Geospatial Analysis of Land Use Changes and Its Implications on Agricultural Production

The conversion of land use patterns generates substantial effects on food cultivation which affects the stability of both food supply and economy. Specifically, this methodology utilizes advanced satellite imagery processing and machine learning to quantify land use transformations and their impacts on agriculture, and geospatial analysis to carry out this process. The pre-analysis processes including cloud mask technique combined with georeferencing and feature extraction yield quality satellite dataset information. ReliefF algorithm serves for feature selection by finding key spectral and temporal indicators that affect agricultural productivity. The research employs CNN+LSTM deep learning techniques which use both spatial feature extraction from CNN and temporal ability of LSTM for land use change classification. The research results show accurate land classification outcomes which enable exact agricultural transition detection. Post-classification validation methods through change detection analysis and statistical assessments help show how much and what patterns in agricultural land transformations appear. The applied research offers vital data-based information for sustainable land management that helps officials and agricultural organizations reduce detrimental land use change effects.