Work

Integrating Multispectral and SAR Satellite Data for Alpine Wetland Mapping and Spatio-Temporal Change Analysis in the Qinghai Lake Basin

Year: 2026

Type: article

Abstract: Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin.

Source: Remote Sensing

DOI: 10.3390/rs18081173

Authors: Qianle Zhuang, Zeyu Tang, Chenggang Li, Meiting Fang, Xiaolu Ling

Institution: China University of Mining and Technology

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,258