Estimation of net primary productivity using multi-scale remote sensing data in Xuzhou, China

An improved Carnegie Ames Stanford Approach model based on two kinds of remote sensing data, Landsat ETM+ and MODIS, and climate variables was applied to estimate the net primary productivity (NPP) of Xuzhou in the June of 2006,2008 and 2010. The NPP of the study area decreases with the spatial scale expanding; The average NPP of terrestrial vegetation in Xuzhou shows decreasing trend in recent years because of the changes in climate and environment; The whole study area was plotted out four sub-regions, which were NPP higher sub-region, NPP high sub-region, NPP low sub-region and NPP lower sub-region. The average NPP of every sub-region was decreasing and the area percentage of lower sub-region was increasing with the scale expanding, so the NPP structure is various in different spatial scales. The NPP of the different vegetation types is significantly influenced by scale effect. In particular, the NPP of urban woodland was estimated lower value because of mixed pixel, it was increasing with the scale expanding.