Name
Abstract | ||
|---|---|---|
Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45 degrees interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12182909 | REMOTE SENSING |
Keywords | DocType | Volume |
waveform lidar,lidar,radiative transfer model,forest remote sensing,leaf area index (LAI),bidirectional reflectance distribution function (BRDF),bidirectional scattering distribution function (BSDF),leaf optical properties | Journal | 12 |
Issue | Citations | PageRank |
18 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin D. Roth | 1 | 0 | 0.34 |
| Adam Goodenough | 2 | 3 | 1.58 |
| Scott Brown | 3 | 12 | 3.25 |
| jan van aardt | 4 | 33 | 10.55 |
| Michael Grady Saunders | 5 | 0 | 0.34 |
| Keith Krause | 6 | 0 | 0.34 |