基于GF-2的乔木生物量估测模型研究

以福建省将乐林场为研究区，使用野外实测样地数据，结合福建省二类调查数据，获取了共192个样地的生物量数据，其中杉木纯林112个，马尾松纯林80个．对覆盖研究区的2景GF-2影像进行预处理，提取光谱信息、植被指数、纹理特征及地形因子，筛选与样地生物量相关性较高的因子作为建模的自变量，采用支持向量机、随机森林及多元逐步回归3种方法分别建立了杉木和马尾松生物量模型．结果表明:支持向量机、随机森林模型拟合效果均比多元逐步回归模型好，其中随机森林模型决定系数R2最高，2种样地的R2分别为0.65和0.72，估计精度也最高，分别为65.28%和76.82%；杉木样地3种模型的均方根误差分别为64.27、48.16和77.03，马尾松样地3种模型的均方根误差分别为54.79、48.18和65.63，其中随机森林模型的最低．在3种模型中，随机森林模型为乔木生物量的最优模型． 

Estimation of tree biomass with GF-2

Biomass data from a total of 192 plots (112 pure forests of Chinese fir, 80 pure forests of Pinus massoniana) in Jiangle State Forest Farm in Sanming City, Fujian Province were obtained from field measured sample plot data and second-class survey data of Fujian Province．Two scene GF-2 images from the study area were preprocessed, spectral information, vegetation index, texture features and topographic factors were extracted, factors highly-correlated with the biomass as independent variables were screened out．Biomass models of fir and Pinus massoniana were established from support vector machine, random forest and multiple stepwise regressions．Fitting of the two machine learning models was found to be better than the multiple stepwise regression model．The random forest model showed the highest determination coefficient R2 (0.65 and 0.72 for the 2 plots), and the highest estimation accuracy (65.28% and 76.82%)．The mean root square errors in the 3 models for the Chinese fir plot were 64.27, 48.16 and 77.03．The mean root square errors in the three models for the Pinus massoniana plot were 54.79,48.16 and 65.63, with the random forest model showing the lowest value．It is concluded that the random forest model is the most optimal among all three models．