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dc.contributor.authorMehtätalo, Lauri
dc.contributor.authorYazigi, Adil
dc.contributor.authorKansanen, Kasper
dc.contributor.authorPackalen, Petteri
dc.contributor.authorLähivaara, Timo
dc.contributor.authorMaltamo, Matti
dc.contributor.authorMyllymäki, Mari
dc.contributor.authorPenttinen, Antti
dc.date.accessioned2022-08-16T08:12:41Z
dc.date.available2022-08-16T08:12:41Z
dc.date.issued2022
dc.identifier.citationMehtätalo, L., Yazigi, A., Kansanen, K., Packalen, P., Lähivaara, T., Maltamo, M., Myllymäki, M., & Penttinen, A. (2022). Estimation of forest stand characteristics using individual tree detection, stochastic geometry and a sequential spatial point process model. <i>International Journal of Applied Earth Observation and Geoinformation</i>, <i>112</i>, Article 102920. <a href="https://doi.org/10.1016/j.jag.2022.102920" target="_blank">https://doi.org/10.1016/j.jag.2022.102920</a>
dc.identifier.otherCONVID_151014083
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82579
dc.description.abstractAirborne Laser Scanning (ALS) results in point-wise measurements of canopy height, which can further be used for Individual Tree Detection (ITD). However, ITD cannot find all trees because small trees can hide below larger tree crowns. Here we discuss methods where the plot totals and means of tree-level characteristics are estimated in such context. The starting point is a previously presented Horvitz–Thompson-like (HT-like) estimator, where the detectability is based on the larger tree crowns and a tuning parameter that models the detection condition. We propose a new method which is based on modeling the spatial pattern of hidden tree locations using a sequential spatial point process model, with a tuning parameter . We also explore whether the variability of the tuning parameters and can be predicted using ALS features to improve the predictions. The accuracy of stand density, dominant height and mean height is used as comparison criteria in a cross-validation procedure. The HT-like estimator with empirically estimated tuning parameter performed the best. The overall performance of the new method was comparable. The new method was computationally less demanding, which makes it attractive for practical use.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesInternational Journal of Applied Earth Observation and Geoinformation
dc.rightsCC BY 4.0
dc.subject.otherforest inventory
dc.subject.otherAirborne Laser Scanning
dc.subject.otherHorvitz-Thompson-like estimator
dc.subject.otherstand density:tree height
dc.titleEstimation of forest stand characteristics using individual tree detection, stochastic geometry and a sequential spatial point process model
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202208164123
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1569-8432
dc.relation.volume112
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Author(s). Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.subject.ysomittausmenetelmät
dc.subject.ysoinventointi
dc.subject.ysotiheys
dc.subject.ysolasertekniikka
dc.subject.ysomittauslaitteet
dc.subject.ysolaserlaitteet
dc.subject.ysopuut (kasvit)
dc.subject.ysolaserit
dc.subject.ysometsänarviointi
dc.subject.ysometsät
dc.subject.ysokaukokartoitus
dc.subject.ysopuusto
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p20083
jyx.subject.urihttp://www.yso.fi/onto/yso/p10397
jyx.subject.urihttp://www.yso.fi/onto/yso/p14628
jyx.subject.urihttp://www.yso.fi/onto/yso/p20011
jyx.subject.urihttp://www.yso.fi/onto/yso/p3583
jyx.subject.urihttp://www.yso.fi/onto/yso/p1144
jyx.subject.urihttp://www.yso.fi/onto/yso/p8147
jyx.subject.urihttp://www.yso.fi/onto/yso/p1145
jyx.subject.urihttp://www.yso.fi/onto/yso/p18894
jyx.subject.urihttp://www.yso.fi/onto/yso/p5454
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p13847
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.jag.2022.102920
jyx.fundinginformationThis research was financially supported by the Academy of Finland through (1) the Finnish Centre of Excellence of Inverse Modeling and Imaging, (2) the flagship program “Forest-Human–Machine Interplay - Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE, decision number 337655)”, and (3) research projects 295100, 310073, 321761, 327211 and 351525.
dc.type.okmA1


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