Spatiotemporal evolution of vegetation in the karst region in southwest China based on the Google Earth Engine platform

Xuepeng Zhang; Yingshuang Huang; Peng Gou; Taixia Wu

doi:10.1117/12.3032125

3 June 2024 Spatiotemporal evolution of vegetation in the karst region in southwest China based on the Google Earth Engine platform

Xuepeng Zhang, Yingshuang Huang, Peng Gou, Taixia Wu

Author Affiliations +

Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 1317007 (2024) https://doi.org/10.1117/12.3032125
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China

Abstract

The karst landforms in the southwest region of China make vegetation coverage susceptible to changes. Clearly understanding the spatiotemporal patterns of vegetation evolution is of great significance for ecological civilization construction and promoting green development in the region. The kernel normalized difference vegetation index (kNDVI) has overcome the "non-linearity" characteristic of NDVI, allowing for a more precise representation of vegetation growth status compared to NDVI. Hence, this paper utilizes Landsat data on the Google Earth Engine platform to retrieve kNDVI from 1990 to 2023. Subsequently, the Theil-Sen slope estimation method and Mann-Kendall test are applied to explore the spatiotemporal evolution patterns of vegetation, while the Hurst index is used to forecast the future trends of vegetation change. The results indicate that: (1) the overall vegetation in the study area has improved, with an increase slope of 0.0057. (2) the area with significant increases account for the majority, approximately 85.21%, suggesting an overall positive trend in the spatial distribution of vegetation in the study area. (3) the northeastern part of the study area and the central part of Guangdong are experiencing a slight but sustainable decrease, yet the future vegetation change is expected to be characterized mainly by significant and sustainable increases. The results of this study can provide scientific theoretical support for strengthening ecological environment protection and governance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuepeng Zhang, Yingshuang Huang, Peng Gou, and Taixia Wu "Spatiotemporal evolution of vegetation in the karst region in southwest China based on the Google Earth Engine platform", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 1317007 (3 June 2024); https://doi.org/10.1117/12.3032125

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KEYWORDS

Vegetation

Sustainability

Landsat

Ecosystems

Remote sensing

Climatology

Soil science

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