Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020, based on machine learning

Abstract

The significance of land use classification has garnered attention due to its implications for climate and ecosystems. This paper establishes a connection by introducing and applying automatic machine learning (Auto ML) techniques to salt lake landscape, with a specific focus on the Qarhan Salt Lake area. Utilizing Landsat-5 Thematic Mappe (TM) and Landsat-8 Operational Land Imager (OLI) imagery, six machine learning algorithms were employed to classify eight land use types from 2000 to 2020. Results show that XGBLD performed optimally with 77% accuracy. Over two decades, salt fields, construction land, and water areas increased due to transformations in saline land and salt flats. The exposed lakes area exhibited a rise followed by a decline, mainly transforming into salt flats. Agricultural land areas slightly increased, influenced by both human activities and climate. Our analysis reveals a strong correlation between salt fields and precipitation, while exposed lakes demonstrate a significant negative correlation with evaporation and temperature, highlighting their vulnerability to climate change. Additionally, human water usage was identified as a significant factor impacting land use change, emphasizing the dual influence of anthropogenic activities and natural factors. This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.