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Article

Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China

1
College of the Environment and Ecology, Xiamen University, Xiamen 361005, China
2
Fujian Geologic Surveying and Mapping Institute, Fuzhou 351005, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(24), 5633; https://doi.org/10.3390/rs15245633
Submission received: 28 September 2023 / Revised: 7 November 2023 / Accepted: 24 November 2023 / Published: 5 December 2023
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)

Abstract

The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed.
Keywords: remote sensing ecological index (RSEI); Huashan Creek watershed; spatiotemporalchange; geographically weighted regression remote sensing ecological index (RSEI); Huashan Creek watershed; spatiotemporalchange; geographically weighted regression

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MDPI and ACS Style

Liao, Y.; Wu, G.; Zhang, Z. Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China. Remote Sens. 2023, 15, 5633. https://doi.org/10.3390/rs15245633

AMA Style

Liao Y, Wu G, Zhang Z. Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China. Remote Sensing. 2023; 15(24):5633. https://doi.org/10.3390/rs15245633

Chicago/Turabian Style

Liao, Yajing, Guirong Wu, and Zhenyu Zhang. 2023. "Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China" Remote Sensing 15, no. 24: 5633. https://doi.org/10.3390/rs15245633

APA Style

Liao, Y., Wu, G., & Zhang, Z. (2023). Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China. Remote Sensing, 15(24), 5633. https://doi.org/10.3390/rs15245633

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