Application of Geospatial Technologies and Predictive Analytics in Sustainable Natural Resource Management Systems

Abstract

The accelerating pace of environmental degradation, land-use changes, climate variability, and increasing human-induced stress on ecosystems puts sustainable natural resource management at the forefront of global issues. Standard approaches to environmental monitoring fall short of sustaining environmental assessments and long-term planning due to insufficient spatial and temporal coverage. In this paper, examine the role of geospatial technologies and predictive analytics in promoting sustainable natural resource management through the integration of spatial analysis and data-driven predictive techniques. The research uses Geographic Information Systems (GIS), remote sensing data collected over 10 years, and predictive analytics to assess changes in land cover, vegetation, and water from 2015 to 2025. Spatial classification, along with thematic mapping, yielded an accuracy of 92.4% (Kappa = 0.89), demonstrating the method's validity for identifying natural phenomena. The analysis identified important changes in the landscape, with the following results in the area changed during the study: a 14.7% reduction in forest cover; a 9.3% increase in urban areas; and an 11.8% reduction in surface water resources. Additional analysis predicted a 91.2% accuracy in resource distribution. The anticipated trends reinforce the validity of the environmental analytical methods. The research illustrates the major temporal and spatial trends and patterns in the resource ecosystem. The results underscore the importance of innovative geospatial and predictive analytics in environmental monitoring and sustainable natural resource management planning. Researchers, environmental managers, and long-term resource governing policymakers will find this a valuable resource.