云南香格里拉地区森林优势树种决策融合分类

基于Google Earth Engine（GEE）云计算平台，协同Sentinel-2影像、WordClim生物气候数据、SRTM地形数据、森林资源二类调查数据等数据，以随机森林（Random Forest， RF），支持向量机（Support Vector Machine， SVM）和最大熵（Maximum Entropy， MaxEnt）3种机器学习算法为组件分类器，开展多源特征、多分类器决策融合的优势树种分类研究。通过3种组件分类器分别构建了两种串行集成和3种贝叶斯并行集成模型，用于确定云南香格里拉地区10种主要优势树种的空间分布。分类结果显示：3个组件分类器的总体精度均低于67.17%；3种并行集成方法总体精度相当，约为72%；两种串行集成方法精度高于78.48%，其中MaxEnt-SVM串行集成方法获得最佳精度（OA：80.66%， Kappa：0.78），与组件分类器相比精度至少提高了13.49%。研究表明：决策融合方法在优势树种分类中比组件分类器精度更高，并且有效改善了小样本树种的分类精度，可用于大范围山区优势树种分类。

Decision Fusion Classification of Forest Dominant Tree Species in Shangri-La Area of Yunnan Province

Based on Google Earth Engine （GEE） cloud computing platform， we collaborate with Sentinel-2 images， WordClim bioclimatic data， SRTM topographic data， forest resources planning and design survey data and other data， and use Random Forest （RF）， Support Vector Machine （SVM） and Maximum Entropy （MaxEnt） machine learning algorithms were used as component classifiers to carry out the study of dominant tree species classification with multi-source features and multi-classifier decision fusion. Two serially integrated and three Bayesian parallel integrated models were constructed by the three component classifiers for determining the spatial distribution of 10 major dominant tree species in Shangri-La region of Yunnan. The classification results showed that the overall accuracy of the three component classifiers was lower than 67.17%， the overall accuracy of the three parallel integration methods was comparable， about 72%， the accuracy of the two serial integration methods was higher than 78.48%. Among them， the MaxEnt SVM serial integration method obtained the best accuracy （OA： 80.66%， Kappa： 0.78）， which improved the accuracy compared with the component classifiers by at least 13.49%. The study shows that the decision fusion method has higher accuracy than the component classifier in dominant tree species classification and effectively improves the classification accuracy of small sample tree species， which can be used for dominant tree species classification in large mountainous areas.