Identificación de la huella urbana utilizando imágenes satelitales radar Sentinel-1 en la Ciudad de Rosario (Santa Fe, Argentina) : año 2018

La utilización de las imágenes radar y sus múltiples aplicaciones están siendo investigadas intensamente estos últimos tiempos. El estudio de la huella o máscara urbana permite entre otras cosas identificar la expansión de una ciudad a fin de servir de herramienta para el ordenamiento territorial y detectar cambios de uso del suelo de rural a urbano. Dado el constante crecimiento y el dinamismo territorial que existe en la ciudad de Rosario, la detección de la huella urbana a través de imágenes satelitales radar podría responder a varias necesidades en cuanto al desarrollo urbanístico que experimenta e incluso su relación con las localidades satélites que conforman la Región Metropolitana. En la identificación de la huella urbana en un área de interés en la ciudad de Rosario (Provincia de Santa Fe, Argentina) se utiliza una metodología propuesta por la EO-College e imágenes satelitales de radar de la misión Sentinel-1 (abril, 2018), con una resolución espacial de 14 m. En un análisis visual de los resultados se utiliza una imagen óptica disponible en Google Maps y en un análisis preliminar cuantitativo, la huella urbana obtenida en el proyecto Global Urban Footprint (GUF) llevado a cabo por el Centro Aeroespacial Alemán (DRL). Como resultado se obtienen dos huellas urbanas con umbrales de 0,6 y 0,7. En el análisis de los resultados considerando el producto GUF y la imagen óptica de Google Maps, se concluye que la metodología seleccionada en este estudio permite identificar mejor el área urbanizada en el casco céntrico de la ciudad y en menor medida en la región periférica.Fil: Tazzioli, Florencia. Universidad Nacional de RosarioFil: Ciattaglia, Brunela. Universidad Nacional de RosarioFil: Aquili, Verónica. Universidad Nacional de RosarioFil: Vicioso, Benito. Universidad Nacional de Rosari

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version

Urban morphology parameters from global digital elevation models: implications for aerodynamic roughness for wind-speed estimation

Urban morphology and aerodynamic roughness parameters are derived from three global digital elevation models (GDEM): Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), and TanDEM-X. Initially, each is compared to benchmark elevation data in London (UK). A moving window extracts ground heights from the GDEMs, generating terrain models with root-mean-square accuracy of up to 3 m. Subtraction of extracted ground heights provides roughness-element heights only, allowing for calculation of morphology parameters. The parameters are calculated for eight directional sectors of 1 km grid-squares. Apparent merging of roughness elements in all GDEMs causes height-based parameter underestimation, whilst plan and frontal areas are over- and under-estimated, respectively. Combined, these lead to an underestimation of morphometrically-derived aerodynamic roughness parameters. Parameter errors are least for the TanDEM-X data. Further comparison in five cities (Auckland, Greater London, New York, Sao Paulo, Tokyo) provides basis for empirical corrections to TanDEM-X-derived geometric parameters. These reduce the error in parameters across the cities and for a separate location. Meteorological observations in central London give insight to wind-speed estimation accuracy using roughness parameters from the different elevation databases. The proposed corrections to TanDEM-X parameters lead to improved wind-speed estimates, which combined with the improved spatial representation of parameters across cities demonstrates their potential for use in future studies

Large-scale 3D Modelling of the Built Environment - Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area

Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information

The appearance of objects and surfaces in synthetic aperture radar (SAR) images significantly differs from the human perception of the environment. In addition, the quality of SAR data is degraded by speckle noise, superposing the true radiometric and textural information of the radar image. Hence, the interpretation of SAR images is considered to be more challenging compared to the analysis of optical data. However, in this paper, we demonstrate how information on the local development of speckle can be used for the differentiation of basic land cover (LC) types in a single-polarized SAR image. For that purpose, we specify the speckle characteristics of the following LC types: 1) water; 2) open land (farmland, grassland, bare soil); 3) woodland; and 4) urban area by means of an unsupervised analysis of scatter plots and standardized histograms of the local coefficient of variation. Next, we use this information for the implementation of a straightforward preclassification of single-polarized TerraSAR-X stripmap images by combining information on the local speckle behavior and local backscatter intensity. The output is either provided as a discrete classification or as a color composite image whose bands can be interpreted in terms of a fuzzy classification. The results of this paper show that unsupervised speckle analysis in high-resolution SAR images supplies valuable information for a differentiation of the water, open land, woodland, and urban area LC types. While the color composite image supports the visual interpretation of SAR data, the outcome of the fully automated discrete LC classification procedure represents a valuable preclassification image, showing overall accuracies of 77%–86%

Improving the representation of the spatial distribution of population for coastal impact and vulnerability assessments

Extreme sea levels and sea-level rise are two hazards that can harm populations situated in low elevated coastal areas. Impact and vulnerability assessments quantify the number of people living in flood prone areas and serve as a basis for planning adaptation measures. One of the prerequisites for these assessments is a realistic spatial representation of the potentially affected coastal population. Census data provide the highest spatial detail for assessments on coarse scales (multi-national to global). Within census units, population is assumed to be distributed homogeneously. This assumption might hold true for small census units (few hectares) but does not represent the actual distribution of population in large census units (several square kilometres). Previous studies use many ancillary data to allocate population within cen-sus units which restricts their transferability to data sparse regions. This thesis in contrast uses a novel approach by deriving settlement extents from the ‘Global Urban Footprint’. It examines whether these derived settlement areas are sufficient to reproduce the actual population distribution within census units. For the German Baltic Sea region, the settlement extents capture 95.3 % of the actual population, qualifying them as a suitable parameter to model the distribution of population. In addition, this thesis compares six approaches to differentiate population density within the derived settlement extents. Compared to a homogeneous distribution of population, the error in the tested approaches is up to six times smaller. In flood prone areas, the tested approaches reduce the overestimation of population exposure by up to 29 %. These results thus show that settlement extents derived from the ‘Global Urban Footprint’ lead to a more realistic distribution of the current popula-tion within census units, which improves the assessment accuracy of population exposure to coastal flooding. The regionalisation of population growth projections in coastal areas is the second focus of this thesis. Previous studies that assess exposure to coastal flooding account for changing sea-levels or the changes in the frequency and intensity of floods due to climate change. However, these studies do not consider socioeconomic development at all, or do not differen-tiate socioeconomic development at subnational level. This thesis extends the ‘Shared Socio-economic Pathways’ for the coastal zone by providing narratives of socioeconomic develop-ment under five scenarios. Furthermore, it combines observed differences in population growth between coastal and inland areas with existing projections on population and urbanisation to develop spatially explicit population projections for the entire globe. Subsequently, this work compares these projections to projections that do not differentiate population growth within countries. Depending on the scenario, it finds the population living in the ‘low elevation coastal zone’ in the regionalised projections in 2100 to be 85 million to 229 million people larger. Furthermore, the thesis investigates, to what extent urbanisation or coastal mi-gration contribute to these differences and whether urban sprawl increases or decreases these differences by employing the ‘Dynamic Interactive Vulnerability Assessment’ Tool. Com-pared to homogeneous population growth within countries, urbanisation increases the assessed exposure by 7 % to 20 %, coastal migration increases the assessed exposure by 1 % to 20 % and urban sprawl decreases the assessed exposure by 12 % to 22 %. The results show that urbanisation, coastal migration and urban sprawl lead to heterogeneous population growth on a subnational level. Accounting for these differences in population growth contributes to improved estimates of the future population exposure to coastal floods.Menschen, die in niedrig gelegenen Küstengebieten leben, sind durch Sturmfluten und den Meeresspiegelanstieg gefährdet. Gefährdungsanalysen quantifizieren die potenziell