Synthetic Aperture Radar Satellite Imagery Research Articles

Relative abundance of Bacillus spp., surfactant-associated bacterium present in a natural sea slick observed by satellite SAR imagery over the Gulf of Mexico

The damping of short gravity-capillary waves (Bragg waves) due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar satellite imagery. Surfactants are produced by natural life processes of many marine organisms, including bacteria, phytoplankton, seaweed, and zooplankton. In this work, samples were collected in the Gulf of Mexico during a research cruise on the R/V F.G. Walton Smith to evaluate the relative abundance of Bacillus spp., surfactant-associated bacteria, in the sea surface microlayer compared to the subsurface water at 0.2 m depth. A method to reduce potential contamination of microlayer samples during their collection on polycarbonate filters was implemented and advanced, including increasing the number of successive samples per location and changing sample storage procedures. By using DNA analysis (real-time polymerase chain reaction) to target Bacillus spp., we found that in the slick areas, these surfactant-associated bacteria tended to reside mostly in subsurface waters, lending support to the concept that the surfactants they may produce move to the surface where they accumulate under calm conditions and enrich the sea surface microlayer.

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

ABSTRACTAlong the season crop classification maps based on satellite data is a challenging task for countries with large diversity of agricultural crops with different phenology (crop calendars). In this paper, we investigate feasibility of delivering early and along the season crop specific maps using available free satellite data over multiple years, including Landsat-8, Sentinel-1 and Sentinel-2. For this study, a test site in Kyiv region (Ukraine) is selected, for which we have been collecting ground data on crop types every year since 2011. Crop type maps are generated through a supervised classification of multi-temporal multi-source satellite data using previously developed artificial neural network algorithms. It is shown, how multi-year crop classification maps are used for crop rotation violation detection. The study shows that in case of considerable cloud cover, synthetic aperture radar (SAR) data, for example acquired by Sentinel-1 satellite, can be interchangeably used with optical imagery to achieve the target 85% accuracy for crop classification.

TanDEM‐X Time Series Analysis Reveals Lava Flow Volume and Effusion Rates of the 2012–2013 Tolbachik, Kamchatka Fissure Eruption

AbstractAssessing eruption volumes is one of the major challenges in volcano research but provides valuable insights into the dynamics of an eruption and the associated hazard. One way to estimate this important parameter is the generation and differencing of digital elevation models (DEMs) acquired before, during, and after an eruption. The satellite mission TanDEM‐X enables generation of time series of DEMs using synthetic aperture radar satellite imagery. We use these data to study the 2012–2013 eruption of Tolbachik in Kamchatka. We developed a processing scheme for generation of 18 DEMs from TanDEM‐X imagery that relies on the generation of a preeruption DEM which is used to process the syneruption and posteruption data pairs. Differencing each DEM with the preeruption DEM enables mapping the lava flows and measuring lava flow volume over time and to estimate lava extrusion rates. We find a final lava flow volume of 0.53 km3 covering an area of 36 km2 by the end of the eruption. An uncertainty analysis is performed while analyzing the DEM differences in areas where no topographic change is expected, leading to an error of ±0.01 km3 for the final lava flow volume. The lava effusion was with 247.92 m3/s very high in the first days of the eruption and rapidly decayed toward its end. While many basaltic eruptions follow an exponentially decaying flow model, the extrusion rates at Tolbachik are more compatible with a 1/t model. This special characteristic might be useful to gain further insight into the eruption process at Tolbachik.

River flood mapping in urban areas combining Radarsat-2 data and flood return period data

Near-real-time flood maps are essential to organize and coordinate emergency services' response actions during flooding events. Thanks to its capacity to acquire synoptic and detailed data during day and night, and in all weather conditions, Synthetic Aperture Radar (SAR) satellite remote sensing is considered one of the best tools for the acquisition of flood mapping information. However, specific factors contributing to SAR backscatter in urban environments, such as shadow and layover effects, and the presence of water surface–like radar response areas, complicate the detection of flood water pixels. This paper describes an approach for near-real-time flood mapping in urban and rural areas. The innovative aspect of the approach is its reliance on the combined use of very-high-resolution SAR satellite imagery (C-band, HH polarization) and hydraulic data, specifically flood return period data estimated for each point of the floodplain. This approach was tested and evaluated using two case studies of the 2011 Richelieu River flood (Canada) observed by the very-high-resolution RADARSAT-2 sensor. In both case studies, the algorithm proved capable of detecting flooding in urban areas with good accuracy, identifying approximately 87% of flooded pixels correctly. The associated false negative and false positive rates are approximately 14%. In rural areas, 97% of flooded pixels were correctly identified, with false negative rates close to 3% and false positive rates between 3% and 35%. These results highlight the capacity of flood return period data to overcome limitations associated with SAR-based flood detection in urban environments, and the relevance of their use in combination with SAR C-band imagery for precise flood extent mapping in urban and rural environments in a crisis management context.

Sea Fetch Observed by Synthetic Aperture Radar

Two satellite synthetic aperture radar (SAR) observations of the fetch in the Bohai Sea of China are presented. The sea surface winds derived from SAR data indicated a high wind of 15–16 m/s that occurred in the fetch zone. The winds are shown to have immediate direct mechanical forcing impacts on the significant wave heights $(Hs)$ of ocean surface gravity waves. Buoy measurements and numerical wave modeling results show that the $Hs$ increased to a maximum of 3.5 m in the semienclosed sea, 3 h after the passage of the fetch winds, and the high $Hs$ in the sea was sustained for a total of 6 h. The Weather Research and Forecasting (WRF) model implemented in our modeling simulation captured the wind field responsible for the evolution of the fetch event. The model-simulated surface horizontal winds agree with the SAR-derived winds. In addition, the vertical wind distribution reveals that the fetch wind field reached the 800 hPa level, and the event lasted less than one day. This study demonstrates the synergy of using SAR imagery and the WRF model as effective tools to investigate the lateral and vertical structures of coastal wind.

A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery

In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter) compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover.

RANDOM FOREST CLASSIFICATION OF SEDIMENTS ON EXPOSED INTERTIDAL FLATS USING ALOS-2 QUAD-POLARIMETRIC SAR DATA

Coastal zones are one of the world’s most densely populated areas and it is necessary to propose an accurate, cost effective, frequent, and synoptic method of monitoring these complex ecosystems. However, misclassification of sediments on exposed intertidal flats restricts the development of coastal zones surveillance. With the advent of SAR (Synthetic Aperture Radar) satellites, polarimetric SAR satellite imagery plays an increasingly important role in monitoring changes in coastal wetland. This research investigated the necessity of combining SAR polarimetric features with optical data, and their contribution in accurately sediment classification. Three experimental groups were set to make assessment of the most appropriate descriptors. (i) Several SAR polarimetric descriptors were extracted from scattering matrix using Cloude-Pottier, Freeman-Durden and Yamaguchi methods; (ii) Optical remote sensing (RS) data with R, G and B channels formed the second feature combinations; (iii) The chosen SAR and optical RS indicators were both added into classifier. Classification was carried out using Random Forest (RF) classifiers and a general result mapping of intertidal flats was generated. Experiments were implemented using ALOS-2 L-band satellite imagery and GF-1 optical multi-spectral data acquired in the same period. The weights of descriptors were evaluated by VI (RF Variable Importance). Results suggested that optical data source has few advantages on sediment classification, and even reduce the effect of SAR indicators. Polarimetric SAR feature sets show great potentials in intertidal flats classification and are promising in classifying mud flats, sand flats, bare farmland and tidal water.

RANDOM FOREST CLASSIFICATION OF SEDIMENTS ON EXPOSED INTERTIDAL FLATS USING ALOS-2 QUAD-POLARIMETRIC SAR DATA

Abstract. Coastal zones are one of the world’s most densely populated areas and it is necessary to propose an accurate, cost effective, frequent, and synoptic method of monitoring these complex ecosystems. However, misclassification of sediments on exposed intertidal flats restricts the development of coastal zones surveillance. With the advent of SAR (Synthetic Aperture Radar) satellites, polarimetric SAR satellite imagery plays an increasingly important role in monitoring changes in coastal wetland. This research investigated the necessity of combining SAR polarimetric features with optical data, and their contribution in accurately sediment classification. Three experimental groups were set to make assessment of the most appropriate descriptors. (i) Several SAR polarimetric descriptors were extracted from scattering matrix using Cloude-Pottier, Freeman-Durden and Yamaguchi methods; (ii) Optical remote sensing (RS) data with R, G and B channels formed the second feature combinations; (iii) The chosen SAR and optical RS indicators were both added into classifier. Classification was carried out using Random Forest (RF) classifiers and a general result mapping of intertidal flats was generated. Experiments were implemented using ALOS-2 L-band satellite imagery and GF-1 optical multi-spectral data acquired in the same period. The weights of descriptors were evaluated by VI (RF Variable Importance). Results suggested that optical data source has few advantages on sediment classification, and even reduce the effect of SAR indicators. Polarimetric SAR feature sets show great potentials in intertidal flats classification and are promising in classifying mud flats, sand flats, bare farmland and tidal water.

Simulating SAR geometric distortions and predicting Persistent Scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: Nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery

We assess the feasibility of monitoring the landmass of Great Britain with satellite Synthetic Aperture Radar (SAR) imagery, by analysing ERS-1/2 SAR and ENVISAT IS2 Advanced SAR (ASAR) archive data availability, geometric distortions and land cover control on the success of (non-)interferometric analyses. Our assessment both addresses the scientific and operational question of whether a nationwide SAR-based monitoring of ground motion would succeed in Great Britain, and helps to understand controlling factors and possible solutions to overcome the limitations of undertaking SAR-based imaging of the landmass. This is the first time such a nationwide assessment is performed in preparation for acquisition and processing of SAR data in the United Kingdom, and any other country in the world. Analysis of the ERS-1/2 and ENVISAT archives reveals potential for multi-interferogram SAR Interferometry (InSAR) for the entirety of Britain using ERS-1/2 in descending mode, with 100% standard image frames showing at least 20 archive scenes available. ERS-1/2 ascending and both ENVISAT modes show potential for non-interferometric and single-pair InSAR for the vast majority of Britain, and multi-interferogram only for 13% to 38% of the available standard frames. Based on NEXTMap® Britain Digital Terrain Model (DTM) we simulate SAR layover, foreshortening and shadow to the ERS-1/2 and ENVISAT Lines-Of-Sight (LOS), and quantify changes of SAR distortions with variations in mode, LOS incidence angles and ground track angles, local terrain orientation, and the effect of scale due to the input DTM resolution. The simulation is extended to the ~230,000 km2 landmass, and shows limited control of local topography on the radar terrain visibility. According to the 50 m to 5 m DTM-based simulations, ~1.0–1.4% of Great Britain could potentially be affected by shadow and layover in each mode. Only ~0.02–0.04% overlapping between ascending and descending mode distortions is found, this indicating the negligible proportion of the landmass that cannot be monitored using either imaging mode. We calibrate the CORINE Land Cover 2006 (CLC2006) using Persistent Scatterer (PS) datasets available for London, Stoke-On-Trent, Newcastle and Bristol, to quantify land cover control on the PS distribution and characterise the CLC2006 classes in terms of the potential PS density they could provide. Despite predominance of rural land cover types, we predict potential for over 12.8 M monitoring targets for each acquisition mode using a set of image frames covering the entire landmass. We validate our assessment by processing with the Interferometric Point Target Analysis (IPTA) 55 ERS-1/2 SAR scenes depicting South Wales between 1992 and 1999. Although absolute differences between predicted and observed target density are revealed, relative densities and rankings among the various CLC2006 classes are found constant across the calibration and validation datasets. Rescaled predictions for Britain show potential for a total of 2.5 M monitoring targets across the landmass. We examine the use of the topographic and land cover feasibility maps for landslide studies in relation to the British Geological Survey's National Landslide Database and DiGMapGB mass movement layer. Building upon recent literature, we finally discuss future perspectives relating to the replication of our feasibility assessment to account for higher resolution SAR imagery, new Earth explorers (e.g., Sentinel-1) and improved processing techniques, showing potential to generate invaluable sources of information on land motions and geohazards in Great Britain.

Fine-scale features on the sea surface in SAR satellite imagery – Part 2: Numerical modeling

Abstract. With the advent of the new generation of synthetic aperture radar (SAR) satellites, it has become possible to resolve fine-scale features on the sea surface on the scale of meters. The proper identification of sea surface signatures in SAR imagery can be challenging, since some features may be due to atmospheric distortions (gravity waves, squall lines) or anthropogenic influences (slicks), and may not be related to dynamic processes in the upper ocean. In order to improve our understanding of the nature of fine-scale features on the sea surface and their signature in SAR, we have conducted high-resolution numerical simulations combining a three-dimensional non-hydrostatic computational fluid dynamics model with a radar imaging model. The surface velocity field from the hydrodynamic model is used as input to the radar imaging model. The combined approach reproduces the sea surface signatures in SAR of ship wakes, low-density plumes, and internal waves in a stratified environment. The numerical results are consistent with observations reported in a companion paper on in situ measurements during SAR satellite overpasses. Ocean surface and internal waves are also known to produce a measurable signal in the ocean magnetic field. This paper explores the use of computational fluid dynamics to investigate the magnetic signatures of oceanic processes. This potentially provides a link between SAR signatures of transient ocean dynamics and magnetic field fluctuations in the ocean. We suggest that combining SAR imagery with data from ocean magnetometers may be useful as an additional maritime sensing method. The new approach presented in this work can be extended to other dynamic processes in the upper ocean, including fronts and eddies, and can be a valuable tool for the interpretation of SAR images of the ocean surface.

Fast terrain modelling for hydrogeological risk mapping and emergency management: the contribution of high-resolution satellite SAR imagery

Geomatic tools fast terrain modelling play a relevant role in hydrogeological risk mapping and emergency management. Given their complete independence from logistic constraints on the ground (as for airborne data collection), illumination (daylight), and weather (clouds) conditions, synthetic aperture radar (SAR) satellite systems may provide important contributions in terms of digital surface models (DSMs) and digital elevation models (DEMs).For this work we focused on the potential of high-resolution SAR satellite imagery for DSM generation using an interferometric (InSAR) technique and using a revitalized radargrammetric stereomapping approach. The goal of this work was just methodological. Our goal was to illustrate both the fundamental advantages and drawbacks of the radargrammetric approach with respect to the InSAR technique for DSM generation, and to outline their possible joint role in hydrogeological risk mapping and emergency management. Here, it is worth mentioning that radargrammetry procedures are independent of image coherence (unlike the interferometric approach) and phase unwrapping, as well as of parsimony (only a few images are necessary). Therefore, a short time is required for image collection (from tens of minutes to a few hours), thanks to the independence from illumination and weather. The most relevant obstacles of the technique are speckle and the lack of texture impact on image matching, as well as the well-known deformations of SAR imagery (layover and foreshortening), which may produce remarkable difficulties with complex morphologies and that must be accounted for during acquisition planning.Here, we discuss results obtained with InSAR and radargrammetry applied to a COSMO-SkyMed SpotLight triplet (two stereopairs suited for radargrammetry and InSAR, sharing one common image) acquired over suburbs of San Francisco (United States), which are characterized by mixed morphology and land cover. We mainly focused on urban areas and zones covered by bare soil and rocks. Image processing was performed using the well-known commercial software SARscape® for InSAR, and the radargrammetric suite implemented in SISAR, software developed at the Geodesy and Geomatic Division of the University of Rome “La Sapienza”.Global accuracies were approximately 5 m using both approaches. However, several differences in terrain morphology reconstruction were determined and are underlined and evaluated here, as well as a possible way to further enhance the results using the integration of InSAR and radargrammetry.

