This PDF file contains the front matter associated with SPIE Proceedings Volume 10783, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Sentinel-2 is a constellation of satellites, Sentinel-2A and Sentinel-2B, launched on 23 June 2015 and 7 March 2017 respectively. They carry virtually identical decametric resolution multi-spectral imager (MSI) with 13 spectral channels in the visible, near infrared and short wave infrared (Drusch et al., 2012). Together, these imagers provide better than 5-day revisit of the Earth’s land surfaces. The imagers have been designed to record spectral bands directly related to widely used land surface variables, including vegetation biophysical parameters (Martimort, 2007). The main aim of this research study is to validate the vegetation biophysical parameters estimated from Sentinel-2A data over agricultural sites located in Canada. The validation task was performed for leaf area index (LAI), vegetation fractional cover (fcover) and canopy water content (CWC). A study area located in Manitoba (Canada), between the latitudes 49.3°N – 49.8°N and longitudes 97.7°W - 98.2°W is selected. For this site, ground-based measurements of vegetation parameters were sampled for 50 fields during the SMAP Validation Experiment 2016 (SMAPVEX16, http://smapvex16-mb.espaceweb.usherbrooke.ca/home.php) field campaign conducted over two 2-week periods from 8–20 June and 10–22 July, 2016. First, the Sen2Cor processor (Mueller-Wilm et al., 2017) was used to convert the available Sentinel-2 L1C product L2A product (top of canopy reflectance). Then, ESA-SL2P algorithm (Weiss and Baret, 2016) was used to derive the vegetation biophysical parameters (LAI, fcover and CWC) over a study area. A 3x3 pixel moving average filter was applied to the derived maps to minimize uncertainties due to product gridding. First validation results showed an important correlation between Sentinel-2 estimates and ground data for LAI (R2 = 0.77) and fcover (R2 = 0.90). However, a lower correlation was obtained for CWC (R2 = 0.49). In terms of RMSE, the error between Sentinel-2 derived data and ground data is about 0.71 for LAI, 0.10 for fcover and 0.42 Kg/m2 for CWC. These results suggest that for the most part the ESA algorithm falls within specification for retrieval of these parameters for crop types evaluated although improvement for dense canopy conditions should be pursued. References 1. M. Drusch, U. Del Bello, S. Carlier, , O. Colin., V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese, and P. Bargellinid, “Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services”, Remote Sens. Environ, vol.20, pp.25–36, 20, 2012. 2. P. Martimort, M. Berger, B. Carnicero, U. Del Bello, V. Fernandez, F. Gascon, et al. “Sentinel-2: The optical high-resolution mission for GMES operational services”, ESA Bulletin, vol. 131, pp. 18–23, 2007. 3. U. Mueller-Wilm, O. Devignot, L. Pessiot, “Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2.4”, Issue 1, 2017 [http://step.esa.int/thirdparties/sen2cor/2.4.0/ Sen2Cor_240_Documenation_PDF/S2-PDGS-MPC-L2A-SUM-V2.4.0.pdf]. 4. M. Weiss and F. Baret, “S2ToolBox Level 2 products”, Version 1.1, Date Issued: 02.05.2016, [step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf].

The two gratest challenges of remote sensing in agricultural monitoring is to provide reliable information on yield and crop variability that is temporally consistent to efficiently analyze the crop in its development cycle, and spatially scalable, with sufficient spatial resolution in adition with large aquiring area which allows analysis inside farm and its regional context. With the technological advancement of orbital sensors mainly in relation to the availability of free data (optics and SAR) provide by Sentinel’s satélite in the Copernicus program, they allow monitoring of agricultural areas every 5 to 12 days, also associated with the processing of data acquired in cloud based plataforms, making the more efficient management of agricultural production. Therefore, the use of SAR data associated with optical data allows the extraction of information on crop development and its productive capacity. Considering this context, the objective of the study was to analyze the temporal development of the backsacattering obtained by the Sentinel-1 satellites, comparing it with vegetation indices associated with biophysical parameters obtained by Sentinel-2 images along the phenological development of the Maize second crop 2017 and soybean crop 2018, and the possibility of integration of the sensors for the agricultural monitoring. The product used for the SAR data acquired by Sentinel-1 was collected in the Interferometric Wide Swath Mode (IW) with VV + VH polarization. The level of processing used was the Ground Range Detect using the following steps: 1. Thermal noise removal 2. Radiometric calibration 3. Terrain correction using SRTM 30. The final terrain corrected values are converted to decibels via log scaling (10 * log10 (x )) and quantized to 16-bits. The images acquired by the Sentinel 2 satellite were used at the L1-C TOA processing level, generating three vegetation indexes associated with NDVI vegetative vigor, NDWI water content and PSRI senescence. The online processing platform Google Earth Engine was used for data processing. We analyzed all Sentinel 1 and 2 acquisitions for maize in the period from 01-03-2017 to 08-01-2017, and for soybean the period from October 10, 2017 to March 1, 2018. To validate the data, field control points were used in areas of the same seeding date with weighing of productive mass. The data were first and correlated for maize 2017 and soybean 2018, characterizing the phenological behavior of each crop for maize and soya. Backscatter data were compared using vegetation indices and correlated using Pearson's correlation test and simple linear regression. The results obtained showed phenologycal characterization for the two crops with the high correlation between VV polarization and NDVI for corn, obtaining values of r² = 0.853 and for soybean r² = 0.678, both with p-value <0.005 . These results evidenced the integration capacity of the two technologies in the monitoring of agricultural crops, with emphasis in spite of the lower values of NDVI for the soybean crop where it is more difficult to acquire optical images due to the intense amount of associated precipitation.

Nowadays, optical and radar remote sensing data are increasingly used for land-cover/vegetation mapping and monitoring. Their technical capabilities and tools are improving all the time and provide more accurate results. By the recent arrival of the Sentinel-1 and Sentinel-2 series, available free, processing and methods of analysis must be increased more and more in the field of cartography. This paper aims to present vegetation mapping method in the Pays de Brest area by using a time series stacking of Sentinel-1, Sentinel-2 and SPOT-6 satellites data using the algorithm Random Forest supervised classification. The types of vegetation mapping in first time are those belonging to the major vegetation types, but especially those that can be observed on the processed images that are the Sentinel-1, Sentinel-2 series and SPOT-6. The types of classes considered for this study are: no vegetation, forest and undergrowth, moors and lawns, summer crops, winter crops, grassland and water. Several time series stacking has been made on that series containing 140 images radar representing different dates (2017) and the best combination method is to use both the two polarizations VV and VH to the calculation of the matrix of confusion. On the other hand, combinations of SAR images with different vegetation indices (NDVI, NDWI, S2rep, IRECI) calculated from the Images Sentinel-2 have been made. The series of times series stacking ends with combinations between SPOT-6 and Sentinel-1. The times series stacking Sentinel-1, Sentinel-2 and SPOT-6 are satisfactory, with an overall accuracy that reaches 93%. Such precision is very good for data that are available free.

This article analyzed the application of Sentinel-2 and Sentinel-1 for monitoring and controlling irrigation in sugarcane production by vinasse (residue of industrial ethanol-sugar manufacturing), a sub-product highly rich in organic matter and minerals, which is used as fertilizer after harvesting, during the months of drought for the plant to regrow. Irrigation with vinasse is a complex process and its lack of use or excess can causes important losses in tonnage of sugarcane. The insufficient spraying of vinasse was identified in the Sentinel-2 image of April 2017 in a farm plantation as the principal cause of stress in the development of the plant in an area representing 21% of the plantation (100 hectares). This represented a total loss of 4335 tons of cane (≈$90000). The identification of the cause of the reduced growth was made possible through analyzing a Sentinel-2 image of July 2016 that showed clearly that no vinasse was spread in the same 100 hectares with cane growing problem. The presence/lack of vinasse was easily detectable using the visible bands of the S-2 image but the difference in growth/stress was better related to the red edge and near infrared bands in the January image. To thoroughly complete our investigation we also acquired a Sentinel-1 radar image of April 2017 where the lack of vinasse could not directly be identified but the effect on the growth can be readily interpreted. This evaluation was extended to other farming facilities and a coefficient of determination of -0.7 was obtained between the production rate per hectare and the plantation where lack of vinasse could be identified in 41 fields. We proposed a systematic approach to monitor the spreading of vinasse with Sentinel-2 images and Sentinel-1 radar images in an effort to increase efficiency.

