Work

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

Year: 2025

Type: article

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.

Source: Sustainability

DOI: 10.3390/su17188436

Authors: Yilin Liu, Bing Yan, Pengyao Zhi, Zhiyou Gao, Lihong Zhao

Institutions: Chinese Academy of Sciences, Institute of Oceanology, Shandong University of Science and Technology

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896