Integrating ecological risk, ecosystem health, and ecosystem services for assessing regional ecological security and its driving factors: Insights from a large river basin in China

Zhu, Qing; Cai, Yongli

doi:10.1016/j.ecolind.2023.110954

Cited by 10 publications

(1 citation statement)

References 44 publications

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“…Forest vegetation conditions, climate factors, and the forest landscape structure are the main drivers affecting the long-term EEQ of the study area [62,63]. Since the forest cover of the study area is as high as 78.2%, the EEQ is closely related to the conditions of the forest growth conditions [24].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

Ren,

Xu,

et al. 2023

Forests

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Evaluating the long-term urban forest ecological environmental quality (EEQ) and analyzing the drivers of its spatiotemporal changes can provide a scientific basis for making long-term urban forest planning decisions. Taking into account the characteristics of urban forest parks with low area proportions of construction land and bare land, high vegetation coverage, and serious forest disturbances, we constructed a modified urban forest park EEQ evaluation index based on a remote sensing ecological index named MRSEI, which is composed of the Landsat enhanced vegetation index (EVI), wetness, land surface temperature (LST), and forest disturbance index (FDI). We selected the Nanjing Zijin Mountain National Forest Park as the study area, used landsat time series stack (LTSS) remote sensing images from 1990 to 2020 as the main data source, and adopted the suggested modified MRSEI, the Theil-Sen median method, and the Hurst index to evaluate the EEQ to analyze its spatiotemporal variations and its driving factors in the study area. The main research results were as follows: (1) the EEQ of Zijin Mountain showed an up-and-down, overall slowly increasing trend from 1990 to 2020, while the spatial auto-correlation coefficient showed an overall decreasing trend; (2) the area percentage of the EEQ-persistent region accounted for 78.69%, and the anti-sustainable region accounted for 21.31%; (3) the spatial centers of the EEQ in the study area were mainly concentrated on the middle and upper part of the southern slope of Zijin Mountain, moving southward from 1990 to 2020; (4) the analysis of drivers showed that climate factors, forest landscape structure, forest disturbances, and forest growth conditions were the main driving factors affecting the EEQ in the study area. These results provide a research framework for the analysis of EEQ changes over a long-term period in the urban forest parks of China.

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Section: Discussionmentioning

confidence: 99%