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Abstract |
Operational pulse density affects the measurements based on airborne laser scanning (ALS) data, especially at the individual tree level. The minimum density required depends on the interpretation methodology used, i.e., knowing the requirements is a prerequisite for a successful ALS data acquisition. We evaluate these requirements for a recently introduced alpha shape metrics approach in which computational volume and complexity metrics derived from ALS point clouds are utilized to produce actual tree-level characteristics. We simulated thinnings to the ALS return data using a test dataset of a total of 92 dominant or codominant trees detected and delineated manually from very high density (approximately 40 returns/m2) initial ALS data and produced species and diameter at breast height estimates with the thinned datasets. We compared the alpha shape metrics approach with alternative methods, making additional use of tree-level ALS data, and examined the sensitivity of the different methods to pulse density. The results show that in addition to the species classification possibilities recognized earlier, alpha shape metrics computed from very high density ALS data are also useful for predicting tree dimensions. Upon analysing the thinned data, the alpha shape metrics were generally discovered to suffer most from a lower pulse density. On the other hand, tree level canopy height distribution variables appeared to be more neutral for the pulse density and could be used at low density levels to complement and stabilize the alpha shape based methods for predicting both species and diameter. The results indicate that, provided individual trees can be accurately delineated, the species and diameter of mature coniferous trees in particular can be predicted using ALS data, even with a very low pulse density. As the alpha shape metrics performed well at densities that were only moderate for the individual tree delineation approach, more research is suggested to determine their full potential. Additionally, identifying trees automatically using more representative data needs to be examined to generalize the obtained result. |
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