Spatio-temporal dynamics of vegetation over cloudy areas in Southwest China retrieved from four NDVI products

文献类型： 外文期刊

第一作者： Li, Xin

作者： Li, Xin;Xu, Jingwen;Jia, Yiyang;Jiang, Yudie;Yuan, Zelin;Du, Huiyu;Ye, Yang;Liu, Shuang;Han, Rui

作者机构：

关键词： Vegetation dynamic; NDVI; Multi-source datasets; Satellite-based remote sensing; Complex landform

期刊名称：ECOLOGICAL INFORMATICS （ 影响因子：5.1； 五年影响因子：4.9 ）

ISSN： 1574-9541

年卷期： 2024 年 81 卷

页码：

收录情况： SCI

摘要： The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi -source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope 2000 -2013 = - 0.0001 & sdot;a - 1 ). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas.

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