基于不同立地质量的松树林分生物量遥感估测

Remote sensing estimation of pinestand biomass based on different site quality

基于2007年建德市森林资源二类调查数据和TM影像数据,采用蓄积量—生物量换算因子连续函数法计算松树生物量,对松树树种分立地质量等级和不分地位等级2种类型建立生物量的遥感估测模型,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0.6以上,最高0.773。(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92.51%,分立地质量等级好、中、差3种类型总体估测精度分别为97.66%、96.56%、97.32%,分不同立地质量等级建模精度明显优于统一建模的精度。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。

Based on the forest resource survey data of Jiande and TM image data in 2007, pine biomass was calculated by the expansion factor function method which was derived from the relationship between biomass and volumeto establish and evaluate the precise of the volume remote sensing estimation model, which is on pine trees with or without the discrete quality grades. Site quality grade according to the average height of the small class and the average age of the establishment of the status table is divided into good, medium and poor three types.Total volume of the sub-compartment is the dependent variable, andeach individual remote sensing contentis the independent variable.As the results showed：（1） the first principal component analysis of R2 Landsat TM image is the best, the coefficient of determination is 0.6, the highest is 0.773.（2） The reserved independent sample on the accuracy of the model is validated, not separate the overall level of quality estimation accuracy was 92.51%, discrete to the overall level of quality estimation accuracy was 97.66%, 96.56%, 97.32% respectively, the classification modeling precision is much better than the unified modeling accuracy. The research results provide an improved method for the estimation of forest volume, and provide a reference for improving the accuracy of forest biomass and carbon storage estimation.