Airborne laser scanning (LiDAR) is used in forest inventories to quantify stand structure with three dimensional point clouds. However, the structure of point clouds depends not only on stand structure, but also on the LiDAR instrument, its settings, and the pattern of flight. The resulting variation between and within datasets (particularly variation in pulse density and footprint size) can induce spurious variation in LiDAR metrics such as maximum height (h max) and mean height of the canopy surface model (C mean). In this study, we first compare two LiDAR datasets acquired with different parameters, and observe that h max and C mean are 56 cm and 1.0 m higher, respectively, when calculated using the high-density dataset with a small footprint. Then, we present a model that explains the observed bias using probability theory, and allows us to recompute the metrics as if the density of pulses were infinite and the size of the two footprints were equivalent. The model is our first step in developing methods for correcting various LiDAR metrics that are used for area-based prediction of stand structure. Such methods may be particularly useful for monitoring forest growth over time, given that acquisition parameters often change between inventories.
To recover verticality after disturbance, angiosperm trees produce 'tension wood' allowing them to bend actively. The driving force of the tension has been shown to take place in the G-layer, a specific unlignified layer of the cell wall observed in most temperate species. However, in tropical rain forests, the G-layer is often absent and the mechanism generating the forces to reorient trees remains unclear. A study was carried out on tilted seedlings, saplings and adult Simarouba amara Aubl. trees-a species known to not produce a G-layer. Microscopic observations were done on sections of normal and tension wood after staining or observed under UV light to assess the presence/absence of lignin. We showed that S. amara produces a cell-wall layer with all of the characteristics typical of G-layers, but that this G-layer can be observed only as a temporary stage of the cell-wall development because it is masked by a late lignification. Being thin and lignified, tension wood fibres cannot be distinguished from normal wood fibres in the mature wood of adult trees. These observations indicate that the mechanism generating the high tensile stress in tension wood is likely to be the same as that in species with a typical G-layer and also in species where the G-layer cannot be observed in mature cells.
ABSTRACT:Developments of LiDAR technology are decreasing the unit cost per single point (e.g. single-photo counting). This brings to the possibility of future LiDAR datasets having very dense point clouds. In this work, we process a very dense point cloud (~200 points per square meter), using three different methods for segmenting single trees and extracting tree positions and other metrics of interest in forestry, such as tree height distribution and canopy area distribution. The three algorithms are tested at decreasing densities, up to a lowest density of ~5 point per square meter. Accuracy assessment is done using Kappa, recall, precision and F-Score metrics comparing results with tree positions from groundtruth measurements in six ground plots where tree positions and heights were surveyed manually. Results show that one method provides better Kappa and recall accuracy results for all cases, and that different point densities, in the range used in this study, do not affect accuracy significantly. Processing time is also considered; the method with better accuracy is several times slower than the other two methods and increases exponentially with point density. Best performer gave Kappa = 0.7. The implications of metrics for determining the accuracy of results of point positions' detection is reported. Motives for the different performances of the three methods is discussed and further research direction is proposed.
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