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Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest

Lindberg, Eva and Hollaus, Markus (2012). Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest. Remote sensing. 4:4, 1004-1023
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Official URL: http://dx.doi.org/10.3390/rs4041004

Abstract

This study compares methods to estimate stem volume, stem number and basalarea from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, sprucedominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with fivedifferent regression models: one model based on height and density metrics from the ALSdata derived from the whole field plot, two models based on similar combinations derivedfrom 0.5 m raster cells, and two models based on canopy volumes from the ALS data. Thebest result was achieved with a model based on height and density metrics derived from0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a modelbased on height and density metrics derived from the whole field plot (RMSE 41.9%). Thestem number and the basal area were estimated with: (i) area-based regression modelsusing height and density metrics from the ALS data; and (ii) single tree-based informationderived from local maxima in a normalized digital surface model (nDSM) mean filteredwith 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 thenDSM (RMSE 63.4%-91.9% and 57.0%-175.5%, respectively). The accuracy of theestimates from the nDSM varied depending on the filter size and the conditions of theapplied filter. This suggests that conditional filtering is useful but sensitive tothe conditions.

Authors/Creators:Lindberg, Eva and Hollaus, Markus
Title:Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest
Series/Journal:Remote sensing (2072-4292)
Year of publishing :2012
Volume:4
Number:4
Page range:1004-1023
Number of Pages:20
Publisher:MDPI
ISSN:2072-4292
Language:English
Publication Type:Journal article
Refereed:Yes
Article category:Scientific peer reviewed
Version:Published version
Full Text Status:Public
Agris subject categories.:K Forestry > K01 Forestry - General aspects
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:forest parameter , LiDAR, regression models, single tree parameter
URN:NBN:urn:nbn:se:slu:epsilon-e-1523
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-1523
Additional ID:
Type of IDID
DOI10.3390/rs4041004
ID Code:10569
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:01 Jul 2013 14:43
Metadata Last Modified:02 Dec 2014 11:01

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