Assessment of Land Desertification and Its Drivers on the Mongolian Plateau Using Intensity Analysis and the Geographical Detector Technique

Abstract

Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

“…The southern Liaodong Peninsula has a warm temperate semi-humid monsoon climate with maritime features. Approximately 70% of the precipitation is concentrated from June to September (Wang et al, 2022). Typhoons in summer and autumn will also bring concentrated heavy rainfall (http://www.weather.gov.cn).…”

“…Additionally, it can be applied to various types of explanatory variables and the outputs have an elegant form and definite physical meaning in the model. The GeoDetector method has been successfully applied to spatial and spatiotemporal prediction and decision-making in a wide field of natural disasters (Wang et al, 2022;Zhang Y. et al, 2023), air pollution (Bai et al, 2019;Xu et al, 2021), public health (Qiao et al, 2019), and engineering (Song et al, 2021), etc. Landslides in various regions exhibit distinct characteristics. The Liaodong Peninsula region, which is situated amidst the sea on three sides, has a typical low mountainous and hilly environment and coastal landform characteristics.…”

A New Remote Sensing Desert Vegetation Detection Index

“…Therefore, after increasing the S and V values, saving the effects as RGB images directly can avoid the color shift again when converting the HSV color space to RGB color space. The specific method is shown in Equation (5).…”

“…2023, 15, 5742 2 of 27 is particularly serious, which is mainly concentrated in the western and northern regions of China, severely restricting local economic development and placing great pressure upon the grassland ecological environment [4]. Among these regions, the Hangjin Banner in the Inner Mongolia Autonomous Region is a key area for the prevention and control of land desertification disasters in China [5]. The Hangjin Banner is rich in vegetation resources, which are mainly dominated by tree species with strong drought tolerance and low shrubs.…”

Normal Difference Vegetation Index Simulation and Driving Analysis of the Tibetan Plateau Based on Deep Learning Algorithms

“…Zhao used a geodetector approach to measure the explanatory degree of each factor affecting the NDVI change in Shaanxi Province to quantitatively assess each factor's contribution to the change in vegetation [31]. Unfortunately, this method could not clarify the spatial distribution of the dominant factors [32]. Although previous studies have, to a certain extent, derived the distributional characteristics of NDVI on the TP and its response relationship with climate factors, they have not considered the influence of topography on the response mechanism.…”