title: Assessing the response of seasonal variation of net primary productivity to climate using remote sensing data and geographic information system techniques in Xinjiang.
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 climate-vegetation 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 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.
authors.affiliation: Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China.
authors.forename: Dai-Liang
authors.initials: DL
authors.lastname: Peng
authors.forename: Jing-Feng
authors.initials: JF
authors.lastname: Huang
authors.forename: Cheng-Xia
authors.initials: CX
authors.lastname: Cai
authors.forename: Rui
authors.initials: R
authors.lastname: Deng
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Xu
mesh_terms: D018533:Biomass; D002681:China; D002980:Climate; D040362:Geographic Information Systems; D005843:Geography; D016014:Linear Models; D058998:Remote Sensing Technology; D012621:Seasons; D014887:Weather