Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data
- Publication Type:
- Journal Article
- Citation:
- Remote Sensing of Environment, 2011, 115 (9), pp. 2330 - 2341
- Issue Date:
- 2011-09-15
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2010004013OK.pdf | 2.74 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
In this study, a combination of low and high density airborne LiDAR and satellite SPOT-5 HRG data were used in conjunction with ground measurements of forest structure to parameterize four models for zero-plane displacement height d(m) and aerodynamic roughness length z0m(m), over cool-temperate forests in Heihe River basin, an arid region of Northwest China. For the whole study area, forest structural parameters including tree height (Ht) (m), first branch height (FBH) (m), crown width (CW) (m) and stand density (SD)(treesha-1) were derived by stepwise multiple linear regressions of ground-based forest measurements and height quantiles and fractional canopy cover (fc) derived from the low density LiDAR data. The high density LiDAR data, which covered a much smaller area than the low density LiDAR data, were used to relate SPOT-5's reflectance to the effective plant area index (PAIe) of the forest. This was done by linear spectrum decomposition and Li-Strahler geometric-optical models. The result of the SPOT-5 spectrum decomposition was applied to the whole area to calculate PAIe (and leaf area index LAI). Then, four roughness models were applied to the study area with these vegetation data derived from the LiDAR and SPOT-5 as input. For validation, measurements at an eddy covariance site in the study area were used. Finally, the four models were compared by plotting histograms of the accumulative distribution of modeled d and z0m in the study area. The results showed that the model using by frontal area index (FAI) produced best d estimate, and the model using both LAI and FAI generated the best z0m. Furthermore, all models performed much better when the representative tree height was Lorey's mean height instead of using an arithmetic mean. © 2011 Elsevier Inc.
Please use this identifier to cite or link to this item: