Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of <i>Suaeda</i> <i>salsa</i> in the Liaohe estuary, China

LI Yubin; WANG Zongming; ZHAO Chuanpeng; JIA Mingming; REN Chunying; MAO Dehua; YU Hao

doi:10.6046/zrzyyg.2023293

2025 Vol. 37, No. 1

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Article navigation > Remote Sensing for Natural Resources > 2025 > 37(1): 195-203

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LI Yubin, WANG Zongming, ZHAO Chuanpeng, JIA Mingming, REN Chunying, MAO Dehua, YU Hao. 2025. Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of Suaeda salsa in the Liaohe estuary, China. Remote Sensing for Natural Resources, 37(1): 195-203. doi: 10.6046/zrzyyg.2023293

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LI Yubin, WANG Zongming, ZHAO Chuanpeng, JIA Mingming, REN Chunying, MAO Dehua, YU Hao. 2025. Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of Suaeda salsa in the Liaohe estuary, China. Remote Sensing for Natural Resources, 37(1): 195-203. doi: 10.6046/zrzyyg.2023293

Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of Suaeda salsa in the Liaohe estuary, China

1. School of Surveying, Mapping and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China;
2. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

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Corresponding author: ZHAO Chuanpeng

Received Date: 22 September 2023

Revised Date: 11 March 2024

Abstract

Abstract

The Liaohe estuary of China boasts the largest red beach landscape in the world. Monitoring the spatiotemporal dynamics of Suaeda salsa in this region is of great significance for revealing the performance of conservation measures such as returning aquaculture to wetlands. Currently, satellite remote sensing technology has been widely applied to the mapping and identification of coastal vegetation including Suaeda salsa. However, existing classification methods rely on black-box models, which are difficult to interpret, while overlooking exploring identification mechanisms. This has hindered the improvement and development of related methods. Fortunately, the advancement in explainable artificial intelligence (XAI) has provided new directions for analyzing the black-box models. Considering that the decision rules in random forests are interpretable, this study developed a new method to extract the optimal decision rules from trained random forest models. Using this method, this study ultimately reconstructed the optimal decision rules used to identify Suaeda salsa, i.e., B3/B4< 0.90 & B5/B3≥1.46, with an overall data accuracy exceeding 90%. Using annual Sentinel-2 images from 2017 to 2022 as a data source, the study successfully extracted the annual dynamics of Suaeda salsa in the Liaohe Estuary. Accordingly, by combining the centroid migration method, this study analyzed the spatiotemporal changes in the Suaeda salsa following the implementation of returning aquaculture to wetlands, revealing the current status that the Suaeda salsa in this region is undergoing rapid restoration.
- returning aquaculture to wetlands /
- Suaeda salsa /
- explainable artificial intelligence (XAI) /
- random forests /
- decision rule

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References

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