Evaluation of Ecological Environmental Quality based on Multi-Temporal Remote Sensing Data

Li, Jie; Tan, Kun; Ou, Depin; Chen, Yu; Xu, Kailei; Ding, Jianwei

doi:10.1109/multi-temp.2019.8866932

Cited by 1 publication

(2 citation statements)

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“…Remote sensing technology can efficiently and accurately objectively gather extensive ground feature information and has been widely applied in assessing EEQ [2,3]. The ecoenvironment is comprehensively affected by multiple factors, and the evaluation results of a single index can only represent changes in a certain aspect of the terrestrial ecological status [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…(2) The Theil-Sen (T-S) estimator and Mann-Kendall (M-K) method were used to analyze the spatiotemporal variation of RSEI. (3) The main factors affecting the spatial heterogeneity of RSEI were obtained by using the optimal parameter-based geographical detector (OPGD) model. (4) The GWR model was used to examine the responses of driving factors to RSEI changes.…”

Section: Introductionmentioning

confidence: 99%

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Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

Zhou,

Li,

Gao

et al. 2024

Remote Sensing

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The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

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