Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index
Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pu...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | https://hdl.handle.net/1721.1/125544 |
| _version_ | 1811083953473323008 |
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| author | Yin, Tiangang Qi, Jianbo Cook, Bruce D. Morton, Douglas C. Wei, Shanshan Gastellu-Etchegorry, Jean-Philippe |
| author2 | Singapore-MIT Alliance in Research and Technology (SMART) |
| author_facet | Singapore-MIT Alliance in Research and Technology (SMART) Yin, Tiangang Qi, Jianbo Cook, Bruce D. Morton, Douglas C. Wei, Shanshan Gastellu-Etchegorry, Jean-Philippe |
| author_sort | Yin, Tiangang |
| collection | MIT |
| description | Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( Pgap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact Pgap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate Pgap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate Pgap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors. Keywords: radiative transfer model; Lidar; airborne laser scan; point cloud; reflectance; leaf area index; gap probability; clumping; Gaussian decomposition; waveform |
| first_indexed | 2024-09-23T12:42:15Z |
| format | Article |
| id | mit-1721.1/125544 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:42:15Z |
| publishDate | 2020 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1255442022-09-28T09:32:59Z Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index Yin, Tiangang Qi, Jianbo Cook, Bruce D. Morton, Douglas C. Wei, Shanshan Gastellu-Etchegorry, Jean-Philippe Singapore-MIT Alliance in Research and Technology (SMART) Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( Pgap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact Pgap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate Pgap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate Pgap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors. Keywords: radiative transfer model; Lidar; airborne laser scan; point cloud; reflectance; leaf area index; gap probability; clumping; Gaussian decomposition; waveform 2020-05-28T14:54:06Z 2020-05-28T14:54:06Z 2019-12-18 2019-11 2020-03-02T13:00:03Z Article http://purl.org/eprint/type/JournalArticle 2072-4292 https://hdl.handle.net/1721.1/125544 Yin, Tiangang, et al., "Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index." Remote Sensing 12, 1 (Dec. 2019): no. 4 doi 10.3390/rs12010004 ©2019 Author(s) 10.3390/rs12010004 Remote Sensing Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Yin, Tiangang Qi, Jianbo Cook, Bruce D. Morton, Douglas C. Wei, Shanshan Gastellu-Etchegorry, Jean-Philippe Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title | Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title_full | Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title_fullStr | Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title_full_unstemmed | Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title_short | Modeling small-footprint airborne lidar-derived estimates of gap probability and leaf area index |
| title_sort | modeling small footprint airborne lidar derived estimates of gap probability and leaf area index |
| url | https://hdl.handle.net/1721.1/125544 |
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