Novel Method for Evaluating Wetland Ecological Environment Quality Based on Coupled Remote Sensing Ecological Index and Landscape Pattern Indices: Case Study of Dianchi Lake Wetlands, China

Yilu Zhao, Aidi Huo (), Zhixin Zhao, Qi Liu, Xuantao Zhao, Yuanjia Huang and Jialu An
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Yilu Zhao: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Aidi Huo: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Zhixin Zhao: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Qi Liu: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Xuantao Zhao: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Yuanjia Huang: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Jialu An: Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China

Sustainability, 2024, vol. 16, issue 22, 1-18

Abstract: Wetlands serve as crucial ecological buffers, significantly influencing temperature reduction, carbon storage, regional climate regulation, and urban wastewater treatment. To elucidate the relationship between wetland landscape patterns and ecological environment, and to accurately assess lake ecosystems, this study proposes a semi-supervised classification method based on RSEI and K-Means. By integrating landscape pattern indices, the Remote Sensing Ecological Index (RSEI), and disturbance proximity, a comprehensive evaluation of the ecological quality of the Dianchi wetlands was conducted. The results indicate that the RSEI-K-Means method, with K set to 50, achieved overall accuracies (OAs) and Kappa values of 0.91 and 0.88, surpassing the SVM’s 0.85 and 0.80. This method effectively combines ecological and landscape indices without relying on extensive training samples, enhancing accuracy and speed in wetland information extraction and addressing the challenges of spatial heterogeneity. This study reveals that from 2007 to 2009, and 2013 to 2015, landscape patterns were significantly influenced by the rapid expansion of Kunming city, exacerbating wetland fragmentation. Notably, significant ecological quality changes were observed in 2009 and 2013, with gradual recovery post-2013 due to strengthened environmental protection policies. The RSEI disturbance proximity analysis indicated that the affected areas were primarily concentrated in regions of high human activity, confirming the method’s high sensitivity and effectiveness. This study can help in wetland ecosystem research and management.

Keywords: RSEI; wetland class; landscape pattern; disturbance identification; Dianchi lake (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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