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

被引:42
|
作者
Yan, Yuchao [1 ]
Wu, Changjiang [2 ]
Wen, Youyue [3 ]
机构
[1] Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China
[2] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China
[3] Minist Ecol & Environm, South China Inst Environm Sci, 18 Ruihe RD, Guangzhou 510535, Peoples R China
基金
美国国家科学基金会;
关键词
Climate change; Urban expansion; Net primary productivity; Spatio-temporal fusion; Remote sensing; LIGHT USE EFFICIENCY; MODIS SURFACE REFLECTANCE; BLENDING LANDSAT; RIVER DELTA; TERRESTRIAL; URBANIZATION; MODEL; SATELLITE; CONSEQUENCES; ENHANCEMENT;
D O I
10.1016/j.ecolind.2021.107737
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Spatio-temporal Change Analysis of Perak River Basin Using Remote Sensing and GIS
    Hanif, Muhammad Faisal
    ul Mustafa, Muhammad Raza
    Hashim, Ahmad Mustafa
    Yusof, Khamaruzaman Wan
    2015 INTERNATIONAL CONFERENCE ON SPACE SCIENCE AND COMMUNICATION (ICONSPACE), 2015, : 225 - 230
  • [32] Multi-time-horizon Traffic Risk Prediction using Spatio-Temporal Urban Sensing Data Fusion
    Dao, Minh-Son
    Nguyen, Ngoc-Thanh
    Zettsu, Koji
    2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 2205 - 2214
  • [33] Detection of ventilation corridors using a spatio-temporal approach aided by remote sensing data
    Wicht, Marzena
    Wicht, Andreas
    Osinska-Skotak, Katarzyna
    EUROPEAN JOURNAL OF REMOTE SENSING, 2017, 50 (01) : 254 - 267
  • [34] Spatio-temporal analysis of the relationship between climate variables and waterlogging using satellite remote sensing
    Sekhon H.S.
    Setia R.
    Singh S.P.
    Kingra P.K.
    Ansari J.
    Arabian Journal of Geosciences, 2021, 14 (14)
  • [35] Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China
    Lin, Huilong
    Wang, Xuelu
    Zhang, Yingjun
    Liang, Tiangang
    Feng, Qisheng
    Ren, Jizhou
    RANGELAND JOURNAL, 2013, 35 (04): : 409 - 425
  • [36] Spatio-Temporal Analysis of Urban Heat Island Using Multisource Remote Sensing Data: A Case Study in Hangzhou, China
    Zhang, Yuzhou
    Cheng, Jie
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (09) : 3317 - 3326
  • [37] Impacts of landscape change on net primary productivity by integrating remote sensing data and ecosystem model in a rapidly urbanizing region in China
    Guo, Lijia
    Liu, Ruimin
    Shoaib, Muhammad
    Men, Cong
    Wang, Qingrui
    Miao, Yuexi
    Jiao, Lijun
    Wang, Yifan
    Zhang, Yan
    JOURNAL OF CLEANER PRODUCTION, 2021, 325
  • [38] Spatio-Temporal Fusion Of UAV Remote Sensing Images Based on Pyramid Method
    Jiang, Chao
    Yu, Yanfeng
    Engineering Intelligent Systems, 2022, 30 (06): : 465 - 474
  • [39] Spatio-Temporal Dynamics Assessment of Coastlines Based on Remote Sensing Data
    Otinar, Pedro
    Silva, Marcus
    Cobos, Manuel
    Magana, Pedro
    Baquerizo, Asuncion
    PROCEEDINGS OF THE 39TH IAHR WORLD CONGRESS, 2022, : 5917 - 5925
  • [40] Spatio-temporal distribution of net primary productivity along the Northeast China Transect and its response to climatic change
    Wen-quan Zhu
    Yao-zhong Pan
    Xin Liu
    Ai-ling Wang
    Journal of Forestry Research, 2006, 17 (2) : 93 - 98