Sciences in Cold and Arid Regions ›› 2015, Vol. 7 ›› Issue (6): 702-708.doi: 10.3724/SP.J.1226.2015.00702

• ARTICLES • Previous Articles    

Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

Pei Liu1,2, PeiJun Du3, RuiMei Han1,2, Chao Ma1,2, YouFeng Zou1,2   

  1. 1. Key Laboratory of Mine Spatial Information Technologies of SBSM, Henan Polytechnic University, Jiaozuo, Henan 454003, China;
    2. School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China;
    3. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, Jiangsu 210093, China
  • Received:2015-03-18 Revised:2015-05-24 Published:2018-11-23
  • Contact: YouFeng Zou
  • Supported by:
    This paper was supported by the Fundamental Research Funds for the Universities of Henan Province (NSFRF140113), the Jiangsu Provincial Natural Science Foundation (No. BK2012018), the Natural Science Foundation of China (No. 41171323), the Special Funding Projects of Mapping and Geographic Information Nonprofit research (No. 201412020), a joint project of the National Natural Science Foundation of China and the Shenhua Coal Industry Group Co., Ltd. (No. U1261206), and the Ph.D. Fund of Henan Polytechnic University (No. B2015-20) and the youth fund of Henan Polytechnic University (No. Q2015-3).

Abstract: In order to monitor the pattern, distribution, and trend of land use/cover change (LUCC) and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing (RS) data and Geographic Information System (GIS) to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM+ remotely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov (CA_Markov) model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Revised Universal Soil Loss Equation (RUSLE) is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall (R), cover management (C), and support practice (P) factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to continue to decline in the next few years, according to our prediction results.

Key words: land use/cover change (LUCC), soil erosion, CA_Markov model, revised universal soil loss equation (RUSLE)

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