Classification and spatio-temporal evolution analysis of coastal wetlands in the Liaohe Estuary from 1985 to 2023: based on feature selection and sample migration methods

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69–5.82%, and the Kappa coefficient was increased by 0.0087–0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration.

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Publication Details

Journal
Frontiers in Forests and Global Change
Published
2024-08-16
DOI
https://doi.org/10.3389/ffgc.2024.1406473
Citations
13
Primary Topic
Environmental Changes in China
Type
article
Field-Weighted Citation Impact
3.67
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article

Classification and spatio-temporal evolution analysis of coastal wetlands in the Liaohe Estuary from 1985 to 2023: based on feature selection and sample migration methods

Yu Zhao, Lei Wang, Yao Lu, Guangshuai Zhang et al.
13 citations
Frontiers in Forests and Global Change
Environmental Changes in China
3.67
article

Classification and spatio-temporal evolution analysis of coastal wetlands in the Liaohe Estuary from 1985 to 2023: based on feature selection and sample migration methods

Yu Zhao, Lei Wang, Yao Lu, Guangshuai Zhang, Quanming Wang, Li-Na Ke, Qin Tan
article en
13 citations

Abstract

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69–5.82%, and the Kappa coefficient was increased by 0.0087–0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration.

Frontiers in Forests and Global ChangeVol. 7
Liaoning Normal University (CN), China National Environmental Monitoring Center (CN), National Marine Environmental Monitoring Center
Life below water
Openalex Percentile: Top 7%
Environmental Changes in China
3.67
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