This PDF file contains the front matter associated with SPIE Proceedings Volume 11535, including the Title Page, Copyright information and Table of Contents.

Hello and welcome to the SPIE Introduction for volume 11535 titled Remote Sensing Technologies and Applications in Urban Environments V

Resilience has become an important necessity for cities, particularly in the face of climate change. Mitigation and adaptation actions that enhance the resilience of cities need to be based on a sound understanding and quantification of the drivers of urban transformation and settlement structures, human and urban vulnerability, and of local and global climate change. Copernicus, as the means for the establishment of a European capacity for Earth Observation (EO), is based on continuously evolving Core Services. A major challenge for the EO community is the innovative exploitation of the Copernicus products in dealing with urban sustainability towards increasing urban resilience. Due to the multidimensional nature of urban resilience, to meet this challenge, information from more than one Copernicus Core Services, namely the Land Monitoring Service (CLMS), the Atmosphere Monitoring Service (CAMS), the Climate Change Service (C3S) and the Emergency Management Service (EMS), is needed. Furthermore, to address urban resilience, the urban planning community needs spatially disaggregated environmental information at local (neighbourhood) scale. Such information, for all parameters needed, is not yet directly available from the Copernicus Core Services mentioned above, while several elements - data and products - from contemporary satellite missions consist valuable tools for retrieving urban environmental parameters at local scale. The H2020-Space project CURE (Copernicus for Urban Resilience in Europe) is a joint effort of 10 partners from 9 countries that synergistically exploits the above Copernicus Core Services to develop an umbrella cross-cutting application for urban resilience, consisting of individual cross-cutting applications for climate change adaptation/mitigation, energy and economy, as well as healthy cities and social environments, at several European cities. These cross-cutting applications cope with the required scale and granularity by also integrating or exploiting third-party data, in-situ observations and modelling. CURE uses DIAS (Data and Information Access Services) to develop a system capable of supporting operational applications and downstream services across Europe. The CURE system hosts the developed cross-cutting applications, enabling its incorporation into operational services in the future. CURE is expected to increase the value of Copernicus Core Services for future emerging applications in the domain of urban resilience, exploiting also the improved data quality, coverage and revisit times of the future satellite missions. Thus, CURE will lead to more efficient routine urban planning activities with obvious socioeconomic impact, as well as to more efficient resilience planning activities related to climate change mitigation and adaptation, resulting in improved thermal comfort and air quality, as well as in enhanced energy efficiency. Specific CURE outcomes could be integrated into the operational Copernicus service portfolio. The added value and benefit expected to emerge from CURE is related to transformed urban governance and quality of life, because it is expected to provide improved and integrated information to city administrators, hence effectively supporting strategies for resilience planning at local and city scales, towards the implementation of the Sustainable Development Goals and the New Urban Agenda for Europe.

The study of urban heat islands (UHI) is of great importance in the context of climate change (CC) and global warming. Cities accumulate heat in urban land covers as well as in built infrastructures, representing true islands of heat in relation to their rural (less artificialized) environment. The densest urban spaces as well as the industrial and commercial areas are characterized by accumulating more heat during the day. On the other hand, the areas of lower density, the Urban Sprawl, tend to have a better climatic behaviour. The lower density as well as the greater amount of vegetation in the Urban Sprawl areas reduce the UHI during daylight hours. Nevertheless, the literature on urban climate has highlighted the singular importance of the nighttime UHI. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems regarding the availability of land surface and air temperature data for night time. This paper aims to develop a model for nocturnal UHI using data from Landsat thermal bands (with spatial resolution of 30 square meters per pixel) and contrasting Landsat's very limited nighttime images with daytime ones. The contrast allows the construction of “cooling” models of the LST based on geographical (longitude, latitude, distance to the sea, DTM, slope, orientation, etc.) and land covers characteristics (density, vegetation and building indexes, impervious surface, and others parameters). Said models will allow evaluating the nighttime LST of the different urban landscapes: historical centers, "ensanches", discontinuous urban fabrics (of different densities), scattered building areas and industrial and commercial areas, trying to clarify the nightly UHI of Urban Sprawl. The case study is the Metropolitan Area of Barcelona (636 km2, 3.3 million inhabitants).

Bangkok Metropolitan Administration (BMA) is the capital city of Thailand. In recent years, city surface has been changed greatly by rapid urbanization. Many green spaces in the urban area are destroyed and replaced with the commercial area. It can have effect on the climate and weather that could lead to the Urban Heat Island (UHI). This research analyzes the correlation between Urban Green Spaces (UGS) and Land Surface Temperature (LST) using quantitative remote sensing technology in BMA. More than five years of Landsat-5 TM and Landsat-8 images are used to study urban temperature. The LST, UGS and their correlation of whole area of BMA are analyzed. The LST is retrieved from mono-window algorithm, which used only one thermal band. Several indices of UGS, including the Normalized Difference Vegetation Index (NDVI), and measurement of vegetation cover percentage are also used to study on UGS. The result showed that from 2008 to 2018 LST is increased while UGS is decreased. In addition, Pearson product-moment correlation coefficient is used to analyze the linear correlation between UGS and LST. The correlation between LST and NDVI indicates the negative correlation. The average correlation coefficient is -0.44. That can imply that the higher vegetated area, the LST was lower. To get more detail relationship between LST and UGS, three typical regional areas are selected to study. LST contour is used to analyze LST and HERCULES system is used to classify land cover. The result showed that firstly, the density of tree affect LST. Secondly, the location of the public park is important. Thirdly, water bodies help to decrease LST. The results of this study have key implications for BMA sustainable urban planning and development; to mitigate UHI effects it is important to not only increase canopy cover or the size of UGS, but also to optimize their spatial configuration.

The problem of ideal hover of an unmanned aerial vehicle in a turbulent atmosphere is considered, and the equations for estimation of turbulent fluctuations of the longitudinal and transverse components of horizontal winds are derived independently of the unmanned aerial vehicle orientation and wind direction. Experiments were carried out on the territory of the Geophysical observatory of Institute of Monitoring of Climatic and Ecological Systems, Siberian Branch, Russian Academy of Sciences. It is situated in Tomsk Akademgorodok, on the territory with complex orography, in a parkland zone with buildings of research institutes and motorways. A DJI Phantom 4 Pro unmanned aerial vehicle flew up to an altitude of 30.7 m and approached an automated weather station, mounted at a mast near the Observatory. Time series of turbulent fluctuations of the longitudinal and transverse components of the horizontal wind were received with the use of the automated weather station, and time series of turbulent fluctuations of estimates of these components, from data of unmanned aerial vehicle in the hovering mode. According to the automated weather station, anisotropic fluctuations of the turbulent flow velocity were observed during the atmospheric measurements: the spectra of fluctuations of the horizontal components coincide, but differ from the spectrum of vertical fluctuations. The spectra of fluctuations of the longitudinal and transverse components of the horizontal wind velocity were comparatively analyzed. The general coincidence of these spectra with the spectra of fluctuations of estimates of the components is shown, with, however, significant differences in the high-frequency spectral region.

The major goal of the German research project “Urban Climate Under Change” [UC]² is the development and evaluation of an innovative urban climate model that should be able to simulate atmospheric processes on spatial scales of 10 m or finer for entire cities like Berlin and Stuttgart. This will enable interdisciplinary analyzes for the planning of measures to improve the urban climate and air quality. For the evaluation of urban climate models and mesoscale chemical-transport models and the three-dimensional processes represented therein, reference measurements are usually only available in the lowest meters of the boundary layer. In addition, an evaluation of city-wide mass balances (e.g. of air pollutants) with point measurements at the surface is only possible under many assumptions, the uncertainties remain high. Satellite and aircraft-based observations are therefore an inevitable addition to the analysis of the urban climate and the evaluation of three-dimensional urban climate models and chemical-transport models, especially if they cover entire cities or agglomerations. The objective of the work presented here is the analysis of atmospheric and chemical parameters by satellite and airborne measurements in order to examine the variability of the urban atmosphere and thereby contribute to the evaluation of the urban climate model PALM-4U and the mesoscale chemical-transport model POLYPHEMUS/DLR. In addition of long-term observations, results from the new instrument Sentinel-5P/TROPOMI will be shown. The capability of the instrument to monitor urban pollution plumes and urban pollution islands is examined. Airborne in-situ measurements were carried out with the DLR Cessna during two intensive measurement campaigns (IOP) in Berlin and Stuttgart.

Urban floods, especially those in dense built-up areas, present high impacts to resident populations and infrastructure. Real-time geographic extents of flooded areas delineated using remote sensing data and technologies is one of the key information inputs for effective disaster management and rapid rescue response. Images from visible band remote sensors are the most common and cost-effective for the real-time applications. Based on an understanding of the differing characteristics of floodwater and those of urban land surface classes, a robust method has been developed and automatized to extract floodwater using RGB band DNs. The methodology has been applied to delineate flood extent visible in imagery from very high-resolution aerial image data. The methodology development involved rule development, segment- and pixel-based feature analysis, automated feature extraction and result validation processing. The accuracies for the visible floodwater class are above 0.8394% and the overall accuracies are above 0.9668% at both pixel and segment levels for three test sites with diverse urban landscapes.

In the next decade, many old bridges will be exposed to increasing traffic loads and destructive environmental conditions. Measurement methods like laser scanning, infrared thermography, photogrammetry, ground penetrating radar, or ultrasonic scanning are used on single robotic systems to partially support the inspectors. However, time-consuming manual inspections for crack detection, measurement, and documentation are still necessary. This paper describes the concept of an autonomous mobile robotic bridge inspection system. The proposed concept for an unmanned ground vehicle (UGV) is achieved by a trade-off of different mobile platforms, sensor systems for mapping, localization and inspection, and fist tests assessing the feasibility. We use a small concrete bridge in Freiburg (Germany) with various cracks for testing the sensors, the UGV concept, and initial tests of the mobile platform. This results in the choice of selecting the weatherproof version of the mobile robotic platform Husky from Clearpath Robotics. It is equipped with Swift Navigation's Duro real-time kinematic (RTK) system, a heading system, an inertial measurement unit (IMU), a base station, and software for semi-autonomous navigation. In the next step, we compare different sensor systems. For mapping and localization, we decide to use the 360 spherical camera Ladybug 5+ from FLIR Systems and a Velodyne VLP-16 light detection and ranging (LiDAR). High-resolution cameras allow recording damages on the bridge's surface. We perform first tests using monochrome and colour cameras. After evaluating different sensor integration concepts, we present a preliminary design of the UGV including integrated sensors.

Ground operations in urban environments present a considerable risk to dismounted and mobile tactical teams. Special operations, counterterrorism, and other missions require forces to operate in complex, densely occluded urban environments. In these situations, an adversary can often exploit in-depth local knowledge of the 3D structure of the environment to establish optimal cover, maintain concealed ring positions, and channelize the attacking force into exposed ingress routes. Small Unmanned Aerial Systems (UAS) operating at low altitude and indoors could potentially provide real-time situational awareness in these conditions but are constrained by the limited availability of GPS in these environments and by a lack of detailed 3D map data required for precision sensor-based navigation in complex environments. This paper, based on our prior work in¹ introduces first responder and militaristic applications for an open- source framework for the rapid processing, exploitation, and dissemination of 3D mapping and navigational data at the tactical edge of the network. The proposed protocol is called 3D Tiles Nav which includes certain navigation cell and visibility metadata in order to improve navigational performance and autonomy in complex environments. The navigation cell data structures organizes the low airspace (below 400ft) in which 3D Tiles NAV data is embedded. The resulting data organization supports efficient data delivery, especially in densely occluded (e.g. low altitude and ground level) use cases. In addition, the resulting metadata fuses information about the visible and navigable structure of the operating environment to provide a unique informational framework for planning and conducting missions in densely occluded, high-threat environments. We demonstrate through experiments the extraction of planar features from point cloud data and illustrate through simulations the reduction network, compute, storage, and sensor requirements made possible using our approach.

Urban land use classes of complex nature are marked by the presence of multiple land covers and/or objects in the specific spatial order. The spatial configuration of the constituent parts of the land use class is generally unique. To the extent that the specific spatial configuration is defining characteristic of a given land use class. These characteristics can be effectively leveraged to identify the land use class. In this research, we exploit the unique spatial structure of the constituent parts for the land use class for its detection. We use capsule network (CapsuleNet) for detecting some of the urban land use classes such as parking lot and golf courses. CapsuleNets use a group of neurons (called capsules) in a convolutional layer to detect a specific image primitive. Each subsequent layer detects higher order primitives, and its relationship with the lower level primitives. Thus, multiple such layers build a hierarchy of parts to learn the whole object, in this case the land use class. We conducted multiple experiments for detecting parking lots and golf courses in a collection of urban images. We used NWPU-RESISC45 dataset for conducting our experiments. Furthermore, we compared the results of CapsuleNet based architecture with standard architecture such as VGG16, which do not consider the spatial structure of the features. Our initial experiments suggest improvement in accuracy in classification of the land use classes such as parking lot and golf courses.

Land use/land cover (LULC) is one of the serious phenomena that can influence human activities. Spatial-temporal analysis of land-use/land-cover (LULC) change, as well as the monitoring and modeling of urban expansion, are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans, and policies and by power, politics, and governance in many developing countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. This paper monitored the changes of the urban suitability area, and prediction of LULC with urban growth using multi-criteria decision analysis and cellular automata (CA)-Markov chain model. The province of Orkhon is the most intensive development area in Mongolia, and a transitional zone on urban area expansion with residential, mining, and industries, where is sampled in this study. The methods are provided by the urban suitability area based on the analytic hierarchy process (AHP) with factor variables and simulation of LULC based on the CA-Markov chain model. The urban suitability area analysis is helpful to validate the prediction of an urban area by relative operating characteristic (ROC) curve. The CA-Markov chain model was calibrated with data from 1989 to 2019 and used to predict expansions for 2030 and 2040 with two datasets of explanatory variables including slope, forest, river, specially protected areas, road, railroad, and urban centers in an urban and non-urban area. All analyses were based on Landsat imageries (TM, ETM+, and OLI) that were used to derive main land use classes. The results show that the urban, agriculture and mining area were expanded intensively. Forest was decreased in the last few years caused by human influences. Besides, the simulation results were validated by ROC curves with urban suitability analysis. Finally, this kind of study results is very important and useful to land managers and urban planners.

The use of remote sensing in archaeological research allows the prospection of sub-surfaces in arid regions non- intrusively before the on-site investigation and excavation. While the actual detection method of expected buried archaeological structures is based on visual interpretation, this work provides a supporting archaeological guidance using remote sensing. The aim is to detect potential archaeological remains underneath the sand. This paper focuses on Saruq Al-Hadid surroundings, which is an archaeologist site discovered in 2002, located about 50 km south-east of Dubai, as archaeologists believe that other archaeological sites are potentially buried in the surroundings. The input data is derived from a combination of wavelength L-band Synthetic Aperture Radar (ALOS PALSAR), which is able to penetrate the sand, and multispectral optical images (Landsat 7). This paper develops a new strategy to help in the detection of suspected buried structures. The data fusion of surface roughness and spectral indices enables tackling the well-known limitation of SAR images and offers a set of pixels having an archaeological signature different from the manmade structures. The potential buried sites are then classified by performing a pixel-level unsupervised classification algorithm such as K-means cluster analysis. To test the performance of the proposed method, the results are compared with those obtained by visual interpretation.

Nighttime light imagery of the earth are a useful way to study the urbanization process. Satellite nocturnal images have been used to identify metropolitan areas as well as urban growth. However, the study of the extent and internal structure of urban systems by nighttime lights has had a fundamental limitation to date: the low spatial resolution of satellite sensors. DMSP Operational Linescan System (OLS), with its 2.7 km/pixel footprint, and Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, with a spatial resolution of 742 m/pixel, still have considerable limitations for the in-depth study of the internal structure of urban systems. The launch of Luojia 1-01 in June 2018 has increased expectations. Its high-resolution nocturnal images (130 metres/pixel) allows a better in-depth study of the landscape impacted by the urbanization. Nevertheless, the areas resulting from urban sprawl process are characterized by weak night lighting, which makes identification extremely difficult. Breaking the rigid boundary that historically distinguished the urban from the rural, the topological inversion of the landscape produced by urban sprawl, makes difficult to identify the territories impacted by dispersed, fragmented and low density urbanization processes. The identification of the sprawled zones and their segmentation of the agricultural covers as well as the rest of the open spaces is especially complex, given the spatial resolution of Luojia 1-01. In this sense, the consideration of the NDVI, altitude, orientation, slope as well as the information provided by the thermal bands of Landsat8 can help to carry out a finer identification of the different urban landscapes, and specifically of the Urban Sprawl. The aim of this paper is to analyse the capacity of Luojia 1-01 to identify different types of urban landscapes, especially the results of the urban dispersion process known as Urban Sprawl. The case study is Barcelona Metropolitan Area (636 km² and 3.2 million inhabitants).

