Preprint Article Version 1 This version not peer reviewed

An Examination of Diameter Density Prediction with k-NN and Lidar

Version 1 : Received: 29 September 2017 / Approved: 30 September 2017 / Online: 30 September 2017 (05:45:21 CEST)

A peer-reviewed article of this Preprint also exists.

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, 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.

Journal reference: Forests 2017, 8, 444
DOI: 10.3390/f8110444

Abstract

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. 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 or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with 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. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

Subject Areas

lidar; forest inventory; k-NN; dbh distribution; diameter distribution; performance criteria; index; indices

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