The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m²/ha), stem volume (m³/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R²) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m²/ha) and stem volume (29.28 m³/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.

中文翻译：

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利用多光谱Sentinel-2数据绘制温带森林林分特征的机器学习方法的比较

林分特征的估计和制图至关重要，因为该信息对于可持续森林管理是必要的。本研究考虑使用贝叶斯加性回归树（BART）算法作为非参数分类器，该算法使用Sentinel-2A数据和地形变量来估计林分特征，即基础面积（m ² / ha），茎量（米³/ ha）和茎密度（数量/ ha）。将这些结果与其他三种流行的机器学习（ML）算法进行了比较，例如通用线性模型（GLM），K最近邻（KNN）和支持向量机（SVM）。对28个变量进行了特征选择，包括Sentinel-2卫星上的多光谱带，相关植被指数以及辅助数据（从数字高程模型（DEM）得出的高程，坡度和地形太阳辐射指数），然后进行通过递归特征消除（RFE）从数据集中删除了大多数无关紧要的变量。研究区域是生物多样性高，海拔梯度从26到1636 m的山区森林。提供了1200个样地的库存数据集，用于训练和测试算法，然后将预测变量输入到ML模型中，以计算和预测林分特征。ML模型的准确性和确定性通过其均方根误差（RMSE），平均绝对误差（MAE）和R平方（R²）值。结果表明，BART产生了最佳的基础面积和茎体积预测，其次是GLM，SVM和KNN。面积（8.12 m ² / ha）和茎干体积（29.28 m ³）的最佳RMSE值/ ha）估算值是通过BART获得的。因此，建立了BART模型在林业中的应用能力。另一方面，对于所有林分变量预测，KNN表现出最高的RMSE值，因此对于该特定应用而言，其表现出的准确性最低。此外，突出显示了红色边缘和海拔附近狭窄的Sentinel-2波段的有效性，可用于预测林分特征。因此，我们得出的结论是，本研究中使用的Sentinel-2产品和源自PALSAR数据的地形变量的组合改善了对温带森林的森林属性的估计。