Utilizing deep transfer learning to discover changes in landscape patterns in urban wetland parks based on multispectral remote sensing

Abstract

Urban wetland parks are essential for protecting ecosystems and alleviating urban heat island effects. Owing to the impact of urban sprawl and human activities, habitats in wetland parks have become increasingly fragmented, evoking an urgent need to accurately monitor and analyze such changes. In this study, a transfer learning-based ResNet-18 method was proposed to classify the landscape patterns of urban wetland parks by integrating the advantages of remote sensing technologies, i.e., long-time series of Gaofen-2 and high-accuracy data of unmanned aerial vehicle remote sensing. The proposed method solves the dual problems of low precision and sparse sample data in landscape pattern classification. By employing the proposed method, we realized long-time-series, high-accuracy analysis of landscape pattern changes in a national wetland park. Our results showed that the overall accuracy was 90.69–97.96 % and Kappa coefficient was stable between 0.865 and 0.968, fully verifying the effectiveness and reliability of our method. We revealed a shrinking trend in the area of water bodies along with an expanding trend in the area of other land types. Thus, our findings reflect the significant impact of urban sprawl on the landscape patterns of wetland parks.