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

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published version
Date
2017
Author(s)
Strunk Jacob L
Gould Peter J
Packalen Petteri
Poudel Krishna P
Andersen Hans-Erik
Temesgen Hailemariam
Unique identifier
10.3390/f8110444
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Citation
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 (11) , 1-16. 10.3390/f8110444.
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CC BY http://creativecommons.org/licenses/by/4.0/
Abstract

While 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.

Subjects
forest inventory   dbh   diameter distribution   performance criteria   
URI
https://erepo.uef.fi/handle/123456789/6001
Link to the original item
http://dx.doi.org/10.3390/f8110444
Publisher
MDPI AG
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  • Luonnontieteiden ja metsätieteiden tiedekunta [1109]
University of Eastern Finland
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