基于Landsat 8 OLI的昆明市主要森林类型生物量遥感估测与反演

基于昆明市2016年森林资源规划设计调查数据与Landsat 8 OLI遥感影像对昆明市12种优势树种分别构建多元线性回归模型、BP神经网络模型和随机森林模型，并选择最优模型对昆明市12种森林类型进行地上生物量反演。结果表明： 3种模型中，随机森林模型有着最好的估测效果，且其中杉木林的模型拟合精度最高为0.683，RMSE为12.68 t/hm2；线性逐步回归模型的拟合精度最低；当AGB小于50 t/hm2和大于100 t/hm2时，3个模型均分别出现不同程度的低值高估和高值低估，但随机森林模型的平均残差值的绝对值较低，在不同生物量段的估测误差相对较低；利用随机森林模型反演研究区森林AGB，反演精度为85.31%，该模型可以较好地反演昆明市森林地上生物量。

Remote Sensing Estimation and Inversion of Biomass for Major Forest Types in Kunming Based on Landsat 8 OLI

This study extracted the distribution data of 12 dominant tree species from the forest management inventory data of Kunming, and the remote sensing variables from Landsat 8 OLI remote sensing images in 2016. Then, liner stepwise regression model(LSR), back propagation neural network model(BPNN), and random forest model(RF) were used to estimate the forest aboveground biomass(AGB), then selected the optimal model for each forest type to have the AGB inversion in Kunming. The results showed that the RF has the highest accuracy in 3 models, which the adjusted determination coefficient() is 0.683 and root mean square error(RMSE) is 12.68 t/hm2 in the Chinese fir forests. And the fitting accuracy of LSR is lowest. When the AGB lower than 50 t/hm2 and larger than 100 t/hm2, the overestimation and underestimation can be found in all 3 models, but the RF often has the lower absolute mean error values. Moreover, it often has the lowest estimation errors in the different biomass segments. The inversion precision of random forest model is 85.31%. The RF would provide a good tool for invert the forest AGB in Kunming.