betroffene Küstenbevölkerung und stellen die Basis bei der Identifikation und Auswahl von geeigneten Anpassungsmaßnahmen dar. Vor allem räumlich hochaufgelöste Informationen zur Bevölkerungsverteilung stellen dabei einen elementaren Eingangsparameter der Gefährdungsanalysen dar. Bei grobskaligen Betrachtungen (multinational bis global) bilden Bevölke-rungsinformationen auf Zensusebene die räumlich höchstaufgelöste Informationsquelle. Die dabei angenommene Gleichverteilung der Bevölkerung innerhalb von Zensuseinheiten kann bei kleinflächigen Zensuseinheiten (wenige Hektar) akkurat sein, entspricht bei großflächigen Zensuseinheiten (mehreren Quadratkilometer) jedoch nicht der tatsächlichen Bevölkerungs-verteilung. Bisherige Studien modellieren die Verteilung der Bevölkerung mit einer Vielzahl von Hilfsvariablen, was die Übertragbarkeit in Gebiete mit spärlicher Datenverfügbarkeit einschränkt. Die vorliegende Arbeit hingegen arbeitet mit einem neuartigen Ansatz, indem Siedlungsgebiete aus dem ‚Global Urban Footprint’ abgeleitet werden. Untersucht wird, ob diese abge-leiteten Siedlungsgebiete hinreichend sind, um die tatsächliche Bevölkerungsverteilung inner-halb von Zensuseinheiten nachzubilden. Für die Untersuchungsregion der deutschen Ostsee-küste zeigt sich, dass die abgeleiteten Siedlungsgebiete bis zu 95,3 % der tatsächlichen Be-völkerung erfassen und somit einen geeigneten Parameter zur Bevölkerungsmodellierung darstellen. Darüber hinaus vergleicht die vorliegende Arbeit sechs Ansätze, die die Bevölke-rungsdichte innerhalb der Siedlungsgebiete differenzieren. Im Vergleich zur Gleichverteilung der Bevölkerung weisen die verwendeten Ansätze bis zu sechs Mal geringere Fehlerwerte auf. In flutgefährdeten Gebieten reduzieren die verwendeten Ansätze die Überschätzung der gefährdeten Bevölkerung um bis zu 29 %. Somit zeigt sich, dass mit Hilfe der aus dem ‚Global Urban Footprint‘ abgeleiteten Siedlungsgebiete eine realistischere Verteilung der aktuellen Bevölkerung innerhalb von Zensuseinheiten abgebildet werden kann, die zu einer verbesserten Abschätzung der Flutgefährdung beiträgt. Den zweiten inhaltlichen Schwerpunkt der Arbeit bildet die Regionalisierung von Bevölkerungsprojektionen im Küstenraum. Viele Gefährdungsanalysen berücksichtigen die Aus-wirkungen des Klimawandels auf den Meeresspiegel sowie auf die Häufigkeit und die Intensität von Sturmfluten, während sozioökonomische Veränderungen entweder nicht betrachtet oder auf subnationaler Ebene nicht differenziert werden. Diese Arbeit beschreibt fünf sozio-ökonomische Entwicklungspfade für den Küstenraum, die die bestehenden „Shared-Socioeconomic-Pathways“ erweitern. Basierend auf beobachteten Unterschieden im Bevölke-rungswachstum zwischen Küsten- und Inlandsgebieten sowie Bevölkerungs- und Urbanisierungsprojektionen werden räumlich explizite Bevölkerungsprojektionen mit globaler Abde-ckung entwickelt. Für die „Low Elevation Coastal Zone“ werden diese Bevölkerungsprojek-tionen anschließend mit Bevölkerungsprojektionen verglichen, die innerhalb eines Landes eine einheitliche Bevölkerungsänderung annehmen. Basierend auf dem regionalisierten Bevölkerungswachstum leben in diesen Gebieten im Jahr 2100 zwischen 85 Millionen und 239 Millionen Menschen mehr als bei einem national einheitlichen Wachstum. Um zu untersuchen, ob Urbanisierung oder Küstenmigration stärker zu diesen Unterschieden beitragen und ob Suburbanisierung diese Unterschiede verstärkt oder verringert, verwendet die vorliegende Arbeit das „Dynamic Interactive Vulnerability Assessment“ Tool. Im Vergleich zu homogenem Wachstum auf nationaler Ebene zeigt sich, dass die geschätzte Gefährdung der Bevölkerung durch Urbanisierung um 7 % bis 20 % ansteigt, durch Küstenmigration um weitere 1 % bis 20 % ansteigt und durch Suburbanisierung um 12 % bis 22 % abfällt. Diese Ergebnisse zeigen, dass Urbanisierung, Küstenmigration und Suburbanisierung auf subnationaler Ebene zu Unterschieden im Bevölkerungswachstum führen und deren differenzierte Berücksichtigung zu einer verbesserten Abschätzung der zukünftigen Flutgefährdung von Bevölkerung im Küstenraum beiträg

