Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China

Abstract

An improved Carnegie Ames Stanford Approach (CASA) model based on two kinds of remote sensing (RS) data, Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS), and climate variables were applied to estimate the Net Primary Productivity (NPP) of Xuzhou in June of each year from 2001 to 2010. The NPP of the study area decreased as the spatial scale increased. The average NPP of terrestrial vegetation in Xuzhou showed a decreasing trend in recent years, likely due to changes in climate and environment. The study area was divided into four sub-regions, designated as highest, moderately high, moderately low, and lowest in NPP. The area designated as the lowest sub-region in NPP increased with expanding scale, indicating that the NPP distribution varied with different spatial scales. The NPP of different vegetation types was also significantly influenced by scale. In particular, the NPP of urban woodland produced lower estimates because of mixed pixels. Similar trends in NPP were observed with different RS data. In addition, expansion of residential areas and reduction of vegetated areas were the major reasons for NPP change. Land cover changes in urban areas reduced NPP, which could chiefly be attributed to human-induced disturbance.

Keywords

multi-scale remote sensing / net primary productivity / improved Carnegie Ames Stanford approach model / urbanization

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