The Landsat and Modis satellites have been providing spectacular imagery of Earth’s surface for over 40 years and 20 years respectively. Joining them is another recent initiative by European Space Agency (ESA) of multispectral Sentinel satellites first launched in 2016. These together provide a variety of vegetation indices, in particular, Normalized Difference Vegetation Index (NDVI) which is widely adopted to analyze vegetation patterns and crop phenological characteristics to understand and predict expected yield, pest attacks, water deficiency etc. However, a big challenge in using the data from multiple satellites to derive common integrated insight is the appropriate bias correction especially as the spatial and temporal resolutions do not match across various satellites. For instance, Landsat has 30mx30m spatial resolution (SR) and 16 days temporal resolution (TR), Modis has 250mx250m spatial resolution but 1 day temporal resolution whereas Sentinel has 10-60mx10-60m spatial resolution with close to 5 days temporal resolution. Along with varying resolutions, presence of significant cloud cover for long time periods is also a factor which affects the performance of vegetation health models based on raw data from these satellites especially in countries like India with long monsoon seasons. We propose a novel blending algorithm to bring together remote sensed data from Landsat, Modis and Sentinel satellites and derive a new enhanced High Definition Normalized Difference Vegetation Index (HDNDVI) for daily vegetation health monitoring at the fine spatial resolution of Sentinel data. The proposed algorithm analyses past observed NDVI data from the above mentioned three satellites, corrects for anomalous sensor data due to cloud cover and sensor saturation, determines the bias between different combinations of satellite data and applies bias correction to derive the HD-NDVI. As it determines the bias from the recent past for a given region, the proposed algorithm can be scaled globally and applied across different geographic regions and seasons. In this paper we present the derived HD-NDVI from our algorithm for Burdwan region of West Bengal in India across the full crop cycle of approximately 98 farmers. We show HD-NDVI images and the latest images of all the three satellites to highlight the differences and significant gain in spatial and temporal resolution achieved by HD-NDVI. The correlation between HD-NDVI and Landsat, Sentinel and Modis satellite data for the entire crop cycle is also analyzed across the full crop cycle. HD-NDVI has the highest correlation with Sentinel and the average, maximum and minimum Pearson Correlation coefficients are 0.9611, 0.992 and 0.59 respectively. Second in terms of correlation is Landsat with average, maximum and minimum correlation coefficients of 0.898, 0.982 and 0.048 respectively. The lowest correlation is with Modis with average, maximum and minimum correlation coefficients of 0.69, 0.87 and -0.00113. The above correlation numbers indicate that HD-NDVI provides daily NDVI at the spatial resolution of Sentinel. Summarizing, HD-NDVI acts as a virtual satellite that has the best of temporal and spatial resolutions compared to the widely adopted Landsat, Sentinel and Modis satellites.

The fast-growing Eucalyptus trees are important for a renewable energy source, carbon storage as well as the variety of industry. For Eucalyptus tree plantation inventory and monitoring, Canopy Height (CH) and Above Ground Biomass (AGB) are required parameters. However, the traditional inventory methods are label intensive, timeconsuming, and inaccurate. This study, we presented the method for estimating CH and AGB from Eucalyptus plantation based on the very high spatial resolution imagery (~5 cm.) from Unmanned Aircraft Vehicle (UAV). To estimate AGB, we first generated the digital surface models (DSMs) from 3 D point-cloud. Then, the digital terrain models (DTMs) were generated from Real Time Kinematic (RTK) Differential Global Positioning System (DGPS) surveying technique. The CH was calculated based on the different of DSM and DTM. We located individual trees location using tree detection software. Next, the allometric equation was generated from the 20 samples (tree) and their subsamples (e.g. foliage, litter, branch). The correlation between sampling tree height and tree weight was relatively high (R² = 0.977) with the exponential relationship. We then apply the correlation to estimate tree weight of the remaining trees. Next, the individual UAVs-based AGB was calculated based on the ratio of fresh weight and dry weight. The total AGB was 3450 kg ha^-1.

Unmanned Aerial Systems (UAS) are a cost-effective means of collecting forest data conventionally used above the forest canopy. Where forest canopies are dense, limited information about stem structures can be extracted directly due to obscuration by foliage. In these circumstances, complementary ground-based methods including manual measurement and terrestrial laser scanning are deployed, but these techniques are often limited in terms of the scope and scale of data collected by factors including time, field cost and site accessibility. This paper describes the application of a UAS flown below the forest canopy as an efficient and effective approach for stem measurement in areas where the canopy is difficult to penetrate, and as a potential solution to measuring trees in areas of dense undergrowth. The study sites were scanned with a helicopter-mounted VUX-1LR LiDAR sensor and the resulting point clouds were used as a comparison dataset. The measurements extracted from these point-clouds were compared with ground-based measurements of diameter at breast height and relative positions. The below-canopy UAS and the VUX-1LR at 30m had the lowest root-mean-squarederror (RMSE) of 4.1cm, followed by the VUX-1LR at 90m with a RMSE of 4.4cm. The VUX-1LR 60m flight was the most consistent with the highest coefficient of determination, however due to a positive bias, there was an RMSE of 4.5cm. The photogrammetry-based, below-canopy UAS was found to be an efficient and accurate method of extracting DBH and relative position of stems in forests.

This paper describes the results of the work, which was divided into three parts: 1) the proof-of-concept of lightweight low cost UAV-borne multispectral NIR (shortwave infrared) sensor, 2) the evaluation of performances of airborne tailored NIR sensor vs. consumer-grade RGB digital camera, and 3) the feasibility study and the comparison of NIR multispectral data vs. Sentinel-2A high-resolution satellite multispectral imagery. The moderated cost-efficient UAV-borne imaging remote sensing solution was requested by the agricultural sector in Ukraine. The existing solutions did not meet the requirements of the end user neither suitable high-resolution satellite multispectral imagery was available too. The designed system integrated consumer-grade RGB digital camera and NIR sensor. Multi-temporary data was collected eight times during one growth season. Eight aerial missions were conducted in midsummer 2015 over the same area, which consisted of 50 plots with various cultivars of wheat, barley and rye. The vegetation indices were calculated for both datasets. Vegetation indices calculated from the NIR sensor highly correlated with plant chlorophyll and plant LAI, and revealed satisfactory correlation with plant fresh mass. Vegetation indices derived from onboard RGB camera did not reveal the significant correlation with any of plant growth parameter. The NDVI values calculated from NIR data demonstrated high correlation with the ones, which were derived from the Sentinel-2 high-resolution satellite multispectral images. The values of UAV-derived NDVI were lower than Sentinel-2-derived NDVI, and the regression slope of this relationship varied in different plant species. Reasons of such variation are discussed in the paper. After the numerous field-tests the customer accepted the developed tailored COTS UAV-borne multispectral solution cost-efficient and sufficient.

Floods are one of the most serious and common natural disasters that cause fatalities and considerable economic losses worldwide every year. In order to reduce and manage risk that floods pose to human health, the environmental and economy flood risk map, as a crucial element of flood risk management, need to be generated. Most often flood risk is estimated based on Digital Elevation Model and projected water levels therefor DEM’s resolution and accuracy highly influence on the reliability of flood risk map especially in lowlands area, where the offset of few decimeters in the elevation data have a significant impact. Airborne light detection and ranging (LiDAR) remote sensing has been a widely used method that provides high-resolution topographical datasets. However LiDAR data are expensive and hard to acquire, usually limited by availability of technology and legal constrains. The main aim of this paper is to present usability of DEM, crated based on UAV RGB images, for flood risk assessment in Vojvodina Porvince, Republic of Serbia therefor flood risk assessment by using the UAV DEM was compared with a flood risk assessment based on LiDAR DEM. Additionally, UAV point cloud was compared with high resolution LiDAR point cloud.

This study proposed the exploitation of Sentinel-2 and Proba-V imagery to assess the heterogeneity in the surroundings of EC stations through the multitemporal analysis of NDVI. The observations by these platforms allow computation of NDVI at 10 m (Sentinel-2) and 100 m (Proba-V) spatial resolutions. Such levels of spatial detail allowed the comparison of pixel values within two relevant geographic extents: the footprint of EC stations and the extent of satellite imagery cells commonly used to force flux modelling. The satellite platforms considered for this study exhibit different but complementary strengths. Proba-V allowed fast processing over a large number of stations in order to screen or rank EC stations as function of the spatial heterogeneity. The fine spatial resolution of Sentinel-2 allowed more in-depth spatial analysis. Three methods were implemented to explore the spatial heterogeneity with Sentinel-2 NDVI: the analysis of simple NDVI histogram, spatial heterogeneity index (SHI) and fitting a semi-variogram. The analysis with Proba-V resulted in a ranking of flux tower stations on the basis of NDVI Interquartile Range. This study was conducted in the framework of a research programme aiming at improving estimates of net primary productivity across different biomes. The methods appeared to complement each other: histograms were simple to interpret and offered a clear view on the spread and shape of data (often bi-model in cropped sites); SHI plots enable the visualization of heterogeneity changes throughout a period of time and semi-variograms allow the analysis at different distances and orientations.

Rain-use efficiency (RUE) acts as a typical indicator of ecosystem function. Land surface phenology (LSP) assesses the vegetation activity during the growing season at the ecosystem level. The Songnen Plain (SNP) is located in semi-humid to semi-arid transition ecological fragile zone in Northeast China. RUE in growing season (May-September) was calculated using time series GIMMS NDVI3g images and precipitation data for the period of 1983-2012. The phenology metrics including the start (SOS) and end (EOS) dates of growing season for each year was extracted. Spatial trends of RUE and LSP were examined by applying a linear regression model with time. The correlation analysis was used to analyze the effects of RUE on LSP. The results showed that RUE increased slightly with an undulating trend. Spatially, the highest positive slopes indicating increased trend of RUE were observed in northern and eastern forest. The advanced in SOS was mainly distributed in northern forest areas. 12.2% of the landscape experienced highly increase trend in EOS with a rate of 0.38 days per year. The length of growing season (LOS) was prolonged in 14.2% of the total land. EOS dates in the southern salinized grassland and cropland were mainly negatively correlated with RUE. The results of the significance test show that 2.95% of the pixels were significantly and positively correlated with RUE, indicating that an increase in the RUE would delay the EOS. Increasing RUE promoted the extension of the LOS, particularly in the forest areas.

In the present paper, the actual evapotranspiration for the territory of Bulgaria on an annual basis for the period 2000- 2014 has been modeled, using satellite data MOD16A3. The data was received by means of remote sensing from a MODIS sensor. Raw evapotranspiration (ET) data were integrated and processed in GIS environment in order to obtain the final goal of finding 2D distribution of the qualitative values of the actual evapotranspiration (АET) for the territory of Bulgaria. In order to generate the relevant values for the annual АET in the area, a model was developed using the MOD16A3 evapotranspiration dataset. In the present paper, the actual evapotranspiration was estimated as a function of the land cover and a digital elevation model. The results obtained show the relationship between the actual evapotranspiration, the land cover and a DEM. In the process of research, some trends for the annual quantity of АET were estimated. The model for the quantitative area estimation of the evapotranspiration developed in the study has already been applied to the catchment area of Lefedzha River (located in northern Bulgaria). It was established that the satellite data give a more representative and reliable information on the spatial distribution of the AET on a regional scale. These methods have less human interference in obtaining information about the individual physical parameters on which the process of evaporation depends. In the MOD16 product the temperature of the spreading surface that influences the evaporation processes was recorded.

Capturing spatial and temporal environmental dynamics in heterogeneous tropical landscapes through remote sensing data has become an important challenge in environmental monitoring studies due to cloud contamination. In Brazilian Amazon, there is a great need of data able to provide information for a wide range of decision makers. Alongside this, a wide variety of free remotely sensed data products have been made available. The combination of information from more than one sensor could maximise the number of cloud-free images as well as their temporal and spatial resolution. Based on this framework, this study investigates the potential and quality of a set of fused images obtained from the Sentinel-2 Multispectral Imager Instrument (MSI) and Landsat OLI satellites sensors over the region of Brazilian Amazon, comparing their performance according to different land cover/land use areas. In this context, the objectives of this study were: (1) assess qualitatively the two main group of image fusion approaches, namely component substitution (CS) and multiresolution analysis (MRA), as well as their suitability for the fusion of Sentinel-2 MSI and Landsat-8 OLI image pair from selected areas in Brazilian Amazon; (2) compare three different image fusion methods for measuring efficacy and performance to determine the best spatio-temporal information: the Intensity Hue Saturation (IHS) method, the Brovey transformation (BT) and the Gram-Schmidt (GS) method; (3) assess quantitatively the methods employed through a set of fusion quality metrics in order to identify the more accurate results based on different reference images.

To meet an increasing demand for food production there is a need for faster genetic gains in Norwegian cereal breeding. Yield gains can be improved by use of High-Throughput Phenotyping (HTP) based on multispectral imaging and application of genomic selection. Several spectral indices have been tested to estimate grain yield, such as the Normalized Differential Vegetation Index (NDVI) and MERIS Terrestrial Chlorophyll Index (MTCI). For the present work, data was gathered from a field trial with 96 plots of 24 wheat cultivars laid out in an alpha-lattice split plot design. The design had two levels of nitrogen (N) fertilization, 75 and 150 kg N/ha, applied at sowing. Also, a larger field trial with 301 breeding lines with two reps of high N fertilization was used. Multispectral images where taken in the wavebands green (550 nm), red (660 nm), red edge (735 nm) and near infrared (NIR) (790 nm) with a Parrot Sequoia multispectral camera combined with a sunshine sensor. This allows vegetation indices to be calculated. In addition, 3D models and Digital Surface Models (DSM) are used to estimate plant height. All cameras and sensors were mounted on a light Unmanned Aerial Vehicle (UAV). Images were taken at regular intervals throughout the growth season. The time series of the vegetation indices showed high values during the period of grain filling for wheat plots that received higher dose of fertilization. The values reached their peak around the period of grain filling before declining when plants approached maturity. For site B, the historical cultivars showed significant differences in NDVI and MTCI, but the indices were weakly correlated with grain yield. On site B, however, the large field with breeding lines, both vegetation indices were associated with grain yield with MTCI showing the strongest correlation coefficient of 0.49. The plant heights computed from the DSM showed deviations of 0.1 to 0.2 meters from the manual measurements, indicating that more sophisticated models are needed for reliable prediction of plant height.

Canopy cover is an important agronomical component for determining grain yield in cereals. Estimates of the canopy cover area of crops may contribute to improving the efficiency of crop management practices and breeding programs. Conventional high resolution RGB cameras can be used to acquire zenithal images taken at ground level or from a UAV (Unmanned Aerial Vehicle). Canopy-image segmentation is complicated in field conditions by numerous factors, including soil, shadows and unexpected objects. Spatial resolution is a key factor for estimating canopy cover area because low spatial resolution may introduce artifacts in the digital image. We propose a comparison of canopy cover segmentation using different spatial resolutions to test the scalability potential of these different techniques. Field trials were carried out during the 2015/2016 crop season in the Arazuri experimental station of INTIA in Navarra, Spain. Three barley genotypes, 10 different N fertilization regimens and three replicates were used in this study. This work uses zenithal RGB images taken from 1 m above the crop and images from the UAV were taken at the intervals of 2 s the during of the flight at distances of 25, 50 and 100 m. Images from the ground were taken at 1 m above the canopy. The CerealScanner plugin for FIJI (Fiji is Just ImageJ) was used to calculate the BreedPix RGB vegetation indices. The comparative results demonstrate the algorithm’s effectiveness in scaling through high correlation values between images with different spatial resolutions taken from the UAV and images taken from the ground.

The Food and Agriculture Organization of the United Nations (FAO) has defined that agriculture is the main source of food supply given the high demand related to the population growth. Therefore, the FAO has also defined the trend to increase agricultural productivity based on the use of technology, information, and com- munication (ICT) tools. Nowadays, recent ICT tools like smartphones, cloud computing, Internet of Things (IoT), and big data support the implementation of precision agriculture to improve crops and their management. Colombia is an abundant country with suitable soils for the cultivation of diverse types of crops. However, the unknown ICT advantages for the farmers and the user rejection to adopt new technology difficult ICT implementation in the Colombian agriculture. On the other hand, the use of smartphones in various industrial applications has brought several advantages, due to their versatility, low-cost, and ease of use. Therefore, smart- phones can be used to address agriculture issues with a promissory implementation. In this sense, this work presents a smartphone application, called AgroTIC, for implementing ICT tools in the traditional work of the Colombian agricultural sector in order to support the productivity of Colombian farmers. AgroTIC takes advantage of smartphone sensors and several ICT tools. Specifically, AgroTIC is composed of four main modules: I) Communication module, II) Image processing and estimation of visible vegetation indices module, III) Production module, and IV) Marketing module. The first allows communication between farmer -to -farmer, and farmers -to -specialists/agronomist for technical assistance remote. The second module is a tool that provides an RGB analysis based on the estimation of two visible vegetation indices to help agronomist in diagnosis. The third module allows farmers to enter information related to their crops to estimate volumes of production and prepare the information for the marketing process. In the fourth module, the farmers can offer their products and establish a direct contact with different potential buyers. AgroTIC has been developed for a community of farmers who grow citrus fruits (orange, tangerine, and Tahiti lime) in the municipality of Simacota, Santander, Colombia.

Agriculture is one of the most important markets in the world. For the agriculture production efficiency and cost reduction, the modern agriculture no longer exists in farm fields only, but expands quickly in information fields as well. The recent trend of agriculture is moving towards precision farming, which gives rise to great demands for IT supports. The future of precision agriculture is considered highly promising, and lots of solution packages will be developed to support farming activities during the entire farming cycle.

The number of ears per unit ground area, or ear density, is in most cases the main agronomic yield component of wheat. A fast evaluation of this attribute may contribute to crop monitoring and improve the efficiency of crop management practices as well as breeding programs. Currently, the number of ears is counted manually, which is time consuming. This work uses zenithal RGB images taken from above the crop canopy in natural light and field conditions. Wheat trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2014/2015 crop season. A set of 24 varieties of durum wheat in two growing conditions with three dates of measurement were used to create the image database. The algorithm for ear counting uses three steps: (i) Laplacian frequency filter (ii) median filter (iii) Find Maxima. Although the image database was collected at the ground level, we have simulated images at lower resolutions in order to test potential application from cameras with lower resolution, such mobiles phones, action cameras (5 – 12 megapixels), or even aerial platforms (e.g. UAV from 25-50 meters). Images were resized to five different resolutions with no interpolation techniques applied. The results demonstrate high accuracy between the algorithm counts and the manual (image-based) ear counts, higher than 90% in success rate, with a decrease of <1% when images were reduced to a half of its original size, and success rates decreasing by 2.29%, 7.32%, 17.32% and 38.82% for images resized by four, eight, 16 and 32 values, respectively.

In the beginning of the century, few desktop review projects were carried out in Ukraine under the Global Water Partnership. In particular, interested in transboundary water research, there are some examples: Environmental Project Water Management of Kakhovka Reservoir and Lower Dnieper River (2000-2001) and Flood Management in Ukraine and Slovakia (2000-2003), and the 16th OSCE Economic and Environmental Forum “Maritime and inland waterways” (2007). The afore-mentioned studies were mostly dedicated to the establishment of a dialog between the governments and stakeholders. So far, basically, there were no applied research in order to perform the risk assessment in particular transboundary areas. For instance, during the past decade, both the Chervonograd coal mining industrial district in the Western Bug river region (the border between Eastern Poland and Western Ukraine) and the mitigating environmental risks for water security have been paid a little attention. The Lviv-Volyn carboniferous basin (Western Ukraine) is one of three major coal mining regions in Ukraine). This paper describes the recent preliminary results of applying GIS technology for simulation of flood risks and associated hazards in the Lviv-Volyn coal basin. The contamination of drinking water (Be, Yb, Co and Pb) was considered as one of main hazards in territory of investigation. Using satellite optical imagery, flood modeling and the prediction of contamination were conducted in the areas of West Bug River and Vistula River. The input parameters were the following: the average water flow velocity 0.45-0.65 m/s, the width of the channel in the meadows 25-70 m, and the depth 1.1-1.7 m. The river runoff is adjusted by the dams of the Dobrotrivska thermal power station and the Sokalsky chemical factory. The water field is 6250 sq. km. The issue of increasing risk of wasting drinking water resources in the region with the estimated population of about 15 million inhabitants is essential.

