Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 11; H. 12; S. 1459
Hauptverfasser:	Zhang, Linjing, Shao, Zhenfeng, Liu, Jianchen, Cheng, Qimin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 20.06.2019
Schlagworte:	aboveground biomass Algorithms Artificial intelligence bioenergy Biomass canopy Carbon carbon sinks Cloud cover combined optical and LiDAR indices Deep learning Estimation forest aboveground biomass (AGB) Forest biomass Forest management forests issues and policy Laboratories Landsat Landsat 8 OLI Landsat satellites LiDAR Machine learning mathematical models Model accuracy Modelling Parameter estimation Prediction models regression analysis Regression models Remote sensing Renewable energy Root-mean-square errors Satellite imagery spatial data Spectra spectral analysis Stacked Sparse Autoencoder network (SSAE) Studies Support vector machines Sustainability management Sustainable forestry synergy Vegetation vegetation index Workflow China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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