parts: (1) Acquire the time-series displacement of scattering features by a dual matching strategy of stereo matching and sequence matching; (2) Obtain the intrinsic and extrinsic parameters of the camera system and 3D reconstruction of the feature points by using a stereo calibration method based on a planar target. (3) Unify the local coordinate systems of different fields of view by calculating the coordinate transformation parameters from the 3D coordinates of the corresponding points. The validity and reliability of the method are verified in the shaking table tests of the slope structure model, which show that the proposed method achieves sub-millimetre positioning accuracy and vibration frequency measurement deviation better than 1%.

Global climate change is a hot issue in the world today, and the terrestrial carbon cycle system is an important part of climate change. Gross primary productivity (GPP) is one of the important indicators to study the carbon cycle of ecosystem. Remote sensing data of solar-induced chlorophyll fluorescence(SIF) provide a method for spatial scale expansion of GPP. Exploring the relationship between GPP-SIF in DingHu Mountain region from different time scales can clarify the utility of SIF in estimating carbon assimilation, comprehensively understand the environmental factors controlling forest productivity in DingHu mountain region, and have great significance for predicting the future carbon cycle feedback of the ecosystem. Environmental factors have a great influence on plant photosynthesis, which reduces the accuracy of GPP-SIF linera relationship model, and the cause of nonlinear part needs to be further studied. Taking DingHu mountain evergreen ecosystem as an example, the linear relationship of GPP-SIF on monthly, seasonal and half-year timespan was fitted to explore the dynamic changes of the two timespan. On this basis, the Random Forest Regression model was used to analyze the importance of different environmental factors to GPP-SIF. The results show that the linear correlation between GPP and remote sensing products of SIF decreases significantly at higher time frequency. The environmental factors that have great influence on SIF and GPP at different time scales are air temperature and relative humidity, and the differences become smaller as the temporal scale increses, providing evidence for the changes in GPP-SIF.

Forest fire seriously affects the stability of the ecosystem, and the fire is very sudden and destructive. With the rapid development of remote sensing technology, remote sensing satellites have been widely used in fire monitoring because of their characteristics of high spatial and temporal resolution and wide monitoring range, but there are still problems such as insufficient use of remote sensing information, missing detection of small fire points and false fire points. Aiming at the above problems, this paper proposes a method of radiation Angle normalization to enhance the information of small fire points, and at the same time creates a prior data set to deeply mine the remote sensing data information, so as to improve the detection accuracy of small fire points.

The high altitude area of Xizang has rich solar energy resources due to its unique geological conditions and climate environment. In order to better utilize and develop these resources, as well as study their impact on the earth's ecosystem, it is necessary to conduct in-depth observation and research on the solar spectrum. The field observation mainly conducted solar spectral observations on three areas with an altitude of more than 5000m in Xizang around the summer solstice in 2022 and 2023, and compared with the spectrum of Daxing District of Beijing (altitude 30.00m), Chengguan District of Lhasa (altitude 3664.48m) and AM0/AM1.5 spectrum. The results show that the strongest value of solar spectrum in Jiacula Pass of Everest National Nature Reserve (altitude 5248.00m) is 2.497W•m^-2•nm^-1, the strongest value of solar spectrum in Mila Pass of Lhasa-Nyingchi boundary point (altitude 5118.50m) is 2.361W•m^-2•nm^-1, and the strongest value of solar spectrum in the top of Drepung Peak of Lhasa (altitude 5352.50m) is 2.69W•m^-2•nm^-1, which exceeds the AM0 standard spectrum to varying degrees. The changes in surface, atmosphere and environment caused by changes in altitude affect the absorption of different gas molecules to the solar spectrum. It can also be inferred that the contents of O₃, O₂, H₂O and other gas molecules in various regions are different. The solar ultraviolet radiation of 287nm-300nm belonging to the UVB band even appears in high altitude areas. The overall performance of the high altitude area is closer to the AM0, but also has the spectral characteristics of AM1.5, showing the advantages of high altitude observation, which can reduce the influence of atmospheric absorption and scattering, provide more accurate solar spectral data, and highlight the importance of high altitude solar spectral observation in Xizang, which is helpful to further promote the research and development of related fields.

Hazards detection plays a significant role in safe soft landing on extraterrestrial bodies. Plane fitting is a usually adopted technique to detect terrain hazards. There are two categories of methods commonly used for plane fitting in hazards detection. One category includes the least squares method and its improved algorithms; the other one includes various robust statistical estimation algorithms. In this paper, representative methods from these two categories are selected for comparison analysis. These different fitting parameters' estimation methods are compared in terms of robustness and time efficiency. Furthermore, a strategy that integrates the Random Sample Consensus (RANSAC) and Robust Weighted Total Least Square (RWTLS) methods is proposed. The experiment results show that this strategy can improve the accuracy of parameter estimation of the RANSAC fitting method.

With the increasing diversification and complexity of satellite imagery, traditional uncontrolled block adjustment algorithms often encounter challenges such as significant disparities in image positioning accuracy and difficulties in weight assignment. This paper proposes utilizing an adaptive LNIFT algorithm to extract ground control point data from public reference Digital Orthophoto Maps (DOM) for preliminary RPCs correction. This can enhance the internal consistency of experimental data, making it easier to determine weights during the adjustment process. Furthermore, we introduce a tie point extraction strategy that integrates coastal and cloud mask data and incorporates a two-step matching strategy. This optimizes extraction effectiveness and ensures robust tie point extraction capability under complex scenes. Finally, block adjustment is conducted to further reduce internal geometric consistency. The experimental results indicate that this method can effectively improve the internal geometric consistency between images, and the proposed matching strategy significantly enhances the adjustment results.

Objective: To study the change characteristics and rules of surface albedo of alpine desert plant communities, for revealing the influence of vegetation status and other factors on surface albedo in alpine desert ecosystem, will provide remote sensing monitoring basis for desert ecosystem monitoring. Methods: Based on the analysis of surface albedo data and the survey data of alpine desert plant community in Qaidam Basin, the spatio-temporal variation of surface albedo of alpine desert plant community was discussed by using statistical methods and multi-scale analysis methods, the relationship between plant community characteristics and surface albedo was revealed. Results: (1) According to the change trend of surface albedo, six kinds of surface albedo can be analyzed in the order of near-infrared white-sky albedo > near-infrared black-sky albedo > short-wave black-sky albedo > visible white-sky albedo > visible black-sky albedo, with near-infrared albedo > short-wave albedo > visible albedo. White-sky albedo > Black-sky albedo. (2) The surface albedo characteristics of different plant communities were as follows: Poacynum hendersonii and Phragmites australis had the highest surface albedo (0.244). The surface albedo of Sympegma regelii desert community was the lowest (0.082). The average surface albedo of the alpine desert community was 0.202. Conclusion: near-infrared black-sky albedo and near-infrared white-sky albedo cannot reflect the type of alpine desert plant community, while BSA_v and WSA_v can be used as the representative indexes of surface albedo of alpine desert plant community.

The construction of digital villages is crucial for rural revitalization, and 3D geographic information data serves as an important support for digital village development. This article primarily introduces the key principles and technical processes of oblique photogrammetry technology, and completes the real sence 3D model construction of Taipingshao Village, Guiyang City, Guizhou Province, and evaluates the accuracy, the mean square error in the horizontal plane was 0.211m, and the mean square error in elevation was 0.148m. The article also analyzes and discusses the application of the 3D model in digital village construction, providing theoretical and technical support for rural revitalization.

Earthquake disaster rescue has often exceeded the capacity of local government; this paper provides a methodology for measuring disaster rescue capacity from the perspective of areas nearby disaster zones. The focus is on four key elements that affect the nearby disaster rescue capacity (hereinafter referred to as NDRC): nearby disaster relief resources, the survival ratio, the time range, and the transportation conditions of disaster areas. We apply our methodology to five disaster areas in the North-South seismic belt of China. The results show that NDRC can reflect the regional differences in rescue vulnerability. For example: Lushan and Minxian turn out to be less vulnerability in post-earthquake rescue, because they are near the capital city and can thus receive a larger rescue force as part of an immediate post-disaster response; on the other hand, Yiliang, Shangri-La, and Yingjiang have a high rescue vulnerability, because their nearby regions have lagging economies, as well as disaster areas have poor conditions of transportation.

The positioning accuracy of real-time precise point positioning (RTPPP) is heavily reliant on the quality of the real-time precise products employed. This study evaluated the real-time precise products provided by the Centre National d’Études Spatiales (CNES), utilizing the Signal-in-Space Range Error (SISRE) as an indicator. An elevation angle model for inter-system weighting is proposed based on the statistical SISRE results. To validate its effectiveness, RTPPP static experiment was conducted at 10 IGS stations. And RTPPP vehicle kinematic experiment was conducted using a Septentrio receiver. Experimental results demonstrate significant RTPPP positioning performance improvements utilizing the proposed elevation angle model for inter-system weighting.

Compared to optical images, SAR image geometric modeling is simpler and more accurate, as its positioning is only related to satellite position. In order to fully utilize the advantages of SAR satellite images in geometric positioning, a high-precision automatic geometric positioning method is proposed in this paper for optical images assisted by SAR satellite images. Firstly, SAR DOMs and optical image are superimposed detection. Secondly, a multi-modal matching algorithm is used to perform image matching and control point optimization. Finally, the matched virtual control points and optical images are used as input information to perform adjustment calculations based on the Rational Function Model. Experiment results show that this method not only significantly improves the geometric positioning accuracy, but also has high computational efficiency and automation level. It has certain guiding significance for the rapid update production of topographic maps in the field of photogrammetry.

In the era of big data, the full utilization of trajectory data has received extensive attention from researchers, who have proposed a large number of trajectory models with different characteristics and have been practical in terms of trajectory representation models. However, the existing trajectory representation models do not model the semantic features of trajectories sufficiently, and there is a lack of fusion between semantic features and spatio-temporal features, and there is no unified model expression. Therefore, there is an urgent need for a model that can represent the trajectory movement process to unify the semantic and spatio-temporal features of trajectory data. The model proposed in this paper constructs a trajectory domain ontology using knowledge graph technology under the premise of trajectory preprocessing and segmentation; then semantically enriches the trajectory entity by combining with open data; and finally verifies the practicability and validity of the model through the display of multi-dimensional features of the trajectory, the temporal deduction of the trajectory moving process, and the query of multi-dimensional features of the trajectory. This model does not need to develop complex data mining algorithms to realize the classification query and correlation analysis of multi-dimensional features of trajectory data.

Ecosystem services are essential for ensuring our survival and development and comimg true regional sustainability. Based on the quantitative indicator assessment method of net primary productivity (NPP) of vegetation and GIS spatial analysis method, the spatial distribution characteristics and importance of the functions of water soil retention, soil and water conservation, wind and sand control, and biodiversity maintenance in the Tibetan Plateau were evaluated during 2000-2015. The results show that: (1) The indices of water conservation, soil conservation, wind protection and sand fixation, and biodiversity maintenance functions are high in the southeast and low in the northwest, showing a reducing trend from the southeast to the northwest. (2) From the grading characteristics of the significance of the ecological service features mentioned above, the four types of ecological system services have the largest area, accounting for 27.06%, 19.73%, 61.44% and 41.7% of the gross area of the plateau, respectively; the area of ecological services with weaker functions is the smallest, accounting for 16.47%, 16.96%, 0.97% and 4.06% of the gross area of the plateau, severally. (3) The ecological function of the Qinghai-Tibet Plateau from 2000 to 2015 shows two obvious characteristics, namely, the southern part of the plateau, the Sanjiangyuan region, the southern end of Qinghai Lake and Qilian Mountain area have a decreasing trend of comprehensive ecological service function, the northern Tibetan Plateau, the southern valley of Tibet, the Hengduanshan area have an increasing trend of comprehensive ecological service function, and the rest of the area stays unchanged.

The accurate and quick detection of urban built-up regions is crucial for better city management and planning, as well as for cognition of urbanization. Nighttime light images and their features provide a reliable means to quantify urban regions. However, only a limited number of studies have delved into the specifics of how light variations across diverse land use types. Therefore, this study made a new attempt using the neural network model method based on VIIRS nighttime light data to detect the spatiotemporal evolution of urban built-up regions in Zhejiang province spanning from 2012 to 2021. The results revealed that the neural network model-based method used in this study had noticeable performance in identifying urban built-up areas, achieving an overall precision of 93.27% and a kappa coefficient of 0.83. Furthermore, there was a significant increase in urban built-up areas and a slight decrease in rural built-up areas in Zhejiang, with more than 35% of built-up regions converting from non-urban regions. The detailed features of urban built-up region dynamics were also detected using a city-level analysis. Moreover, this study extracted urban built-up regions based on the light variation characteristics of land use itself through the neural network method, which provides a new insight into the applications of nighttime light data in urban studies.

The subway holds a significant role within the urban public transportation network. Addressing the challenges of subway connectivity has become crucial for enhancing the commuting experience of citizens. This study, focusing on the main urban area of Guangzhou, employs GIS network analysis tools to evaluate the bus service coverage, identifying regions that lack subway accessibility and require bus transfers for commuters. Building on these findings, Guangzhou Higher Education Mega Center (HEMC) was selected as the specific area of study. The site selection and allocation model were utilized to optimize bus station configuration, while the best path model was applied to determine feasible route directions. Ultimately, the closest facility point model and OD cost matrix model were implemented to strategically place shared bicycle docking stations in areas characterized by high population density and significant transit likelihood, aiming to solve the "last kilometer" travel issue for city residents. The outcomes of this research could serve as a reference for the development of public transportation in Guangzhou.

The stable supply of cultivated land resources directly affects regional food security and sustainable development. Therefore, a clear and accurate grasp of the spatial distribution of cultivated land stability has become a necessary prerequisite for national food security and sustainable development of cultivated land resources construction planning. In this paper, the instability factors of cultivated land are measured comprehensively from the two angles of the fragile natural environment condition and the contradiction between man and land in social economic development, and a set of evaluation index system of cultivated land resource stability is constructed. By using remote sensing and GIS technology, entropy method, spatial analysis, multivariate statistics, econometric model, rule-based classification model and other technical methods, a set of key technologies and methods for the stability evaluation and zoning of cultivated land resources were studied and constructed from the perspectives of comprehensive integration of multiple information and automatic classification and recognition. The stability data of cultivated land resources in major grain-producing areas of China were quantitatively analyzed and evaluated under the multi-scale of districts, counties and plots, and the cultivated land with poor stability and difficult to be used stably was classified as unstable cultivated land. The pattern of unstable cultivated land in China's major grain-producing areas was extracted with a scale accuracy of 1:10,000. The results show that by 2020, there are significant spatial differences in the stability of cultivated land resources in China's main grain-producing areas, which generally show a decreasing spatial pattern from south to north and from east to west. The cultivated land area with average stability or above is above 95% in the three major grain-producing areas, which is in a stable state and the scale of unstable cultivated land is generally controllable. The methods and conclusions of the comprehensive evaluation and zoning of cultivated land stability in China established in this study can provide references for the construction planning of food security and sustainable development of cultivated land resources.

The 12345 public hotline service is one of the primary channels for residents to provide feedback on urban issues and suggestions. Extracting spatial information from this feedback is crucial for government departments to accurately locate various events and explore their spatial characteristics. Traditional methods, such as those based on regular expressions, often struggle with inaccurate extraction of geographic entity features and low recognition efficiency. To address these issues and improve fine-grained geographic entity recognition, a method for extracting and correcting geographic information based on 12345 text data is proposed in this paper. Initially, official data from the 12345 hotline in Sanya City were used as a corpus, and the geographic entities within it were annotated and classified. Subsequently, three geographic entity extraction models were selected for testing, and relevant evaluation metrics were calculated. Finally, a spatial correction method for geographic entities based on semantic matching was proposed. This method utilizes the Jaccard similarity function and spatial correction buffers to establish correction rules, further optimizing the extraction results. The results show that the geographic entity recognition method based on BERT-BiLSTM-CRF achieves a precision of 89.21%, recall of 92.59%, and F1 score of 90.86%. Additionally, after spatial correction, all geographic entities fall within the POI and road network buffer zones, making the recognition results more accurate and practically relevant.

The geographical scope of urban physical area is the basis for distinguishing urban and rural areas, establishing urban-rural statistics, formulating urban-rural policies, and implementing national spatial planning. It clarifies the scope for conducting research on cities, and contributes to control the scale of urban development, intensively utilize land, and accurately obtain urban population. This paper defines city in terms of morphological and functional dimensions, and constructs a conceptual model of the geographical scope of urban physical area. Based on the national population census data and historical population data, the national land survey data, the geographic monitoring data, map POI and other geographic spatial big data, it designs the overall technical framework for identifying the geographical scope of urban physical areas, extracting the urbanization geographical scope of national land space. The extracted urbanized physical geographical scope provides a basic framework for the analysis of urban spatial patterns and feature extraction.

