An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image

Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery which records the backscatters that carry information of kind, direction, heterogeneity and relationship of the features. This paper put forward a computer program for texture analysis for high resolution radar data. Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM). To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery from Terengganu area, Malaysia was analyzed and pixel-based and object-based classification were attempted. The thematic maps derived by pixel-based method could not achieve acceptable visual interpretation and for that reason no accuracy assessment was performed on them. The overall accuracy achieved by object-based method was 83.63% with kappa coefficient of 0.8. Results on image texture classification showed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtained textural analysis resulted in high classification accuracy. The proposed texture analysis program can be used in many applications such as land use/cover (LULC) mapping, hazard studies and many other applications

Cartography from Pole to PoleSelected Contributions to the XXVIth International Conference of the ICA, Dresden 2013 /

XVII, 496 p. 224 illus., 164 illus. in color.onli

Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.https://doi.org/10.3390/rs1203055

Snow cover distribution in the Aksu catchment (Central Tien Shan) 1986-2013 Based on AVHRR and MODIS data

Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatiotemporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986–2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures