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Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling

Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture (USP/ESALQ), University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, Brazil
Department of Forestry, Michigan State University, East Lansing, MI 48824, USA
National Institute for Amazon Research (INPA), Av. André Araújo, 2936, Manaus 69067-375, Brazil
US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843, USA
Department of Forestry, Federal University of Vales do Jequitinhonha e Mucuri—Campus JK, Rodovia MGT 367—Km 583, n° 5000, Teófilo Otoni CEP 69900-056, Brazil
Department of Plant Sciences, Forest Ecology and Conservation, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu, Finland
Embrapa Acre, Rodovia BR-364, km 14, Rio Branco CEP 69900-056, Brazil
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 92;
Received: 9 December 2018 / Revised: 2 January 2019 / Accepted: 3 January 2019 / Published: 7 January 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m−2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems. View Full-Text
Keywords: LAI; LAD; leaf area index; canopy; Beer–Lambert law LAI; LAD; leaf area index; canopy; Beer–Lambert law
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Almeida, D.R.A.; Stark, S.C.; Shao, G.; Schietti, J.; Nelson, B.W.; Silva, C.A.; Gorgens, E.B.; Valbuena, R.; Papa, D.A.; Brancalion, P.H.S. Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling. Remote Sens. 2019, 11, 92.

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