An Area-Based Image Fusion Scheme for the Integration of SAR and Optical Satellite Imagery

The task of enhancing the perception of a scene by combining information captured from different image sensors is usually known as multisensor image fusion. This paper presents an area-based image fusion algorithm to merge SAR (Synthetic Aperture Radar) and optical images. The co-registration of the two images is first conducted using the proposed registration method prior to image fusion. Segmentation into active and inactive areas is then performed on the SAR texture image for selective injection of the SAR image into the panchromatic (PAN) image. An integrated image based on these two images is generated by the novel area-based fusion scheme, which imposes different fusion rules for each segmented area. Finally, this image is fused into a multispectral (MS) image through the hybrid pansharpening method proposed in previous research. Experimental results demonstrate that the proposed method shows better performance than other fusion algorithms and has the potential to be applied to the multisensor fusion of SAR and optical images.

Service-oriented infrastructure for flood mapping using optical and SAR satellite data

In this paper, we present the service-oriented infrastructure within the Wide Area Grid project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites. The developed infrastructure integrates services and computational resources of several regional and national Grid systems: Ukrainian Academician Grid (with satellite data processing Grid segment, UASpaceGrid) and Grid system at the Center on Earth Observation and Digital Earth of Chinese Academy of Sciences. The study focuses on integrating geo-information services on flood mapping provided by Ukrainian and Chinese entities to benefit from information acquired from multiple sources. We also describe services for workflow automation and management in Grid environment and provide an example of workflow automation for generating flood maps from optical and synthetic-aperture radar satellite imagery. We also discuss issues of enabling trust for the infrastructure using certificates and reputation-based model. Applications of utilizing the developed infrastructure for operational flood mapping in Ukraine and China are given as well.

Monte Carlo cluster refinement for noise robust image segmentation

This paper explores a stochastic approach to refining clustering results for data with spatial-feature context such as images under the presence of noise. We formulate the clustering problem as a maximum a posteriori (MAP) problem, and refine clustering results using importance-weighted Monte Carlo posterior estimates based on between-neighborhood error statistics to account for local spatial-feature context within a global framework. This cluster refinement approach is non-iterative and can be integrated with existing clustering methods to achieve improved clustering performance for image segmentation under high noise scenarios. Experiments on synthetic gray-level images, real-world natural images, and real-world satellite synthetic aperture radar imagery illustrate the proposed method’s potential for improving clustering performance of existing clustering algorithms for image segmentation under high noise situations.

Fuzzy Uncertainty Representations of Coseismic Displacement Measurements Issued From SAR Imagery

An emerging way to reduce the geodetic parameter uncertainty is to combine the large numbers of data provided by satellite synthetic aperture radar (SAR) images. However, the measurements by radar imagery are subject to both random and systematic uncertainties. Thus, mathematical theories that are adequate for each type of uncertainty representation and handling have to be selected. Probability theory is known as the adequate theory for uncertainties corresponding to random variables, but questionable for systematic uncertainties, arising from information incompleteness. Fuzzy theory, being a generalization of interval mathematics, is more adapted to such uncertainty. Moreover, it provides a bridge with probability theory by its ability to represent a family of probability distributions. Therefore, we consider here the conventional probability and the fuzzy approaches for handling the random and systematic uncertainties of displacement measurements derived from differential SAR interferometry (D-InSAR) and SAR amplitude image correlation. The applications are performed on the measurement of the displacement field due to the 2005 Kashmir earthquake. The fuzzy approach, being free from distribution and independence hypotheses, gives the most pessimistic uncertainty assessment, while the conventional probability approach gives the most optimistic uncertainty assessment. As confirmed by the Monte Carlo simulation applied to an earth deformation model, the actual uncertainty should be situated between the fuzzy and conventional uncertainties.

Comparison of high‐resolution wind fields extracted from TerraSAR‐X SAR imagery with predictions from the WRF mesoscale model

We discuss the status of our efforts to determine a suitable Geophysical Model Function (GMF) that relates X‐band normalized radar cross section (NRCS) to the near surface wind vector over the ocean. Development of an X‐band GMF has become particularly relevant due to the recent launches of several X‐band satellite synthetic aperture radar (SAR) systems. We concentrate here on SAR data collected by the TerraSAR‐X satellite launched by the German Space Agency in 2007. Inversion of TerraSAR‐X NRCS imagery to wind speed is accomplished using both a simple physics‐based GMF as well as an empirical GMF derived by interpolating more accurately tested C‐ and Ku‐band GMFs to X‐band. We compare the retrieved wind speeds from three TerraSAR‐X scenes with in situ data when available and also with predictions from the Weather Research and Forecast (WRF) model. At vertical polarization, these comparisons show reasonable agreement with in situ data for both the physics‐based and empirical GMFs. At horizontal polarization however, the NRCS from the physics‐based GMF is generally too low, resulting in an under prediction results of the corresponding retrieved wind speeds. An unexpected result from the WRF comparisons is the similarity between the small‐scale structure (on scales ≈ 5–10 km) observed in the SAR imagery and the corresponding WRF output. We believe that this similarity may allow TerraSAR‐X imagery to provide a quantitative measure of the quality of the WRF boundary‐layer parameterization schemes.

저수지 저수량 추정을 위한 위성 SAR 자료의 활용성

This study discussed the applicability of satellite SAR (Synthetic Aperture Radar) imagery with regard to reservoir monitoring, and tried the extraction of reservoir storage from multi-temporal C-band RADARSAT-1 SAR backscattering images of Yedang and Goongpyeong agricultural reservoirs, acquired from May to October 2005. SAR technology has been advanced as a complementary and alternative approach to optical remote sensing and in-situ measurement. Water bodies in SAR imagery represent low brightness induced by low backscattering, and reservoir storage can be derived from the backscatter contrast with the level-area-volume relationship of each reservoir. The threshold segmentation over the routine preprocessing of SAR images such as speckle reduction and low-pass filtering concluded a significant correlation between the SAR-derived reservoir storage and the observation record in spite of the considerable disagreement. The result showed up critical limitations for adopting SAR data to reservoir monitoring as follows: the inappropriate specifications of SAR data, the unreliable rating curve of reservoir, the lack of climatic information such as wind and precipitation, the interruption of inside and neighboring land cover, and so on. Furthermore, better accuracy of SAR-based reservoir monitoring could be expected through different alternatives such as multi-sensor image fusion, water level measurement with altimeters or interferometry, etc.

Ship Detection in Ice-Infested Waters Based on Dual-Polarization SAR Imagery

This letter discusses the potential of automatic ship detection in ice-infested waters based on satellite synthetic aperture radar (SAR) imagery. The popular K -distribution is used to model the backscatter statistics of sea ice clutter. The goodness of fit of this model is assessed with the Kolmogorov-Smirnov and Anderson-Darling test statistics for both VV and VH polarizations. We also test the impact of introducing the Method of Log Cumulant (MoLC) estimator for the shape parameter of the K-distribution. Finally, a constant false-alarm rate ship detection algorithm, applying the K -distribution with the MoLC estimator, is evaluated on dual-polarization RADARSAT-2 SAR data. Our results demonstrate that this is a viable approach to ship detection in ice-infested waters.

Wind Class Sampling of Satellite SAR Imagery for Offshore Wind Resource Mapping

Abstract High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical–dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5% from the mast observations whereas the deviation on power density and the Weibull shape parameter is up to 7%. These results are promising and may be improved further through a better population of the wind classes. Advantages of the wind class sampling method over random sampling include, in principle, selection of the most representative SAR scenes such that wind resources can be predicted from a lower number of SAR samples. Furthermore, the wind class weightings can be adjusted to represent any time period.

Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and Japan Meteorological Agency (JMA)-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1)/SAR (L-band SAR) images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.