The classification of the Yellow River Delta wetlands is crucial for their protection and planning. This study proposes a method for wetland mapping in the Yellow River Delta based on multi-source and multi-feature integration using the GEE platform. First, the Sentinel-1/2 and SRTM data were utilized, combined with the Random Forest Recursive Feature Elimination algorithm (RF_RFE) for feature selection. We compared the classification performance of four machine learning algorithms to determine the best classification scheme, and completed the Yellow River Delta wetland mapping from 2020 to 2023. Research shows that: (1) Multi-source feature collection significantly improves the accuracy of wetland classification, and feature selection can reduce redundant features and further improve classification accuracy; (2) The Random Forest algorithm based on feature selection performs exceptionally well, with an overall classification accuracy of 96.48% and a Kappa coefficient of 0.96, surpassing the classification accuracies of KNN, GBDT and CART; (3) In 2023, the effect of clearing S. alterniflora was significant, and its area was reduced to 9.07 km², which was 48.26 km² less than that in 2022, freeing up living space for native vegetation such as Phragmites. The research results are of great significance for biodiversity conservation and ecological restoration in the Yellow River Delta.

The calculation of meridian arc length is typically expressed as a series expansion of geodesic latitude. However, during the course of our research into map projection, we discovered that for different types of map projections, it is often necessary to introduce three types of auxiliary latitude: equal angle latitude, equal distance latitude and equal area latitude. Mathematica was employed to derive the relationship between these latitudes and the arc length of the meridian, with the third flattening n being used instead of the first eccentricity to re-derive their coefficients. The analysis indicates that the formula for the length of a meridian expressed in terms of equiangular latitude is more accurate and efficient than that expressed in terms of equidistant area latitude. Consequently, it is more appropriate to use the formula for the length of a meridian expressed in terms of equiangular latitude in the related fields of geodesy, map projection and so on.

In order to realize the unified storage of geographic information data of different structural types of land change survey, a geographic information data storage system of land change survey based on ArcGIS is designed. As for the system hardware, data server, data collector and data storage are designed to form the system hardware structure. The software part sets the group key, stores and processes the geographic information data of land change survey, and completes the design of data storage system. The experimental results show that the storage performance of the designed system is good, with excellent user interface, response speed, data synchronization, security, expansibility and compatibility. At the same time, the data storage processing delay of the system in this paper is low, which can meet the needs of large-scale geographic information data storage.

With the continuous development of railway informatization in China, the integration and application of GIS and railway oblique models on the web have gradually deepened^[2]. The railway oblique photogrammetric model obtained through automated modeling is essentially a continuous TIN network. Apart from roaming and browsing, it is impossible to perform actual operations on the model. However, most applications of railway informatization require the ability to select individual objects, assign and query attributes, and other basic GIS functions. Therefore, individualization is an important issue that needs to be addressed in the application of oblique photogrammetric models in GIS. Based on the 3D open-source engine Cesium framework, combined with the front-end Vue framework, server-side NodeJS framework, and MySQL database, this paper constructs a railway model visualization system capable of data management. This system can individualize various types of railway objects in a 3D spatial scene, enabling the management of individual objects. Additionally, it develops corresponding spatial analysis functions to facilitate railway inspection personnel in examining and analyzing railway data.

Along with the fast development of Chinese economy and the speed of urbanization, it is inevitable to expand the city. As the center of economy in the Middle and West, Chongqing is expanding rapidly. However, while urban expansion drives economic development, it also exerts tremendous pressure on the urban ecological environment, with the changes in the urban green space pattern being particularly prominent. Based on RS data and statistics year books, this paper analyses the influence of urban expansion on urban green pattern from 2000 to 2020. The study finds that the area of urban construction land in Chongqing has expanded significantly, showing characteristics of central radiation and annular expansion. The influence of urban expansion on urban green pattern is mainly reflected in the decrease of farmland and nature, and the ratio of industry and residence. The analysis of the temporal and spatial characteristics of urban expansion shows that the urban expansion in Chongqing has experienced three development stages of “moderate-high-slow”, with the overall urban form showing a “southwest-northeast” strip distribution and tending to be compact and regular.

The barrier effect on wildlife is one of the most important ecological impacts caused by highway construction in ecologically sensitive areas. More and more scholars are paying attention to the type and size of animal passages, while there is less research on the scientific site selection of animal passages. Therefore, this paper studies the scientific site selection of wildlife passages during highway construction. In the paper, taking the highway section of the Tomur glacier region in Xinjiang as the study area, the Capra ibex as the study object. Quantitatively evaluate the suitability of animal habitats based on the entropy method, simulate their activity paths through hydrological analysis, and discusses and analyses the results. The research results show that after overlapping with the highway line, the location of the eight animal passages determined are relatively consistent with those determined through field survey. The habitat suitability index fluctuates between 0.117-0.606, indicating low habitat suitability and obvious heterogeneity in spatial distribution. The research results can provide some theoretical basis for the site selection of wildlife passages in the highway transportation and environmental protection industry and respond to the sustainable development of biodiversity.

The spatial data information of Guanzhong grottoes heritage is obtained through GIS. The elevation distribution, distance relationship with rivers, distribution type, aggregation degree and distribution direction of Guanzhong grottoes are systematically analyzed by means of nearest neighbor index, kernel density estimation and standard deviation ellipse. The spatial distribution characteristics of Guanzhong grottoes from the Northern and Southern Dynasties to the Ming and Qing Dynasties are revealed, and discusses the influence of three factors: political economy, belief foundation and ancient river on the distribution pattern of Guanzhong grottoes. Research funding comes from Shaanxi Social Science Fund: Study on the Environment and Art of Grotto Temples in Shaanxi Province from the Perspective of Historical Geography (Project Number: 2022J074). The research shows that the distribution of Guanzhong grottoes generally shows a trend of clustering distribution, and its nearest neighbor distance index R=0.559767<1, forming two core gathering areas of Tongchuan City and Xianyang City. It gathered and distributed in the Northern and Southern Dynasties and Sui and Tang Dynasties, and dispersed in the Song and Jin Dynasties and Ming and Qing Dynasties. The distribution of grottoes in Guanzhong generally showed a trend of first diffusion and then contraction. It showed a diffusion trend from the Northern and Southern Dynasties to the Sui and Tang Dynasties, and a contraction trend from the Sui and Tang Dynasties to the Song and Jin Dynasties. The distribution center of Guanzhong grottoes moved from southwest to northeast as a whole.

In this study, we utilize Landsat 8's OLI/TIRS imagery as the primary data sources for our analysis. Based on the analysis of the Landsat images of Jinan City in 2014, 2018 and 2023, the RSEI model is established to conduct spatial scale monitoring and dynamic change analysis. The research results show that from 2014 to 2023, the RSEI index continues to increase, and the ecological environment quality generally presents a good state, and the areas where the ecological environment changes are mainly concentrated in the northern and southwestern areas of Jinan City. At the meantime, the overall improvement area of ecological environment in Jinan city is greater than its deterioration area. The research aims to establish a scientific foundation for the ecological and environmental conservation in Jinan, thereby fostering the city's sustainable growth and development.

Preventing wheel slippage is crucial for ensuring safe and prolonged operation of the rovers on the lunar surface. Therefore, investigating the mechanism of wheel slippage during lunar traversal is of significant importance. Inspired by the wheels of the Yutu and Yutu-2 lunar rovers, a three-dimensional discrete element method (DEM) model was established to analyze wheel-soil interactions. To simulate the mechanical properties of the lunar soil, a three-dimensional lunar soil contact model, taking into account particle rotational resistance and van der Waals forces, was utilized. Comparative analysis was conducted to examine surface soil damage and sub-surface particle movement under different slip conditions, thereby exploring the impact of wheel slippage on the wheel-soil interaction process and elucidating the underlying mechanisms. Experimental results indicate that wheel slippage alters the magnitude and direction of sub-surface lunar soil particle movements, affecting the interaction forces between the wheel and the soil. Within certain limits, increased slippage can enhance wheel traversal capability; however, excessive slippage leading to significant sinking can diminish traversal performance.

The efficiency of conventional attitude domain search is low, and its real-time performance highly depends on the angular constraint. In this paper, given a constant baseline length constraint, we proposed a modified attitude domain search method to resolve integer ambiguity. First, we rewritten the least-squares (LS) objective function to get an rigorous LS attitude domain search model. Second, we used rounding to modify this attitude domain search model, and then derived an orthogonal-diagonal decomposition method and the corresponding attitude search step-lengths to simplify the search procedure. All the ambiguity candidates are determined by traversal search in the attitude search space. Finally, the LS objective function is used to fix the integer ambiguities. As the results of tests, our new method has high success rate and good real-time performance, and shows its potential in high dimensional ambiguity resolution. Because of its good performance, our new method may be considered in real-time GNSS attitude determination.

