The study indicates that analysis of morphometric parameters with the help of geographic information system (GIS) would prove a viable method of characterizing the hydrological response behaviour of the watershed. It is also well observed that remote sensing satellite data is emerging as the most effective, time saving and accurate technique for morphometric analysis of a basin. This technique is found relevant for the extraction of river basin and its stream networks through ASTER (DEM) in conjunction with remote sensing satellite data (Landsat etm+, 2013 and georeferenced survey of Indian toposheet, 1972). In this study, Kanhar basin a tributaries of Son River has been selected for detailed morphometric analysis. Seven sub-watersheds are also delineated within this basin to calculate the selected morphometric parameters. Morphometric parameters viz; stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, etc., are calculated. The drainage area of the basin is 5,654 km² and shows sub-dendritic to dendritic drainage pattern. The stream order of the basin is mainly controlled by physiographic and lithological conditions of the area. The study area is designated as seventh-order basin with the drainage density value being as 1.72 km/km². The increase in stream length ratio from lower to higher order shows that the study area has reached a mature geomorphic stage.

Land use change models are tools to support the analysis of the causes and consequences of land use dynamics. Land use and land cover change (LUCC) has been recognized as an important driver of environmental change on all spatial and temporal scales. The primary objective of this paper is to predict and analyze the present and future growth of Muzaffarpur city and its surrounding, Bihar (India) using the Landsat satellite images of 1988 and 2010. These data are used for change prediction and for preparation of prediction map of year 2025 and 2035. IDRISI, Land Change Modeler (LCM) was used to analyze the land use and land cover changes between various classes during the period 1988-2008. Erdas Imagine software (ver-9.3) were also used to prepare land use/cover classification using image processing supervised classification method in a multi-temporal approach. The prediction of land use land cover change was done on neural network built-in module in the Selva version of IDRISI. The accuracy was obtained as 72.28% for all the conversion types

Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.

This work focuses on comparing results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory factors including a new one, geomorphology, have been utilized. Out of 1000 randomly generated flood-points from 2008 Landsat 5 TM image derived flood polygon, 70% have been utilized for the training purpose of Shannon’s entropy (SE) model and 30% for area under receiver operating characteristic (AUROC) method of validation of both, SE and frequency ratio (FR), models. Result from FR shows that the contributions of specific classes of different explanatory factors to flooding susceptibility vary whereas the SE model suggests that geomorphology is the most contributing factor. The AUROC curve for SE (0.90) was better than that for FR (0.85). Hence, the SE model predicts flood-susceptible areas more accurately than the multivariate statistical model FR in the study area.

The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.