Remote sensing information extraction for mangrove forests based on multi-feature parameters:A case study of Guangdong Province
Accurate mangrove forest distribution information is critical to the conservation and management of mangrove forests.Despite extensive studies on the remote sensing mapping of mangrove forests,it is necessary to improve their mapping accuracy by effectively utilizing multi-source remote sensing features.First,this study designed 15 feature associations using temporal features,including spectral,scattering,texture,and terrain features,which were extracted from multi-source remote sensing data.Then,using a random forest model,it analyzed the accuracy of different feature associations in mangrove forest identification,obtaining the optimal feature association.Finally,this study mapped the 10-m-resolution mangrove forest distribution of Guangdong Province in 2021 based on platform Google Earth Engine(GEE).The results show that spectral features in winter exhibited the highest importance,with richer feature types corresponding to higher mapping accuracy.The optimal feature association yielded overall accuracy of 92.25%and a Kappa value of 0.91.Overall,this study extracted information on mangrove forests in Guangdong based on multi-feature parameters and the optimal feature association.The results of this study will provide a scientific reference for accurate mapping of mangrove forests on a large scale.
information extraction of mangrove forestsmulti-source remote sensing dataGEEmachine learningGuangdong Province
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