基于多源遥感数据的森林生物量估算模型研究

Study on forest biomass estimation model based on multisource remote sensing data

探讨了利用光学遥感图像HJ1B和多极化L波段微波遥感数据ALOS/PALSAR建立森林生物量估算模型的方法,利用统计回归方法建立了4种模型:1利用雷达图像ALOS/PALSAR后向散射系数和实测生物量建立的生物量回归模型;2利用多光谱图像HJ1B进行混合像元分解(spectral mixture analysis,SMA)后的组分图像与雷达图像ALOS/PALSAR进行图像融合建立的生物量回归模型;3利用多光谱图像HJ1B进行混合像元分解后的组分图像与实测生物量建立的生物量回归模型;4利用HJ1B图像的NDVI指数与实测生物量建立的生物量回归模型。对4种模型估算的生物量进行了对比分析。结果表明:第2种方法融合后图像与森林地上生物量之间存在较好的定量关系,估算生物量与实测生物量一致性较好,估算生物量精度优于其他模型结果。利用光学和微波图像协同遥感能够有效地提高森林生物量估算的精度,但并非所有的融合方法都能提高生物量估算的精度。利用雷达和光学图混合像元分解法进行植被生态系统监测研究具有一定的应用潜力。

The development of forest above - ground biomass （AGB） estimation model by using muhispectral HJ1B and multi - polarization L band ALOS/PALSAR remote sensing data is discussed, based on which, four estimation models were established by statistical regression method, including the model to estimate the biomass by using radar image ALOS/PALSAR baekscattering coefficients and measured biomass, by fusing the fraction image from HJ1B spectral mixture analysis and ALOS/PALSAR data, by using the fraction image from H J1 B spectral mixture analysis and field -measured data and by NDVI index derived from H J1B images and field - measured biomass. The estimated results of the four models were compared. It showed that the image fusion of the fraction image and ALOS/PALSAR data is quantitatively related with filed - measured biomass. A good fit could be found between the estimated AGB and ground - measured biomass with a R2 （ Coefficient of Determination） and RMSE （ Root Mean - Square Error） of 0.60 and 10.45 t/hm2 , respectively and its estimated accuracy is superior to that of other models. Integrating the optical and microwave sensing remote images can effectively improve the accuracy of biomass estimation, but not all fused images can significantly improve the accuracy. Consequently, it demonstrated a good application potential for monitoring vegetation ecosystem by the use of radar image and spectral mixture analysis of optical image.