Satellite images taken in the night (a.k.a night lights) have been extensively used as a proxy for economic activity and urbanization. The recent Visible Infrared Imaging Radiometer Suite (VIIRS) data available every 24h open up a new domain of applications i.e. monitoring the function of the city. A method is needed however in order to remove lunar illumination that is different in each night so to form a meaningful time series, free of noise. A method is proposed here and tested in several case studies. It is found to perform acceptably with minimum input and processing requirements.

In this paper, we present a novel fusion and classification process for remotely sensed images developed in the framework of possibility theory. Unlike probability theory, possibility theory has the ability to handle both uncertainty and imprecision of classified pixel through a possibility and a necessity measures. Proposed multisource fusion and classification process involves several steps: First of all, the probability distribution of each spectral class from the samples representing thematic classes is estimated. This estimation is based on the histogram analysis of each class. Then, the possibility distribution is estimated from probability distributions using the Klir probability-possibility transformation. Next, once the possibility of samples is determined, we apply the Lagrangian interpolation method to estimate the other observations (grey level/reflectance) of the information sources. Combination operators: conjunctive and disjunctive are then used to combine possibilities of each source. This operation is performed a pixel level. Finally, decision is taken by using a criterion based on maximization of the possibility measures in order to select the optimal class. Experimental results obtained as land cover maps and land change maps indicate by using a statistical assessment (confusion matrix, Kappa coefficient, local spectral analysis method) that the proposed possibilistic fusion and classification process outperform the existing probabilistic fusion and classification process: It is important to emphasize that classical probabilistic data fusion models do not provide an adequate combination tool to detect and label change areas. Thus, possibility theory seems to be the best methodological framework which allows the development of multisource and multitemporal image fusion and classification process.

Spaceborne Synthetic Aperture Radar (SAR) and Optical sensors, are one of the main sources of Earth observation in the present age. Both the data types have their inherent advantages and disadvantages. Spaceborne Optical sensor are restricted by clouds but can offer strong information content in ideal conditions. On the other hand, SAR sensors rely on their own energy and can see through clouds. SAR is potentially an all-weather day/night imager. But SAR sensors have limitations in terms of data collection geometry and algorithmic approximations. Both sensors offer complimentary information for exploitation in data fusion for enhanced results. This research is focused on capitalizing the fusion potential for spaceborne High resolution SAR and Optical data in urban settings. The fusion of high reflection of SAR energy from urban areas and optical features of such areas can be combined to enhance the urban infrastructure detection and monitoring in a SAR/Optical fused scenario. SAR/Optical fusion can take place at three levels 1) pixel level, 2) feature level; and 3) information level. Pixel level fusion is often considered most difficult for high resolution data as precise registration up to subpixel level is required and even slight misregistration results in unfavorable circumstances. Simon Fraser University (SFU) Burnaby Mountain Campus has been chosen for area of interest because of its ongoing student housing and university infrastructure developmental projects. TerraSAR-X High Resolution Spotlight (TSX-HS) Single Look Complex (SLC) images of 1.0 m resolution have continuously being acquired over SFU; along with high resolution Optical (RGB) and Infrared (IR) images (3.0 m resolution each) from “The Planet” acquisitions. Limited high-resolution images from “Google Earth” (GE) in the coinciding period of TSX-HS acquisitions were also acquired for the study. Six fusion techniques have been studied for urban infrastructure detection and have been categorized based on their performance. Precision change maps will be created based on time series analysis for SAR/optical fused data in conjunction with Interferometric SAR (InSAR) analysis to study the long-term effect of urban infrastructure developments over a period of two years.