Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning

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http://hdl.handle.net/10138/182254

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Yu , X , Hyyppa , J , Litkey , P , Kaartinen , H , Vastaranta , M & Holopainen , M 2017 , ' Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning ' , Remote Sensing , vol. 9 , no. 2 . https://doi.org/10.3390/rs9020108

Title: Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning
Author: Yu, Xiaowei; Hyyppa, Juha; Litkey, Paula; Kaartinen, Harri; Vastaranta, Mikko; Holopainen, Markus
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
Date: 2017-02
Language: eng
Number of pages: 16
Belongs to series: Remote Sensing
ISSN: 2072-4292
URI: http://hdl.handle.net/10138/182254
Abstract: This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensor solution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.
Subject: multispectral laser scanning
ALS
individual tree detection
tree species classification
random forest
DISCRETE-RETURN LIDAR
STEM VOLUME
INDIVIDUAL TREES
CANOPY STRUCTURE
RANDOM FORESTS
HEIGHT
INTENSITY
IDENTIFICATION
NORMALIZATION
ENVIRONMENT
4112 Forestry
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