大气校正对毛竹林生物量遥感估算的影响

基于LandsatTM影像对毛竹林生物量进行了估算,并利用6种大气校正方法(FLAASH、6S、DOS1～DOS4)分析了大气校正对毛竹林生物量遥感估算的影响.结果表明:6种大气校正模型均能有效地消除大气影响;不同大气校正模型校正后,归一化植被指数(NDVI)与毛竹林生物量之间的关系得到很好改善;对于同一种大气校正方法而言,NDVI、红外指数(II)和近红外指数(MI)与生物量之间关系的差异较大,说明在探讨植被指数的生物物理意义时必须进行大气校正;与其他5种模型相比,DOS3模型校正后的LandsatTM数据与毛竹林生物量之间具有最高的相关系数,但6种校正模型校正前后LandsatTM数据与毛竹林生物量之间的相关系数没有显著差异,说明采用单一时相遥感影像建立多元线性回归模型估算生物量时,可以不进行大气校正.

Effects of atmospheric calibration on remote sensing estimation of Moso bamboo forest biomass

Landsat Thematic Mapper (TM) image was used to estimate Moso bamboo forest biomass, a
nd six atmospheric calibration methods (FLAASH model, 6S model, and DOS1-4 models) were adopted to analysis the effects of atmospheric calibration on
 the remote sensing estimation of Moso bamboo forest biomass. All the six calibration m
ethods could effectively reduce the atmospheric impacts on TM spectral responses
. The relationships between NDVI and Moso bamboo forest biomass under the calibrati
on by the six calibration methods were improved. Great differences were observ
ed in the relationships of Moso bamboo forest biomass with NDVI, II , and MI when using
the same calibration methods, suggesting that atmospheric calibration should be
made for studying the biophysical significance of vegetation indices. The Landsa
t TM data corrected with DOS3 model had the highest correlation coefficient with
 Moso bamboo forest biomass, but there were no significant differences in the correlati
on coefficients after corrected with the six calibration methods, which indicate
d that atmospheric calibration might be not required if a single TM image was
used for biomass estimation with multiple linear regression model.