Overview of Results of Spaceborne Imaging Radar C-, X-Band Synthetic Aperture Radar (SIR-C/X-SAR).

SIR-C/X-SAR wurde an Bord der Raumfaehre Endeavour im Fruehjahr und Herbst 1994 gestartet. Die waehrend der 10taegigen Missionen erhobenen Radardaten von ueber 300 Gebieten der Erde werden zum besseren Verstaendnis der Umweltbedingungen ausgewertet. Waehrend des zweiten Fluges wurde ein Experiment zur Radar-Interferometrie erfolgreich durchgefuehrt. In dem Artikel wird ein Ueberblick der Missionen sowie erste Ergebnisse dargestellt

MirrorSAR: An HRWS Add-On for Single-Pass Multi-Baseline SAR Interferometry

This paper reports the Phase A study results of the interferometric extension of the High-Resolution Wide-Swath (HRWS) mission with three MirrorSAR satellites. According to the MirrorSAR concept, small, low cost, transponder-like receive-only satellites without radar signal demodulation, digitization, memory storage, downlink, and synchronization are added to the planned German X-band HRWS mission. The MirrorSAR satellites fly a triple helix orbit in close formation around the HRWS orbit and span multiple single-pass interferometric baselines. A comprehensive system engineering and performance analysis is provided that includes orbit formation, MirrorLink, Doppler steering, antenna pattern and swath design, multi-static echo window timing, SAR performance, height performance and coverage analysis. The overall interferometric system design analysis of Phase A is presented. The predicted performance of the global Digital Elevation Model (DEM) is improved by one order of magnitude compared to presently available global DEM products like the TanDEM-X DEM

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Monte Carlo simulation model for electromagnetic scattering from vegetation and inversion of vegetation parameters

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 171-185).In this thesis research, a coherent scattering model for microwave remote sensing of vegetation canopy is developed on the basis of Monte Carlo simulations. An accurate model of vegetation structure is essential for the calculation of scattering from vegetations, especially those with closely spaced elements in clusters. The Monte Carlo approach has an advantage over the conventional wave theory in dealing with complex vegetation structures because it is not necessary to find the probability density functions and the pair-distribution functions required in the analytic formulation and usually difficult to obtain for natural vegetation. To achieve a realistic description of the vegetation structure under consideration, two methods may be employed. One method requires the specification of the number of each type of component and the relative orientations of the components. In a structural model which incorporates this method, the detailed features can be preserved to the desired level of accuracy. This structural model is applied to two types of vegetation- --rice crops and sunflowers.(cont.) The developed structural model for rice crops takes into account the coherent wave interactions made prominent by the clustered and closely spaced structure of rice crops, and is validated with the ERS-1 and RADARSAT data. It is utilized to interpret the experimental observations from the JERS-1 data, such as the effects of the structure of rice fields, and to predict the temporal response of rice growth. The structural model developed for sunflowers is validated using the airborne Remote Sensing Campaign Mac-Europe 91 multi-frequency and multi-polarization data acquired for sunflower fields at the Montespertoli test site in Italy. Another method to characterize vegetation structure uses growth rules. This is especially useful in modeling trees, which are structurally more complex. The Lindenmayer systems (L-systems) are utilized to fully capture the architecture of trees and describe their growth. Monte Carlo simulation results of the scattering returns from trees with different structures and at different growth stages are calculated and analyzed. The concept of the "structure factor" which extracts the structural information of a tree and and provides a measure of the spatial distribution of branches is defined, and computed for trees with different architectures.(cont.) After study of the forward scattering problem in which the scattering coefficients are determined on the basis of known physical characteristics of the scattering objects or medium, the inverse scattering problem is considered in which the characteristics of the scattering objects or medium are to be calculated from the scattering data. In this thesis research, neural networks are applied to the inversion of geophysical parameters including soil moisture and surface parameters, sunflower biomass, as well as forest age (or equivalently, forest biomass). They are found to be especially useful for multi-dimensional inputs such as multi-frequency polarimetric scattering data. For the inversion of soil moisture and surface parameters, neural networks are trained with theoretical surface scattering models. To retrieve the sunflower biomass, neural networks are trained with the scattering returns obtained from the developed vegetation scattering model based on the Monte Carlo approach. To assess the performance of the use of experimental data to train the neural networks, the polarimetric radar data acquired by the Spaceborne Imaging Radar-C (SIR-C) over the Landes Forest in France are utilized as the training data to retrieve the forest age. Different combinations of backscattering data are used as input to the neural net in order to determine the combination which yields the best inversion result.by L-i-Fang Wang.Ph.D

Estimation of the Degree of Polarization in Polarimetric SAR Imagery : Principles and Applications

