Analysis and optimization model of rural landscape pattern based on remote sensing technology

The rural landscape has undergone significant transformations, leading to increased fragmentation and ecological challenges. This thesis presents an integrated analysis and optimization framework that leverages remote sensing technology for sustainable rural landscape planning. The proposed method integrates remote sensing-based semantic segmentation with a multi-objective landscape optimization model. High-resolution satellite imagery is first processed to generate detailed land cover maps, and these serve as the basis for optimization. The multi-objective model simultaneously reduces landscape fragmentation, improves connectivity between habitat patches, and enhances land-use diversity. In a case study, the optimized landscape pattern exhibited larger contiguous green spaces, more connected ecological networks, and a richer mix of land-use types compared to the current pattern. The major contributions of this work lie in demonstrating how coupling advanced image analysis with spatial optimization can yield measurable improvements in landscape metrics. This approach provides decision-makers with a data-driven tool to guide rural land use planning towards greater ecological integrity and sustainability.