Estimation of canopy height based on multi-source remote sensing data using forest structure aided sample selection

Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps.