Construction of a Territorial Space Classification System Based on Spatiotemporal Heterogeneity of Land Use and Its Superior Territorial Space Functions and Their Dynamic Coupling

Case Study on Qionglai City of Sichuan Province, China

Bibliographic Data

ID15465490
AuthorsDinghua Ou (0000-0001-5956-1935, Sichuan Agricultural University), Qi Zhang (0009-0000-5229-2496, Sichuan Agricultural University), Yijie Wu (0009-0005-3696-0243, Sichuan Agricultural University), Jing Qin (0000-0002-3293-0929, Sichuan Agricultural University), Xia Jian-guo (Sichuan Agricultural University), Jianguo Xia (Sichuan Agricultural University), Ouping Deng (0000-0001-7450-9529, Sichuan Agricultural University), Xuesong Gao (0000-0002-3426-8721, Sichuan Agricultural University, corresponding author), Jinhu Bian (0000-0003-1472-5259, Chinese Academy of Sciences), Shangqi Gong (Sichuan Agricultural University)
Year2021
Volume18
Issue17
Pages9052-9052
Publication date2021-08-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18179052
PMID34501641
OpenAlexW3198146118
LanguageEN
Citations received2
References cited46

Territorial space classification (TSC) provides the basis for establishing systems of national territory spatial planning (NTSP) and supervising their implementation in China, thus has important theoretical and application significance. Most of the current TSC research is related to land use/land cover classification, ignoring the connection of the NTSP policies and systems, failing to consider the spatiotemporal heterogeneity of land use superior territorial space functions (TSFs) and the dynamic coupling between land use and its superior TSFs on the result of TSC. In this study, we integrated the factors influencing the connection of NTSP policies and systems and established a theoretical framework system of TSC from the perspective of spatial form and functional use. By integrating the q-statistic method with spatiotemporal geographical analysis, we propose a method to construct a TSC system for Qionglai City of Sichuan Province in China based on the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs. It makes up for the deficiency of directly taking land use/land cover classification as TSC and solves the problems of ignoring the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs. Using this method, we found that the TSC of Qionglai City consists of 3, 7, and 14 first-, second-, and third-level space types, respectively. The key findings from this study are that land use superior TSFs show spatiotemporal heterogeneity in Qionglai, and coupling effects in spatial distribution were noted between land use types and their superior TSFs, as was temporal heterogeneity in the coupling degree and the structure of the TSFs corresponding to the land use types, which show obvious dynamics and non-stationarity of the functional structure. These findings confirm the necessity of considering the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs in TSC. This method of establishing a TSC system can be used to address a number of NTSP and management issues, and three examples are provided here: (a) zoning of urban, agricultural, and ecological space; (b) use planning of production, living and ecological space; (c) delimitation of urban development boundary, permanent basic farmland protection redline, and ecological protection redline

Cartography · China · Civil engineering · Coupling (piping · Geography · Land cover · Land use · Perspective (graphical · Spatial heterogeneity · Computer Science · Diverse Aspects of Tourism Research · Engineering · Land Use and Ecosystem Services · Urban Transport and Accessibility · Artificial Intelligence · Ecology

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