The continuous forest inventory is critical to forestry monitoring, Earth science applications, and global carbon cycle researching. Traditional field measurements of forest inventory are time-consuming. Targeting the problem, our study proposes an approach for monitoring the forest tree heights by combining geoscience laser altimeter system data with land surface reflectance data from Landsat TM based on artificial neural network (ANN) model in the Nanning research region. The land surface reflectance data are used as inputs of ANN model. The GLAS data are used to derive tree heights for training ANN model. Then the forest height spatial distribution and corresponding height change during 2003 to 2006 are investigated. The validation shows the high precision of modeling tree heights by, respectively, calculating determination coefficient (R2) and root-mean-square error (RMSE) of evaluations from 2003 to 2006 (R2 = 0.667, mean RMSE = 3.219 m). Finally, the change trends of forest tree heights in Nanning region are obtained by calculating the difference of tree heights from 2003 to 2006.
Forest canopy height is a very important forest structural attribute. LiDAR and SAR are able to penetrate the forest canopy to obtain information on the understory and canopy vertical structure. But the single data of LiDAR or SAR has its own shortcomings in forest height extraction. We jointly use LiDAR and ALOS PALSAR data to retrieve forest canopy height. First, the extinction degree of the canopy is extracted using airborne LiDAR. The canopy is assumed to be uniform, and the extinction degree is divided by the canopy height to obtain the average extinction coefficient. Then, the extinction coefficient is substituted into random volume over ground (RVoG), and the forest canopy height is obtained. Experimental results showed that the collaborative inversion algorithm based on RVoG model proposed in this paper improves the accuracy of forest canopy height retrieval.