Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

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http://hdl.handle.net/10138/159576

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Kantola , T , Vastaranta , M , Yu , X , Lyytikäinen-Saarenmaa , P , Holopainen , M , Talvitie , M , Kaasalainen , S , Solberg , S & Hyyppä , J 2010 , ' Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images ' , Remote Sensing , vol. 2 , no. 12 , pp. 2665-2679 . https://doi.org/10.3390/rs2122665

Title: Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images
Author: Kantola, Tuula; Vastaranta, Mikko; Yu, Xiaowei; Lyytikäinen-Saarenmaa, Päivi; Holopainen, Markus; Talvitie, Mervi; Kaasalainen, Sanna; Solberg, Svein; Hyyppä, Juha
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
Date: 2010
Language: eng
Number of pages: 16
Belongs to series: Remote Sensing
ISSN: 2072-4292
URI: http://hdl.handle.net/10138/159576
Abstract: Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.
Subject: 411 Agriculture and forestry
ALS
defoliation
Diprion pini
forest disturbance
logistic regression
MSN
random forest
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