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Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Lindberg, Eva and Holmgren, Johan and Olsson, Håkan (2021). Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method. International Journal of Applied Earth Observation and Geoinformation. 100 , 102334
[Research article]

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Abstract

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.

Authors/Creators:Lindberg, Eva and Holmgren, Johan and Olsson, Håkan
Title:Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method
Series Name/Journal:International Journal of Applied Earth Observation and Geoinformation
Year of publishing :2021
Volume:100
Article number:102334
Number of Pages:13
Publisher:ELSEVIER
ISSN:0303-2434
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:Multispectral LiDAR, Tree species maps, Laser wavelengths, Laser light reflectance
URN:NBN:urn:nbn:se:slu:epsilon-p-112081
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-112081
Additional ID:
Type of IDID
DOI10.1016/j.jag.2021.102334
Web of Science (WoS)000647733500002
ID Code:24403
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:03 Jun 2021 17:22
Metadata Last Modified:04 Jun 2021 05:01

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