摘要
基于香格里拉市2006年TM影像、森林资源二类调查数据、外业调查数据,利用随机点所在小班的遥感因子平均值建立数据集,通过数据筛选和相关性分析,选出123个样地数据及14个遥感因子,建立了基于遥感因子的高山松生物量估测的非线性和线性模型,并讨论了模型精度及其预测精度。研究结果表明,线性模型(R2adj=0.406、RMSE=34.18 t/hm^2、rRMSE=38.54%)比非线性模型的精度(R_(adj)~2=0.286、RMSE=37.79 t/hm^2、rRMSE=42.60%)高;运用交叉验证法得到的线性模型的预测精度(RMSE’=35.12 t/hm^2、rRMSE’=39.59%)也要高于非线性模型的预测精度(RMSE’=38.44 t/hm^2、rRMSE’=43.34%)。与其它2个同类研究相比,重建的高山松生物量模型虽然在模型精度上还略有欠缺,但是建模数据更为随机、合理,对因子提取方法进行了一定改进,建立的模型为高海拔地区利用遥感数据和森林资源二类调查数据估测典型乔木森林生物量提供参考,为地形起伏较大区域进行森林生物量估测提供了较为完整的技术方法。
This paper took the TM images of Shangri - La City in 2006 2006 and field survey data as the data source, and built datasets by ran compartment＇ s mean values based on remote sensing indexes. In this , forest resource inventory domly points, extracted fro data in m study, 123 sampling points sub- were selected by eliminating the abnormal values, and 14 indexes were collected as the alternative variables through correlation analysis. Finally, a nolinear model and a linear model for estimating Pinus densatabiomass were established, and the model precision and prediction accuracy was also evaluated. The re- suits showed that the linear model precision （Radj2 = 0. 406, RMSE = 34. 18 t ·hm2, rRMSE -38.54% ） was better than the nolinear（ Radj2 = 0. 286, RMSE = 37.79 t · hm2, rRMSE = 42.60% ） , and prediction accuracy of the linear model （ RMSE＇ = 35.12 t · hm2, rRMSE＇ = 39.59% ） , which calculated using cross - validation method, was higher than the nolinear（ RMSE＇ = 38.44 t · hm2 , rRMSE＇ = 43.34% ）. Comparing with other two similar research, this model precision was slightly lower than the other two, but the modeling data source was much more random and reasonable, the extracting indexes has also been im- proved. The model would provide reference for other research using remote sensing images to estimate for- est biomass of typical arbor in high elevation region. At the same time, it would provide relatively com- plete technical methods to estimate forest biomass for the larger terrain area.
出处
《林业调查规划》
2016年第6期1-7,共7页
Forest Inventory and Planning
基金
西南林业大学云南省省级重点学科(林学)
西南林业大学林学一级学科中青年后备人才培养计划(5009750101-1)
云南省教育厅重点项目(2015Z143)
昆明市林业信息工程技术研究中心建设项目
