Semi-Automated Land Use Monitoring Prioritization Scheme Based on Forest Loss, Cropland Loss, and Built-up Gain from 2000-2020 of Philippine Local Government Units

Abstract. Local Government Units (LGUs) in the Philippines are mandated to craft and enforce Comprehensive Land Use Plans (CLUPs) and Zoning Ordinances to guide sustainable land use. However, limited resources hinder effective monitoring and enforcement, leading to issues such as environmental degradation and urban sprawl. To support LGUs, the Environmental, Land Use, and Urban Planning and Development Bureau of the Department of Human Settlements and Urban Development (DHSUD-ELUPDB) seeks a data-driven approach to identify and prioritize areas most affected by cropland loss, forest degradation, and urban expansion. Leveraging global geospatial datasets from the GLAD Laboratory, this study proposes a semi-automated land use monitoring prioritization workflow based on LCLUC indicators. This approach enables national agencies to efficiently assess over 1,600 LGUs, target technical support, and optimize resource allocation for improved land use governance. Principal Component Analysis (PCA) was employed to derive indicator weights at the provincial level, allowing for the assessment of each factor's contribution to overall LCLUC. Using these weighted scores, high-priority LGUs were identified at both national and regional scales, with Dumaguete City in Negros Oriental, Bongao in Tawi-Tawi, and Mercedes in Eastern Samar ranking the highest overall. PCA-weighted scores aligned high-scoring LGUs with provincial development goals, while provincial weights improved consistency with the national urban settlement hierarchy. Notably, high-scoring LGUs were situated within land use change hotspots clusters. Distance correlation analysis showed stronger interactions among LCLUC indicators at the provincial scale, revealing regional land use dynamics that may be masked nationally.