Soil water erosion is one of the challenges that the European Union should deal with in the next years, due to its significant impacts on agriculture and natural hazards. In this work, a RUSLE (Revised Universal Soil Loss Equation)-like model has been applied to estimate soil water erosion in a Northern Italian Alpine basin (Val Camonica) by combining meteorological forcing with topography, soil properties and land cover. In the traditional formulation, land cover classes are assigned categorized cover management factor (Cfactor) value retrieved from existing literature (C-Land Cover formulation). However, Earth observation data have been proven effective in tuning the protective effect of vegetation on soil erosion dynamics. Thus, this method has been compared with two approaches (C-Satellite and C-Land Cover+Satellite) based on satellite-derived NDVI values to discretize C-factor values at a pixel scale. The C-Satellite formulation is based on an exponential law for correlating observed NDVI and C-factor values, irrespective of land cover classes. The C-Land Cover+Satellite method is based on the integration of land cover classification with NDVI maps. NDVI values have been retrieved from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI time series imaged from 2000 to 2017. Results of the application of the RUSLE-like proposed approach to estimate soil water erosion in an Italian alpine basin, have shown that integrating satellite-derived spectral information within the land-cover based C-factor estimate can generate a more reliable soil loss estimate related to seasonal and long-term land cover changes.

The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel1 constellation provides synthetic aperture radar (SAR) images coverage with a 6 days revisit period at a high spatial resolution of pixel spacing 20 m. Sentinel-1 data are considerable useful, as they provide valuable information of the vegetation cover. The objective of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for rice height and dry biomass retrievals. To do this, we train Sentinel1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multi-temporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce an intensity radar data stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R² = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R² = 0.9 and RMSE = 18% (162 g.m⁻² ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks

Climate change, food insecurity and limited land and water resources strengthen the need for operational and spatially explicit information on vegetation condition and dynamics. The detection of vegetation condition as well as multiannual and seasonal changes using satellite remote sensing, however, depends on the choice of data including length and frequency of time series. Thus, this contribution focuses on the derivation of the optimal remotely sensed data for vegetation monitoring and extraction of relevant metrics. Time series of satellite data from Landsat-8, Sentinel-1/2, and MODIS were used to identify characteristics of vegetation at different spatiotemporal scales. We derived parameters, such as: maximum and amplitude based on vegetation index time series, as well as Land Surface Temperature (LST). Along with optical data, we used backscattering intensity over consecutive vegetation growing seasons. The analysis was carried out using Google Earth Engine, a cloud computing platform which allows to access various data archives and conduct data-intensive analysis. Taking advantage of this platform, we developed a web-based application named GreenLeaf. The application is computing metrics and plotting time series, based on parameters defined by the user. The derived vegetation condition parameters provide sufficient information to detect vegetation change. In addition, the images acquired from near-coincident dates provide similar information over continuous surfaces. The developed application contributes to the use of satellite data and the simplification of data access for users with limited remote sensing experience and/or restricted processing power. Aiming at providing this knowledge to stakeholders can further support decision making on multiple scales.

Ecosystem productivity, biome distribution, and forest carbon stocks are likely to be changed by the climate change. These ecosystems changes can be identified using Satellite based normalized difference vegetation index (NDVI). In this study, global inventory modeling and mapping studies (GIMMS) NDVI data acquired by the advanced very highresolution radiometer (AVHRR) was used for analyzing the trend over Turkey for the period 1982-2015. The acquired data has bi-monthly nature, and the maximum value of composite method was used for finding monthly NVDI. The obtained NDVI was then clipped to the study area, and then the trend was estimated using Annual aggregated time series (AAT) and seasonal adjusted time series (SAT) methods. In AAT method the annual averages were calculated and then the trend was estimated. In SAT, the seasonal component removed from the time series and the seasonal adjusted time series used for estimating the trend. The gradient latitudinal and longitudinal trend was also implemented to investigate the spatial trend. The gradient was calculated as the trend of the blocks of a specific latitude of longitude over the whole Turkey to have better interpretation of the spatial trend from south to north and east to west. The results showed that throughout Turkey the NDVI has an increasing and decreasing trend, but the increasing trend is dominant as 89.9% and 79.1% of the total area using AAT and SAT respectively are significant increasing trend. One the other hand, only 0.45% and 0.36% of the total area has significant decreasing trend using AAT and SAT respectively and the rest of the area has no significant trend. The seasonal adjusted method showed most of the no trend areas is distributed through the eastern part and the far western part of Turkey. The Annual aggregated trend showed similar pattern with largest no trend area centered in the far eastern part of Turkey. The gradient analysis showed decreasing in the magnitude of the positive NDVI trend when moving from the west to the east, and no specific pattern in the south north direction.

Nowadays, the ecosystem service framework can be accepted for designing and completion of different management strategies to preserve and restore ecosystems. The present study will contribute to increase the stakeholders’ interest to wetlands condition and possibilities for better management aiming at improving the ecosystems services they provide. For the research of the wetland in the area of quarry lakes in Negovan village interim ecological monitoring (IEM) using remote sensing data after restoration measures have been implemented. The aim of IEM is to obtain results for the wetland actual condition and restoration process efficiency for ecosystems services assessment. Based on the generated data an interim ecological monitoring methodology (MIEM) implying different remote sensing data (different temporal intervals, spectral and spatial resolution) has been designed. The model functions entirely within Geographical Information System (GIS). It generates data for the actual environmental condition of the wetland in different temporal intervals. The obtained results for the wetland actual condition have been used for defining the values of Disturbance Index (DI), which in turn is defined based on orthogonalization of multispectral satellite data from Sentinel 2 - Multi Spectral Instrument (MSI) and Landsat for different temporal intervals. DI rates are determined through Wetness component and Greenness component. Ecosystem services are assigned based on the correlation between vegetation and water surface area that is connected to water balance/budget. The space distribution of vegetation is specified on the base of Normalized Differential Vegetation Index (NDVI), Normalized Differential Greenness Index (NDGI) for different temporal intervals of the research. This paper presents results for evaluation the potential for delivery of ecosystem services taking into consideration the environmental and ecological processes behind the services that have been assessed at a relevant scale generated.

In recent years, the Normalized Difference Vegetation Index (NDVI) has been used to help in the analysis of plant productivity, especially for rice crops. In this research, we analyze time series of NDVI (2007 to 2015) for Bangladesh to predict crop yields. A key ingredient is the rice classification of the fields. The crop yield estimations are made using rice masks and pixel-based season alignment. Furthermore, the pixel-based growing seasons are aggregated to district level, to correlate with national yield data. NDVI ~ Yield models were trained with data from 2007-2013. District specific regression models provide model fits of Adjusted R² = 0.6 ± 0.3, estimating ricle yield with a Root Mean Square Error (RMSE) of 0.09 ± 0.05 tons/ha. Model validation with data from the results between 2014 and 2015 in rice yields estimates with prediction errors of 14.7%. In conclusion, we show with this research that the method of aggregation of NDVI temporally as well as spatially can lead to improving correlation and can predict rice yields.

Hyperspectral remote sensing has the potential to provide quantitative information on the spatial cover, acquiring relevance for the agronomic management. Traditionally, the diagnosis, management, and control of diseases in oil palm crops is a time-consuming and difficult task given that it needs a visual symptom observation. Currently, oil palm crops deal with diseases and infections. The bud rot disease (PC in Spanish) of the oil palm is one of the most common diseases in Central and South American countries, especially in Colombia. A viable alternative for the identification of diseased palms is the use of hyperspectral images and classification algorithms. Nevertheless, the usual assumption that every pixel of the hyperspectral image can be associated with a unique class label is no longer verified, and mixed pixels cannot be correctly addressed by traditional classifiers. This paper presents an unmixing-based approach as a tool for classification of stress oil palms caused by the bud rot disease, conducted on hyperspectral datasets of oil palm crops from Colombia, through the estimation of abundance maps with three labels: diseased oil palm, healthy oil palm and background (grass-shadow).

This study aims at reviewing existing remote sensing approaches to assess drought impact on desertification in the East Mediterranean region. Drought and desertification are interconnected phenomena. The World Meteorological Organization (WMO) defines that an area is affected by drought when the annual precipitation is lower than 60 % of the normal values, at least during 2 consecutive years in more than 50 % of its area. Drought is a phenomenon that may trigger or exacerbate desertification. Desertification is usually reported as the process during which land becomes more arid and loses its vegetation, water bodies (lakes, streams), and wildlife. Being one of the major causes of desertification, drought is a complex phenomenon and its monitoring is crucial for early warning and risk management of desertification. A number of approaches are possible for assessing drought. This paper reviews remotely-sensed drought indices that are particularly relevant to the East Mediterranean Region, discussing their strengths and weaknesses, as well as their present challenges. As the East Mediterranean Region is dominated by semi-arid to arid climates, focus is here given to methods applied to assess drought in semi-arid or arid regions. The present paper analyses the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Drought Index (NDDI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Vegetation Health Index (VHI) and the Composite Drought Index (CDI). For their validation, the indices need to be compared to ancillary data, recorded at meteorological stations or acquired from in-situ measurements. Thus, the paper suggests Goodness-of-fit criteria, which correlate the derived data spatially and temporally. Examples of such criteria are the Pearson product-moment correlation coefficient r, the coefficient of determination R2, the Root Mean Squared Error (RMSE) and the Nash Sutcliffe Efficiency (NSE).

Water temperatures are crucial for biological and physiological processes and are an integral part of water quality monitoring. Thermal infrared remote sensing can help to acquire extensive temperature information at low cost, but for inland water bodies, the available satellite data often has an insufficient spatial resolution to monitor water surface temperatures (WST). Hence, thermal sharpening to increase temperature data availability is evaluated in this study. Landsat-8 TIRS data was sharpened based on land cover information from Landsat-8 OLI data and a gyrocopter survey over the research area in Koblenz, Germany. This area presents a challenging composition of two rivers, infrastructure, urban and vegetation areas. The water and non-water pixels were identified with a pixel based, two-step modified parallelpiped classification algorithm. The sharpening water thermal imagery (SWTI) algorithm is available for water bodies to unmix temperatures of water-land boundary pixels based on the relation between their radiance and land cover composition. It was modified in order to test its applicability to the research area. Two auxiliary datasets were used to investigate the effect of large resolution differences between temperature and land cover data. In most cases the SWTI improves the RMSE and in all cases reduces the average difference of the Landsat-8 compared to reference WST. The quality of the results depends mainly on the congruency of the input data. The SWTI also proved to be superior to the cubic convolution algorithm when increasing the spatial resolution over water areas and reduced the required minimum river width.

Drought is commonly occurring natural hazard. It has vicious impact on agricultural production as well as on socioeconomic status of an area. Meteorological drought will induce with the deficit of rainfall and leads to agricultural drought as it prolongs. Rainfall is crucial parameter to assess meteorological drought and NDVI based indices can capture agricultural drought satisfactorily. The present study aims to assess meteorological and agricultural drought in the Ghataprabha river basin using Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI). Monitoring of SPI and VCI will benefits to mitigate drought impacts with the proper water resources managements. Ghataprabha river basin is the sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Average annual rainfall of the basin varies from 600 mm to 2000 mm. Gridded rainfall data was procured from the Indian Meteorological Department for the period of forty three years (1970- 2013) and considered same as input for SPI. To calculate SPI with multiple time scale, two parameter gamma distribution was implemented. MODIS NDVI products from 2000-2013 was considered for calculation of VCI. Significant number of meteorological drought episodes were observed during the study period while severe agricultural drought was observed during 2001-2003 and in 2012. SPI and VCI were compared to quantify variation of VCI with respect to SPI. Good agreement between SPI and VCI was observed during drought and non-drought periods. Results indicates that eastern part of the basin was more prone to severe droughts as compare to other part of the basin. This study assistances to formulate drought mitigation strategies and to establish effective water resources policies in the study region.

This article describes the “Sao Francisco Floodplain Project” (SFFPP) aiming at defining a Mean Ordinary Flood Line (MOFL) on the banks of the Sao Francisco River, in East Central Brazil. Land inserted within the MOFL of large rivers in Brazil are characterized as government-owned and are ruled by a special legislation. The lack of consensus for an effective method for this delimitation has raised much conflicts between dwellers, land owners and the federal government. To solve this, the SFFPP first aims to determine the mean flood plain level using historical water level data and then find all the dates since the launch of the first satellite sensor with a 30 m resolution (Landsat-4, 1982) corresponding to these particular water levels with a small margin of error. All Landsat images corresponding to these dates were acquired to produce a delineation of the MOFL. In a thorough series of tests to extract the water surface, the K-means segmentation using the shortwave infrared band of Landsat yielded the best results. A first refining of the MOFL was performed by interpolating the Landsat bands to improve the smoothness of the waterline. This refinement reduced the average distance error between the Landsat water edge and the true water edge from 9 to 7.5 meters. Then, some sections of the MOFL was further completed or refined using high-resolution multi-source satellite images where available. These first results were very encouraging and we were able to acquire Landsat images for each section of the river corresponding to the mean flood water level. Because Brazil has been suffering a significant reduction in rainfall since 2013, no recent SAR or optical images such as Sentinel-1 and -2 could be used. Analysis of the water level time series confirmed an alarming decreasing trend in the water discharge of the Sao Francisco River.

This study is a pilot project for the “San Francisco Flood Plain Project” (SFFPP), meant to delimit flood plain areas owned by the Brazilian federal government. The objective is to determine the attainable accuracy in river water surface delineation using satellite imagery from Landsat, Sentinel-1 and -2. We prioritize the evaluation of Landsat data due to its long systematical time series, allowing hydrological analysis requiring observations of at least 40 to 60 years and data from the Sentinel missions with their high frequency of revisit, improved spatial resolution (compared with Landsat) and possibility of observation in wet season (S-1). In our approach, we evaluated the accuracy by spectral bands individually and in combination, as well as polarization. We also tested a number of thematic information extraction techniques unsupervised (K-means and EM Cluster Analysis) and supervisioned (Random Forest - RF, k-nearest neighbors - KNN, Maximum Likelihood Classification – ML, Support Vector Machine – SVM, Mahalanobis). To validate our results, we used a PlanetScope mosaic (3 m). Results indicate that shortwave infrared bands have a higher capacity to separate water surface from other classes. For SAR data, the best separation was obtained by VV polarization (compared with VH). Techniques all reached agreement values >94% for the Sentinel-2 image, >93% for the Sentinel-1 image and >;86% for the Landsat-8. We consider both methodologies effectives to extract the water surface and appropriate for the real estate issues of the SFFPP project.

Monitoring dam displacements using different techniques allows an evaluation of their structural behaviour over time. In this study, dam displacements (for the Castello dam, Agrigento, Italy) have been investigated using different Interferometric Synthetic Aperture Radar (InSAR) techniques exploiting a freely available dataset from the EU Copernicus Sentinel-1 SAR built by the European Space Agency (ESA). The dataset includes Sentinel 1A (S1A) images acquired in dual-polarization and Interferometric Wide (IW) swath using the Terrain Observation with Progressive Scans SAR (TOPSAR) mode. Three main Multi-Baseline Construction methods based on the identification of Persistent Scatterers (PS) have been tested, within a scene including an extra-urban area surrounding the dam. The evaluation of the best strategy is carried out over few images (7) with a constant acquisition time-span of 12 days, except for the first image, acquired 24 days before the next one. Three different multi-baseline construction methods have been investigated in this preliminary research to test the capability of these InSAR techniques in finding a time series of displacements with high accuracy in extra-urban areas also. The star graph results in displacement appear to be more in agreement with GNSS measurements than other techniques.

Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.

Bluewater EYE analytics bring the power of remote sensing backed by artificial intelligence to address several water pollution related challenges at low cost as it requires minimal calibration with Internet of Things (IoT) data and can provide useful water quality insights via spatio-temporal pollution signatures mapped over possibly large distances, applicable to any global water body. We leverage geo-spatial data analytics in combination with cloud based, real-time in-situ sensing, that is capable of mapping changes in water turbidity of a water resource. Spatio-temporal pollution insights are derived and represented via color-based heatmaps and matrices that are overlaid on Google maps and facilitate visualization and identification of pollution hotspots. Methods include first-order correlations derived from surface reflectance of different satellite band/s (single or a normalized difference combination) and high resolution, geo-tagged, in-situ turbidity sensing via a moving multi-parameter sensor platform. Significant improvements in correlation (upto 80%) were obtained by using statistical methods such as moving average for filtering out the sensing data associated noise. Moreover, we use machine learning based approach for training a turbidity model based on Support vector machine (SVM) regression with Radial Basis Kernel (RBF). The predicted turbidity values for the selected region based on Landsat 8 Level 2 surface reflectance data, was then applied for time-series data for historical years of 2016 and 2017. The turbidity time series so obtained is analyzed to capture any significant variations in water quality due to various factors (event or season based). We propose a method to analyze pollution contributions from different individual sources, that is based on assigning an individual spatio-temporal signature in the form of a color-coded matrix. Overall, Bluewater EYE could be useful for capturing water quality spatio-temporal information and trends, providing data driven proofs for timely alerts and advisory and other useful insights for implementation of appropriate remediation measures and interventions– all this at an affordable cost.

The Water Framework Directive of the European Union aims to protect water bodies from feature degradation. Monitoring is essential for assessment and comprehensive overview of water status. Annex V of WFD define tree type of water quality parameters which need to be monitored (biological and two supported one – hydro morphological and physic chemical) in order to assess ecological status of water bodies. Remote sensing data can be used for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. However, this technique must to be integrated with traditions in situ sampling method and field surveying in order to provide precise results. Various empirical, semi-analytics and machine learning algorithms exist to derive relationship between multi spectral image surface reflectance and water quality indicators derived from in situ measurement. In this study we evaluate the capabilities of Landsat 8 satellite image for assessment of abundance of phytoplankton’s (biological parameters) and Turbidity, Dissolved oxygen, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia. The Neuron Networks are used to analyzing correlation between in situ measurements and 7 Landsat 8 atmospherically corrected satellite images acquired in 2013. In situ data are obtained from Agency for environment protection of Serbia. Our results shows that satellite-based monitoring, in combination with in situ data, provide an improved basis for more effective monitoring of large number of water bodies over large geographical area. Relationship between derived and WFD quality parameters is established in order to provide usage of remote sensing data for ecological status classification according to WFD.

