Dynamic identification of soil erosion risk in the middle reaches of the Yellow River Basin in China from 1978 to 2010

Abstract

Soil erosion has become a significant environmental problem that threatens ecosystems globally. The risks posed by soil erosion, the trends in the spatial distribution in soil erosion, and the status, intensity, and conservation priority level in the middle reaches of the Yellow River Basin were identified from 1978 to 2010. This study employed a multi-criteria evaluation method integrated with GIS and multi-source remote sensing data including land use, slope gradient and vegetation fractional coverage (VFC). The erosion status in the study region improved from 1978 to 2010; areas of extremely severe, more severe, and severe soil erosion decreased from 0.05%, 0.94%, and 11.25% in 1978 to 0.04%, 0.81%, and 10.28% in 1998, respectively, and to 0.03%, 0.59%, and 6.87% in 2010, respectively. Compared to the period from 1978 to 1998, the area classed as improvement grade erosion increased by about 47,210.18 km 2 from 1998 to 2010, while the area classed as deterioration grade erosion decreased by about 17,738.29 km 2. Almost all severe erosion regions fall in the 1st and 2nd conservation priority levels, which areas accounted for 3.86% and 1.11% of the study area in the two periods, respectively. This study identified regions where soil erosion control is required and the results provide a reference for policymakers to implement soil conservation measures in the future.

Identification of Priority Areas for Soil and Water Conservation Planning Based on Multi-Criteria Decision Analysis Using Choquet Integral

“…In recent years, MCDA has been introduced as a new qualitative approach for the evaluation of the soil erosion risk [7,15,33], and has been applied in various environmental fields due to its flexibility and ability for accommodating a variety of data types (quantitative and qualitative). The applications of this method include regional risk assessment [34,35], land management [36], policy making [37], and identification of forest landscape restoration priorities [38].…”

“…Such quantitative methods are usually designed to estimate certain types of soil erosion, such as the wind erosion model (IWEMS) and the water erosion model (RUSLE), based on complex calculations and large amounts of data [14]. However, multiple types of soil erosion usually occur simultaneously in one area, and thus a single quantitative method is hardly suitable for integrated soil erosion assessment [5,15,16]. Meanwhile, the data requirements might constrain their application in data-poor regions [17,18].…”

Hyper-spectral response and estimation model of soil degradation in Kenli County, the Yellow River Delta

“…The main soil type is saline soil derived from alluvial deposits of the Yellow River, which has a light loam soil texture and nutrient deficiencies. Soil degradation in the region manifests mainly as salinization, alkalization, and diminishing fertility, which cause negative effects to the local agricultural production [31][32][33]. The local crops include mainly winter wheat (Triticum aestivuml), corn (Zea mays), paddy (Oryza sativa), and cotton (Gossypium), and most of the natural vegetation consists of salt-tolerant herbaceous plants and shrubs, such as reed (Phragmites australis), cogongrass (Imperata cylindrica), seepweed (Suaeda glauca), and salt cedar (Tamarix chinensis).…”

Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation

“…TRMM 3B43 is a monthly rainfall product and available at a 0.25˚ grid. Its spatial resolution is low, but it is considered that a good indication for temporal rainfall distribution (Vrieling et al, 2008;Zhang et al, 2012;Zhao et al, 2018). This dataset is selected because rain gauge measurements do not always exit or are often hard to obtain, while TRMM data is available for any region between latitudes 50˚N and 50˚S, furthermore, its spatial distribution is more authentic relative to the interpolated results of point measured data.…”

“…Soil erosion is the combined result of natural factors and human factors, and has become one of the most significant environmental problems worldwide, threatening agricultural productivity and food security (DeGraffenried & Shepherd 2009;Zhang et al, 2010;Verachtert et al, 2011;Zhao et al, 2018). Efficient intervention to control soil erosion requires assessment models for predicting the spatial location and intensity of degradation (Cohen et al, 2005;Zhang et al, 2012).…”