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The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

Temilola Fatoyinbo, John Armston, Marc Simard, Sassan Saatchi, Michael Denbina, Marco Lavalle, Michelle Hofton, Hao Tang, Suzanne Marselis, Naiara Pinto, Steven Hancock, Brian Hawkins (+19 others)
2021 Remote Sensing of Environment  
and support of the AfriSAR airborne and field campaigns in Gabon.  ...  We would also like to thank our collaborators from ESA, DLR, ONERA and CNES for the invitation to participate in the campaign.  ...  Here our results show that lidar waveforms and L-band radar tomograms have similar overall responses over forest canopies even though they are based on measurements at different wavelength and thus different  ... 
doi:10.1016/j.rse.2021.112533 fatcat:zjjfcgditratzbqyzzpjdaddzy

Modeling carbon-water-vegetation dynamics using remote sensing and climate data

Nasreen Jahan
Carbon and water fluxes are essential components of biospheric processes which directly or indirectly influence climate, surface energy balance, hydrologic processes and hence influence the vegetation  ...  productivity, distribution and characteristics.  ...  The Howland forest west tower is located at 775 m distant from the Howland forest main tower.  ... 
doi:10.7939/r3260j fatcat:obculbimv5ad5htn7udjackvn4

Requirements of a habitat specialist in Swiss mountain forests – an assessment of forest structure and composition using laser remote sensing and field data

Florian Zellweger, Felix Morsdorf, Veronika Braunisch, Kurt Bollmann
This research investigates the estimation of forest structural variables from small-footprint airborne LiDAR capturing both discrete return (DR) and full waveform (FW) data.  ...  To study the habitat requirements of hazel grouse (Bonasa bonasia), an indicator species of structurally rich forest stands, we assessed the structure and composition of Swiss mountain forests over three  ...  This study compared the accuracy of linear regression, GWR, gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons  ... 
doi:10.5167/uzh-77306 fatcat:gtpnt3jpdfcjpcdlxw6sxjei44