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Forests 2018, 9(5), 268; https://doi.org/10.3390/f9050268

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science & Technology, 50, UNIST-gil, Ulsan 44919, Korea
2
Division of Global Forestry, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Korea
3
Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA
*
Author to whom correspondence should be addressed.
Received: 3 February 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 14 May 2018
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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Abstract

Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approaches—support vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species. View Full-Text
Keywords: forest stand height; plot-level to stand-level expansion methods; airborne LiDAR; machine learning forest stand height; plot-level to stand-level expansion methods; airborne LiDAR; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lee, J.; Im, J.; Kim, K.; Quackenbush, L.J. Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data. Forests 2018, 9, 268.

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