Abstract
In this analysis, a method for construction of forest canopy three-dimensional (3D) models from terrestrial LiDAR was used for assessing the influence of structural changes on reflectance for an even-aged forest in Belgium. The necessary data were extracted by the developed method, as well as it was registered the adjacent point-clouds, and the canopy elements were classified. Based on a voxelized approach, leaf area index (LAI) and the vertical distribution of leaf area density (LAD) of the forest canopy were derived. Canopy–radiation interactions were simulated in a ray tracing environment, giving suitable illumination properties and optical attributes of the different canopy elements. Canopy structure was modified in terms of LAI and LAD for hyperspectral measurements. It was found that the effect of a 10% increase in LAI on NIR reflectance can be equal to change caused by translating 50% of leaf area from top to lower layers. As presented, changes in structure did affect vegetation indices associated with LAI and chlorophyll content. Overall, the work demonstrated the ability of terrestrial LiDAR for detailed canopy assessments and revealed the high complexity of the relationship between vertical LAD and reflectance.
Highlights
Remote sensing technologies are extensively used in forestry for mapping physical-structural features of the land and in forest surveys
The difference can be attributable to the approach used and the occlusion effect since Terrestrial laser scanning (TLS) coverage at higher parts of dense canopies canopies is limited, limited, forest forest canopy canopy biomass biomass is is underestimated
A 3D canopy structure of an even-aged forest stand was represented in detail by means of TLS
Summary
Remote sensing technologies are extensively used in forestry for mapping physical-structural features of the land and in forest surveys. Canopy geometry (i.e., canopy closure and density) has been documented by Guyot et al [7] as the most significant factor in the optical properties of forest canopies All these factors should be taken into account when using HS remote sensing from forested areas. A fundamental input for developing such models encompass a correct description of canopy structure in terms of tree position, crown shapes, leaf density, and spatial distribution of leaf area [9]. This heterogeneity of canopy structure is greatly influenced by the clumping index and the leaf inclination angle distribution [10,11]. An accurate representation of the spatial variation of the canopy structure, both vertical and horizontal, is hard to achieve
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