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A fundamental theorem for eco-environmental surface modelling and its applications |
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| Authors: | Yue Tianxiang Zhao Na Liu Yu Wang Yifu Zhang Bin Du Zhengping Fan Zemeng Shi Wenjiao Chen Chuanfa Zhao Mingwei Song Dunjiang Wang Shihai Song Yinjun Yan Changqing Li Qiquan Sun Xiaofang Zhang Lili Tian Yongzhong Wang Wei Wang Ying’an Ma Shengnan Huang Hongsheng Lu Yimin Wang Qing Wang Chenliang Wang Yuzhu Lu Ming Zhou Wei Liu Yi Yin Xiaozhe Wang Zong Bao Zhengyi Zhao Miaomiao Zhao Yapeng Jiao Yimeng Naseer Ufra Fan Bin Li Saibo Yang Yang Wilson John P |
| Institution: | 1.State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China ;2.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China ;3.College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang, 330045, China ;4.College of Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China ;5.College of Forestry, Beijing Forestry University, Beijing, 100083, China ;6.School of Land and Resources, China West Normal University, Nanchong, 637002, China ;7.College of Geographical Information and Tourism, Chuzhou University, Chuzhou, 239000, China ;8.Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China ;9.Department of Information Engineering, Shandong University of Science and Technology, Tai’an, 271019, China ;10.College of Resources, Sichuan Agricultural University, Chengdu, 611130, China ;11.College of Geography and Tourism, Qufu Normal University, Rizhao, 276828, China ;12.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China ;13.School of Geographical Science, Southwest University, Chongqing, 400715, China ;14.National Disaster Reduction Center of China, Ministry of Emergency Management, Beijing, 100721, China ;15.Department for Population Data, China Population and Development Research Center, Beijing, 100081, China ;16.Department for Industrial Standard, China Standardization Administration, Beijing, 100088, China ;17.The Academy of Digital China, Fuzhou University, Fuzhou, 350002, China ;18.National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China ;19.School of Information Engineering, China University of Geoscience, Beijing, 100083, China ;20.Qian Xuesen Laboratory of Space Technology, Beijing, 100094, China ;21.College of Architecture and Urban Planning, Chongqing Jiaotong University, Chongqing, 400074, China ;22.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China ;23.Spatial Sciences Institute, University of Southern California, Los Angeles, CA, 90089-0374, USA ; |
| Abstract: | We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth's surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM. |
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