Application of geographical detector and geographically weighted regression for assessing landscape ecological risk in the Irtysh River Basin, Central Asia

Abstract

The exponential growth of human activities has resulted in a substantial increase in land use practices that not only modify the characteristics of landscape patterns but also pose significant landscape ecological risk (LER), with the latter being pivotal for ecosystem conservation and sustainable social development. However, research on LER and driving factors of Irtysh River Basin (IRB) are limited. Objectively assessing the LER of the high latitudes within Central Asia (Irtysh River Basin) and quantitatively identifying the environmental factors driving its changes holds significant research value for ensuring the ecological security of human habitation amidst global change. In this study, the spatial autocorrelation analysis method and geographically weighted regression (GWR) and geographical detector (Geo-Detector) models were utilized to reveal the spatiotemporal changes in LER based on land use/land cover (LULC) changes in the IRB from 1992 to 2020. The findings indicate that (1) the temporal scale reveals a slight increasing trend in LER within the IRB. (2) The spatial distribution is characterized by a dominance of lower- and medium-risk regions, with evident positive spatial autocorrelation. (3) The spatial pattern of LER is influenced by various factors, with a significant impact from temperature in the geo-detector model. In addition, the spatial heterogeneity of the effects of major factors was further obtained using the GWR model. The findings presented herein can serve as scientific references for the development of sustainability and ecological safety management in global arid zones and high-latitude cold regions, thus promoting environmental protection in various countries, enhancing consensus on ecological protection and facilitating international cooperation on conservation.