Classification of Compact Polarimetric Synthetic Aperture Radar Images

The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

Forest attributes mapping with SAR data in the romanian South-Eastern Carpathians requirements and outcomes

Esta tesis doctoral se centra en la estimación de variables forestales en la zona Sureste de los Cárpatos Rumanos a partir de imágenes de radar de apertura sintética. La investigación abarca parte del preprocesado de las imágenes, métodos de generación de mosaicos y la extracción de la cobertura de bosque, sus subtipos o su biomasa. La tesis se desarrolló en el Instituto Nacional de Investigación y Desarrollo Forestal Marín Dracea (INCDS) y la Universidad de Alcalá (UAH) gracias a varios proyectos: el proyecto EO-ROFORMON del INCDS (Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests), y el proyecto EMAFOR de la UAH (Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapes). El proyecto EO-ROFORMON fue financiado por la Autoridad Nacional para la Investigación Científica de Rumania y el Fondo Europeo de Desarrollo Regional. El proyecto EMAFOR fue financiado por la Comunidad Autónoma de Madrid (España). El objetivo de esta tesis es el desarrollo de algoritmos para la extracción de variables forestales de uso general como la cobertura, el tipo o la biomasa del bosque a partir de imagen de radar de apertura sintética. Para alcanzar dicho propósito se analizaron posibles fuentes de sesgo sistemático que podrían aparecer en zonas de montaña (ej., normalización topográfica, generación de mosaicos), y se aplicaron técnicas de aprendizaje de máquina para tareas de clasificación y regresión. La tesis contiene ocho secciones: una introducción, cinco publicaciones en revistas o actas de congresos indexados, una pendiente de publicación (quinto capítulo) y las conclusiones. La introducción contextualiza la importancia del bosque, cómo se recoge la información sobre su estado (ej., inventario forestal) y las iniciativas o marcos legislativos que requieren dicha información. A continuación, se describe cómo la teledetección puede complementar la información de inventario forestal, detallando el contexto histórico de las distintas tecnologías, su funcionamiento, y cómo pueden ser aplicadas para la extracción de información forestal. Por último, se describe la problemática y el monitoreo del bosque en Rumanía, detallando el objetivo de la tesis y su estructura. El primer capítulo analiza la influencia del modelo digital de elevaciones (MDE) en la calidad de la normalización topográfica, analizando tres MDE globales (SRTM, AW3D y TanDEM-X DEM) y uno nacional (PNOA-LiDAR). Los experimentos se basan en la comparación entre órbitas, con un MDE de referencia, y la variación del acierto en la clasificación dependiendo del MDE empleado para la normalización. Los resultados muestran una menor diferencia ente órbitas al utilizar un MDE con una mejor resolución (ej. TanDEM-X, PNOA-LIDAR), especialmente en el caso de zonas con fuertes pendientes o formas del terreno complejas, como pueden ser los valles. En zonas de alta montaña las imágenes de radar de apertura sintética (SAR) sufren frecuentes distorsiones. Estas distorsiones dependen de la geometría de adquisición, por lo que es posible combinar imágenes adquiridas desde varias órbitas para que la cobertura sea lo más completa posible. El segundo capítulo evalúa dos metodologías para la clasificación de usos del suelo utilizando datos de Sentinel-1 adquiridos desde varias órbitas. El primer método crea clasificaciones por órbita y las combina, mientras que el segundo genera un mosaico con datos de múltiples órbitas y lo clasifica. El acierto obtenido mediante combinación de clasificaciones es ligeramente mayor, mientras que la clasificación de mosaicos tiene importantes omisiones de las zonas boscosas debido a problemas en la normalización topográfica y a los efectos direccionales. El tercer capítulo se enfoca en separar la cobertura forestal de otras coberturas del suelo (urbano, vegetación baja, agua) analizando la utilidad de las variables basadas en la coherencia interferométrica. En él se realizan tres clasificaciones de máquina vector-soporte basadas en un conjunto concreto de variables. El primer conjunto contiene las estadísticas anuales de la retrodispersión (media y desviación típica anual), el segundo añade la coherencia a largo plazo (separación temporal mayor a un año), el tercero incluye las estadísticas de la coherencia a corto plazo (mínima separación temporal). Utilizar variables basadas en la coherencia aumenta el acierto de la clasificación hasta un 5% y reduce los errores de omisión de la cobertura forestal. El cuarto capítulo evalúa la posibilidad de detectar talas selectivas utilizando datos de Sentinel-1 y Sentinel-2. Sus resultados muestran que la detección resulta muy difícil debido a la saturación de los sensores y la confusión introducida por el efecto de la fenología. El quinto capítulo se centra en la clasificación de tipos de bosque basado en una serie temporal de datos Sentinel-1. Se basa en la creación de un conjunto de modelos que describen la relación entre la retrodispersión y el ángulo local de incidencia para un determinado tipo de bosque y fecha concreta. Para cada píxel se calcula el residuo respecto al modelo de cada uno de los tipos de bosque, acumulando dichos residuos a lo largo de la serie temporal. Hecho esto, cada píxel es asignado al tipo de bosque que acumula un menor residuo. Los resultados son prometedores, mostrando que frondosas y coníferas tienen un comportamiento distintivo, y que es posible separar ambos tipos de bosque con un alto grado de acierto. El sexto capítulo está dedicado a la estimación de biomasa utilizando datos Sentinel-1, ALOS PALSAR y regresión Random Forest. Se obtiene un error similar para ambos sensores a pesar de utilizar una banda diferente (band-C vs. -L), con poca reducción en el error cuando ambas bandas se utilizan conjuntamente. Sin embargo, el ajuste de un estimador adaptado a las condiciones locales de Rumanía sí ofreció una reducción de del error al ser comparado con las estimaciones globales de biomasa

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD