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Updated: Jun 23, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
Geospatial assessment of land use transformation and potential ecological vulnerability in Saharsa District, India
Sushmita Kumari1, Somnath Mandal1, Sajad Nabi Dar2
1Department of Geography, Lovely Professional University, Phagwara, Punjab, India.
Abstract:
This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) in Saharsa District, Bihar, India, over 21 years (2002-2023), using multi-temporal Landsat satellite imagery (Landsat TM and Landsat OLI/TIRS) and geospatial analysis. The novel scientific contribution of this work lies in the integrated application of a Potential Ecological Vulnerability Index (PEVI) framework alongside LULC change detection to identify ecological stress hotspots and assess the cumulative environmental consequences of land transformation in a flood-prone Megafan environment, a combination not previously applied to the ecologically sensitive Kosi Basin. Satellite images from four time points (2002, 2009, 2016, and 2023) were subjected to radiometric, atmospheric, and geometric pre-processing, including cross-sensor radiometric calibration to ensure spectral consistency between Landsat 5 TM and Landsat 8 OLI/TIRS sensors, and classified using the supervised Maximum Likelihood Classifier (MLC) in ERDAS IMAGINE. Key quantitative findings reveal a 100.60 km² (6.04%) increase in built-up area and a 77.42 km² (4.66%) decline in forest cover over the study period. PEVI mapping identified a progressive shift from moderate to poor and very poor ecological quality, particularly in the central and southeastern zones of the district, corresponding to areas of accelerated deforestation and urban expansion. These results underscore the ecological vulnerability of the district and highlight the urgent need for sustainable land management policies, reforestation programmes, and flood-resilient urban planning. Future research should incorporate higher-resolution imagery (e.g., Sentinel-2), machine learning classifiers (Random Forest, SVM), socio-economic variables, and hydrological modelling for more comprehensive land-use policy formulation.
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