Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 10; no. 12; pp. 5569 - 5582
• Main Authors: Shao, Zhenfeng, Zhang, Linjing, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Back propagation networks; Biomass; Biomedical optical imaging; Computer simulation; Data; deep learning (DL); Density stratification; Detection; Forest biomass; Forest growth; Forest management; Forests; Image detection; Landsat; Landsat 8; Landsat satellites; Laser radar; Lidar; light detection and ranging (LiDAR); Machine learning; Mapping; Mathematical models; Model accuracy; Modelling; Neural networks; Optical imaging; Optical saturation; Policies; Prediction models; Reflectance; Remote sensing; SAR (radar); Satellite communication; Satellite imagery; Satellites; Sentinel-1A; stacked sparse autoencoder network (SSAE); Stock assessment; Stratification; Workflow

Timely, spatially complete, and reliable forest above-ground biomass (AGB) data are a prerequisite to support forest management and policy formulation. Traditionally, forest AGB is spatially estimated by integrating satellite images, in particular, optical data, with field plots from forest inventor...