Monitoring and Analysis of Coastal Salt Pans Using Multi-Feature Fusion of Satellite Imagery: A Case Study Along the Laizhou Bay

Yilin Liu, Bing Yan, Pengyao Zhi (), Zhiyou Gao and Lihong Zhao
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Yilin Liu: College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Bing Yan: College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Pengyao Zhi: College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Zhiyou Gao: College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Lihong Zhao: College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Sustainability, 2025, vol. 17, issue 18, 1-25

Abstract: Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method—Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)—based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy—R(MWI)-G(SCI)-B(DL)—was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources.

Keywords: coastal zone; crystallization ponds; deep learning; evaporation ponds; multi-feature fusion; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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