Monitoring Land Cover Change Using Change Vector Analysis (CVA) in Central Bengkulu Regency, Indonesia

Abstract

Monitoring land cover change is essential for sustainable spatial planning in regions undergoing rapid development. This study aimed to evaluate Land cover changes in Central Bengkulu Regency during the 2008–2024 period were analyzed using the Maximum Likelihood Classification (MLC) method, which identified nine land cover classes with moderately good accuracy (Overall Accuracy of 66.7% and Kappa coefficient of 61.3% for Landsat 5 imagery in 2008, and 58.0% OA and 50.1% Kappa in 2024). Significant land cover dynamics were observed, with notable increases in shrub and plantation areas, and substantial decreases in primary dryland forest, rice fields, and open land. Change Vector Analysis (CVA), combining NDVI and NDBI indices, proved effective in detecting both the magnitude and direction of land cover change. The largest change category was "Stable/Not Significant" (±65,000 ha), followed by "Rehabilitation/Recovery" (±24,700 ha), and "Urban Development" (±10,800 ha). These changes reflect the strong influence of socio-economic drivers such as population growth, land conversion for oil palm plantations, and settlement expansion, as well as ecological factors such as degradation and natural succession. The results indicate that the integrative approach of CVA and spectral indices can serve as a reliable spatio-temporal analysis tool to support spatial planning and sustainable land management policies, particularly in newly established regions vulnerable to land conversion.