Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass
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The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest (RF) and support vector machine were adopted to estimate above ground biomass (AGB) using widely used Landsat imagery in the region, especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.
KeywordsAbove ground biomass Ecological forest Forest management Landsat Random forest
The authors are thankful to the USGS and NASA for the open archives of Landsat imagery, and would like to give sincere thanks to the R Development Team for the open-source package for statistical analysis. The authors also thank the Editor and anonymous reviewers for their constructive comments, suggestions, and help in enhancing the manuscript.
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