Biomass estimation of Shorea robusta with principal component analysis of satellite data  

卫星数据主成分分析估计娑罗双树生物量(英文)

作　　者：Nilanchal Patel Arnab Majumdar 

机构地区：[1]Birla Institute of Technology Mesra

出　　处：《Journal of Forestry Research》2010年第4期469-474,524,共7页林业研究（英文版）

摘　　要：Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.由于测定树木胸径、树高等参数较为困难,使得地上生物量的时空估计成为一项很困难的任务。本研究在印度兰契市的Birla技术学院进行,该校区长满娑罗双树。野外测定的地上生物量分别与遥感数据的个体条带、条带主成分、植被指数、植被指数主成分进行线性回归分析,以及地上生物量分别与这些参数进行多元线性回归分析,决定地上生物量与遥感参数之间关联性。线性回归分析表明,只有NDVI回归系数值在0.8以上,其他参数的回归系数值均较低。另外,多元线性回归方程计算得到的地上生物量与野外测定的值的相关系数在0.9以上,说明用多元线性回归法估计地上生物量具有更好的可靠性。多元回归分析中植被指数主成分与地上生物量之间的相关性系数是0.99。

关 键 词：above ground biomass spectral response modeling vegetation indices principal component analysis linear and multiple regression analysis. 

分 类 号：S792.99[农业科学—林木遗传育种]