The role of vegetation, especially forest ecosystems, is outstanding as a climatic indicator. In our sample area in Hungary the proportion of the trees is high and the afforestation is intense. For the geographic evaluation of the climate changes, MOD13Q1 NDVI / EVI data in 2000–2017 summer period were investigated. In the whole index series the biomass does not show a trend change in either the deciduous, the coniferous or the mixed forest categories. Reduction process is typical for consecutive drier years, but a subsequent wet season is sufficient for the forests to avoid decrease over the longer term. In the main droughty periods of July-August, the effect of dry year following the rainy years immediately reduces the green biomass. The changes in the vegetation period are also wellassessed; such as a change in spring intensity or an increase in summer biomass product, for example the mixed forest show growth not only in the spring but also at the highest summer values. The deciduous characteristic is the decrease in the maximum biomass production. Changes over the nearly two decade period are currently compensated, but forests are vulnerable within a short period of time. According to the standardized deviations in the consecutive years with significant differences, biomass production in the north and southwest of the sample area was reduced. The determination coefficients between VI data and drought index data show strong relationship (R² ~0,85). This validation is verified by the LANDSAT VI data as well.

Remote sensing technologies can provide accurate, cost-effective and real time information for sustainable forest management. Present study demonstrates the use of high resolution IRS1C data and Principal Component Analysis (PCA) with supervised classification to define different types of forest cover in protected and unprotected areas for growing forest stock assessment of Shorea robusta Gaertn. F. (Sal) dominated forest. Further, a map of the same was generated which was subsequently validated using phyto-sociological field data in Dehradun, India. Three forest canopy density classes, viz., 10-30%, 30-70% and <70% could be differentiated. Aim of this study was to test usefulness of LISS-III and test its efficacy for assessment of total growing stock and further in generalising the method for the whole area. The homogeneous forest strata were field inventoried for individual tree (≥10 cm dbh) diameter using sample quadrats at each of the study sites. The plot inventory data was analysed to arrive at image level growing stock estimates. An inspection review was carried out to be familiar with the study area using hard copy of LISS-III data to correlate the image. A scheme of forest classification was developed and the forests were stratified into moist Siwalik sal forest (3C/C2a), dry Siwalik sal forest (5B/C1a) and non-forest. High level of accuracy was achieved by ground truthing. The objective of the stratification was to categorize the forest into homogeneous strata. The biomass measurement from same sample plots was also integrated into the remote sensing technique for large spatial information on AGB distribution. The study revealed that protected sal forest has maximum volume and growing stock per hectare followed by unprotected sal forest. Same was also true for sal associates. The study also shows good scope of high resolution data for growing stock and forest carbon assessment, opening scope in estimating carbon sequestration potential of forest which may help researchers and policy makers to understand global and regional CO₂ cycle.

This research presents the results of a survey on the extent of the affected areas in the municipality of Bregovo from the flood of the Timok River, which occurred on 11.03.2018. The application of SAR and optical data for assessment of the spatial and temporal characteristics of the occurred flood is the objective of this paper. This methodology applies orthogonal transformation of different multispectral images from Sentinel 2 mission combined with SAR data from Sentinel 1. The assessment was made on the basis of the orthogonal transformation’ components of the bands from different multispectral Sentinel 2 imagery: Tasseled Cap Transformation TCT-brightness, TCT-wetness, TCT-greenness. Indicators of quantitative changes in areas affected by the floods have been obtained. Satellite images from Sentinel 1- SAR and Sentinel 2-MSI satellite missions, orthophotography, terrestrial and in-situ terrain data from the Bregovo municipality affected by flooding were used. The processed satellite images are from different sensors and are selected by different dates before and after the day of the natural disaster. Pseudocomposite radar images with different polarization (vv,vh) were used to clarify and more precisely visualize the territorial coverage of the flood in the surveyed area. Various normalized quantitative differentiation indices (vegetation, humidity, vapors, etc., NDGI,) are generated after image processing. Results are presented for correlation between the values obtained for the different data types. On the basis of the obtained data and results, a comparative analysis of the dynamics of the changes occurred as a result of the disaster was carried out and a quantitative assessment of the changes occurred and respectively the registered negative environmental impacts in the territorial extent of the flood.

The United Arab Emirates (UAE) is one of the fastest agriculture economical growing country in the world. One aspect of this agriculture growth is the development of the date palm trees sector in the UAE. The date palm tree is considered one of the oldest and most widely cultivated tree, which is commercially the most important tree in the life of its people and their heritage. Moreover, the date palm tree was believed to be a part of the UAE strategy to control desertification. With this huge investment and interest in palm trees in the UAE, there is limited knowledge of the actual tree counts and their exact spatial locations, which is a requirement for any agricultural census. WorldView-3 satellite images were used to develop an algorithm to detect and count palm trees in the UAE. The processing was done in two steps: the first step is to detect palm trees which involved supervised classification using maximum likelihood with four feature classes: Red, Blue, Green and Near infrared (NIR) bands associated with palm trees objects taken by the labeling. The second step is to count palm trees which involved extracting local spatial maxima of Laplacian blob from Normalized Difference Vegetation Index (NDVI) masking. The algorithm was tested in different regions of interest in AlAin city, part of the capital Emirate Abu Dhabi. The algorithm and final results are compared with ground truth images for accuracy assessment. The results were satisfactory with an accuracy of 89% and higher and very minimum negligible misclassification.

The objective of this study is to monitor precision paddy field area and crop classification on a small town area. Monitoring of this agricultural sector is useful for evaluating the cultivated environmental condition, especially in mixed crop cultivated areas. This study evaluated the small town in Chungbuk, South Korea, using drone images for the years 2016. The images were acquired 15 times using the RGB and NIR sensors, respectively. Using the collaboration of drone images and smart farm map, we improved the accuracy of crop classification and crop identification. This study also evaluated the relationship between vegetation health and growth state of the study area using remote sensing and GIS techniques. The study area, Daeso town was produced by several crops such as rice, red pepper, corn, sweet potato, sesame, welsh onion, bean, tobacco, ginseng. This study acquired images using fixed wing drone from August 5 to August 6, 2016. It was found that the range of DN values of drone images by crop was different for each crop. The NDVI of this area showed the highest value in the rice with active vegetation and the value of 0 or more in the crops such as sweet potato, soybean, corn and grassland. An object-based technique is used for crop classification. The results showed that scale 250, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5 were the optimum parameter values in image segmentation. As a result, the collaborated method showed that the kappa coefficient was 0.85 and the overall accuracy of classification was 88.0 %. The result of the present study validates our attempts for crop classification using high resolution drone image and collaborated smart farm map (SFM) established the possibility of using such remote sensing techniques widely to access the difficulty of remote sensing data acquisition in agricultural sector.

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of highland Kimchi cabbage growth prediction equation by using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main producing district of highland Kimchi cabbage. UAV imagery was taken on the Anbandeok twelve times from early June to early September during the highland Kimchi cabbage growing season. Meanwhile, field reflectance spectra and three plant growth parameters, including plant height (P.H.), leaf height (L.H.) and leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. The NDVIField for each of the 40 plants was measured using an active plant growth sensor (Crop CircleTM) at the same time. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. Of the output independent variables of the stepwise regression, NDVIUAV performs best (r=0.933). And correlation coefficients of highland Kimchi cabbage growth parameters, accumulated temperature, irradiation and rainfall showed positive relationship; the results were respectively 0.931, 0.926, 0.893 (p<0.01). But minimum temperature had negative relationship; the results were respectively -0.789 (p<0.01). NDVIUAV and rainfall in the model explain 93% of the P.H. and L.H. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVIUAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVIUAV and other agro-meteorological factors were well reflected in the model. These results will also be useful in determining the UAV multi-spectral imagery necessary to estimate parameters of highland Kimchi cabbage.

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI_UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI_UAV and growth parameters was shown. Correlation analysis between NDVI_UAV and barley growth parameters revealed that NDVI_UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI_UAV, the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI_UAV was applied to power function. From these results, NDVI_UAV can be used as a new tool for monitoring barley growth.

The introduction of unmanned aerial vehicles is one of the most dynamic areas of civil aviation development. The great part of aircraft area their application is the performance of aerial works. For most aerial work is characterized by a small distance of aircraft from humans, animals, plants and infrastructure. Thus the influence of inductive velocity of these devices on the objects and processes of aviation operations is the topical issue. The problem of assessment of vortex trace formation and description of inductive velocity with in the flying area of multicopters, are considered in the present paper. The studies have conducted using a numerical experiment using the developed in Delphi software package. Created package provides the defined parameters of the multicopter, conditions and flight modes simulation of the formation of a vortex trace of multicopter. Visualization of the vortex trace and calculations according to the inductive velocities in the area of flight with the construction of the vector diagrams is the result of a set of interference П - shaped vortices of rotors of the multicopter, as well as addressing some immediate problems. Graph data on the spatial configuration of vortex trace and distribution of inductive velocities in the transverse planes are not uniformly distant from the multicopter on the example of a hexacopter «Odonata agro» for the accepted conditions obtained using the package are presented in the paper. General regularities and features of the formation and configuration of the vortex trace and inductive velocity distributions in the flying area of multicopters from the calculated data in the work are formulated. The results of paper can be used for estimation of parameters and the influence of the vortex trace of light multicopters on the surrounding objects and develop design solutions and modes of flight in the performance of aviation operations.

