J Integr Plant Biol. ›› 2008, Vol. 50 ›› Issue (12): 1580-1588.DOI: 10.1111/j.1744-7909.2008.00696.x

Special Issue: Ecology and Global Changes

• Molecular Ecology and Evolution • Previous Articles     Next Articles

Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

Liang-Dai Peng, Feng-Jing Huang, Xia-Cheng Cai, Rui Deng and Feng-Jun Xu   

  • Received:2007-06-11 Accepted:2008-02-07

Abstract: Net primary productivity (NPP) is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climatevegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) data and geographic information system (GIS) techniques. The impact of climatic factors (air temperature, precipitation and sunshine percentage) on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0 lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2month lag with air temperature, and the impact of which could persist 1month as a whole. No correlation was found between NPP and sunshine percentage.

Key words: climate, geographic information system techniques, net primary productivity, remote sensing, seasonal variation, serial correlation ageing, time lag.

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