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

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...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 10; H. 12; S. 5569 - 5582
Hauptverfasser:	Shao, Zhenfeng, Zhang, Linjing, Wang, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.12.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Back propagation networks Biomass Biomedical optical imaging Computer simulation Data Deep learning 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
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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