Remote Sensing Based Habitat Mapping and Biodiversity Monitoring for Enhancing Wildlife Conservation and Environmental Management

Abstract

Environmental degradation, habitat fragmentation, deforestation, urbanization, and global warming have adversely affected biodiversity and stability in ecosystems. The current research was conducted in order to identify the efficiency of remote sensing and geographic information system-based technologies in habitat mapping and biodiversity monitoring. Remote sensing data from various sources such as Landsat, Sentinel, MODIS, and UAVs have been used for conducting habitat mapping. Preprocessing of images, classification of habitats, vegetation index mapping, biodiversity hotspots determination, wildlife corridors mapping, and environment changes detection techniques have been applied for ecological evaluation of the habitat. Classification of habitat and ecological evaluation were performed using machine learning techniques like Random Forest and Support Vector Machines. Dense forests were found to take up the highest share of the study area amounting to 425 km² (34.2%) and agricultural land was found to cover 320 km² (25.8%). Biodiversity evaluation showed that there are 12 biodiversity hot spots and 7 major wildlife corridors in the study region. NDVI analysis showed that there is relatively healthy vegetation in the study region with an average of 0.71 and there is also moderate ecological disturbance with an index of 0.46. Species richness analysis showed that there are approximately 148 species within various habitats in the study area. Other environmental threats include forest degradation, wetland reduction, and urbanization. Overall, the study shows how the use of remote sensing techniques and geographic information systems can be effectively used for environmental assessment.