An Examination of Diameter Density Prediction with k-NN and Airborne Lidar
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. In conclusion, 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.
- Authors:
-
- Univ. of Washington, Seattle, WA (United States)
- Washington State Dept. of Natural Resources, Olympia, WA (United States)
- Univ. of Eastern Finland, Joensuu (Finland)
- Oregon State Univ., Corvallis, OR (United States)
- Publication Date:
- Research Org.:
- USDA Forest Service-Savannah River, New Ellenton, SC (United States)
- Sponsoring Org.:
- USDOE Office of Environmental Management (EM), Acquisition and Project Management
- OSTI Identifier:
- 1415439
- Report Number(s):
- 17-05-P
Journal ID: ISSN 1999-4907; PII: f8110444; TRN: US1800825
- Grant/Contract Number:
- AI09-00SR22188
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Forests
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 11; Journal ID: ISSN 1999-4907
- Publisher:
- MDPI
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; forest inventory; dbh; diameter distribution; performance criteria
Citation Formats
Strunk, Jacob L., Gould, Peter J., Packalen, Petteri, Poudel, Krishna P., Andersen, Hans -Erik, and Temesgen, Hailemariam. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. United States: N. p., 2017.
Web. doi:10.3390/f8110444.
Strunk, Jacob L., Gould, Peter J., Packalen, Petteri, Poudel, Krishna P., Andersen, Hans -Erik, & Temesgen, Hailemariam. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. United States. https://doi.org/10.3390/f8110444
Strunk, Jacob L., Gould, Peter J., Packalen, Petteri, Poudel, Krishna P., Andersen, Hans -Erik, and Temesgen, Hailemariam. Thu .
"An Examination of Diameter Density Prediction with k-NN and Airborne Lidar". United States. https://doi.org/10.3390/f8110444. https://www.osti.gov/servlets/purl/1415439.
@article{osti_1415439,
title = {An Examination of Diameter Density Prediction with k-NN and Airborne Lidar},
author = {Strunk, Jacob L. and Gould, Peter J. and Packalen, Petteri and Poudel, Krishna P. and Andersen, Hans -Erik and Temesgen, Hailemariam},
abstractNote = {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. In conclusion, 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.},
doi = {10.3390/f8110444},
journal = {Forests},
number = 11,
volume = 8,
place = {United States},
year = {2017},
month = {11}
}
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Works referencing / citing this record:
Prediction of diameter distributions and tree-lists in southwestern Oregon using LiDAR and stand-level auxiliary information
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Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators
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Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests
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