A real-time PPP method based on broadcast ephemeris was designed to solve the problem that IGS precision products have a certain delay and the network may be unstable in driving. Equation is deduced in this paper, based on the original observation applies to multi-frequency multi-mode under three frequency (TDF) and the ionosphere two combination model, considering the dynamic factors such as environment, data processing using the extended kalman filtering through the station observation data dynamically in imitation experiment to evaluate the proposed approach. Results show that compared with single GPS system, multiple systems on the positioning accuracy and convergence time is improved, especially at the zenith direction, from the original 5.32cm to 0.82cm, have larger ascension. Compared with the dual-frequency ionosphere-free model, although there is no significant improvement in the horizontal direction, there is a significant improvement in the zenith direction. The positioning accuracy reaches 0.82cm, although there is no significant improvement in the horizontal direction, and the zenith direction accuracy is improved by 46.4%.

To ensure that ensure the normal operation of the Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) satellite mission, it is crucial to perform precise orbit determination (POD) using the onboard GPS data. This study aims to enhance the accuracy of POD by performing an in-flight correction of the phase center variation (PCV) for the GPS receiver antenna on the COSMIC-2 satellite. Using the simplified dynamics method to perform orbit determination for COSMIC-2 satellites, we estimate the PCV model using both the direct approach and residual approach, and evaluate the orbit determination results through internal and external consistency assessments. The results show that the root mean square (RMS) value of overlapping orbit comparisons accuracy without considering PCV correction is at the centimeter level, while the RMS value of the reference orbit comparison accuracy is at the decimeter level. With the PCV model corrections approximated by the direct approach and residual approach, 3D RMS values experience a reduction of 2.5mm and 1.3mm, respectively, based on 4-hour overlap orbit comparison.

Ionospheric delay, a crucial error source in precise point positioning (PPP) technology, can significantly hamper positioning accuracy and dependability, particularly during geomagnetic storm conditions. This study presents a high-precision ionospheric model to address this challenge. Initially, total electron content (TEC) is retrieved from the global ionosphere map (GIM) and subtracted from the TEC derived from undifferenced and uncombined PPP data to derive differential TEC (dTEC). Subsequently, a polynomial model of dTEC is established, correlating it with the disturbance storm time (Dst) index. This model is then utilized to compute dTEC and correct the TEC estimate, resulting in an accurate and reliable TEC value. Ultimately, this refined TEC serves as prior ionospheric information for PPP positioning. To validate the positioning performance of our proposed method, positioning experiments proceeded with observational data from six stations at various latitudes from September 6 to 9, 2017. The findings indicate that with the imposition of the high-precision ionospheric model, convergence times to achieve a 10cm accuracy in the East-North-Up (ENU) coordinates are approximately 18 min, 18 min, and 15 min, respectively. In contrast, devoid of the ionospheric model, achieving the same level of convergence takes approximately 35 min, 38 min, and 40 min. Furthermore, in the event of geomagnetic storms, the application of the high-precision ionospheric model substantially reduces positioning errors, with maximum errors in the ENU coordinates decreasing from 0.8m, 0.7m, and 1.2m to 0.4m, 0.35m, and 0.6m, respectively.

Through solving the difference between zenith tropospheric delay (ZTD) obtained from the empirical tropospheric model and real ZTD, this research determines a refined tropospheric model by utilizing the relationship between the foregoing difference and latitude and altitude. Moreover, the model proposed is introduced in the precise point positioning (PPP) for ZTD-constrained PPP, which reduces the convergence time of PPP while improving its positioning accuracy. Concurrently, this research selects the data from 180 IGS stations around the world on September 1st, 2015, to facilitate further positioning experiments. The experimental results reveal that the convergence of traditional PPP to 5 cm takes approximately 20 minutes and 18 minutes respectively for eastward and northward errors, whereas PPP constrained by the refined tropospheric model solely takes 15 minutes and 10 minutes. Additionally, regarding the elevation direction error, the convergence of traditional PPP to 10 cm takes 20 minutes, while that of troposphere-constrained PPP only takes 10 minutes.

The sparse distribution of international GNSS service (IGS) reference stations in China has led to the reduced accuracy of the global ionosphere map (GIM) data within the region, limiting the precise measurement of fine-scale ionospheric variations. In the present work, a regional ionospheric model for the middle reaches of the Yangtze River was constructed utilizing data from the Hubei Continuous Operational Reference System (HBCORS), the crustal movement observation network of China (CMONOC), and IGS. Additionally, we assessed the accuracy of the proposed ionospheric model. The results show that both the estimated differential code bias (DCB) values for 32 satellites and those published by the Center for Orbit Determination in Europe (CODE) are under 0.3ns. Furthermore, the DCB values of the 45 observation stations contributing to the proposed model demonstrate stable measurements, all below 0.3ns. The difference between the ionospheric vertical total electron content (VTEC) values in the middle reaches of the Yangtze River and those obtained through linear interpolation from IGS exhibits a maximum root mean square (RMS) value of 1.72TECu and a maximum standard deviation (STD) value of 1.46TECu. For stations in HBCORS that were not included in the modeling process, leveraging the CODE global ionospheric model precise point positioning (PPP) resulted in maximum errors of 0.25m, 0.24m, and 0.48m in the N, E, and U directions, respectively. However, using the regional ionospheric model PPP in the middle reaches of the Yangtze River yielded maximum errors of 0.14m, 0.18m, and 0.21m in the N, E, and U directions, respectively. Compared to the CODE global ionospheric model, the regional ionospheric model PPP in the middle reaches of the Yangtze River enhanced positioning accuracy by 49.91%, 35.15%, and 53.53% in the N, E, and U directions, respectively.

As global urbanization continues to accelerate, cities have become the centers of global population and economic activities. This study investigates the driving forces behind urban expansion in the Xiamen-Zhangzhou-Quanzhou region, a significant urban agglomeration in southeastern Fujian Province, China. Utilizing GIS technology, the research visualizes factors influencing urban expansion to better understand the growth and evolution patterns of the area. Data from various sources, including socio-economic statistics, road networks, land use classifications, and DEM elevation data, were analyzed using the GeoDetector to assess influence of various elements. The results reveal that the density of the population and the density of the road network have the highest interaction interpretability for urban land expansion, with a value of 0.985. The interaction between population density and GDP follows closely, with a value of 0.972. The synergistic effects of these factors indicate that their combined impact on urban expansion is greater than their individual effects. The study concludes that urban expansion in the region is primarily driven by the combined effects of population density, economic development, and transportation infrastructure. These findings provide important perspectives for regional city planning and long-term ecological sustainability approaches, offering guidance for the future development of similar urban clusters.

Trajectory data inevitably contain various errors which would affect the effectiveness of the trajectory data in various applications. Therefore, trajectory uncertainty modelling is a prerequisite problem that needs to be solved. Trajectory uncertainty is generally estimated by using only speeds or positions of the moving objects. However, both of the speeds and positions of the moving objects have influences on the trajectory uncertainty. Therefore, in this paper, a speed and position weighting (SPW) model is designed to integrate the classical speed based model and the geometric distance based model by using an adaptive weighting strategy. The weights of these two models are determined based on the relationship between the Euclidean distance and the distance of the uniform motion between sampled points. Two indicators of uncertainty area proportion and the point coverage are adopted to validate the effectiveness of the SPW model. The results of the experiment prove that the SPW model can provide a moderate uncertain region contain almost the whole trajectory.

Precise localization of a planetary lander during descent phase is crucial for ensuring a safe landing. Terrain Relative Navigation (TRN) is a critical technology for achieving precise localization in hazardous terrains. Initial localization which aligns the descent image from lander camera with the reference map, is essential for TRN to generate precise localization results. This paper introduces a novel initial localization method utilizing the Bag of Visual Words (BoVW) model to pinpoint the descent image on the global map. This method enhances localization efficiency by narrowing the search range for image-map matching. This approach was validated with both synthesized and actual descent image data from the Mars 2020 mission.

The submarine base array provides the calculation datum for long-baseline tracking and positioning, and the positioning results of subsea beacon directly affect the accuracy of long-baseline navigation. Therefore, the establishment of a tight underwater positioning model is the key issue to improve the accuracy of undersea beacon point position, and the traditional monostatic underwater positioning model fails to take into account the displacement of the shipborne transducer in the acoustic signal round-trip process, which leads to a large error introduced into the positioning results. To address the above problems, the positioning accuracy of the transceiver separation underwater positioning model in the round-trip mode is investigated and analyzed, and the positioning accuracy of the monostatic and transceiver separation underwater positioning models is compared and analyzed through simulation. The experimental results show that, in the case of the transponder deviating from the center of the track or the speed of the ship fluctuating, the positioning accuracy of the transceiver separation model is higher than that of the monostatic model, and more accurate positioning results can be obtained.

