Exploring the Variations of Redbed Badlands and Their Driving Forces in the Nanxiong Basin, Southern China: A Geographically Weighted Regression with Gridded Data

Gusong Luo,Yuxiang Dong,Shaoyun Zhang,Hua Peng,Luobin Yan,Yuan Li

doi:10.1155/2021/6694407

Abstract

At present, most of the international research cases on badlands are based on semiarid regions, while there are few studies on badlands in humid regions. Therefore, the research on badlands in humid regions has strong theoretical and practical significance. By taking the Nanxiong Basin, which is located in the humid regions of southern China as the research object, this paper analyzes the scale and spatial distribution variation characteristics of redbed badlands and builds a set of factors that influence redbed badlands to explore the driving forces influencing the variation of redbed badlands based on remote sensing images of the American KH-4A satellite from 1969 and a Landsat 8 image from 2017. The result shows that the scale of redbed badlands in the Nanxiong Basin had generally decreased from 1969 to 2017. The area of redbed badlands decreased from 1693.97 hm2 in 1969 to 127.4 hm2 in 2017, with a decrease of 92.48%. The spatial distribution of redbed badlands had gradually changed from the contiguous planar distribution form in 1969 to the dispersed island distribution form in 2017, forming four agglomerations. The influence degree of the driving forces for the scale variation of redbed badlands is in the order of lithology > road > aspect > residential locations > slope > water system > vegetation > garden plots. Among these driving forces, except vegetation and garden plots, which have a negative correlation with the variation of redbed badlands, other factors have a positive correlation. Lithology is positively correlated with the variation of redbed badlands and has the strongest influence on the redbed badlands of all the influencing factors. The road factor is second to the lithological factor; the more accessible an area is, the stronger the human influence will be and the more serious the damage to vegetation will be, which easily cause surface vegetation damage, induce land degradation, and form redbed badlands.

Highlights

Badlands represent a type of surface landscape cut by strong running water, and they are barren areas that feature dense ravines without vegetation coverage [1, 2]
The geographically weighted regression (GWR) model integrates the spatial attributes of data into the regression model to estimate local parameters, which has been widely used in revealing the relationship between variables changing with spatial location
Lithology is positively correlated with the variation of redbed badlands and has the strongest influence on the redbed badlands of all the influencing factors

Summary

Introduction

Badlands represent a type of surface landscape cut by strong running water, and they are barren areas that feature dense ravines without vegetation coverage [1, 2]. The research direction mainly involves the material basis of badlands [3, 4], development and influencing factors [5,6,7,8], influence of human activities [9, 10], badland erosion [11, 12], badland ecology [13, 14], and other aspects. Research on badlands in humid regions has strong theoretical and practical significance. A redbed badland is a geomorphologic shape developed based on the characteristics of the parent rocks of redbeds, which has typical characteristics such as the loss of the topsoil layer, the direct exposure of contiguous redbed bedrock or thin weathered crust of redbed, and the dense distribution of chicken feet-shaped erosion ditches [16]. The redbed badland may occur in any climatic region, and it is especially

Methods

Results

Discussion

Conclusion