A layer-wise classification framework for coastal wetlands mapping using SAR time series and locally optimal spatiotemporal features

• A novel LOSF_LC framework for salt marsh mapping; • 10 m resolution wetland maps for the YRD and JDS from 2017 to 2024; • Spartina alterniflora removal improve native salt marsh habitat; • Wetlands face new ecological risks from declines in salt marsh and land areas. Coastal wetland patterns in China have undergone dramatic changes in recent years due to the invasion and subsequent management of Spartina alterniflora ( S. alterniflora ). The ecological benefits of clearing S. alterniflora still lack sufficient exploration. SAR data can provide complete time series observations covering the full phenological cycle of salt marsh vegetation, thereby offering a valuable data source for accurate wetland monitoring. However, the similarity in backscatter values among salt marsh vegetation types poses a challenge for wetland classification. To address this issue, we propose a layer-wise classification framework based on local optimal spatiotemporal features fusion (LOSF_LC). We selected the Yellow River Delta wetland (YRD) and Jiuduansha wetland (JDS), which differ in geographical location and have been severely invaded by S. alterniflora , as the study areas. Using 443 Sentinel-1 images, we generated 10-m resolution wetland maps from 2017 to 2024. Classification experiments using LightGBM, XGBoost, and RF demonstrated that LOSF_LC improved OA by 1.64%–3.44% compared to RFE. Under the optimal scheme, the OA reached 96.07% and 96.30% for the YRD and JDS, respectively. In addition, long-term mapping results revealed that the removal of S. alterniflora has achieved significant restoration outcomes, with native salt marsh species regaining habitat and Phragmites australis becoming the dominant type. Nonetheless, the reduction in salt marsh vegetation and land areas highlights emerging risks for wetland ecosystems. The proposed method provides a new technical reference for accurate SAR-based wetland mapping.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-10-15
DOI
https://doi.org/10.1016/j.jag.2025.104904
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
Field-Weighted Citation Impact
0.00
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article

A layer-wise classification framework for coastal wetlands mapping using SAR time series and locally optimal spatiotemporal features

Zhenhai Gao, Zhenjin Li, Zhiyong Wang, Silong Cheng et al.
International Journal of Applied Earth Observation and Geoinformation
Coastal wetland ecosystem dynamics
article

A layer-wise classification framework for coastal wetlands mapping using SAR time series and locally optimal spatiotemporal features

Zhenhai Gao, Zhenjin Li, Zhiyong Wang, Silong Cheng, Yanan Zuo, Haoran Song
article en

Abstract

• A novel LOSF_LC framework for salt marsh mapping; • 10 m resolution wetland maps for the YRD and JDS from 2017 to 2024; • Spartina alterniflora removal improve native salt marsh habitat; • Wetlands face new ecological risks from declines in salt marsh and land areas. Coastal wetland patterns in China have undergone dramatic changes in recent years due to the invasion and subsequent management of Spartina alterniflora ( S. alterniflora ). The ecological benefits of clearing S. alterniflora still lack sufficient exploration. SAR data can provide complete time series observations covering the full phenological cycle of salt marsh vegetation, thereby offering a valuable data source for accurate wetland monitoring. However, the similarity in backscatter values among salt marsh vegetation types poses a challenge for wetland classification. To address this issue, we propose a layer-wise classification framework based on local optimal spatiotemporal features fusion (LOSF_LC). We selected the Yellow River Delta wetland (YRD) and Jiuduansha wetland (JDS), which differ in geographical location and have been severely invaded by S. alterniflora , as the study areas. Using 443 Sentinel-1 images, we generated 10-m resolution wetland maps from 2017 to 2024. Classification experiments using LightGBM, XGBoost, and RF demonstrated that LOSF_LC improved OA by 1.64%–3.44% compared to RFE. Under the optimal scheme, the OA reached 96.07% and 96.30% for the YRD and JDS, respectively. In addition, long-term mapping results revealed that the removal of S. alterniflora has achieved significant restoration outcomes, with native salt marsh species regaining habitat and Phragmites australis becoming the dominant type. Nonetheless, the reduction in salt marsh vegetation and land areas highlights emerging risks for wetland ecosystems. The proposed method provides a new technical reference for accurate SAR-based wetland mapping.

International Journal of Applied Earth Observation and GeoinformationVol. 144
Washington University in St. Louis (US), Geospatial Research (United Kingdom) (GB), Shandong University of Science and Technology (CN)
Openalex Percentile: Top 72%
Coastal wetland ecosystem dynamics
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