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Remote Sens. 2012, 4(4), 1004-1023; doi:10.3390/rs4041004

Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest

1
Department of Forest Resource Management, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
2
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology (TU Wien), Gußhausstraße 27-29, A-1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Received: 6 February 2012 / Revised: 3 April 2012 / Accepted: 6 April 2012 / Published: 13 April 2012
(This article belongs to the Special Issue Laser Scanning in Forests)
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Abstract

This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%–91.9% and 57.0%–175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions. View Full-Text
Keywords: forest parameter; LiDAR; regression models; single tree parameter forest parameter; LiDAR; regression models; single tree parameter
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lindberg, E.; Hollaus, M. Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest. Remote Sens. 2012, 4, 1004-1023.

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