LAUSR

Recently, remote sensing (RS) technology are becoming an increasingly important technology in estimating forest growing stem volume (GSV), and the saturation issue of spectral variables from various optical sensors severely hinders the improvement of mapping forest GSV, especially in the planted forest with high GSV. Forest canopy height is widely considered as one of the major factors to increase the saturation levels in mapping GSV. However, it is rather difficult to invert the forest canopy height without precisely external DEM for large regions. In this study, the canopy height model (CHM) was derived from ZY-3 stereo images with subtracting open-sourced external DEM and the response of saturation levels was analyzed by adding forest height in the planted coniferous forest (Larch and Chinese pine). To further describe the relationships between the forest height and saturation levels, five datasets with five estimation models (Linear, MLR, SVR, KNN, and RF) and three methods of variable selection (Stepwise, LASSO, and Pearson) were applied to estimate the forest GSV using corrected CHM and 49 alternative variables extracted from ZY-3 multispectral images. Meanwhile, a spherical model was employed to quantitatively describe the saturation levels of combined variables. The results showed that values of relative root means square error were decreased from 29.3% to 25% for Larch and from 26.5% to 22.2% for Chinese pine after adding the corrected CHM, respectively. Meanwhile, the saturation level of each combined variable set was successfully determined by the spherical model. The results illustrated that the saturation levels of GSV were significantly increased by adding corrected CHM from open-sourced external DEM. Specially, the averaged saturation levels were increased from 220 m3/ha to nearly 300 m3/ha for Chinese pine and from 150 m3/ha to 220 m3/ha for Larch, respectively. It is proved that ZY-3 stereo and multispectral images have great potential for accurate estimation of forest GSV by delaying the saturation levels using extracted CHM with open-sourced external DEM.