Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco)

被引：101

作者：

El Jazouli, Aafaf ^{[1
]}

Barakat, Ahmed ^{[1
]}

Khellouk, Rida ^{[1
]}

Rais, Jamila ^{[1
]}

El Baghdadi, Mohamed ^{[1
]}

机构：

[1] Sultan My Slimane Univ, Fac Sci & Tech, Georessources & Environm Lab, Beni Mellal, Morocco

来源：

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT | 2019年 / 13卷

关键词：

Oum Er Rbia high-basin; Soil erosion; LULC; Remote sensing; GIS; CA_Markov model; RUSLE; DEPOSITION RATES; CA-MARKOV; AREA; DEGRADATION; IMPACTS; CS-137; RUSLE;

D O I：

10.1016/j.rsase.2018.12.004

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Soil erosion by water is one of the most important causes of land degradation throughout the world, particularly in arid and semi-arid areas. The high basin of the Oum Er Rbia River, located in the Middle Atlas (Morocco), is a mountainous area dominated by steep slopes and clayey soils, which make it vulnerable to soil erosion due to human activities and severe weather. Multidate satellite images of Sentinel 2A, Landsat Oli-8 and ETM acquired respectively in 2017, 2013 and 2003, and the Cellular Automata Markov (CA_Markov) model were used to forecast the land use/land cover map and their change detections. The Revised Universal Soil Loss Equation (RUSLE) was integrated in a GIS environment to quantify soil loss and to map erosion risk of the specific years. The estimated annual soil losses average was 58 t ha(-1) yr(-1) in 2003, and the predicted annual soil loss average for 2030 was 142 60 t ha(-1) yr(-1). The results demonstrate that using CA_Marcov and RUSLE in combination with remote sensing and GIS is a suitable method for forecasting LUCC and accurately measuring the quantity of soil losses in the future.

引用

页码：361 / 374

页数：14

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