Ranging algorithm is one of the key technologies of hydroacoustic positioning technology in ocean mapping, in which time delay estimation and sound speed are the key factors to determine the ranging accuracy. In this paper, a hydroacoustic signal ranging processing method is designed for the influence of time delay estimation and sound speed error on ranging accuracy. The method integrates orthogonal demodulation algorithm, carrier synchronization algorithm and matched filtering and other algorithms. The simulation results show that under the conditions of different signal-to-noise ratios, the mean absolute value of the experimental ranging error is 0.328m, which exhibits good ranging accuracy and stability, and maintains a good ranging performance. Through the tests at different distances in the pool, the average ranging error of each group of tests is no more than 0.5m, which verifies that the ranging accuracy of the algorithm can reach the decimeter level and can effectively improve the positioning accuracy of the hydroacoustic system. The study shows that, although the time delay estimation method and the sound speed parameter have certain errors, the algorithm can still maintain a more ideal accuracy for the system ranging, help the hydroacoustic positioning system to carry out ranging, and effectively support the oceanographic mapping and geographic information applications in related fields.

Since the successful launch of the GRACE gravity satellites in 2002, they have been able to monitor the Earth's gravity field with high temporal and spatial resolution. Currently, GRACE products are primarily provided in the form of spherical harmonic coefficients. Although the algorithms for calculating the Earth's gravity field model using spherical harmonic coefficients are well-established, there is a lack of program design and algorithms suitable for beginners. This design, based on MATLAB software, provides programs for calculating geoid undulation, gravity anomaly, and equivalent water height using the CSR-06 spherical harmonic coefficients.

Antenna phase center model is one of the most important error correction models for high-precision satellite navigation and positioning. This paper uses a high precision mechanical arm to realize an outdoor absolute antenna phase center calibration algorithm based on three-difference observations, and successfully calibrate a multi-frequency navigation signal antenna phase model including Global Navigation Satellite System (GNSS), with a calibration accuracy of the model better than 1mm.

Water is an irreplaceable strategic resource for human survival and social development. Since it flows all the time and changes very fast, it is quite important to have the capability to monitor the change of water quantity and spatial distribution accurately, quickly and timely. With the fast development of remote sensing satellite industry in China recent years, monitoring surface water on a large scale several times a year has become a task of challenge but achievable. Anhui Province is about 0.14 million km² large and full of water resources. There are 31 big rivers, grade one to grade three, flowing through Anhui Province. This paper develops the DeepLab V3+ model with river buffer strategy to carry out river extraction both in the dry and wet seasons in 2022 and uses the area accuracy, the percentage of the area of extracted water to the actual water body area, to measure river extraction accuracy. The overall area accuracy of river extraction in Anhui Province is better than 95%. 2022 satellite river monitoring result in Anhui Province shows an unique phenomena which is the total river area during the wet season does not change much compared with that during the dry season. In 2022, the total area of the 31 grade one to grade three rivers is 1375.89 km² during the dry season while 1397.78 km² during the wet season, an increase of only 21.89 km² or 1.59%. Analysis is carried out with satellite images and meteorological information, this phenomena is caused by frequent rain and snowfall in the first half of 2022 and persistent drought in the latter half of 2022. This research will be helpful for water resources monitoring, water environment and ecology protection and management in Anhui Province, China.

GML data has strong semantic expression ability, strong modeling ability, open data structure, etc. It develops rapidly online and constitutes unique geographic information resources. This paper intends to carry out a study on geographic information retrieval technology based on GML, which can not only improve the query performance of existing online electronic maps, but also enrich the data content of online electronic maps.

To address the current issues of insufficient visualization and low digitalization level in chemical plant systems, this study proposes a construction method for a chemical plant digital twin based on a five-dimensional digital twin model. This method integrates multi-source data such as remote sensing images of chemical plants, plant area distribution maps, enterprise detection data, and historical archives to build a comprehensive system that includes a digital twin model and a multi-source monitoring database. By constructing a digital twin scenario and applying virtual-physical mapping technology, and combining web front-end and back-end technologies with Cesium three-dimensional globe technology, it achieves interaction between the virtual and real chemical plant environments and digital correspondence. The aforementioned functions can enhance the interactivity and three-dimensional visualization capabilities of the chemical plant decision-making system.

As to grasp the quantity of land for construction in the Yangzonghai Basin and analyze its spatiotemporal variation characteristics to alleviate conflicts between human activities and the environment, this research utilized remote sensing image data from 2005, 2010, 2015, and 2020 and using ArcGIS, Fragstats4.2 to employ various analytical methods including , dynamic change degree, assessment of different terrain conditions, transfer matrix analysis, geospatial information charting, and landscape pattern index calculation. The results indicate an increasing trend in the quantity of construction land in the research area from 2005 to 2020, with an increase in area of 809.31hm² and a dynamic change degree of 1.86%. At the county level, apart from Guandu District and Songming County, the other three counties exhibited a consistent increase in construction land over the study periods. Terrain conditions significantly influenced the spatial distribution of construction land, with preference for areas below 2000m in altitude and slopes less than 8° for development. Analysis based on geospatial information atlas and landscape pattern indices revealed fluctuations in construction land over the study period, with predominant changes involving conversions from unused land, forest land, and cultivated land, collectively accounting for about 95% of the changes, with cultivated land having the largest proportion. The amount of land for construction patches decreased from 2005 to 2020, while the average patch size and patch fractal dimension increased, indicating enhanced spatial aggregation and landscape connectivity. This study provides insights into the dynamics of construction land in the Yangzonghai Basin over the past 15 years, aiming to give data support and decision-making basis for village planning and ecological environment protection in the region.

The task of crevasses segmentation in remote sensing images plays an important role in the fields of crevasses change monitoring and ice shelf stability study. In order to improve the fragmentation problem of the current crevasses segmentation results, this paper improves the high-precision extraction method of crevasses by taking into account the morphological characteristics of the zigzag and staggered distribution of crevasses. The experimental results show that the improved algorithm improves the F1-score, MIoU and Recall by 1.01%, 1.69% and 2.43%, respectively, compared with the traditional algorithm, which effectively improves the connectivity of the crevasses extraction results, and it has the potential to be applied in crevasses segmentation scenarios based on remotely sensed images.

Ecological environment protection is one of the key concerns of the Chinese government. With the release of more and more satellite remote sensing datasets or products, it has become possible to conduct comprehensive evaluation and monitoring of global and local ecological environment quality (EEQ). In this paper, the multi-source data from satellite remote sensing, ground observations, atmospheric models, and source emission inventories are used to evaluate the EEQ in the Yangtze River Delta (YRD) region from 2012 to 2022 and to monitor its spatiotemporal change. Compared with the traditional ecological indicators, a new comprehensive remotely sensed ecological index (still denoted as RSEI) that additionally considers air pollution and human activities is proposed. Preliminary monitoring results show that compared to 2012, due to economic development and urban expansion, there was a decrease in forest and cropland in the YRD region in 2022, which has also caused a deterioration of the EEQ in some areas (approximately 30%). It also indicates that in future development, the government and the public should pay more attention to monitoring and protecting the ecological environment, in order to truly achieve green and sustainable development.

Ozone is an important trace gas in the atmosphere. Due to the uneven distribution of ground monitoring stations and the lack of spatial continuity, as well as the low time resolution caused by the orbital characteristics of polar-orbiting satellites and the inability of some monitoring instruments to obtain valid data at night, this study utilizes the advantages of the Himawari-8/AHI infrared band at night, which has high temporal and spatial resolution and features a strong ozone absorption band at 9.6μm. Using ERA5 TCO (Total Column Ozone) as label data and choosing the time range of 2019-2021 with the Himawari-8 full-disk as the research area, a nighttime TCO retrieval method based on the FCNN model was developed. This method can provide TCO with a spatial resolution of 2km×2km and a temporal resolution of 10 minutes. The validation accuracy shows Pearson’s r of 0.98, R² of 0.95, RMSE of 10.7DU, and MAE of 7.29DU. The model's retrieval accuracy in different regions was verified, revealing higher retrieval accuracy in low-latitude areas.

Assessing ecological vulnerability is crucial for targeted ecological restoration and management within a region. This study utilizes GIS and the Analytic Hierarchy Process (AHP) within the "Exposure-Sensitivity-Adaptability" (ESA) framework to assess ecological vulnerability in the Guangdong-Hong Kong-Macao Greater Bay Area. By incorporating nine impact factors across human, social, and ecological dimensions, we developed a five-tiered assessment system to identify and analyze the distribution patterns of ecological vulnerability across the region. The data is progressively organized to quantitatively analyze the overall distribution patterns of ecological vulnerability. Findings indicate that areas of higher ecological vulnerability in the northern regions of the Greater Bay Area necessitate stringent protective measures for the natural environment. Conversely, the central and southern regions, characterized by higher levels of urban development, stable ecological structures, and strong self-regulatory capacities, require careful planning and construction while preserving the existing ecological environment. This detailed evaluation supports informed decision-making for sustainable urban development and ecological management, fostering regional coordination and sustainability.

