Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

Indian Remote Sensing Satellite-1A (IRS-1A) LISS-II data of 24th Nov., 1988 was analysed digitally to differentiate three density classes viz. dense/closed forest, open forest and degraded forest within each vegetation type in the district, Jalpaiguri, West Bengal. Stratification approach was used to classify separately forest cover into pure sal forests, mixed forests, riverine forests along with man-made sal/teak plantations. In this approach the forested and non-forested areas were classified separately through supervised classification techniques using maximum likelihood algorithm using VAX 11/780 based VIPS-32 Image Processing software. Later the two classified outputs were composited to provide entire area of the district. The forest cover of the district was 1420.89 sq. km, (22.82 percent). Other broad landuse/landcover dominant in the district include agricultural areas.(45.20 percent) and tea gardens (10.49 percent). The accuracy of the classified output was estimated to be 90 percent for forested areas and 85 percent in case of other landuse/landcover classes.     

Changing water resources study using GIS and spatial model — A case study of Bhatar Block,district Burdwan,West Bengal,India

Markov chains have been used to model spatial changes in a variety of spheres. Changes in social situations, economic standards, natural resource availability, and even weather conditions have been explored and predicted using Markov Random Function (MRF) and Markov Random Chains (MRC). In this section, we try to use data of Mahata village of Bhatar Block, extracted from GIS based maps/images in a MRC to obtain present transition probabilities and predict future changes. The village is facing the problem of decreasing the water table and at the same time the number of surface water bodies is also decreasing. This is a serious situation for the development of the agricultural activities in general and at the same time it poses threat to the human habitation of the village in the long run. The average depth of the ground water table from ground level increased from 8 meter to 15 meter within the last 10 years. The threat is coming from the changes in land use and land cover, especially due to substantial extension of agricultural activities which is expanding at a very fast rate. Increasing population is also demanding more lands for settlement and industrial uses. The surface water bodies i.e. the ponds etc. are used for such intensive irrigation purposes. As a result the surface water bodies depletes before the onset of summer. The cultivators use those dried up ponds or surface water bodies for agricultural purposes also. There is thus a serious trend to convert the surface water bodies into the agricultural land. It is estimated using MRC, that in next 25 years, the number of surface water bodies will deplete by 50% in the agriculturally active Bhatar PS at the current rates of depletion. Shifting to less water needy crops, prevention of LULC conversion, and water harvesting would provide some solace to the situation.     

An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands

Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data.     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.