Spatial differentiation and formation mechanism of ecological sensitivity in large river basins: A case study of the Yellow River Basin, China

Kong, Xianglun; Han, Mei; Li, Yunlong; Kong, Fanbiao; Sun, Jinxin; Zhu, Wenjun; Wei, Fan

doi:10.1016/j.ecolind.2024.111571

Cited by 9 publications

(1 citation statement)

References 49 publications

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“…Fortunately, the geo-informatic Tupu model provides a spatio-temporal analysis technique that is capable of delineating the spatial structural traits and temporal shifts of events through advanced data mining and specific processing techniques. This method, by creating a series of intuitive graphs, images, and schematic information in the form of Tupu units, aids in the visualization of the processes of LUCs [59][60][61][62][63]. Essentially, the geo-informatic Tupu model allows for the recording of complex spatiotemporal land-use data using Tupu units, which enhances the precision, clarity, and realism of LUCs research.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Land-Use Patterns and the Associated Impacts on Ecosystem Services Value in Putian City, China

Peng,

Wu,

Lin

et al. 2024

Applied Sciences

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Human actions have led to consistent and profound alterations in land use, which in turn have had a notable effect on the services provided by ecosystems. In this research, the Google Earth Engine (GEE) was initially employed to perform a supervised classification of Landsat satellite images from 2000 to 2020, which allowed us to obtain land-use data for Putian City, China. Next, the geo-informatic Tupu model and the revised valuation model were used to explore the spatial attributes and ecological effects of land-use changes (LUCs). Subsequently, EEH (eco-economic harmony), ESTD (ecosystem services tradeoffs and synergies degree index), and ESDA (exploratory spatial data analysis) methods were employed to further analyze the coordination level, trade-offs, synergies, and spatial patterns of ecological-economic system development. The findings revealed that: (1) The land-use composition in Putian City was predominantly cultivated land and forest land, with other types of land intermixed. Concurrently, there was an ongoing trend of expansion in urban areas. (2) ESV in Putian City exhibited an upward trend, increasing from 15.4 billion CNY to 23.1 billion CNY from 2000 to 2020. (3) ESV exhibited an imbalance in spatial distribution, with high-high agglomeration areas concentrated in the central part of Putian City and the coastal region of Hanjiang District, while low-low agglomeration areas were prevalent in Xianyou County in the southwest, Xiuyu District along the coast, and Licheng District in the urban center. (4) Synergistic relationships among ESs predominated, though the trade-off relationship showed a tendency to expand. (5) The ecological environment and economic progress in Putian City collectively faced a region of potential risk. The findings of this study are intended to serve as a guide for improving the distribution of land resources and for developing strategies that ensure the sustainable development of the region’s socio-economic framework.

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