South Korea's paddy field agriculture is exposed to major risks due to the water shortage during the irrigation period lasting three years. Especially the conditions of the west and southern coastal areas are serious. For this purpose, it should be considere d to adopt sophisticated technology to ensure adequate water management methods. Droughts cause severe human and economic damage and appropriate drought monitoring is important to reduce serious damage. In order to solve such a drought problem, it should be considered to adopt advanced monitoring technology to exploit appropriate water management methods by utilizing new technologies such as ICT and drone. Drought indices using drone image data are one of the effective methods for the paddy field drought monitoring. Drought indices which can evaluate spatial and temporal drought conditions are required for detailed paddy field drought monitoring. The main objective of this study was t o develop a drought index using RGB and NDVI (normalized difference vegetation index). These indexes were aiming at design and development of an information system which supports paddy rice monitoring, using drone images for spatial evaluation. Blue wavelength band NDVI were used to evaluate the usefulness of drought damage conditions. The results showed that drought damage conditions and NDVI have good correlation in the beginning of the growing season and that Blue band and NDVI are valid for drought monitoring in these periods.

Obtaining accurate satellite precipitation data is crucial for monitoring flood and flood disasters and analysing climate change. As a new generation of satellite precipitation product, global rainfall observation program GPM is integrated with advanced microwave detection technology and data correction algorithm, providing more choices for studying precipitation in global regions. The main purpose of this paper is to evaluate the adaptability of GPM three-stage precipitation product IMERG (Final Run mode) at different temporal resolutions in the Beijing-Tianjin-Hebei region. After screening, the precipitation data of 107 national meteorological stations in the Beijing-Tianjin-Hebei region was used as the verification data, and the detection capability of GPM satellite precipitation data in different time scales of day, month and season as well as extreme precipitation events was analysed. The following conclusions were obtained: (1) Compared with the IMERG products with half -hourly and daily temporal resolution, the IMERG satellite precipitation data with 1month temporal resolution has a strong correlation, reaching 0.90. It can reflect the precipitation event accurately in the Beijing-Tianjin-Hebei region. The poor performance of IMERG satellite is concentrated in the north-western and south-eastern edges of the Beijing-Tianjin-Hebei region.(2) The performance of IMERG precipitation data in the humid season on the monthly scale is better than that in the dry season, and GPM satellite precipitation data are generally overestimated.(3) In the process of extreme precipitation event detection using 0.5h temporal resolution data, the tendency of IMERG satellite precipitation data to accumulate precipitation is roughly consistent with that of the station, but the phenomenon of overestimation still exists, especially in places with large precipitation, overestimation is more obvious. In view of this precipitation event, satellite precipitation data covers almost the entire precipitation process, and some values are still high. Although the data has good detection capability for extreme precipitation events, the monitoring of precipitation peak is not accurate enough. According to the comprehensive analysis, its Final GPM product has a strong ability to detect rainfall events, which can provide data support for the long time series analysis in the Beijing-Tianjin-Hebei region.

A simple yet effective scheme to estimate volumetric soil moisture (VSM) using infrared (IR) land surface emissivity retrieved from satellite IR spectral radiance measurements was recently developed, assuming other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme was applied to a decade of global IR emissivity data retrieved from MetOp-A Infrared Atmospheric Sounding Interferometer measurements. Global datasets of monthlymean 0.25° spatial-grid VSM, estimated from IR emissivity data (denoted as IR-VSM), are assembled to compare with those routinely provided by satellite microwave (MW) multi-sensor measurements (obtained from the ESA CCI website). The IR-VSM dataset is used to demonstrate its spatial variations as well as seasonal-cycles and inter-annual variability which are spatially coherent and consistent with that from MW measurements. Herein we report on some initial evaluation of the IR-VSM absolute values using in-situ measurements available from the Soil Climate Analysis Network of the USDA website. From the 114 ground station locations, we infer a mean bias between monthly-mean in-situ VSM and satellite derived IR-VSM of 0.005 (m³ /m³ ) over the past decade, despite the differences between physical measurements. The absolute accuracy of IR-VSM is also evaluated using an independent source of soil moisture measurements.

In this work we study the problem of mapping soil moisture by means of Synthetic Aperture Radar (SAR) images. A test site has been set in Companhia das Lezirias, close to Lisbon, Portugal. The main advantage of using SAR images is their capability to map soil moisture at a very high spatial resolution. This opens interesting perspectives for agricultural applications, where soil moisture can abruptly change across field boundaries depending on the agricultural practices. The study area is characterized by flat topography, large agricultural areas and sparse vegetation. Five sensors have been deployed in a test area to measure soil moisture with a sampling time of one hour for a period of seven months. In-situ measurements are compared with the results obtained by processing 33 C-band Sentinel-1 images using the SAR interferometry technique. The aim of the study is to analyze the relation between the interferometric phase and time varying soil moisture. The main advantage of SAR interferometry with respect to the use of radar cross-section is that the information about soil moisture can be recovered using a reduced number of in-situ measurements. In particular, we combine three interferograms obtained from three SAR images, acquired over the same area at different times, to derive maps of bi-coherence and phase triplet. This last quantity allows to disentangle the phase contribution due to soil moisture from those related to microwave propagation in atmosphere and terrain displacements. Results are compared to those obtained using the interferometric phase and coherence to emphasize the importance to split the effects due to propagation (e.g. atmosphere) from those related to volume scattering.

The nature restoration project ‘Lenzener Elbtalaue’, realised from 2002 to 2011 at the river Elbe, included the first large scale dike relocation in Germany (420 ha). Its aim was to initiate the development of endangered natural wetland habitats and processes, accompanied by greater biodiversity in the former grassland dominated area. The monitoring of spatial and temporal variations of soil moisture in this dike relocation area is therefore particularly important for estimating the restoration success. The topsoil moisture monitoring from 1990 to 2017 is based on the Soil Moisture Index (SMI)¹ derived with the triangle method² by use of optical remotely sensed data: land surface temperature and Normalized Differnce Vegetation Index are calculated from Landsat 4/5/7/8 data and atmospheric corrected by use of MODIS data. Spatial and temporal soil moisture variations in the restored area of the dike relocation are compared to the agricultural and pasture area behind the new dike. Ground truth data in the dike relocation area was obtained from field measurements in October 2017 with a FDR device. Additionally, data from a TERENO soil moisture sensor network (SoilNet) and mobile cosmic ray neutron sensing (CRNS) rover measurements are compared to the results of the triangle method for a region in the Harz Mountains (Germany). The SMI time series illustrates, that the dike relocation area has become significantly wetter between 1990 and 2017, due to restructuring measurements. Whereas the SMI of the dike hinterland reflects constant and drier conditions. An influence of climate is unlikely. However, validation of the dimensionless index with ground truth measurements is very difficult, mostly due to large differences in scale.

Land degradation is a serious environmental issue in the world. Both space and ground-based observations could be used to define the land changes and develop the assessment of land degradation. This study assessed land degradation in the Orkhon sub-province, the best representation sample in the prominent agricultural zone of Mongolia, using Landsat Thermal Mapper (TM) and Landsat Operation Land Imager (OLI) satellite images during the periods of 1990, 1994, 2000, 2006, 2010, and 2015. The land degradation of a region could be detected by changes in spectral indices and correlation of these indices. The most frequently used spectral indices include Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). These indices were selected as indicators for representing land surface conditions vegetation biomass, landscape pattern, micrometeorology and human activities. The land degradation analysis was described by descriptive statistics, correlation distributions and correlation coefficients of changes in index outputs. In addition, the validations of these indices were also verified by comparing LST and NDWI index values with in-situ, realtime climate data from 1984 to 2010. The Land Degradation Risk Mapping (LDRM) analysis shows that the agricultural and urban areas experienced degradation due to human activities and this has led to decline in the soil moisture in this region.

As streets occupy a growing proportion of our world, they possess great potential as reference for optical, atmospheric research. Moreover, the demand for infrared sensors which distinguish asphalt from other grounds increases due to automation in agriculture, and more generally the introduction of highly autonomous vehicles. Thus, it is favorable to investigate and especially understand the optical properties of asphalt in the nearinfrared (NIR). In our study, we investigate the infrared reflectance of more than 15 asphalt types with different measurement geometries. As a prerequisite, we analyze the dependence of the diffuse reflectance factor on the light spot size, in order to determine a minimum light spot size for any further optical study. By comparing the BRF (bidirectional reflectance factor) to a nearly-Lambertian Spectralon white standard, we show that asphalt is in general a good diffuse scatterer. While the absolute value of the near-infrared reflectance factor varies dependent on asphalt type and wear, its slope shows typically only slight variations; nevertheless, one asphalt differs significantly from the others. For a more detailed analysis, pieces of this and other asphalts were pulverized and pressed into pellets, suitable for broadband infrared spectroscopy. With this, we are able to identify a feature between 460 nm and 6300 nm, whose intensity determines the slope of the NIR response. Here, we discuss its dependence on the asphalt’s ingredients, as well as the contribution of the grain size structure to the actual reflectance of real asphalt.

The advancements in remote sensing in combination with sensor technology, both passive and active, enable growers to analyze an entire crop field, its local features and crop conditions. Nutrient deficiency, and in particular nitrogen deficiency, may cause substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, an applicability of digital color imaging to monitor nitrogen uptake in crops is demonstrated. The measurements were performed in a carrot and wheat fields as well as in a greenhouse. The images of canopy were taken during the entire growing season by use a hand held digital color (RGB) camera together with plants collection for lab analysis. The nitrogen weight in plant leaves, kg/ha, was obtained by image processing and compared with the standard laboratory analysis. Applicability of digital color imaging to monitor N uptake in crops instead of laboratory test is successfully demonstrated. The availability of RGB image-based data for N uptake is faster, timely and less expensive than that of laboratory test and can be obtained by low-cost ground-based imaging devices, unmanned aerial vehicles (UAV) and satellites.

