Spatiotemporal pattern evolution for vegetation using remote sensing technology: a case study of Zhoushan Island, China

Yankun Chen; Chao Chen; Zhisong Liu

doi:10.1117/12.3029768

21 June 2024 Spatiotemporal pattern evolution for vegetation using remote sensing technology: a case study of Zhoushan Island, China

Yankun Chen, Chao Chen, Zhisong Liu

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670U (2024) https://doi.org/10.1117/12.3029768
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

For ecological environment protection research and regional sustainable development, vegetation, as a crucial land cover type, necessitates long-term, large-scale, and highly accurate monitoring. This paper formulates an enduring vegetation monitoring approach using Landsat satellite remote sensing imagery on the Google Earth Engine (GEE) cloud platform. By computing the Normalized Difference Vegetation Index (NDVI), we formulate a method for prolonged remote sensing monitoring of vegetation, unveiling its temporal and spatial pattern evolution. Firstly, based on Landsat remote satellite imagery and GEE, we calculated the NDVI. By leveraging the Normalized Difference Water Index (NDWI), we removed inland water bodies and outliers within the study area, enabling the precise generation of spatiotemporal NDVI distribution maps. Subsequently, the NDVI was analyzed for time trends in conjunction with grade transfer matrices. Finally, combined with Moran's I, high/low clusters, clustering and outlier, cold/hot spots, etc., the NDVI spatial pattern characteristics were analyzed. Using the Zhoushan Island in China as an example, the results indicated that: (1) The most significant changes were observed in the low NDVI area (0-0.2), which expanded from 28.84 km² to 67.29 km². (2) Concentrated mainly in the inland areas are the NDVI High-High clusters and the significant hot spots, while the coastal areas primarily feature NDVI Low-Low clusters and significant cold spots.

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

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Yankun Chen, Chao Chen, and Zhisong Liu "Spatiotemporal pattern evolution for vegetation using remote sensing technology: a case study of Zhoushan Island, China", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670U (21 June 2024); https://doi.org/10.1117/12.3029768

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KEYWORDS

Vegetation

Landsat

Remote sensing

Statistical analysis

Autocorrelation

Satellites

Climate change

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