**1. Introduction**

Soil erosion is considered to be the greatest threat to land degradation, seriously affecting terrestrial ecosystem security [1,2]. Soil erosion disrupts the soil agglomeration structure [3], resulting in the redistribution of soil nutrients [4], which changes soil carbon transport [5] and affects soil ecological service functions [6]. Soil erosion also reduces soil resources, exacerbates the scarcity of land resources [3], affects vegetation growth [7] and food security, and hinders sustainable socio-economic development [8]. To effectively control soil erosion and its negative socio-environmental impacts, the formation process, dynamic evolution, and hazards of soil erosion must be robustly assessed [9,10]. China is one of the countries most severely affected by soil erosion in the world, especially in the karst region of southwestern China [11]. Soil erosion leading to rock desertification has become a major environmental disaster limiting people's production and development [12]. In recent years, numerous studies have focused on soil erosion in karst areas, including erosion processes, spatial and temporal evolution, driving mechanisms [13], dynamic modeling [14], sensitivity evaluation [15], and control measures [16]. However, constrained

**Citation:** Shen, C.; Xiong, K.; Shu, T. Dynamic Evolution and Quantitative Attribution of Soil Erosion Based on Slope Units: A Case Study of a Karst Plateau-Gorge Area in SW China. *Land* **2022**, *11*, 1134. https://doi.org/ 10.3390/land11081134

Academic Editor: Xiaoyong Bai

Received: 10 June 2022 Accepted: 20 July 2022 Published: 24 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Guiyang 550006, China

by the highly heterogeneous geographical environment and complex erosion patterns [17], many research methods are difficult to perform and apply in karst areas, and little is known about soil erosion processes and drivers in karst areas. These analyses are fundamental requirements for combating soil erosion, and in particular, understanding the long-term dynamic evolution of soil erosion and its interactive drivers is essential for land managers to assess soil erosion changes and formulate soil and water conservation policies.

With the increasing abundance of environmental data, scientists have developed several mathematical models to emulate soil erosion processes at different spatial and temporal scales on the basis of topographic, climatic, soil, land use, and vegetation cover data [18,19]. Among the existing erosion models, the RUSLE model [16], the SWAT model [20], and the WEPP model [21] have been proven to be applicable at different spatial scales. They are widely used in complex topographic units due to their simple structure and GIS compatibility [22], such as in the karst areas of southwest China [23] or Cuba [24]. However, many studies have not considered the control of soil erosion by karst conditions and the direct use of the RUSLE model may overestimate soil erosion in karst areas [25]. Karst landscapes have a double-layer structure of surface and subsurface. Large exposures of carbonate rocks on the surface alter surface runoff velocity and flow patterns and intercept sediment runoff; secondary pore spaces are developed underground and contribute to the rapid transport of runoff sediment. Dai found a correlation coefficient of −0.076 (*p* < 0.01) between soil erosion and bedrock exposure on the basis of an artificially designed soil trough device with a double-layer spatial structure and simulated rock desertification [26]. Gao and Wang optimized the RUSLE model by introducing the rock desertification factor on this basis [23], and the results showed that the simulation accuracy of the RUSLE model was significantly improved after optimization. Therefore, this study used the RUSLE optimized to estimate soil erosion in karst areas.

Appropriate study units are an important prerequisite for scientific spatial analysis. In current soil erosion studies, the common study units are administrative units, grid units, and geographical feature units. However, the assessment results based on these study units are difficult to meet the requirements for fine-grained soil erosion assessment or control, which may make it difficult to carry out accurate soil and water conservation work. The slope cell, proposed by Carrara, is a topographic unit cut by a combination of ridgelines, valley lines, terrace boundaries, and valley bottom boundaries [27]. Slope units are constructed according to hydrological processes, ensuring maximum homogeneity within the unit and maximum heterogeneity between different units [28], and are currently widely used in the spatial distribution of landslides [29], sensitivity analysis [30], and prediction studies [31], among others, with the slope unit being a more sophisticated unit than traditional units. Compared with traditional units, the slope unit has higher classification performance and more stable estimation coefficients, which better reflect the actual geographical environment and reduce the uncertainty of control factors [32]. Using slope units as a basis for analyzing soil erosion can help in the analysis of dominant factors of soil erosion.

In this study, we aimed to investigate the long-term soil erosion evolution patterns in karst areas and the interaction of their driving forces. To achieve the objectives, the following analyses were made: (1) simulation of soil erosion by the RUSLE model optimized by the rocky desertification factor; (2) analysis of the long-term spatial and temporal dynamic evolution pattern of soil erosion; (3) identification of the dominant and interacting factors of soil erosion evolution. The results of the study can provide a scientific reference for determining suitable soil erosion control schemes in karst areas, and this contribution will also help to advance the sustainable development goals of soil and water conservation in karst areas.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The karst zone of southwest China, centered on Guizhou, is the largest continuous zone of karst and has the most typical and complex karst landscape in the world. The study area (105◦34 59~105◦43 06 E, 25◦37 18~25◦42 37 N) was selected in southwest Guizhou, south of Guanling County and north of Zhenfeng County, on both sides of the Beipanjiang River Gorge, with a total area of 51.6197 km2 (Figure 1). The altitude range of the area is between 443 and 1366 m a.s.l., which is a typical plateau canyon landform. The region is under a dry and hot southern subtropical river valley climate, with warm and dry winters and springs, and high temperatures and rain in summer and autumn [33]. Meteorological data show no significant increase or decrease in temperature or precipitation, both varied within regular ranges from 2000 to 2020. The average annual temperature is 18.4 ◦C, and the average annual precipitation is 1100 mm, with May to October accounting for more than 80% of the total annual precipitation. The lithology is mainly Middle Tertiary limestone and dolomite, and the soil is calcareous [34].

The research region is characterized by a rocky desert landscape, with fragmented and shallow soils, being prone to soil erosion in the presence of water, and having an extremely fragile ecological environment. Coupled with extensive deforestation and agricultural activities, the region has been caught in a vicious cycle of "environmental fragility–resource shortage-poverty, resource plunder–environmental degradation–further poverty" [35]. Since the beginning of the 21st century, the study area has been designated as a model area for the integrated management of karstic desertification ecology and environment, implementing natural restoration measures such as returning farmland to forest and grass. Therefore, the selected study area is typical, representative, and exemplary in the management of karst soil erosion.

#### *2.2. Data*

The data required for this study included remote sensing images, rainfall data, land use data, topographic and geomorphological data, and soil type data. The consistency and reliability of all data were strictly checked and controlled by the data production department.

(1) Remote sensing images and topographic data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn (accessed on 7 October 2020)), with a resolution of 30 m. Remote sensing images without clouds in the study area were selected as the data source. (2) Rainfall data were obtained from the ChinaMeteorological Data Network (http://data.cma.cn (accessed on 15 October 2020)), using ArcGIS10.2 to spatially interpolate and rasterize the rainfall dataset of the meteorological stations. (3) Land use data were obtained in two parts: The data from 2000 and 2005 were obtained from remote sensing images as the source data due to the long period and accuracy problems, and the initial land use data were obtained through supervised classification and manual interpretation at a later stage. The data from 2010 to 2020 were obtained through remote sensing image interpretation and correction by the research team through long-term field investigation. (4) Soil type data were obtained from the Resource and Environment Science and Data Centre of the Chinese Academy of Sciences (http://www.resdc.cn (accessed on 15 October 2020)) and calibrated concerning the 1:50,000 soil type map of Guizhou Province and the results of the team's field soil sample collection.

#### *2.3. Methods*
