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应用生态学报 ›› 2010, Vol. 21 ›› Issue (01): 1-8.

• 研究报告 •    下一篇

大气校正对毛竹林生物量遥感估算的影响

范渭亮|杜华强|周国模**|徐小军|崔瑞蕊|董德进   

  1. 浙江林学院环境科技学院|浙江临安 311300
  • 出版日期:2010-01-20 发布日期:2010-01-20

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

FAN Wei-liang, DU Hua-qiang, ZHOU Guo-mo, XU Xiao-jun, CUI Rui-rui, DONG De-jin   

  1. School of Environmental Science and Technology|Zhejiang Forestry University, Lin’an 311300, Zhejiang, China
  • Online:2010-01-20 Published:2010-01-20

摘要: 基于Landsat TM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Landsat TM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Landsat TM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.

关键词: 大气校正, 生物量, 遥感估算, 植被指数, 地表温度, MODIS, 土地利用类型, 天山区域

Abstract: Landsat Thematic Mapper (TM) image was used to estimate Moso bamboo forest biomass, and 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 methods could effectively reduce the atmospheric impacts on TM spectral responses. The relationships between NDVI and Moso bamboo forest biomass under the calibration by the six calibration methods were improved. Great differences were observed 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 Landsat TM data corrected with DOS3 model had the highest correlation coefficient with Moso bamboo forest biomass, but there were no significant differences in the correlation coefficients after corrected with the six calibration methods, which indicated that atmospheric calibration might be not required if a single TM image was used for biomass estimation with multiple linear regression model.

Key words: atmospheric calibration, biomass, remote sensing estimation, vegetation index, land surface temperature (LST), MODIS, land use type, Tianshan Mountains area.