基于不同立地质量的杉木生物量遥感估测

摘要

[目的]研究不同立地质量对杉木生物量遥感估测精度的影响，为进一步提高和完善森林生物量遥感监测体系提供一种新的思路和方法。[方法]以2007年建德市森林资源二类调查数据和 TM影像为研究材料，采用蓄积量—生物量换算因子连续函数法计算杉木林生物量和地位级法评价立地质量等级，比较杉木立地质量好、中等、差和不分地位等级4种生物量遥感估测模型，并进行精度检验。[结果]表明：（1）以 TM遥感影像主成分分析中第一主成分为自变量的模型拟合效果最好，决定系数 R2均在0．69以上，最高0．855。（2）利用预留独立样本对模型精度进行验证，不分地位级总体估测精度为87．78％，分立地质量等级好、中、差3种类型总体估测精度分别为97．37％、95．82％、98．23％。分不同立地质量类型可以提高杉木生物量遥感估测精度。[结论]研究结果为森林生物量遥感估测提供一种改进的思路，且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。%Objective]To understand the influence of site quality in the remote sensing Chinese fir biomass estima-tion.[Method]Based on the forest resource management inventory data and TM image of Jiande city obtained in 2007,the biomass of Chinese fir was calculated by forest volume-biomass conversion factor continuous function method and the site quality was evaluated by site class method.Four biomass estimation models for different site classes (good,moderate,poor and no ranking)were compared and the accuracy of them was tested.[Result](1)The per-formance of regression model based on the first principal component analysis of TMremote sensing image is the best, the determination coefficients R2 is higher than 0.69 and the maximum is 0.855.(2)Verifying the model accuracy by reserved independent samples,the whole model accuracy without site class is 87.78% and the accuracies of good, moderate,poor site quality models are respectively 97.37%,95.82%,and 98.23%.[Conclusion]Distinguishing different site qualities could improve remote sensing estimation precision of Chinese fir biomass.The research results provide with an improved method for the remote sensing estimation of forest biomass,and a reference for improving the remote sensing estimation accuracy of forest biomass and carbon storage.