期刊
SENSORS
卷 14, 期 8, 页码 15348-15370出版社
MDPI
DOI: 10.3390/s140815348
关键词
bag fraction; biosphere-atmospheric interactions; learning rate; high resolution RapidEye imagery; tree complexity; variable importance and variable selection
资金
- ACCESS-SA: Applied Center for Climate and Earth Systems Science in South Africa under the theme Land Use and Land Cover Change
The quantification of aboveground biomass using remote sensing is critical for better understanding the role of forests in carbon sequestration and for informed sustainable management. Although remote sensing techniques have been proven useful in assessing forest biomass in general, more is required to investigate their capabilities in predicting intra-and-inter species biomass which are mainly characterised by non-linear relationships. In this study, we tested two machine learning algorithms, Stochastic Gradient Boosting (SGB) and Random Forest (RF) regression trees to predict intra-and-inter species biomass using high resolution RapidEye reflectance bands as well as the derived vegetation indices in a commercial plantation. The results showed that the SGB algorithm yielded the best performance for intra-and-inter species biomass prediction; using all the predictor variables as well as based on the most important selected variables. For example using the most important variables the algorithm produced an R-2 of 0.80 and RMSE of 16.93 t.ha(-1) for E. grandis; R-2 of 0.79, RMSE of 17.27 t.ha(-1) for P. taeda and R-2 of 0.61, RMSE of 43.39 t.ha(-1) for the combined species data sets. Comparatively, RF yielded plausible results only for E. dunii (R-2 of 0.79; RMSE of 7.18 t.ha(-1)). We demonstrated that although the two statistical methods were able to predict biomass accurately, RF produced weaker results as compared to SGB when applied to combined species dataset. The result underscores the relevance of stochastic models in predicting biomass drawn from different species and genera using the new generation high resolution RapidEye sensor with strategically positioned bands.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据