An Examination of Diameter Density Prediction with k-NN and Airborne Lidar
AbstractWhile lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria. View Full-Text
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Strunk, J.L.; Gould, P.J.; Packalen, P.; Poudel, K.P.; Andersen, H.-E.; Temesgen, H. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. Forests 2017, 8, 444.
Strunk JL, Gould PJ, Packalen P, Poudel KP, Andersen H-E, Temesgen H. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. Forests. 2017; 8(11):444.Chicago/Turabian Style
Strunk, Jacob L.; Gould, Peter J.; Packalen, Petteri; Poudel, Krishna P.; Andersen, Hans-Erik; Temesgen, Hailemariam. 2017. "An Examination of Diameter Density Prediction with k-NN and Airborne Lidar." Forests 8, no. 11: 444.
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