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

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...

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Autores principales:	Yuchao Yan, Changjiang Wu, Youyue Wen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Climate change Urban expansion Net primary productivity Spatio-temporal fusion Remote sensing Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/363d7dcfd7084665aed77bd9c9eab8d5
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spelling	oai:doaj.org-article:363d7dcfd7084665aed77bd9c9eab8d52021-12-01T04:52:23ZDetermining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data1470-160X10.1016/j.ecolind.2021.107737https://doaj.org/article/363d7dcfd7084665aed77bd9c9eab8d52021-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21004027https://doaj.org/toc/1470-160XClimate 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 areas, the contribution ratio of URE to the 30-m NPP is much higher than that to the 250-m NPP. Moreover, in natural vegetation cover areas, CLC, which is measured by the interannual changes in temperature, precipitation, and solar radiation, is the leading factor of the change in the NPP. However, within the urban areas, residual factors other than CLC and URE, such as the introduction of exotic high-productivity vegetation, irrigation, fertilization, and pest control, dominate the change in the NPP. The results of this study are expected to contribute to a deeper understanding of the influences of CLC and URE on terrestrial ecosystem carbon cycles and provide an important theoretical reference for urban planning.Yuchao YanChangjiang WuYouyue WenElsevierarticleClimate changeUrban expansionNet primary productivitySpatio-temporal fusionRemote sensingEcologyQH540-549.5ENEcological Indicators, Vol 127, Iss , Pp 107737- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Climate change Urban expansion Net primary productivity Spatio-temporal fusion Remote sensing Ecology QH540-549.5
spellingShingle	Climate change Urban expansion Net primary productivity Spatio-temporal fusion Remote sensing Ecology QH540-549.5 Yuchao Yan Changjiang Wu Youyue Wen Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
description	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 areas, the contribution ratio of URE to the 30-m NPP is much higher than that to the 250-m NPP. Moreover, in natural vegetation cover areas, CLC, which is measured by the interannual changes in temperature, precipitation, and solar radiation, is the leading factor of the change in the NPP. However, within the urban areas, residual factors other than CLC and URE, such as the introduction of exotic high-productivity vegetation, irrigation, fertilization, and pest control, dominate the change in the NPP. The results of this study are expected to contribute to a deeper understanding of the influences of CLC and URE on terrestrial ecosystem carbon cycles and provide an important theoretical reference for urban planning.
format	article
author	Yuchao Yan Changjiang Wu Youyue Wen
author_facet	Yuchao Yan Changjiang Wu Youyue Wen
author_sort	Yuchao Yan
title	Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
title_short	Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
title_full	Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
title_fullStr	Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
title_full_unstemmed	Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
title_sort	determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/363d7dcfd7084665aed77bd9c9eab8d5
work_keys_str_mv	AT yuchaoyan determiningtheimpactsofclimatechangeandurbanexpansiononnetprimaryproductivityusingthespatiotemporalfusionofremotesensingdata AT changjiangwu determiningtheimpactsofclimatechangeandurbanexpansiononnetprimaryproductivityusingthespatiotemporalfusionofremotesensingdata AT youyuewen determiningtheimpactsofclimatechangeandurbanexpansiononnetprimaryproductivityusingthespatiotemporalfusionofremotesensingdata
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Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data

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