The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period, even for Texas A&M users with NetID.
Spaceborne Lidar for Estimating Forest Biophysical Parameters
MetadataShow full item record
The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and while this mission primarily serves to capture ice topography measurements of the earth’s surface, it also offers a phenomenal opportunity to estimate biophysical forest parameters at multiple spatial scales. This study served to develop approaches for utilizing ICESat-2 data over vegetated areas. The main objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data and examine the use of simulated PCL metrics for modeling AGB and canopy cover, (2) create wall-to-wall AGB maps at 30-m spatial resolution and characterize AGB uncertainty by using simulated PCL-estimated AGB and predictor variables from Landsat data and derived products, and (3) investigate deep learning (DL) neural networks for producing an AGB product with ICESat-2, using simulated PCL-estimated AGB Landsat imagery, canopy cover and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using existing airborne lidar data and known ICESat-2 track locations for the first two years of the mission. Three scenarios were analyzed; 1) simulated data without the addition of noise, 2) processed simulated data for nighttime and 3) daytime scenarios. AGB model testing with no noise, nighttime and daytime scenarios resulted in R^2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Canopy cover (4.6 m) models achieved R^2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the no noise, nighttime and daytime scenarios respectively. Random Forest (RF) and deep neural network (DNN) models used with predicted AGB estimates and the mapped predictors exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha. Overall, findings from this study suggest the potential of ICESat-2 for estimating AGB and canopy cover and generating a wall-to-wall AGB product by adopting a combinatory approach with spectral metrics derived from Landsat optical imagery, canopy cover and land cover.
Narine, Lana Landra (2019). Spaceborne Lidar for Estimating Forest Biophysical Parameters. Doctoral dissertation, Texas A & M University. Available electronically from