THE IMPACT OF DESERTIFICATION ON AGRICULTURAL LANDS IN LAYLAN SUB-DISTRICT THROUGH THE APPLICATION OF ARTIFICIAL INTELLIGENCE

Abstrak

Objective: This study aims to assess land degradation and desertification risk in Lailan Subdistrict by integrating remote sensing indices to identify environmental stressors affecting agricultural productivity. Method: Landsat OLI 8 satellite imagery and ArcGIS 10.4.1 were employed to analyze three key indicators: the Desertification Risk Index (DRI), Normalized Multi-band Drought Index (NMDI), and Soil Salinity Index (SI). Results: The total study area of 694.84 km² was classified into five desertification risk levels, ranging from low (6.51%) to extreme (10.15%). Moderate and severe drought collectively affected over 57% of the area, with extreme drought alone covering 39.14%. Soil salinity varied widely, with moderate (31.20%) and high salinity (27.45%) dominating the landscape, while very high salinity (>30 dS/m) affected 8.65% of the area. Overall, more than 70% of the subdistrict was exposed to critical risks driven by combined drought stress, salinization, and unsustainable land use. Novelty: By integrating multiple remote sensing indices, this research provides a comprehensive spatial assessment of desertification risks, offering practical insights for sustainable land management strategies, including improved irrigation, salt-tolerant crops, vegetation restoration, and long-term monitoring.

Kata Kunci