Author(s)

Jianfeng Liu¹, Xiwang Zhang^2,3*

¹Yellow River Conservancy Technical Institute, Kaifeng, China.
²Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng, China.
³College of Environment and Planning, Henan University, Kaifeng, China.

Vegetation cover derived from remote sensing image is widely used for soil erosion risk assessment, but there is no clear guideline to select the most appropriate temporal satellite data. It is common practice that satellite data during growing season are randomly selected and used in soil erosion risk assessment. However, the effectiveness of vegetation in protecting the soil is quite different even if it is the same growing season since vegetation covers change as they grow. This article aims to provide a method of choosing optimal vegetation cover for studying soil erosion risk using remote sensing, that is, the vegetation cover in the most appropriate temporal period. Based on the temporal relationship of the two most active impact factors, rainfall and vegetation, an index of RV is developed and used to indicate the relative erosion risk during the year. The results show that annual variation of rainfall is significant, and vegetation is relatively stable, resulting in their matching relationship is different in each year. The correlation coefficient reaches 0.89 between RV and real sediment transport during the period when rainfall can cause soil erosion. In other words, RV is a good indicator of soil erosion. Therefore, there is a good correlation between RV maximum and the optimal vegetation cover, which can help facilitate erosion research in the future, showing good potential for successful application in other places.

Optimal Vegetation Cover, Remote Sensing, Soil Erosion Risk, Temporal Matching Relationship

Liu, J. and Zhang, X. (2019) Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation. Journal of Geoscience and Environment Protection, 7, 22-36. doi: 10.4236/gep.2019.72002.