| Title | Retrieving crown leaf area index from an individual tree using ground-based lidar data |
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| Author | Moorthy, I; Miller, J R; Hu, B; Chen, J; Li, Q |
| Source | Canadian Journal of Remote Sensing vol. 34, no. 3, 2008 p. 320-332, https://doi.org/10.5589/m08-027 |
| Year | 2008 |
| Alt Series | Natural Resources Canada, Contribution Series 20181530 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing |
| Program | Canada Centre for Remote Sensing Divsion |
| Abstract | Light detection and ranging (lidar) sensors, both at the terrestrial and airborne levels, have recently emerged as useful tools for three-dimensional (3D) reconstruction of vegetated environments. One
such terrestrial laser scanner (TLS) is the Intelligent Laser Ranging and Imaging System (ILRIS-3D). The objective of this research was to develop approaches to use ILRIS-3D data to retrieve structural information of an artificial tree in a
controlled laboratory experiment. The key crown-level structural parameters investigated in this study were gap fraction, leaf area index (LAI), and clumping index. Measured XYZ point cloud data from a systematically pruned tree were sliced to
retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction, LAI, and clumping index. Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2 =
0.95). LAI estimates from lidar data were corrected for the confounding effects of woody material and nonrandom foliage distribution and then compared with direct LAI measurements (r2 = 0.98, RMSE = 0.26). The methods developed in this research
provide valuable lessons for application to field-level TLS data for structural parameter retrievals. Successful demonstration of analysis protocols to extract crown-level structural parameters like gap fraction, LAI, and clumping index from TLS data
will be important for detailed assessments of 3D canopy radiative transfer modeling and likely will lead to more robust inversion algorithms. |
| GEOSCAN ID | 311885 |
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