引入地形因子的高山松地上生物量动态估测  

Estimation of aboveground biomass dynamics of Pinus densata by considering topographic factors

作　　者：廖易 张加龙 鲍瑞 许冬凡 王书贤 韩冬阳 LIAO Yi;ZHANG Jialong;BAO Rui;XU Dongfan;WANG Shuxian;HAN Dongyang(Faculty of Forestry,Southwest Forestry University,Kunming 650224,Yunnan,China)

机构地区：[1]西南林业大学林学院,昆明650224

出　　处：《生态学杂志》2023年第5期1243-1252,共10页Chinese Journal of Ecology

基　　金：国家自然科学基金(31860207);2020年云南省高层次人才培养支持计划“青年拔尖人才”专项(81210468);西南林业大学科研启动基金(111932)资助。

摘　　要：构建基于遥感的香格里拉市高山松地上生物量变化估测模型,对比引入地形因子前后模型的估测精度。利用1987-2017年国家森林资源连续清查样地和对应年份的Landsat TM/OLI影像提取遥感因子,计算遥感因子与对应样地地上生物量的变化率,采用多元线性回归(MLR)、随机森林(RF)及梯度提升回归树(GBRT)建模,在3种模型中分别引入海拔、坡度和坡向因子并对比其模型效果。3种建模方法的变化率模型拟合结果R2分别为0.468、0.946和0.887,RMSE分别为2.431、0.692和1.027 t·hm^(-2)·a^(-1);预测效果rRMSE分别为56.66%、33.17%和35.30%,预测精度分别为44.31%、78.77%和70.95%。加入地形因子后,3种模型精度指标均有所提升,RF和GBRT模型的置信区间变窄;结合坡向因子的RF模型效果最优,其R2为0.976,提升3.17%;RMSE为0.502 t·hm^(-2)·a^(-1),降低27.46%;rRMSE为31.50%,降低5.05%;预测精度为82.07%,提升4.18%。在基于RF的变化率模型中引入地形因子能够提高模型的精度,坡向因子对模型精度的提升效果最佳,可为香格里拉市高山松地上生物量精确估测提供参考。Estimation models of aboveground biomass change of Pinus densata in Shangri-La were established based on remote sensing.The estimation accuracy of the models with or without terrain factors being considered was compared.Based on the continuous inventory plots and the Landsat TM/OLI images during 1987 to 2017,the remote sensing factors were extracted,and the change rates of those remote sensing factors and aboveground biomass of the corresponding plots were calculated.Multiple linear regression(MLR),random forest(RF)and gradient boosting regression tree(GBRT)were then used in the modeling.Finally,the factors of elevation,slope and aspect were added to these three models and their modeling effects were compared.The change rate models constructed with the three methods varied a lot,with the fitted results of R^(2) being 0.457,0.946 and 0.887,the RMSE being 2.431,0.692 and 1.027 t·hm^(-2)·a^(-1),the rRMSE of predicted effect being 56.66%,33.17%and 35.30%,and the precision being 44.31%,78.77%and 70.95%,respectively.The accuracy of the three models was improved,and the confidence intervals of RF and GBRT were narrowed after adding terrain factors.In all models,the result of RF model with slope aspect factor was best,the R^(2) was 0.976(with an enhancement of 3.1%),the RMSE was 0.502 t·hm^(-2)·a^(-1)(a reduction of 27.46%),the rRMSE was 31.50%(a reduction of 5.05%),the precision was 82.07%,(an increase of 4.18%).Adding terrain factor into the change rate model of RF can improve the accuracy of the model,and the aspect factor has the best performance on improving the model accuracy.Our results can provide a useful basis for accurate estimation of aboveground biomass of Pinus densata in Shangri-La.

关 键 词：LANDSAT 随机森林 梯度提升回归树 变化率 地形因子 地上生物量 

分 类 号：S718.5[农业科学—林学]