The increasing frequency of deadly earthquakes, devastating rainstorms, raging storms, and other extreme natural disasters is putting the whole society at great financial risk. In this paper, the evaluation criteria of natural disaster risk were divided into 9 indicators in three aspects: Perniciousness, Exposure, and Defense. The AHP, EWM, and CRITIC were then combined to calculate the mixed weights of those indicators. The TOPSIS model was utilized to calculate the risk factors, which were subsequently divided into four levels using Fuzzy Cluster. Finally, Logistic Regression and ARIMA models were employed to predict the trend in risk in the coming years. The model was applied to California and Japan, revealing that the risk of natural disasters in these regions may decrease between 2024 and 2027.

With the change of climate, extreme weather in many places in the world is becoming more and more frequent and intense. As one of the countries with frequent flood disasters in the world, it is urgent to do a good job in risk prevention under extreme weather conditions. The current study is focused on the flood disaster under extreme rainfall conditions in Fogang-Qingcheng-Qingqing-Yingde administrative division of Qingyuan City. Based on multi-source data, the flood inundation range and water depth distribution of 30m resolution in the study area were evaluated and mapped rapidly, on a large scale and with high resolution by HAND model. And compared with the remote sensing monitoring results of inundation during the flood of Beijiang No. 2. The results show that the spatial distribution of inundation area predicted by HAND model is in good agreement with the remote sensing monitoring results, but the predicted inundation range is more complete. Combined with satellite images, a relatively complete maximum inundation area can be quickly obtained. The inundation mapping of flood disaster under extreme rainfall conditions based on HAND model is feasible and reasonable. It provides powerful theoretical support for disaster prevention under flood disaster situation caused by extreme rainfall.

Karst areas are usually dry due to the thin thickness of the soil, posing challenges to the local vegetation ecology such as soil erosion and water scarcity, resulting in sparser vegetation than in other areas, further contributing to ecological imbalances and affecting human societies. This paper investigates the changes of vegetation cover over 22 years for Guangxi, which has been biggest karst area in global. Based on MOD13Q1 remote sensing imagery data, we conducted an analysis of the spatiotemporal patterns of vegetation dynamics in Guangxi using NDVI index. Additionally, this study employed residual analysis to investigate the contributions of anthropogenic and natural factors on vegetation NDVI. The results indicate that, (1) from 2001 to 2022, the vegetation NDVI fluctuations have increased, with a gradual decrease in spatial distribution from the periphery to the center. (2) In most areas, vegetation NDVI has shown significantly improved conditions, accounting for 59% and 20% of the total area, respectively, while degraded areas constitute only 6%. (3) Residual analysis indicates that 88.5% of the regions are influenced jointly by climate and anthropogenic factors, with the latter's contribution predominating over the former, primarily concentrated in the southern regions of Guangxi. (4) Interaction detection revealed that the strongest effects were produced by interactions centered on land use, while the influence of interactions between temperature, precipitation, and slope is relatively small. These findings can provide data support for the green low-carbon transformation and the development of green productivity in Guangxi.

Aerosol optical depth (AOD) is an important physical parameter to characterize atmospheric pollution, and inversion of AOD is of great significance to monitor the atmospheric quality of Qilian Mountain National Nature Reserve. This study employs MODIS remote sensing data and utilizes the Dark Target algorithm, the Deep Blue algorithm, and a fusion of both to retrieve AOD. The findings are as follows: 1) The average AOD values retrieved by all three methods generally show an initial increase followed by a decrease. The fusion algorithm covers a larger area and achieves accuracy above 80%. 2) The Dark Target-Deep Blue fusion algorithm exhibits high correlation with the MCD19A2 product, with a correlation coefficient exceeding 70% and consistent trends in high and low AOD values. 3) Spatial distribution and factor analysis from 2006 to 2021 indicate a shift in AOD changes from a dispersed to a more clustered distribution, with a significant negative correlation between NDVI and AOD. Decrease in AOD across 2006 to 2021 reflects significant recent improvements in ecological restoration of mines within the reserve.

Spatial simulation and projection for municipalities in arid areas affected by climate and complex topography using cellular automata (CA) modeling is essential to promote urban sustainability. In this paper, a new BHO-CA model is developed using the black hole optimization (BHO) algorithm and applied to the urban simulation of Urumqi for the years 2010 and 2020. The results indicated that Urumqi's urban growth in the period 2000-2020 occurred mainly in the area perimeter of the city center, with obvious agglomeration characteristics; the overall accuracy for the BHO-CA model in 2010 was 98.43% and the figure of merit (FOM) was 43.37%, the overall accuracy in 2020 was 98.52% and the FOM was 37.06%, adequately proving the validity of this new model. Two possible scenarios were projected for Urumqi in 2030 using the BHO-CA model to contribute to the optimization of urban development and planning strategies.

In recent years, Sichuan Province has taken strong measures to protect green space. Revealing change characteristics of surface greenness will help to understand the ecological status of surface vegetation and provide a basis for ecological management. Normalized Difference Vegetation Index (NDVI) is produced by satellites to quantify surface greenness. To detect trends of surface greenness in Sichuan, FengYun-3D (FY-3D) mersi-ii monthly NDVI from January 2020 to December 2023 are utilized. Linear trend fitting and coefficient of variation analysis showed that the annual average NDVI increased from 2020 to 2023 in most areas. The slope of the annual average NDVI trend reached 0.0195 per year. Further, significant increasing trends of seasonal average NDVI appeared with rates of 0.0274, 0.0048, 0.0242 and 0.022 per year in four seasons. Annual and seasonal average NDVI increased in most plateaus and north-eastern Sichuan except summer. In north-western Sichuan, NDVI decreased all the time. In summer, most regions showed decreasing trends.

When constructing a 3D surface grid using point cloud data closely matched with high-resolution satellite stereo images, holes often appear due to matching failures or limitations of triangulation algorithms, affecting the grid's integrity and the accuracy of subsequent applications. To solve this problem, a boundary contraction algorithm is studied for repairing mesh holes. The research focuses on different terrain conditions such as mountains, plains, and cities, and evaluates the effectiveness of boundary contraction algorithms in repairing holes. Using visual inspection methods, the quality of the repaired mesh is checked to ensure its integrity, smoothness, continuity, and topological correctness. The experimental results of GF-7 satellite images show that the boundary contraction algorithm can effectively fill holes in various terrain environments, significantly improving the geometric quality and overall integrity of the mesh, verifying its feasibility as a reliable mesh repair method, and providing strong support and verification for the application of the boundary contraction algorithm in actual terrain data processing.

Ecological sensitivity assessment is an important basis for formulating ecological environment planning and regional environmental policies. This manuscript is based on the theory of ecological sensitivity and utilizes GIS data platforms to select six sensitive factors, including slope, aspect, elevation, precipitation, temperature, and vegetation, from the Zhengxiangbai Banner in Xilingol League, and construct an evaluation index system. Then, the Analytic Hierarchy Process was used to determine the weights of each sensitive factor. Divide the sensitivity of the six factors into five levels: extremely high sensitivity, high sensitivity, medium sensitivity, low sensitivity, and non sensitivity, and finally obtain the spatial distribution of ecological sensitivity of the Zhengxiangbai Banner. The results show that the ecological sensitivity of Zhengxiang Baiqi is mainly medium to high sensitivity, with extremely high sensitivity, high sensitivity, medium sensitivity, low sensitivity, and non sensitivity areas accounting for 9%, 20%, 27%, 27%, and 16% of the total area, respectively. Based on existing data, analyze the ecological sensitivity of Zhengxiangbai Banner and obtain a comprehensive ecological sensitivity evaluation. Based on the evaluation results, propose ecological protection suggestions to provide scientific basis for the ecological protection of Zhengxiangbai Banner.

Traditional soil Ni detection methods are time-consuming, labor-intensive and expensive, and can’t achieve large-scale, real-time and dynamic monitoring. To solve the above problems, this study proposed a hyperspectral remote sensing inversion method based on Principal component analysis (PCA) and Multi-scale geographical weighted regression (MGWR), in order to achieve rapid and accurate detection of soil Ni in large areas. In this study, the soil spectrum was transformed by First Derivative (FD), and the sensitive bands of soil Ni were extracted by PCA method. Two principal components were extracted as independent variables and input into Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR) and MGWR models for modeling and inversion. To this end, a total of 78 sampling points were selected in Longkou City. The results showed that (1) FD transform can effectively enhance the sensitivity of soil characteristic bands. (2) MGWR model was better than GWR and OLS, the R², AIC and RMSE of MGWR were 0.536, 187.643 and 0.172, respectively.

