Predictive Analytics for Wildlife Habitat Conservation Using Remote Sensing and Machine Learning

Abstract

Wildlife habitat degradation caused by deforestation, climate change, urbanization, and land-use change has significantly threatened global biodiversity, necessitating intelligent approaches for habitat monitoring and conservation. This study proposes a Random Forest (RF)-based predictive analytics framework for wildlife habitat conservation by integrating remote sensing data with machine learning techniques. Multispectral satellite imagery obtained from Sentinel-2, Landsat-8/9, and other geospatial datasets is combined with environmental variables, including vegetation indices, land cover, elevation, rainfall, land surface temperature, slope, soil characteristics, and proximity to water bodies, to characterize habitat conditions. The collected data undergo preprocessing and feature engineering before training the Random Forest classifier for habitat suitability prediction. The developed model classifies the study area into highly suitable, moderately suitable, and unsuitable habitat zones while generating GIS-based habitat suitability maps for conservation planning. The proposed framework is evaluated using standard performance metrics, including Accuracy, Precision, Recall, F1-score, Area Under the Curve (AUC), and Cohen's Kappa coefficient. Experimental results demonstrate that the Random Forest model achieves superior predictive performance compared with conventional machine learning algorithms, owing to its ensemble learning capability and robustness against heterogeneous environmental data. Feature importance analysis further identifies vegetation density and land cover as the primary determinants of habitat suitability. The proposed framework provides an efficient and scalable decision-support system for biodiversity conservation, ecological monitoring, and sustainable wildlife habitat management.