Analysis of Factors Influencing the Lake Area on the Tibetan Plateau Using an Eigenvector Spatial Filtering Based Spatially Varying Coefficient Model

• Published in: Remote Sensing
• Main Authors: Zhexin Xiong, Yumin Chen, Huangyuan Tan, Qishan Cheng, Annan Zhou
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: lake expansion; spatial heterogeneity; climate change; Tibetan Plateau; Science; Q; permafrost
• Online Access: https://doi.org/10.3390/rs13245146; https://doaj.org/article/112c6f0358194cd0a1c4457199f82900

Lakes on the Tibet Plateau (TP) have a significant impact on the water cycle and water balance, and it is important to monitor changes in lake area and identify the influencing factors. Existing research has failed to quantitatively identify the changes and influencing factors of lakes in different regions of the TP. Thus, an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model was used to analyze the relationship between lake area and climatic and terrain factors in the inner watershed of the TP from 2000 to 2015. A comparison with ordinary regression and spatial models showed that the ESF-SVC model eliminates spatial autocorrelation and has the best model fit and complexity. The experiments demonstrated that precipitation, snow melt, and permafrost moisture release, as well as the area of vegetation and elevation difference in the watershed, can significantly promote the expansion of lakes, while evapotranspiration and days of mean daily temperature above zero have an inhibitory effect on lake area expansion. The degree of influence of each factor also differs significantly over time and across regions. Spatially quantitative modeling of lake area in the TP using the ESF-SVC method is a new attempt to provide novel ideas for lake research.