Integration of Satellite Imagery, AI, and GIS Technologies for Landscape-Level Biodiversity Monitoring

Abstract

Abstract: Monitoring of landscape level biodiversity has grown in significance to support ecosystem conservation, habitat management and sustainable land-use planning. The traditional field-based monitoring methods typically have high labor requirements, time demands and limited spatial and temporal coverage. The proposed monitoring framework in this study is an integration of the satellite image-based monitoring with the Artificial Intelligence (AI) and Geographic Information System (GIS) technologies, which is improving assessment of biodiversity in heterogeneous landscape. The multi-source satellite data are preprocessed in order to produce high quality environmental information by performing radiometric correction, geometric alignment, cloud masking and image enhancement. Biodiversity and ecological variables are linked to a GIS database, which allows for a spatial analysis and habitat characterisation. AI-based classification and prediction models are used to classify the biodiversity, estimate species distribution, and to identify environmentally sensitive regions. Experimental results show that classification accuracy, habitat mapping accuracy and biodiversity prediction accuracy are high, and the visualization of spatial ecological trends is easy to understand through GIS. The integrated framework seamlessly brings together remote sensing observations, intelligent analytics and geospatial decision support for timely and scalable biodiversity monitoring. The proposed approach improves environmental assessment, prioritisation for conservation, ecological restoration planning, and provides a user-friendly decision support tool for policy makers, environmental agencies and researchers engaged in large-scale biodiversity management and sustainable ecosystem conservation.