基于随机森林算法的橡胶林地上生物量遥感反演研究--以景洪市为例

Counter-Estimation on Aboveground Biomass of Hevea brasiliensis Plantation by Remote Sensing with Random Forest Algorithm——A Case Study of Jinghong

以景洪市橡胶林为研究对象,将获得的样地植被指数和生物量数据用随机森林方法建立相关关系,将建立的关系用Landsat TM影像反演出整个研究区域的生物量分布,用影像和实地样地调查数据进行分析和验证,实现光学遥感的大范围生物量反演。在反演过程中将植被指数作为自变量,使用R语言环境下的随机森林方法进行变量筛选和建模,并对该方法的适用性进行分析评价。结果表明:随机森林算法适用于森林生物量反演;选择的变量为可见光大气阻抗植被指数(VARI)、简单比值指数(RVI)、归一化植被指数(NDVI)、水分胁迫指数(MSI)、中红外指数(MidIR);总体模型反演精度R2=0.43,RMSE=46.05。反演结果对于生物量密度较低的区域回归效果较好,对于生物量超过200 t/hm2的地区,反演结果偏低,且随着生物量密度的增加,反演结果偏差逐渐增大。

The correlations between the vegetation indexs and biomass data obtained from Hevea brasiliensis planta-tion in Jinghong Municipality were established by means of random forest algorithm.The biomass distribution throughout the study area was counter-estimated with Landsat TMimage based on the correlations,and the counter-estimation of bi-omass by optical remote sensing in a wider range was realized through analyses and validation by the Landsat TMimage data and the field survey data of the sample plots.The vegetation indexes were taken as the independent variables in the counter estimation process,The random forest multiple regression method was used to select variables and to model under R language environment,and the applicability of this method was analyzed and evaluated.The results showed that the random forest algorithm was appropriate to be applied for forest biomass estimation.The variables selected were VARI, RVI,NDVI,MSI,MidIR.The overall precision of the counter estimation of the model was that R2 value was 0.43,and the value of RMSE was 46.05.The counter estimation result for the area with lower biomass density was better.Whereas the counter estimation result for the area with over 200 t/hm2 biomass would be lower than the actual figure.And the de-viation of counter estimation would increase gradually along with the increase of biomass density.