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

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dc.contributor University of Helsinki, Department of Forest Sciences en
dc.contributor University of Helsinki, Department of Forest Sciences en
dc.contributor University of Helsinki, Department of Forest Sciences en
dc.contributor University of Helsinki, Department of Forest Sciences en
dc.contributor.author Kantola, Tuula
dc.contributor.author Vastaranta, Mikko
dc.contributor.author Yu, Xiaowei
dc.contributor.author Lyytikäinen-Saarenmaa, Päivi
dc.contributor.author Holopainen, Markus
dc.contributor.author Talvitie, Mervi
dc.contributor.author Kaasalainen, Sanna
dc.contributor.author Solberg, Svein
dc.contributor.author Hyyppä, Juha
dc.date.accessioned 2016-01-20T13:44:01Z
dc.date.available 2016-01-20T13:44:01Z
dc.date.issued 2010
dc.identifier.citation 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 en
dc.identifier.issn 2072-4292
dc.identifier.other PURE: 11800968
dc.identifier.other PURE UUID: 2e807486-4b13-4cab-a99e-179e2eaa14b2
dc.identifier.other WOS: 000208402200002
dc.identifier.other Scopus: 79957766071
dc.identifier.other ORCID: /0000-0003-1884-3084/work/41082565
dc.identifier.other ORCID: /0000-0002-1683-016X/work/30288993
dc.identifier.other ORCID: /0000-0001-6552-9122/work/29615083
dc.identifier.uri http://hdl.handle.net/10138/159576
dc.description.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. en
dc.format.extent 16
dc.language.iso eng
dc.relation.ispartof Remote Sensing
dc.relation.uri http://www.mdpi.com/2072-4292/2/12/2665/
dc.rights en
dc.subject 411 Agriculture and forestry en
dc.subject ALS en
dc.subject defoliation en
dc.subject Diprion pini en
dc.subject forest disturbance en
dc.subject logistic regression en
dc.subject MSN en
dc.subject random forest en
dc.title Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.3390/rs2122665
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
dc.contributor.pbl
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