Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data

作者：Yuchao Yan, Changjiang Wu, Youyue Wen · 发表于：Ecological Indicators · 年份：2021 · DOI：10.1016/j.ecolind.2021.107737 · 被引用次数：65 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Urban Heat Island Mitigation

Climate change (CLC) and urban expansion (URE) have profoundly altered the terrestrial net primary productivity (NPP). Many studies have determined the effects of CLC and URE on the NPP. However, these studies were conducted at low resolutions (250–1000 m), making it difficult to detect many smaller new urban lands, and thus potentially underestimating the contribution of URE. To accurately determine the contributions of CLC and URE to the NPP, this study takes Beijing as an example and uses an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse the spatial resolution of the Landsat Normalized Difference Vegetation Index (NDVI) and the temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI to generate a new NDVI with a high spatio-temporal resolution. Compared with the Landsat NDVI, the NDVI fused by the ESTARFM is found to be reliable. The fused NDVI was then inputted into the Carnegie–Ames–Stanford Approach (CASA) model to generate the NPP with a high spatio-temporal resolution, namely, the 30-m NPP. Compared with the 250-m NPP generated by directly inputting the MODIS NDVI into the CASA model, the 30-m NPP as a new ecological indicator is more accurate than the 250-m NPP. Due to the high resolution of the 30-m NPP and its increased ability to detect more new urban lands, the total loss of the 30-m NPP caused by URE is much higher than that of the 250-m NPP. For the same reason, especially in rapidly urbanized are...