Les radars à synthèse d’ouverture (RSO) polarimétriques sont devenus incontournables dans le domaine de la télédétection, grâce à leur zone de couverture étendue, ainsi que leur capacité à acquérir des données dans n’importe quelles conditions atmosphériques de jour comme de nuit. Au cours des trois dernières décennies, plusieurs RSO polarimétriques ont été utilisés portant une variété de modes d’imagerie, tels que la polarisation unique, la polarisation double et également des modes dits pleinement polarimétriques. Grâce aux recherches récentes, d’autres modes alternatifs, tels que la polarisation hybride et compacte, ont été proposés pour les futures missions RSOs. Toutefois, un débat anime la communauté de la télédétection quant à l’utilité des modes alternatifs et quant au compromis entre la polarimétrie double et la polarimétrie totale. Cette thèse contribue à ce débat en analysant et comparant ces différents modes d’imagerie RSO dans une variété d’applications, avec un accent particulier sur la surveillance maritime (la détection des navires et de marées noires). Pour nos comparaisons, nous considérons un paramètre fondamental, appelé le degré de polarisation (DoP). Ce paramètre scalaire a été reconnu comme l’un des paramètres les plus pertinents pour caractériser les ondes électromagnétiques partiellement polarisées. A l’aide d’une analyse statistique détaillée sur les images polarimétriques RSO, nous proposons des estimateurs efficaces du DoP pour les systèmes d’imagerie cohérente et incohérente. Ainsi, nous étendons la notion de DoP aux différents modes d’imagerie polarimétrique hybride et compacte. Cette étude comparative réalisée dans différents contextes d’application dégage des propriétés permettant de guider le choix parmi les différents modes polarimétriques. Les expériences sont effectuées sur les données polarimétriques provenant du satellite Canadian RADARSAT-2 et le RSO aéroporté Américain AirSAR, couvrant divers types de terrains tels que l’urbain, la végétation et l’océan. Par ailleurs nous réalisons une étude détaillée sur les potentiels du DoP pour la détection et la reconnaissance des marées noires basée sur les acquisitions récentes d’UAVSAR, couvrant la catastrophe de Deepwater Horizon dans le golfe du Mexique. ABSTRACT : Polarimetric Synthetic Aperture Radar (SAR) systems have become highly fruitful thanks to their wide area coverage and day and night all-weather capabilities. Several polarimetric SARs have been flown over the last few decades with a variety of polarimetric SAR imaging modes; traditional ones are linear singleand dual-pol modes. More sophisticated ones are full-pol modes. Other alternative modes, such as hybrid and compact dual-pol, have also been recently proposed for future SAR missions. The discussion is vivid across the remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full polarimetry. This thesis contributes to that discussion by analyzing and comparing different polarimetric SAR modes in a variety of geoscience applications, with a particular focus on maritime monitoring and surveillance. For our comparisons, we make use of a fundamental, physically related discriminator called the Degree of Polarization (DoP). This scalar parameter has been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. Based on a detailed statistical analysis of polarimetric SAR images, we propose efficient estimators of the DoP for both coherent and in-coherent SAR systems. We extend the DoP concept to different hybrid and compact SAR modes and compare the achieved performance with different full-pol methods. We perform a detailed study of vessel detection and oil-spill recognition, based on linear and hybrid/compact dual-pol DoP, using recent data from the Deepwater Horizon oil-spill, acquired by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Extensive experiments are also performed over various terrain types, such as urban, vegetation, and ocean, using the data acquired by the Canadian RADARSAT-2 and the NASA/JPL Airborne SAR (AirSAR) system

Advancing Indonesian Forest Resource Monitoring Using Multi-Source Remotely Sensed Imagery

Tropical forest clearing threatens the sustainability of critically important global ecosystems services, including climate regulation and biodiversity. Indonesia is home to the world's third largest tropical forest and second highest rate of deforestation; as such, it plays an important role in both increasing greenhouse gas emissions and loss of biodiversity. In this study, a method is implemented for quantifying Indonesian primary forest loss by landform, including wetlands. A hybrid approach is performed for quantifying the extent and change of primary forest as intact and degraded types using a per-pixel supervised classification mapping followed by a GIS-based fragmentation analysis. The method was prototyped in Sumatra, and later employed for the entirety of Indonesia, and can be replicated across the tropics in support of REDD+ (Reducing Emissions from Deforestation and forest Degradation) initiatives. Mapping of Indonesia's wetlands was performed using cloud-free Landsat image mosaics, ALOS-PALSAR imagery and topographic indices derived from the SRTM. Results quantify an increasing rate of primary forest loss over Indonesia from 2000 to 2012. Of the 15.79 Mha of gross forest cover loss for Indonesia reported by Hansen et al. (2013) over this period, 38% or 6.02 Mha occurred within primary intact or degraded forests, and increased on average by 47,600 ha per year. By 2012, primary forest loss in Indonesia was estimated to be higher than Brazil (0.84 Mha to 0.47 Mha). Almost all clearing of primary forests (>90%) occurred within degraded types, meaning logging preceded conversion processes. Proportional loss of primary forests in wetlands increased with more intensive clearing of wetland forests in Sumatra compared to Kalimantan or Papua, reflecting a near-exhaustion of easily accessible lowland forests in Sumatra. Kalimantan had a more balanced ratio of wetland and lowland primary forest loss, indicating a less advanced state of natural forest transition. Papua was found to have a more nascent stage of forest exploitation with much of the clearing related to logging activities, largely road construction. Loss within official forest-land uses that restrict or prohibit clearing totaled 40% of all loss within national forest-land, another indication of a dwindling resource. Methods demonstrated in this study depict national scale primary forest change in Indonesia, a theme that until this study has not been quantified at high spatial (30m) and temporal (annual) resolutions. The increasing loss of Indonesian primary forests found in this study has significant implications for climate change mitigation and biodiversity conservation efforts