AUTHOR=Ke Lina , Tan Qin , Lu Yao , Wang Quanming , Zhang Guangshuai , Zhao Yu , Wang Lei 

TITLE=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

JOURNAL=Frontiers in Forests and Global Change

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2024.1406473

DOI=10.3389/ffgc.2024.1406473

ISSN=2624-893X

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, the study area chosen was the Liaohe Estuary wetland. 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 RF-RFE feature selection, the accuracy of the model reached 0.88, the separation of the selected feature set was good, and the first three dominant features were obtained as DVI_10, NWI_10 and Greenness_10. (2) The sample migration method utilized in this study resulted in an overall accuracy of sample classification in the target year ranging 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. The findings of this study offer valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary, laying a strong groundwork for shaping policies on ecological protection and restoration.