Trends of NDVI, precipitation and their relationship in different forest ecological zone of China during 1982 to 2006

Sa Wang; Xiaoling Chen; Xi Li; Guo Zhang; Ting Yang

doi:10.1117/12.2067883

8 November 2014 Trends of NDVI, precipitation and their relationship in different forest ecological zone of China during 1982 to 2006

Sa Wang, Xiaoling Chen, Xi Li, Guo Zhang, Ting Yang

Author Affiliations +

Proceedings Volume 9260, Land Surface Remote Sensing II; 92602H (2014) https://doi.org/10.1117/12.2067883
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

This study analyzes the change of Normalized Difference Vegetation Index (NDVI) and precipitation for forest in different ecological zones in China and their correlation over the period of 1982-2006. The specific aim of this paper was to identify the changing trends of NDVI and precipitation and understand their relations, especially, on which duration the precipitation influence NDVI strongly during growing season of forest in different ecological aspects. The results showed that 1) the break points of NDVI and precipitation appeared in different years in most ecological zones, but in temperate continental forest and temperate mountain system, they have a high degree of consistency; 2) the NDVI in boreal coniferous forest, temperate mountain system and tropical moist deciduous forest showed a increasing trend during 1982-2006 and the lowest value were appeared in different time and the precipitation in boreal coniferous forest and temperate mountain system showed a decreasing trend; 3) the forest in different ecological zones has different patterns with different periods and lags and the peak value of pearson correlation coefficients were showed in different duration and lag, and NDVI and precipitation generally have the negative but weak relation.

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Sa Wang, Xiaoling Chen, Xi Li, Guo Zhang, and Ting Yang "Trends of NDVI, precipitation and their relationship in different forest ecological zone of China during 1982 to 2006", Proc. SPIE 9260, Land Surface Remote Sensing II, 92602H (8 November 2014); https://doi.org/10.1117/12.2067883

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