Integrating terrestrial and airborne lidar to calibrate a 3D canopy model of effective leaf area index Chris Hopkinson a,e, ⁎, Jenny Lovell b , Laura Chasmer c , David Jupp a , Natascha Kljun d , Eva van Gorsel a a CSIRO Marine and Atmospheric Research, Pye Laboratory, Clunies Ross St, Canberra, ACT, Australia b CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart 7001, Tas, Australia c Department of Geography, Wilfrid Laurier University, Waterloo, Ontario, Canada d Department of Geography, College of Science, Swansea University, UK e Department of Geography, University of Lethbridge, Lethbridge, Alberta, Canada abstract article info Article history: Received 8 January 2013 Received in revised form 9 May 2013 Accepted 9 May 2013 Available online xxxx Keywords: Lidar Leaf area index LAI Canopy structure Airborne/terrestrial laser scanning Echidna Point cloud Percentile distribution Terrestrial laser scanning (TLS) with the Echidna Validation Instrument (EVI) provides an effective and accurate method for calibrating multiple-return airborne laser scanning (ALS) point cloud distributions to map effective leaf area index (LAIe) and foliage profile within a 1-km diameter test site of mature eucalyptus forest at the Tumbarumba research site, New South Wales, Australia. Plot-based TLS foliage profiles are used as training datasets for the derivation of a scaling function applied to calibrate effective leaf area index (LAIe) from a coincident ALS point cloud. The results of this study show that: a) the mean proportion of the total number of returns within 11.3 m radius of the TLS scan station was 64%. Increasing the radius decreased the level of detail due to occlusion; b) the relationship between TLS LAIe profile and ALS foliage percentile distribution (PD) using all, primary and sec- ondary returns are not linearly related; and c) regressions between TLS LAIe profile and ALS PD, demonstrate better correspondence using a 5th order polynomial applied to all returns (r 2 = 0.95; SE = 0.09 m 2 m -2 ) than aquasi- physically-based Weibull scaling function. The calibration routine was applied to ALS data within a GIS environment to create a 500 m radius 3D map of LAIe. This localised 3D calibration of LAIe was then used as the basis to calculate the overhead canopy extinction coefficient parameter (k), and thereby facilitate upscaling of spatial LAIe estimates to larger domains using a Beer Lambert Law assumption. © 2013 Elsevier Inc. All rights reserved. 1. Introduction 1.1. Leaf area index (LAI) The spatial distribution of foliage within a forest canopy controls light and energy transfer between the sky and the ground (Chen et al., 1999; Loranty et al., 2010; Oke, 1996; Traver et al., 2010), the interception of precipitation (Whitehead & Kelliher, 1991; Wilson et al., 2001) and aerosols (Wedding et al., 1975), rates and magnitudes of photosynthesis (Amthor et al., 1990; Chasmer et al., 2008) and evapotranspiration (Blanken et al., 1997; Brümmer et al., 2012; Engel et al., 2002; Ge et al., 2011), atmospheric flux footprint density and extent (Kljun et al., 2002, 2004; McAneney et al., 1994), as well as animal habitat and foraging pathways (DeWalt et al., 2003; Goetz et al., 2010). Consequently, param- eters describing leaf properties and canopy structure are necessary inputs to eco-physical models used to simulate mass and energy fluxes throughout forest environments (Davi et al., 2006; Kobayashi et al., 2012; Kowalczyk et al., 2006; Richardson et al., 2012). Recent comparisons between land surface model (LSM) results and ob- served CO 2 flux (net ecosystem exchange, NEE) at a range of FluxNet sites indicate that models often misrepresent the variability of CO 2 fluxes over short time scales (Schwalm et al., 2010). Phenological processes frequently related to C exchange are most often estimated from remote sensing methods. This has been identified as an area where improvements are needed in model input data (Richardson et al., 2012), and because phenology is inherently related to the amount of photosynthesizing biomass, this requires accurate esti- mates of leaf area. Leaf area index (LAI) is typically defined as the vertically integrated one sided area of leaf or needle cover per unit ground surface area on a horizontal plane (e.g. Chen et al., 2006; Gower et al., 1999; Watson, 1947). LAI is expressed in units of m 2 m -2 and is traditionally measured through destructive sampling. LAI cannot easily be measured directly through non-destructive means, so another metric, effective leaf area index (LAIe), is more commonly measured in the field and either cali- brated to true LAI (e.g. Chen et al., 2006) or used directly in model simu- lations (e.g. Ives et al., 2011, who use LAIe; and Coops et al., 2012; Schwalm et al., 2010 who use LAI). LAIe is analogous to LAI but does not differentiate between woody or leafy foliage components or account for variations in apparent leaf area due to leaf, branch and shoot Remote Sensing of Environment 136 (2013) 301–314 ⁎ Corresponding author at: Department of Geography, University of Lethbridge, Lethbridge, Alberta, Canada. Tel.:+1 902 840 1164. E-mail address: c.hopkinson@uleth.ca (C. Hopkinson). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.05.012 Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse