基于随机森林的杉木适生性预测研究

以中国林业科学研究院热带林业实验中心杉木树种为研究对象，从森林资源二类调查数据中提取优势树种为杉木的小班，将样本数据按7:3的比例分为训练样本和测试样本。以海拔、地貌类型、坡度、坡向、坡位、土壤种类、成土母岩、土壤厚度、腐殖质层厚度为输入变量，以杉木生长适宜性为输出变量，运用随机森林算法建立杉木适生性预测模型，对不同立地条件下的造林地进行杉木适生性预测。同时，利用随机森林模型的变量重要性评估功能，分析了各立地因子对杉木生长的影响权重。结果表明:基于随机森林的杉木适生性预测模型的训练精度为84.3%，泛化精度达到89.5%，具有较高的预测准确率；研究区域内对杉木生长影响较大的立地因子依次为坡度、坡向、腐殖质层厚、海拔，影响因素较小的是土壤种类、土层厚度；就单因素的影响而言，海拔≥350 m的低山和中山地区，坡度在25°~34°之间比较适宜杉木生长。基于随机森林的杉木适生性预测模型可处理复杂的非线性关系，可将模型应用到无林地的造林决策，实现有林地与无林地对杉木适生性判断的有机统一，也可推广到其他树种，为适地适树提供依据。 

Prediction of adaptability of Cunninghamia lanceolata based on random forest

In this paper, Cunninghamia lanceolata was taken as research object in the Experimental Center of Tropical Forestry of Chinese Academy of Forestry, Pingxiang County of Guangxi Province of southern China, we selected the sub-compartments with dominant species of Cunninghamia lanceolata, divided the experimental data into training samples and test samples at 7:3 ratio and established a random forest model with altitude, physiognomy type, slope degree, slope aspect, slope position, soil type, parent rock, soil thickness, humus layer thickness as input variables and growth adaptability of Cunninghamia lanceolata as output variable to predict its adaptability for afforestation sites. At the same time, we analyzed the weight of main site factors on the growth of Cunninghamia lanceolata using the established model. This study showed that the training accuracy of adaptability of Cunninghamia lanceolata based on random forest model was 84. 3% and the generalization accuracy reached 89. 5%. Site factors greatly affecting the growth of Cunninghamia lanceolata were slope degree, slope aspect, the humus layer thickness and altitude, while soil type and soil thickness less affected the growth of Cunninghamia lanceolata. In terms of single site factor, the slopes ranged from 25° to 34° and the altitude greater than 350 m were more suitable for the growth of Cunninghamia lanceolata. The established model based on random forest could deal with complex nonlinear relations and could be applied to make afforestation decision to non-forest lands, then to realize the organic unification of the suitability judgment of Cunninghamia lanceolata with forest land and non-forest land, and the model can be extended to other tree species and provide theoretical support to the problem of matching species with site.