OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Sentinel-1 mission provides a freely accessible opportunity for urban interpretation from synthetic aperture radar (SAR) images with specific resolution, which is of paramount importance for earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, it is urgently needed to construct a large-scale SAR dataset leading urban interpretation. This paper presents OpenSARUrban: a Sentinel-1 dataset dedicated to urban interpretation from SAR images, including a well-defined hierarchical annotation scheme, the data collection, the well-established procedures for dataset construction and organizations, the properties, visualizations, and applications of this dataset. Particularly, the OpenSARUrban provides 33358 image patches of SAR urban scene, covering 21 major cities of China, including 10 different categories, 4 kinds of formats, 2 kinds of polarization modes, and owning 5 essential properties: large-scale, diversity, specificity, reliability, and sustainability. These properties guarantee the achievable of several goals for OpenSARUrban. The first is to support urban target characterization. The second is to help develop applicable and advanced algorithms for Sentinel-1 urban target classification. The dataset visualization is implemented from the perspective of manifold to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmark algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially challenging

Catchment Hydrology In The Anthropocene: Impacts Of Land-Use And Climate Change On Stormwater Runoff

Urbanization and climate change are associated with extreme changes to hydrologic processes that alter the amount and timing of runoff delivery in the Anthropocene. This dissertation research examined the degree of urbanization, climate change, and hydrologic responses in Rocky Branch Watershed (RBW), a small, highly urbanized catchment with dense vegetation canopy in Columbia, South Carolina. This dissertation is composed of three parts: (1) an automated algorithm for mapping building impervious areas (BIA) from remote sensing data for estimating percent impervious area (PIA), (2) a paired watershed study contrasting a forested with an urban watershed, and (3) a hydrologic simulation model to compare land-use and climate changes in an urban watershed. One key cause of hydrologic change, and also a measure of the degree of urbanization, is the PIA. However, mapping PIA under dense vegetation canopy is a challenge. Moreover, hydrologic changes to surface runoff in response to high PIA include an increase in peak flows and a decrease in peak flow arrival times. Although these relationships are general understood, details are missing—especially for small watersheds. This research presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes. The Building Extraction from LiDAR Last Returns (BELLR) model, uses a non-spatial, local vertical-difference filter on LiDAR point-cloud data to automatically identify and map building footprints under dense vegetation canopy. The BELLR- estimated BIAs were tested using two different types of hydrologic models to compare BELLR results with results using the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness data. The BELLR BIA values provide more accurate results than the use of the 2011 NLCD PIA data in both models. Comparisons between RBW and a forested watershed under different land-use conditions utilized field measurements of rainfall and streamflow to characterize storm hydrographs in order to quantify hydrologic responses to anthropogenic changes in small, heavily urbanized watersheds. It contrasts peak discharges, stormflow durations, volumes of storm water, shapes of storm hydrographs, and runoff coefficients generated by a variety of storm events between the two watersheds. The EPA Storm Water Management Model (SWMM) was used to study the effects of urbanization and climate change on stormwater in RBW. SWMM was applied to a series of scenarios to compare relative effects of projected PIA and climate-change scenarios on runoff for the near term (2035) and far term (2060). This analysis showed that climate change has generated a greater impact on runoff than urbanization

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Tsunami Risk and Vulnerability

The research focuses on providing reliable spatial information in support of tsunami risk and vulnerability assessment within the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) project. It contributes to three major components of the project: (1) the provision of spatial information on surface roughness as an important parameter for tsunami inundation modeling and hazard assessment; (2) the modeling of population distribution, which is an essential factor in tsunami vulnerability assessment and local disaster management activities; and (3) the settlement detection and classification from remote sensing radar imagery to support the population distribution research. Regarding the surface roughness determination, research analyses on surface roughness classes and their coefficients have been conducted. This included the development of remote sensing classification techniques to derive surface roughness classes, and integration of the thus derived spatial information on surface roughness conditions to tsunami inundation modeling. This research determined 12 classes of surface roughness and their respective coefficients based on analyses of published values. The developed method for surface roughness classification of remote sensing data considered density and neighborhood conditions, and resulted in more than 90% accuracy. The classification method consists of two steps: main land use classification and density and neighborhood analysis. First, the main land uses were defined and a classification was performed applying decision tree modeling. Texture parameters played an important role in increasing the classification accuracy. The density and neighborhood analysis further substantiated the classification result towards identifying surface roughness classes. Different classes such as residential areas and trees were combined to new surface roughness classes, as “residential areas with trees”. The density and neighborhood analysis led to an appropriate representation of real surface roughness conditions. This was used as an important input for tsunami inundation modeling. By using Tohoku University’s Analysis Model for Investigation Near-field Tsunami Number 3 (TUNAMI N3), the spatially distributed surface roughness information was integrated in tsunami inundation modeling and compared to the modeling results applying a uniform surface roughness condition. An uncertainty analysis of tsunami inundation modeling based on the variation of surface roughness coefficients in the Cilacap study area was also undertaken. It was demonstrated that the inundation modeling results applying uniform and spatially distributed surface roughness resulted in high differences of inundation lengths, especially in areas far from the coastline. This result showed the important role of surface roughness conditions in resisting tsunami flow, which must be considered in tsunami inundation modeling. With respect to the second research focus, the population distribution, a concept of population distribution modeling was developed. Within the modeling process, weighting factor determination, multi-scale disaggregation and a comparative study to other methods were conducted. The basis of the developed method was a combination of census and land use data, which led to an improved spatial resolution and accuracy of the population distribution. Socio-economic data were used to derive weighting factors to distributing people to land use classes. Moreover, in case of missing input data, an approach was developed that allows for the determination of generalized weighting factors. The approach to use specific weightings, where possible and generalized ones, where necessary, led to a flexible methodology with respect to the achievable accuracy and availability of data. A comparative study was performed by comparing this new model with previously developed population distribution models. The newly developed model showed a higher accuracy. The detailed population distribution information was a valuable input for the vulnerability assessment being the main data source for human exposure assessment and an important contribution to evacuation time modeling. In support of the population distribution research, settlement classification using TerraSAR-X imagery was conducted. A current classification method of speckle divergence analysis on SAR imagery was further developed and improved by including the neighborhood concept. The settlement classification provided highly accurate results in dense urban areas, whereas the method needs to be further developed and improved for rural settlement areas. Finally, it has been shown how the results of this research can be applied. These applications cover the integration of surface roughness conditions into the tsunami inundation modeling and hazard mapping. The contributions to tsunami vulnerability assessment and evacuation planning were shown. Additionally, the results were integrated into the decision support system of the Tsunami Early Warning Center in Jakarta

Approches tomographiques structurelles pour l'analyse du milieu urbain par tomographie SAR THR : TomoSAR

SAR tomography consists in exploiting multiple images from the same area acquired from a slightly different angle to retrieve the 3-D distribution of the complex reflectivity on the ground. As the transmitted waves are coherent, the desired spatial information (along with the vertical axis) is coded in the phase of the pixels. Many methods have been proposed to retrieve this information in the past years. However, the natural redundancies of the scene are generally not exploited to improve the tomographic estimation step. This Ph.D. presents new approaches to regularize the estimated reflectivity density obtained through SAR tomography by exploiting the urban geometrical structures.La tomographie SAR exploite plusieurs acquisitions d'une même zone acquises d'un point de vue légerement différent pour reconstruire la densité complexe de réflectivité au sol. Cette technique d'imagerie s'appuyant sur l'émission et la réception d'ondes électromagnétiques cohérentes, les données analysées sont complexes et l'information spatiale manquante (selon la verticale) est codée dans la phase. De nombreuse méthodes ont pu être proposées pour retrouver cette information. L'utilisation des redondances naturelles à certains milieux n'est toutefois généralement pas exploitée pour améliorer l'estimation tomographique. Cette thèse propose d'utiliser l'information structurelle propre aux structures urbaines pour régulariser les densités de réflecteurs obtenues par cette technique

Analysis of the 2015 Sagavanirktok River flood: associated permafrost degradation using InSAR and change detection techniques

Thesis (M.S.) University of Alaska Fairbanks, 2020In 2015, the Sagavanirktok River experienced a sequence of high, early-winter temperatures that lead to a buildup of aufeis. The buildup displaced the spring runoff causing widespread flooding. Flood waters inundated the surrounding tundra introducing heat into ground ice-baring soils. The Sagavanirktok River flood was caused by an extensive ice dam that developed the previous winter. The first flooding pulse started in April 2015, when an aufeis obstruction diverted river water to the surface. The obstruction caused flooding along 24 km of the Dalton Highway and its surroundings, necessitating a prolonged highway closure and emergency repairs. A second flooding pulse was caused by annual spring runoff in May 2015, which was driven by rapid snowmelt due to warm seasonal temperatures. The washed-out highway had to be closed again. Field investigations showed that thermal erosion of ice wedges in the tundra adjacent to the Dalton Highway caused local subsidence by several meters. However, the full environmental impact of the flood has not yet been quantified regionally or temporally. Thermokarst formation, can cause rapid ecological and environmental changes. Thawing of permafrost can lead to terrain instability as the melting of ground ice induces subsidence and loss of soil strength. The processes involved in permafrost degradation are complex, as is predicting terrain stability and the associated impacts to permafrost surrounding infrastructure. The immediate impact of the 2015 Sagavanirktok River flood is evident, which caused rapid terrain collapse in the vicinity of the Dalton Highway and the Trans-Alaska Pipeline near Deadhorse, North Slope Borough, Alaska. Thermal degradation of permafrost can be expressed as the change in the surfacemicrotopography over several years following a flood. Change detection, digital elevation model differencing, and InSAR were employed within the area of interest to understand the extent of the flood and deformation within inundated areas. To determine the likely impacted areas within the area of interest and expanse of the flood, an unsupervised change detection technique of high resolution TerraSAR-X and Sentinel-1 amplitude images was utilized. The topographic deformation analysis to determine the motion on the ground surface used a short baseline subset InSAR analysis of Sentinel-1 data during the summer season following the Sagavanirktok River flooding events. Additional deformation analysis was conducted with ALOS-2 data for annual comparison of the 2015 to 2019 summers. TanDEM-X digital elevation model differencing compared surface models generated from before and after the Sagavanirktok River flood. Elevation model differencing would identify the absolute change between the acquisition time of the surface models. A joint data analysis between deformation and differenced elevation models analyzed the contrast within inundated and flood-unaffected areas; thus, the changes and impact to the permafrost following the 2015 Sagavanirktok River flood. The Sagavanirktok River flood highlights the vulnerability of ice-rich permafrost to flooding. A change in the vicinity of the Sagavanirktok River Delta to the hydrological cycle led to widespread increases in terrain instability. Analysis of summer season deformation data suggested inundated permafrost areas showed lower seasonal deformation in years following the flood. Analysis of annual deformation shows permafrost subsidence intensified in inundated areas in the years following the flood. Digital elevation model differencing produced a statistically ambiguous result. This research illustrates the value of combining TerraSAR-X, TanDEM-X, Sentinel 1, and ALOS-2 microwave remote sensing missions for evaluating widespread surface changes in arctic environments. However, annual deformation data proved the most usable tool in observing the changing permafrost ecosystems around the Sagavanirktok River.Geomatics Office of the National Geospatial-Intelligence Agenc

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps