Forest height estimation and change monitoring based on artificial neural network using Geoscience Laser Altimeter System and Landsat data

Abstract

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 ( R 2) and root-mean-square error (RMSE) of evaluations from 2003 to 2006 ( R 2 = 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.