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Soil Erosion Assessment of a Hilly Terrain by RUSLE Model - A Case Study of Chittagong Hill Tracts 基于RUSLE模型的丘陵地形土壤侵蚀评价——以吉大港丘陵为例
Pub Date : 2023-04-03 DOI: 10.48123/rsgis.1197801
Easmat Ara AFRİN, M. M. Abdullah Al MAMUN, Mohammed Mozaffar HOSSAİN, Li ZHANG
Among many environmental problems, soil erosion poses a serious threat to the region known as Chittagong Hill Tracts (CHTs) in Bangladesh, comprising three districts, namely Bandarban, Rangamati, and Khagrachari. The annual soil erosion rate for this hilly terrain was calculated using the Revised Universal Soil Loss Equation (RUSLE) model integrated with Remote Sensing and Geographic Information System (GIS). The ranges of the estimated erosivity of rainfall, erodibility of the soil, slope length and slope steepness, crop management factor and conservation practices are 806.2 to 1513.2 MJ.mm.ha-1.h-1.yr-1 (or an average of 1121.5 MJ.mm.ha-1.h-1.yr-1), 0 to 0.02 t.h.MJ-1 mm-1, 0 to 78.8 (or average 0.41), 0 to 0.63 (or average 0.57) and 0.55 to 1 (or average 0.73), respectively. As per the findings, the study area is expected to lose 182621.5 tons of soil annually, with the estimated annual soil erosion rate of 15.18 t.ha-1.yr-1 also predicted. The weighted overlay index approach was used to produce the probability zone map, which shows that the majority of the research region falls within the slight probability zone and that only a small percentage falls inside the high and very high probability zones. This study proves RS-GIS is useful for predicting erosion and can be used in soil conservation programs.
在众多环境问题中,土壤侵蚀对孟加拉国吉大港山地(CHTs)地区构成了严重威胁,该地区包括班达班、兰加马蒂和哈格拉查里三个地区。利用修正通用土壤流失方程(RUSLE)模型与遥感和地理信息系统(GIS)相结合,计算了该丘陵地形的年土壤侵蚀速率。估算的降雨侵蚀力、土壤可蚀性、坡长和坡陡、作物管理因子和保护措施的范围为806.2 ~ 1513.2 MJ.mm.ha-1.h-1。年-1(或平均1121.5 MJ.mm.ha-1.h-1.yr-1)、0 ~ 0.02 t.h.MJ-1 mm-1、0 ~ 78.8(或平均0.41)、0 ~ 0.63(或平均0.57)和0.55 ~ 1(或平均0.73)。研究结果表明,研究区年土壤流失量为182621.5吨,年土壤侵蚀速率为15.18 t.ha-1。Yr-1也可以预测。采用加权叠加指数法绘制概率区图,结果表明,研究区大部分位于小概率区,只有一小部分位于高概率区和甚高概率区。该研究证明了RS-GIS对土壤侵蚀预测的有效性,并可用于土壤保持项目。
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Turkish Journal of Remote Sensing and GIS
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