Satellite images are widely used for identification of different types of crops, health monitoring and estimating crop production. This study is to analyze the effect of seasonal monsoon rainfall on different types of crops using multispectral and polarimetric synthetic aperture radar (SAR) images. The study area for this analysis is around Tezpur town located centrally in the north-eastern state of Assam in India. The major crops produced in this area are rice, tea, sugarcane, lychee. Multispectral images from the Indian Space Research Organization’s (ISRO) ResourceSat 2 satellite are used along with dual polarization (HH and HV) SAR images from RISAT 1. The health of the different varieties of crops before and after the monsoon is measured from the multispectral images using modified 2 band Enhanced Vegetation Index (EVI2). The ratio of the cross-polarized to co-polarized back scatter (HV/HH) is used as another metric to track the change in volume scatter from the different types of crops. The change in the vegetation index along with the amount of volume backscatter pre- and post-monsoon gives an idea of the effect of rainfall on the vegetation health along with changes in the structure and leaf density for the different types of crops. The results show that there is an increase in HV/HH and decrease in EVI2 for tea, sugarcane and rice during the season while both EVI2 and HV/HH decreased for lychee. This preliminary study shows how complementary information from multispectral and polarimetric SAR images can be fused with improved vegetation monitoring capability.

Soil erosion is one of the most implicit hazards as it degrades quality of water as well as soil in watershed. Lesser Himalayan region is highly susceptible to natural hazards particularly which are instigated by action and movement of water such as soil erosion, flood and mass movements. Hilly watersheds with diversified land uses and fragile ecosystem are responsible for accelerating soil erosion. Satellite based remote sensing data has been used in this study to demonstrate its utilisation in analysing soil erosion susceptible areas in Tons river watershed (India). In this study an attempt is made to understand interrelationship and role of morphometry, hypsometry and land cover together as coupled criteria in soil erosion. Remote sensing data and Multi-Criteria Analytical (MCA) framework has been used to estimate soil erosion susceptibility of sub watersheds of Tons river basin. The watershed was delineated using ArcGIS to generate the natural drainage network and stream orders were defined using Strahler’s method in hydrology tool. Land cover was classified using Forest Survey of India (FSI) forest cover map and ASTER Digital Elevation Model (30m) for calculating morphometric parameters and hypsometric integral for further analysing geological stage with the help of ArcGIS 10.3. Soil erosion susceptibility maps were generated for sub watersheds for each criterion and also for coupled criteria. Outcomes indicated that sub watershed with more vegetation cover and mature geological stage is least prone to erosion. It can be inferred from results that morphometry, land cover and hypsometry all together as coupled criteria can be better indicator for assessing soil susceptibility rather than any single criteria. Also, the study suggested that remote sensing can be one of the most competent tools with lots of scopes to study watershed management and can help efficiently in decision making for formulation of conservation strategies, it is implementation and monitoring.

Forests are dynamic, complex and multidimensional ecosystems and play an irreplaceable role in social, economic, environmental, ecological and cultural context. Eucalyptus is the most common exotic species in Portugal forests. This species is fundamental in the industries related to the pulp paper production and the concern about their effects in ecosystems is growing. Geographical Information Systems (GIS) combined with Remote Sensing (RS) data can help to understand this complex ecosystem. Moreover, GIS and RS are commonly used in forest management. GIS allows the manipulation, analysis, and generation of considerable amounts of environmental information. This information can be used in the evaluation of ecosystems’ conditions and for decision making. The study case of this project was the municipal lands included in “Serras do Porto” and Valongo’s Nature 2000 network (Porto district, Portugal). The study zone considered in this work is a landscape of extreme relevance to Porto Metropolitan Area. For decades this area was extensively explored with eucalyptus plantations in order to produce cellulose for paper industry. Due to the characteristics of the area and its extension (40 hectares) the use of GIS became the most accurate and reliable alternative to characterize it. The combination of GIS tools and RS data allows the characterization of terrain relief, namely the analysis of altimetry, hypsometry, hydrography, the creation of environmental indexes such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), and the Digital Elevation Model (DEM). RS technology offer the potential to explore the effects of land-use changes and disturbances on forest dynamics at large spatial scales. A Sentinel-2A image was used to produce NDVI, EVI, and NDWI environmental indexes and to generate the Land Use Land Cover (LULC) map, through Semi-Automatic Classification Plugin from QGIS software using Minimum Distance algorithm. The LULC was classified with two classes because the study area only presents two types of species: eucalyptus and bare soil. The LULC map obtained was validated through field points collected in the study area with a GPS receptor. An overall accuracy of 92.98% and a kappa statistic of 0.842 was obtained. Also, some of the geographic information obtained in the field was then integrated in QGIS software. Moreover, a phenological study was performed using NDVI values obtained from Sentinel-2A images, to understand the eucalyptus behavior in a certain period of time.. Because of that RS data provided useful information about the landscape dynamics allowing the assess to forest cover change and land use helping to create decision making plans and forest conservation measures.

Sustaining forest resources in Romania requires a better understanding of forest ecosystem processes, and how management decisions and climate and anthropogenic change may affect these processes in the future. This paper aims to provide an application of time series anomalies and harmonic analysis to extract information about vegetation phenology from NDVI/EVI and LAI time series for a forest ecosystem Prahova Valley, placed in Carpathian Mountains, Romania, from MODIS Terra/Aqua data over 2000 – 2017 period. Spatio-temporal forest vegetation dynamics have been quantified also based on LANDSAT TM/ETM/OLI satellite data. Several daily climatic variables, were used as explanatory variables for the discussion of the vegetation phenology behavior. For investigated test area, considerable NDVI/EVI and LAI decline were observed for drought events during 2003, 2007, and 2012 years. The vegetation phenology analysis was correlated with associated time series of climatic variables in order to detect recorded anomalies. Temperature, rainfall and radiation were significantly correlated with almost all forest land-cover classes, while vegetation phenology was not correlated with climatic variables for the same period of analysis, suggesting a delay between climatic biophysical parameters variations and forest vegetation response. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI/EVI dynamics.

Malaria is one of deadly infectious diseases commonly found in tropical countries, and until now its preventive efforts are still going on. From a spatial-analytical perspective, the preventive efforts can be done by developing malaria vulnerability maps, which can be used as a basis for risk management. Remotely sensed imagery is a powerful source for collecting relevant spatial data for that purpose. Among various models, there are four analysis methods for generating such maps, i.e. scoring, matching, spatial multi-criteria evaluation (SMCE) and geographically weighted regression (GWR), which have been compared according to their effectiveness and accuracies. The authors tested those methods in Purworejo Regency, Central Java, Indonesia, which has been recognized as a malaria endemic area. This study used Landsat-8 OLI imagery as a basis for deriving spatial parameters closely related to malaria vulnerability . Each vulnerability spatial model’s accuracy was then evaluated by calculating the number of cases found in the field, with respect to each vulnerability class, and then compiling all values using cross tabulation. It was found that, among other methods, the SMCE-based malaria vulnerability map statistically delivered the best result.

The stripe noise is a key factor that affects imaging quality of satellite multi-hyperspectral remote sensing images, which also has a serious effect on the interpretation and information extraction of remote sensing images. Complex surface textures mixed with strip noises in the high-resolution multi-spectral remote sensing of satellite are extremely difficult to remove, this paper analyzes the Markov random field prior model method, combines the Huber function to propose a universal, fast and effective Huber Markov destriping method. According to the statistical characteristics of the image gray level variation, the distribution features and mutual relationship between each pixel and its neighborhood pixels in the image, the co-occurrence matrix reflecting the contrast gray characteristics of the image is connected with the threshold T of Huber function, which is automatically iteratively determined during the noise removal process, and will be able to remove image noises as well as preserving its edges and details effectively. In order to solve the time complexity of the algorithm caused by the pixel space information introduced by the Huber Markov random field algorithm, the GPU adaptive partitioning technique is adopted to accelerate the algorithm. Experimental results show that the destriping method based on Huber function Markov random field can remove the strip noise effectively, while preserving texture details of the image, which can be applied to a variety of noise-containing images. Meanwhile, GPUbased adaptive partitioning technology has been adopted, which has greatly improved the computational efficiency of processsing massive remote sensing images, and lays a foundation for the application of remote sensing satellite images in China.

Rice is the world’s major staple food crop occupying over 12% of global cropland area which produces around 800 million tons. Nearly 90% of the world’s rice is produced and consumed in Asian countries. Therefore, information on agricultural plantation area, yield, and production are essential to ensure food security of nearly 3 billion people. At the moment this information is either lacking in many countries or only available post-harvest, this is too late to input into any effecting policy in a specific year. Therefore, there is a pressing need to provide accurate and reliable yield estimation well ahead of harvest. In this project we explore potential of multi source remote sensing data coupled with crop model to provide country scale yield estimation in Thailand. For optical sensor, the study utilised Landsat8 OLI/TIRS satellite data to develop common vegetation indexes (VIs) approach to derive essential crop biophysical variables such as Leaf Area Index. This is supplemented with information from microwave sensor such as Sentinel 1 to overcome issues with cloud. At the end, we produced a regular time series of crop biophysical variable across the growing season. These satellite-based estimates were validated with dedicated field campaign in three provinces covering the entire growing season. Initial results suggest a good agreement between the optical/microwave derived crop biophysical variables and ground data. Finally, these will be used as an input to the ORYZA 2000 crop model to adjust the model parameters and develop a high resolution yield prediction.