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 ( R 2), 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 km 2 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 amore »
- 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 (EM-50)
- 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. doi: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. doi: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 referenced in this record:
Stand structure and development of an insect-mediated boreal forest landscape
journal, March 2007
- McCarthy, John W.; Weetman, Gordon
- Forest Ecology and Management, Vol. 241, Issue 1-3
A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data
journal, May 2012
- Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I.
- Forest Ecology and Management, Vol. 271
Evaluation of Methods to Predict Weibull Parameters for Characterizing Diameter Distributions
journal, April 2013
- Poudel, Krishna Prasad; Cao, Quang V.
- Forest Science, Vol. 59, Issue 2
The use of quantile trees in the prediction of the diameter distribution of a stand
journal, January 2006
- Mehtätalo, Lauri; Maltamo, Matti; Kangas, Annika
- Silva Fennica, Vol. 40, Issue 3
Model-Assisted Forest Yield Estimation with Light Detection and Ranging
journal, April 2012
- Strunk, Jacob L.; Reutebuch, Stephen E.; Andersen, Hans-Erik
- Western Journal of Applied Forestry, Vol. 27, Issue 2
Comparison of basal area and stem frequency diameter distribution modelling using airborne laser scanner data and calibration estimation
journal, August 2007
- Maltamo, M.; Suvanto, A.; Packalén, P.
- Forest Ecology and Management, Vol. 247, Issue 1-3
The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs
journal, August 2007
- Packalén, Petteri; Maltamo, Matti
- Remote Sensing of Environment, Vol. 109, Issue 3
Diameter distributions and structural sustainability in forests
journal, February 2006
- Rubin, Benjamin D.; Manion, Paul D.; Faber-Langendoen, Don
- Forest Ecology and Management, Vol. 222, Issue 1-3
A Bivariate Distribution Approach to Modeling Forest Diameter Distributions at Two Points in Time
journal, March 1991
- Knoebel, Bruce R.; Burkhart, Harold E.
- Biometrics, Vol. 47, Issue 1
Estimation of diameter distributions by means of airborne laser scanner data
journal, June 2008
- Breidenbach, Johannes; Gläser, Christian; Schmidt, Matthias
- Canadian Journal of Forest Research, Vol. 38, Issue 6
Statistical distributions for fitting diameter and height data in even-aged stands
journal, September 1977
- Hafley, W. L.; Schreuder, H. T.
- Canadian Journal of Forest Research, Vol. 7, Issue 3
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data
journal, January 2009
- Maltamo, Matti; Peuhkurinen, Jussi; Malinen, Jukka
- Silva Fennica, Vol. 43, Issue 3
Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs
journal, July 2008
- Packalén, Petteri; Maltamo, Matti
- Canadian Journal of Forest Research, Vol. 38, Issue 7
Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data
journal, January 2007
- Martin Bollandsås, Ole; Næsset, Erik
- Scandinavian Journal of Forest Research, Vol. 22, Issue 1
LiDAR and Weibull modeling of diameter and basal area
journal, December 2008
- Thomas, V.; Oliver, R. D.; Lim, K.
- The Forestry Chronicle, Vol. 84, Issue 6
The fickle P value generates irreproducible results
journal, February 2015
- Halsey, Lewis G.; Curran-Everett, Douglas; Vowler, Sarah L.
- Nature Methods, Vol. 12, Issue 3
yaImpute : An R Package for k NN Imputation
journal, January 2008
- Crookston, Nicholas L.; Finley, Andrew O.
- Journal of Statistical Software, Vol. 23, Issue 10
The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases
journal, June 2009
- Eskelson, Bianca N. I.; Temesgen, Hailemariam; Lemay, Valerie
- Scandinavian Journal of Forest Research, Vol. 24, Issue 3
Estimation of stem volume using laser scanning-based canopy height metrics
journal, February 2006
- Maltamo, Matti; Eerikäinen, Kalle; Packalén, Petteri
- Forestry: An International Journal of Forest Research, Vol. 79, Issue 2
Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data
journal, December 2004
- Gobakken, Terje; Næsset, Erik
- Scandinavian Journal of Forest Research, Vol. 19, Issue 6
Comparison of stratified and non-stratified most similar neighbour imputation for estimating stand tables
journal, April 2008
- Eskelson, B. N. I.; Temesgen, H.; Barrett, T. M.
- Forestry, Vol. 81, Issue 2
A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario
journal, November 2015
- Penner, Margaret; Woods, Murray; Pitt, Douglas
- Forests, Vol. 6, Issue 12
Calibration Estimators in Survey Sampling
journal, June 1992
- Deville, Jean-Claude; Särndal, Carl-Erik
- Journal of the American Statistical Association, Vol. 87, Issue 418