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The sensitivity of synthetic aperture radar (SAR) and interferometric SAR (InSAR) to surface properties, especially changes in height and roughness, combined with an all-weather capability, makes radar remote sensing a potential tool for mapping urban damage caused by earthquakes. With InSAR, surface displacement has been mapped successfully and in detail, but for urban-damage mapping, results have so tar been less conclusive. ENVISAT Advanced SAR images of Barn, Iran, that were acquired before and after the 2003 earthquake were used. Between preseismic and coseismic image pairs, coherence decreased with increasing damage levels. However, contrary to previous studies, earthquake damage caused both increases and decreases in amplitude. Therefore, its absolute value, which correlated with damage level, was used. Individually, coherence difference led to better results than absolute amplitude change, although still with limited accuracy. The combination of both resulted in an overall accuracy of 52%. Since vegetation causes decorrelation, a predisaster Advanced Spaceborne Thermal Emission and Reflection Radiometer image was used to mask out vegetated areas, which improved the accuracy by 4%. The ground-truth map showed damage levels per district instead of per pixel. Therefore, the ratio of pixels classified as 'damage' and 'nondamage' was calculated for each damage class of the map, and a clear relation was found. This shows that the aggregation level of the map partly explains the low-accuracy figures of the pixel-based evaluation. Although improvement was made, InSAR techniques for urban-damage mapping do not yet provide the accuracy levels needed for disaster mitigation. However, substantial improvements can be expected from instruments with higher spatial resolution, such as the recently launched Phased Array type L-band SAR and Terra SAR-X. Index Terms--Amplitude, Bam, Iran, coherence, earthquake, interferometic synthetic aperture array (InSAR), urban damage assessment.

Meteosat satellites with the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) sensor onboard provide remote-sensing images nowadays every 15 min. This paper investigates and applies image-mining methods to make an optimal use of images. It develops a simple, time-efficient, and generic model to facilitate pattern discovery and analysis. The focus of this paper is to develop a model for monitoring and analyzing forest fires in space and time. As an illustration, a diurnal cycle of fire in Portugal on July 28, 2004 was analyzed. Kernel convolution characterized the hearth of the fire as an object in space. Objects were extracted and tracked over time automatically. The results thus obtained were used to make a linear model for fire behavior with respect to vegetation and wind characteristics as explanatory variables. This model may be useful for predicting hazards at an almost real-time basis. The research illustrates how image mining improves information extraction from the Meteosat SEVIRI images. Index Terms--Forest fires, image mining, Meteosat, Spinning Enhanced Visible and Infrared Imagery (SEVIRI) Portugal, space-time modeling.

Abstract: This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM+ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM+ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6km) spatial dependence as compared to those varying only within 1km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM+ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape. [Copyright &y& Elsevier]