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Remote Sens. 2017, 9(9), 944; doi:10.3390/rs9090944

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

1
Department of Plant Sciences, University of Cambridge, CB2 3EA Cambridge, UK
2
Ontario Ministry of Natural Resources and Forestry, Forest Research and Monitoring Section, 1235 Queen Street East, Sault Ste. Marie, ON P6A 2E5, Canada
3
Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada
4
Department of Biology, University of Regina, 3737 Wascana Parkway, Regina, SK S4S0A2, Canada
*
Author to whom correspondence should be addressed.
Received: 29 May 2017 / Accepted: 19 June 2017 / Published: 12 September 2017
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Abstract

We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution. View Full-Text
Keywords: airborne LiDAR; stem diameter distribution; tree allometry; area-based approach; aboveground carbon map airborne LiDAR; stem diameter distribution; tree allometry; area-based approach; aboveground carbon map
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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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MDPI and ACS Style

Spriggs, R.A.; Coomes, D.A.; Jones, T.A.; Caspersen, J.P.; Vanderwel, M.C. An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape. Remote Sens. 2017, 9, 944.

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