Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR |
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| Institution: | 1. Department of Geographical Sciences, University of Maryland, College Park, MD, USA;2. School of Environment and Technology, University of Brighton, United Kingdom;3. Bay Area Environmental Research Institute (BAERI), West Sonoma, CA, USA;4. NASA Ames Research Center, Moffett Field, CA, USA;1. Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis, One Shields Avenue, 139 Veihmeyer Hall, Davis, CA 95616, USA;2. Faculty of Forest Sciences, University of Talca, 2 Norte # 685, Talca, Chile;3. Department of Geology, Geography and Environment, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain;4. Institute of Economics, Geography and Demography, Spanish National Research Council (CSIC), Albasanz 26–28, 28037 Madrid, Spain;5. Associated Research Unit GEOLAB, University of Alcalá - Spanish National Research Council (CSIC), Alcalá de Henares, 28801/ Albasanz 26-28, 28037, Madrid, Spain;6. School of Environment and Natural Resources, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 44691, USA |
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| Abstract: | As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R2 = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding. |
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| Keywords: | Leaf area index (LAI) Leaf area density (LAD) profile Effective LAI (LAIe) Unified cumulative LAI (ucLAI) Light detection and ranging (LiDAR) Static terrestrial laser scanning (TLS) |
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