Nahib Irmadi, Wahyudin Yudi, Amhar Fahmi, Ambarwulan Wiwin, Nugroho Nunung Puji, Pranoto Bono, Cahyana Destika, Ramadhani Fadhlullah, Suwedi Nawa, Darmawan Mulyanto, Turmudi Turmudi, Suryanta Jaka, Karolinoerita Vicca
Research Center for Geospatial, National Research and Innovation Agency of Indonesia (BRIN), Jalan Raya Jakarta Bogor KM 47 Cibinong, Bogor, West Java 16911, Indonesia.
Faculty of Agriculture, Universitas Djuanda, Jl. Tol Ciawi No. 1, Ciawi, Bogor, West Java 16720, Indonesia.
Scientifica (Cairo). 2024 Jan 11;2024:7251691. doi: 10.1155/2024/7251691. eCollection 2024.
The application of remote sensing data has been significant in modeling soil erosion. However, previous studies have fallen short in elucidating and lacked an understanding of the multifactor influencing erosion. This study addresses these limitations by employing the InVEST and the Geodetector models. Specifically, it aims (1) to delineate both spatial and temporal variations in soil erosion within the Citarum watershed from 2010 to 2020, (2) to identify the key drivers of soil erosion and unravel the underlying mechanisms, and (3) to identify the high-risk zones for soil erosion. Both models consider a range of natural predictors, including topography (slope factor), climate (precipitation factor), and vegetation cover (vegetation factor). In addition, they incorporate social parameters such as income per capita and population density, which interact with the watershed's position in the downstream, middle, and upper streams. The results reveal that, over a decade, the average soil erosion increased by 15.50 × 10 tons, marking a 16.65% surge. The impact of factors varies significantly across different subwatershed areas. For example, fraction vegetation cover interactions influence upper- and middle-stream regions, while the downstream area is notably affected by precipitation interactions. The high-risk erosion areas in the watershed are primarily influenced by slope, precipitation, and fractional vegetation cover. In these areas, factors causing high erosion risks include slope, precipitation, and other environmental variables categorized into strata. The study highlights the varying influential factors in different watershed areas.
遥感数据在土壤侵蚀建模中具有重要作用。然而,以往的研究在阐明和理解影响侵蚀的多因素方面存在不足。本研究通过采用InVEST模型和地理探测器模型来解决这些局限性。具体而言,其目标是:(1)描绘芝塔龙河流域2010年至2020年期间土壤侵蚀的时空变化;(2)确定土壤侵蚀的关键驱动因素并揭示其潜在机制;(3)识别土壤侵蚀的高风险区域。这两个模型都考虑了一系列自然预测因子,包括地形(坡度因子)、气候(降水因子)和植被覆盖(植被因子)。此外,它们还纳入了社会参数,如人均收入和人口密度,这些参数与流域在上游、中游和下游的位置相互作用。结果显示,在十年间,平均土壤侵蚀量增加了15.50×10吨,增幅为16.65%。不同子流域地区各因素的影响差异显著。例如,植被覆盖比例相互作用影响上游和中游地区,而下游地区则明显受降水相互作用的影响。流域内的高风险侵蚀区域主要受坡度、降水和植被覆盖比例的影响。在这些区域,导致高侵蚀风险的因素包括坡度、降水以及归类为不同层次的其他环境变量。该研究突出了不同流域地区影响因素的差异。