Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis

Abstract

AbstractLandscape change is intricately linked to natural resource utilization. Landscape dynamics are closely tied to land use and land cover (LULC), serving as a representation of ecosystems and human activities. In the Citarum River Basin, Indonesia, a comprehensive approach is necessary to comprehend landscape dynamics as a manifestation of human interaction with the environment. This research aims to analyze landscape dynamics and its factors that can significantly drive changes. We focused on the Cirasea Watershed, which serves as an upper region of the Citarum River Basin. Data was acquired from Landsat-series imageries from 1993 to 2023, and LULC analyses were conducted using classification and regression trees (CART), random forest (RF), and support vector machine (SVM). We analyzed seven independent variables, including slope (X1), elevation (X2), main river (X3), population (X4), central business district (X5), distance from the past settlements (X6), and accessibility (X7) using multivariate analysis. This research found that RF stands out as the optimal choice for LULC analysis over CART and SVM because it had the highest overall accuracy and overall kappa (0.91–0.92, 0.88–0.89). Notably, there was a substantial 273.43% increase in built-up areas, coupled with significant declines in plantations and horticultures. LULC changes was more pronounced in the lower areas near Bandung City. LR model highlighted X1, X3 and X6 as the significant driving forces for built-up areas expansion (r-square 0.44 with p-value < 0.01 and 95% confidence level). Without effective spatial planning, flat areas near rivers and past settlements have the greatest potential for LULC changes.

Keywords

• Cirasea watershed
• google earth engine
• logistic regression
• LULC
• random forest