Wetland classification using remote sensing data is significant for ecological environmental protection. In this paper, a high-precision wetland classification method was proposed to address the problem that wetland categories are easily misclassified. To improve the application of BiSeNet in wetland classification, the feature attention refined module (FARM), feature fusion module (FFM) and Dense connected Atrous Spatial Pyramid Pooling (DenseASPP) were designed to form Dense-BiSeNet. In addition, we adopted Short-Term Dense Concatenate network (STDC network) as backbone network. In order to validate the effectiveness of the model, we performed comparison on the Yellow River Delta and Dafeng Nature Reserve dataset between Dense-BiSeNet and other advanced wetland classification methods. The results show that the model has high accuracy and has objective potential for application.

Vegetation has the ability to regulate the runoff and sediment yield in the underlying surface, thus, watershed vegetation monitoring is of great significance to the effectiveness of soil and water conservation programs in the Loess Plateau watersheds. Taking the typical Chabagou watershed, the first sub-region of the Loess Hilly and Gully Region, as the research area, this research considers the unmanned aerial vehicle (UAV) hyperspectral data as the main data and draws the auxiliary data from the UAV orthophoto model, field surveys, and surface feature spectral measurement, in an attempt for multi-phase aerial surveying and data acquisition. Based on the multi-phase vegetation spectral features extracted, the integrated SVM-SAM method is suggested for vegetation classification. The accuracy evaluation shows that: (1) the UAV-based hyperspectral measurement is capable of generating centimeter-level hyperspectral data of the underlying surface. It is proven applicable in the Loess Plateau; (2) by virtue of a combination of UAV data acquisition, field survey, and surface feature spectral measurement, an elaborate database with high precision for vegetation features is attainable; (3) this integration possesses an overall classification accuracy of 82.82% and a Kappa coefficient of 0.785. (4) the research results can effectively enhance the accuracy of vegetation monitoring, and improve its spatiotemporal scale. It is expected that the research achievements will provide essential data for exploring the soil and water erosion mechanisms in the Loess Plateau and establishing soil erosion models, which demonstrates great significance to the practices of soil and water conservation in the region.

Aiming at the problem that the traditional deep learning prediction model has low accuracy in predicting sea ice density and is difficult to capture the spatiotemporal variation of sea ice density, this paper constructed an improved spatiotemporal prediction model ConvLSTM to predict the Arctic sea ice density. A deep learning network was used to capture temporal and spatial changes in Arctic sea ice density over a 45-year period. Finally, the monthly average data of 2023 is output and displayed as the result. The results show that compared with the traditional prediction model, the proposed model is superior to the LSTM algorithm in prediction accuracy. LSTM network can overestimate the extent of sea ice density data to different degrees, especially in the melting season of sea ice, and ConvLSTM model has a better performance. The RMSE, MSE, MAE, R2 and SSIM of the model in this paper are 0.0644, 0.0043, 0.0145, 0.9318 and 0.9427, respectively.

This study takes Liangping District in Chongqing as an example. Utilizing ENVI5.6 software, the 2023 remote sensing image data of the study area were interpreted through supervised classification of land use. By combining the principles of landscape ecology and geographic information system (GIS) technology, the current characteristics of the land use landscape pattern of the wetland city of Liangping were analyzed. Landscape indices were obtained using Fragstats 4.2 software, and the current characteristics of Liangping’s urban landscape pattern were analyzed through the correlation analysis of these indices. Results: Among natural landscapes, wetland patches dominated by grassland have the largest area, highest dominance, and highest aggregation degree. Grassland is the matrix landscape of Liangping. The number of wetland patches is the highest, specifically characterized by the prevalence of small and micro wetland landscapes. These landscapes have the highest shape complexity and are the least affected by human intervention.

Based on the MODIS Net Primary Productivity (NPP) data from 2000 to 2014, the spatial and temporal evolution of NPP and its multi-dimensional driving factors in Hubei Province were quantitatively analyzed by using RS and GIS technology. The results show that: 1) The annual NPP in Hubei province presents the increasing trend in space, from north to south, which the NPP value is higher at the southwest mountainous and east region, while the northwest is the lowest. In the past 15 years, the annual NPP in Hubei Province showed a slight decline, with the maximum in 2002 and the minimum in 2001 and 2011, which was 494gC m^-2a^-1. 2) The annual average NPP was significantly positively correlated with the annual average rainfall, and had no significant correlation with the annual average temperature. However, the NPP reached the maximum when the annual average temperature was 16-16.5°C.

Helan Mountain region serves as a crucial ecological security barrier in northwest China. Excessive mining has severely impacted the ecological environment, making it urgent to assess ecological quality changes for better governance. Utilizing long-term MOD17A3 remote sensing data, along with DEM, vegetation type, and land use data, this research quantitatively examines the spatiotemporal variations in vegetation Net Primary Productivity (NPP) within the Helan Mountain National Nature Reserve in Ningxia over the period 2001 to 2020, assessing alterations in ecological environment quality. The results indicated: (1) The average NPP exhibited a declining trend from southwest to northeast. (2) Throughout the study duration, the mean annual NPP exhibited a variable upward trend, accompanied by an average yearly growth rate of 1.66 gC/m²/yr. (3) Vegetation NPP exhibited spatial heterogeneity. The southwest region had a marked upward trend, the northeast region a moderate increase, while some areas experienced a decline due to human disturbances. The findings can inform decisions on ecological restoration and regional management across different areas of Helan Mountain.

Soil moisture is a key variable in terrestrial surface systems, and obtaining high-resolution long-term soil moisture information is of great significance. This article conducts inversion experiments based on multi temporal Sentinel-1 radar data and Sentinel-2 optical data from 2020 in the Shandian River Basin. Firstly, within the framework of the water cloud model, three different optical remote sensing indices were used to estimate the vegetation moisture content in the model, in order to correct the scattering contribution of the vegetation layer. On this basis, a soil moisture observation equation system is constructed based on the Dobson soil dielectric model and the Alpha approximation model. The least squares method is used and a regularization term is added to solve the soil dielectric constant, thereby achieving the inversion of soil moisture. The study used corresponding ground observation data for verification, and the results showed that the inversion of soil moisture values had good consistency with ground observation values. Especially based on normalized difference moisture index (NDMI), the inversion effect is the best after removing the backscatter effect of vegetation. The correlation coefficient (R) between the inversion results and ground measurement data reaches 0.864, and the root mean square error (RMSE) is 0.038cm³/cm³, indicating that this method can accurately estimate soil moisture. At the same time, analyzing soil moisture changes in time series, reflecting their seasonal fluctuations and correlation with regional precipitation events, verified the applicability of the inversion method considering vegetation phenology in the Shandian River Basin.

Ammonia, a major alkaline gas in the atmosphere, reacts with acidic gases, speeding up PM2.5 formation and negatively affecting the ecological environment. Wind speed is one of the key factors influencing the distribution of atmospheric ammonia. This study utilizes the observational data from the Infrared Atmospheric Sounding Interferometer (IASI) and the M2I1NXASM dataset from MERRA-2 meteorological reanalysis data. The emission and lifetime of point source ammonia were estimated using the rotational wind direction method and particle swarm optimization (PSO) algorithm. Golmud, an inland city in China, was selected as the study area to analyze NH₃ emissions from May to September from 2007 to 2023. The results indicate that the NH₃ emission intensity in the Golmud area is approximately 5.88×10^-12kgꞏm^-2s^-1, with ammonia persisting in this region for 14.61 hours. By comparing the estimation results of this study with existing emission inventories, the reliability of the adopted method was validated, demonstrating the potential of using satellite data to constrain emission inventories.

Unreasonable threshold setting is an important factor leading to high false alarm rate of landslide monitoring and warning. In order to obtain accurate threshold warning intervals for most landslide warning levels, we taken GNSS horizontal displacement parameters as an example, selected 60 successful landslide warning cases in Ankang City, Shaanxi Province, and extracted the corresponding GNSS horizontal displacement changes. After data filtered, applied parameter standard normalization and K-means clustering algorithm to obtain four centroid positions adaptively, thus divided five landslide monitoring threshold warning level intervals. Used this threshold level standard, we judged the threshold warning level of the remaining 5 successful landslide warning cases and obtained good warning results. The research showed that the K-means clustering method can obtain the monitoring and warning threshold level suitable for most landslide hazard points, which was conducive to reduce the false alarm rate of landslide disaster threshold warning.

This study proposes a novel thinning algorithm to effectively preserve terrain features in response to the problem of increased processing burden caused by high density of airborne laser point cloud data. This study is based on constructing a spatial triangulation using a regular triangulation network, with the maximum distance point between the laser point cloud and the spatial triangulation within the projection range of the triangulation as the terrain feature points to be retained. This algorithm achieves a significant reduction in data volume while ensuring the main terrain features of point cloud data, achieving good results in balancing the efficiency and accuracy of the algorithm.

Taking the coal seam mining under the river in a certain well field in Chongqing as an example, in this article, the development characteristics of overlying rock damage are analyzed and studied in coal seam mining by calculating the height of the caving zone and the height of the water conducting fracture zone of the minable coal seam. To ensure the safety of surface water bodies and production, the probability integration method is used to analyze the mining impact on the riverbed directly above the coal seam in the mining area. Calculation and expected results indicate that the vertical distance between the mining coal seam and the water barrier is much greater than the calculated vertical height of the waterproof and safe coal rock pillar, and it has little impact on the riverbed elevation and river channel. It will not change the position and trend of the river bend, and affect the vegetation growth on both sides of the river. Therefore, underwater mining is safe and feasible.

Extreme precipitation events are expected to happen more frequently due to global climate change, affecting societal development and human life. Therefore, six extreme precipitation indices were computed using the daily precipitation data of 139 meteorological grid points in the Liaohe River Basin (LRB) from 1961 to 2020. The spatial and temporal characteristics of the extreme precipitation indices in the basin were then examined using trend analysis and mutation testing., and their correlation with latitude, longitude, altitude, and atmospheric circulation indices were also analyzed. The results show that: The extreme precipitation indices, with the exception of RX1day, all showed an increasing trend; however, R95p, R99p, R20, R25, and CWD all showed decreasing trends; none of them displayed obvious mutation years; decreases within the basin dominated the changes in the extreme precipitation indices, with the northeastern part of the basin experiencing the greatest decreases; the majority of the extreme precipitation indices demonstrated a significant positive correlation with longitude and a significant negative correlation with latitude and elevation. The majority of the extreme precipitation indices have a considerable negative correlation with the Sunspot Number (SN) in the atmospheric circulation.

As human societies undergo rapid urbanization, coastal areas have emerged as prominent regions. The concentration of urban populations and the expansion of built-up areas have exerted a significant impact on coastal wetlands, leading to concerns over wetland area shrinkage and degradation, which have become the focal points of academic investigation. This study aims to examine the changes in coastal wetlands on the Shandong Peninsula over the past 30 years using Landsat remote sensing imagery. Additionally, it explores the influence of human activities on these wetlands by employing methods such as the human disturbance intensity index model. The following conclusions have been drawn from the study: (1) Between 1990 and 2020, there was an overall increase in wetland areas, primarily driven by a substantial expansion of constructed wetlands. The rate of increase was significant. However, natural wetlands exhibited a declining trend, with a considerable portion of previously unused land, including natural wetlands, being converted into constructed wetlands. (2) The changes in wetland areas between 1990 and 2020 can be attributed mainly to human activities. (3) Analysis of the development patterns in different cities reveals variations in the impact of human activities on wetland areas. It is evident that the influence of human activities on coastal wetlands differs among various cities.

Based on patent data spanning 2000 to 2020 from 168 counties in Hebei Province, this study investigates the spatio-temporal evolution of county-level innovation and its determinants using methodologies such as standard deviation ellipses, spatial Markov chains, spatial autocorrelation, and geographical detectors. The findings indicate: (1) A rising trend in overall innovation levels across Hebei's counties, with a shift in innovation structure from low concentration to multi-type equilibrium. Core areas of county-level innovation, centered around Shijiazhuang, Langfang, and Tangshan, are gradually forming spatially, transitioning from a dispersed “point-like” pattern to a “concentrated and contiguous” distribution. (2) The “Northeast-Southwest” innovation pattern at the county level remains stable, but exhibits unstable focal points shifting predominantly southwestward, indicative of a trend towards following innovative resources and emerging industries. (3) The spatial distribution of innovation levels in counties is shaped by multiple factors, including temporal dynamics and economic development models, reflecting noticeable shifts over time.

Landscape ecological risk assessment provides a basis for determining the potential adverse impacts of external risk sources on regional ecosystems. This study selected evaluation indicators from the landscape ecology perspective to construct a risk assessment system. We assessed the risk of Jilin province from 2000 to 2020 based on a 1km moving window, explored the spatiotemporal variation characteristics of risk areas using the center of gravity shift model, and revealed the response relationship between landscape ecological risk and terrain gradient by combining the topographic position index and distribution index. The results showed that the risk of the province first increased and then decreased, with a predominantly the lowest risk distribution, accounting for over 32% of the province. The changes mainly occurred in the lowest risk regions, with the area decreasing and then increasing, and the center of gravity shifting southeastward and then northwestward. The distribution of risk across different terrain gradients showed significant differences. The lowest risk zones had a distribution advantage at medium, higher, and the highest gradients. The lower risk zones shifted towards higher gradients after 2010, while the medium, higher, and the highest risk zones exhibited the most significant distribution advantage at low gradients. This study aimed to provide scientific reference for maintaining the smooth development of regional ecosystems.

Flooding is one of the most destructive and sudden natural disasters, and it is of great significance to quickly and accurately extract the flood inundation range and assess the disaster situation for emergency rescue and disaster loss reduction. Flood monitoring using SAR backward scattering coefficient often needs to extract the pre-disaster water body and the water body in the disaster firstly, and then carry out the phase subtraction operation in order to determine the flood inundation range. In this paper, based on the object-oriented classification method, we constructed a rapid extraction method of flood range with joint backward scattering coefficient and interferometric coherence. Taking the July 2020 flooding in Poyang Lake Basin, Jiangxi Province as a test area, the 2-view Sentienl-1B GRD and 2-view Sentinel-1B SLC dual-polarized data before and during the disaster are used as the data source, and firstly, the RGB pseudo-color composite display is carried out by introducing the interferometric coherence to analyze whether the flood disaster information can be accurately reflected, which provides a basis for the subsequent selection of the flood samples, and then, the flood extent is quickly extracted by using the object-oriented classification method. Then, the feature fusion is carried out by using pre-disaster and in-disaster dual-polarization backward scattering coefficients and pre-disaster-in-disaster interferometric coherence, and finally, the direct classification extraction and segmentation of flood, normal water, and non-water ranges are accomplished based on the object-oriented Support Vector Machine Classification method, and combined with the data of the AI Earth China 10-meter Feature Classification product to carry out the disaster assessment. The experimental results show that the overall accuracy and Kappa coefficient of the method in this paper are 95.79% and 0.88, respectively, of which the extraction accuracy of the flood range reaches 94.57%. The method constructed in this paper is fast and direct compared with the traditional flood extraction method, and can provide new ideas for flood emergency response.

The Yellow River Basin plays a crucial role as an ecological barrier in China. The rapid urban population growth in these agglomerations results in a continuous increase in urbanization levels, leading to a sharp rise in the demand for land for residential and public construction. As a consequence of the population surge driven by rapid urbanization, there is an increased need for food in the region. Within the arable land protection system, constraints such as "arable land occupation and compensation balance" lead to the conversion of part of eco-land into production and eco-land to compensate for losses caused by urban expansion. The current land use classification system primarily focuses on production and habitation purposes but fails to adequately consider ecological functions. This study analyzes the ecological land use changes across the seven main urban clusters within the Yellow River Basin over the past two decades, utilizing data on land use from 2000 to 2020. The analysis includes examining the spatial distribution characteristics of different land categories, quantifying level fluctuations, assessing inter-class spatial arrangement alterations, and studying shifts in land categories through quantitative, exchange, shift-level, and intensity analysis methods. By examining the trajectory of ecological land use transformation and developmental trends in these seven urban clusters, several conclusions are drawn from this study. Between 2000 and 2020, there was a discernible shift in active land utilization towards residential and industrial purposes within these clusters. In contrast, other land categories displayed a trend opposing the transition to ecological land use or the promotion of ecological resource production. Particularly noteworthy is the observed transition in the Central Plains urban clusters - initially showing a net decrease in ecological land use area from 2000 to 2020, which notably reversed into a net increase specifically between 2010 and 2020. This transition highlights the positive impact resulting from efforts focused on ecological preservation.

Water is the source of life, the essence of production and the foundation of ecology. Water resources is one of important elements of natural resources and satellite technology is an irreplaceable method of high-quality water resources monitoring. With Chinese Ziyuan and Gaofen serial satellites as well as the Landsat satellites, a 1970-2022 long-time monitoring and analysis is performed on 132 major lakes which areas are above 1km² in Anhui province, China. This paper use the OTSU threshold method as well as best-fit buffer area algorithms to preform the lake area extraction. Results reveals that since 1970, the total water surface area of the 132 major lakes in Anhui Province has shown a fluctuating trend. The smallest total lake area was 3650.33km² in 1970. Due to severe torrential rains and flooding in the Yangtze and Huaihe River basins in July 2020, the total lake area reached its maximum of 4840.60km² in 2020, an increase of 1190.27km² or 32.61% compared to 1970. The lake long-time monitoring results will be beneficial to the related research on water resources investigation, monitoring and evaluation in Anhui province, China.