Luke
 

A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data

dc.contributor.authorChirici, Gherardo
dc.contributor.authorMura, Matteo
dc.contributor.authorMcInerney, Daniel
dc.contributor.authorPy, Nicolas
dc.contributor.authorTomppo, Erkki O.
dc.contributor.authorWaser, Lars T.
dc.contributor.authorTravaglini, Davide
dc.contributor.authorMcRoberts, Ronald E.
dc.contributor.departmentLuke-
dc.contributor.departmentidLuke-]
dc.date.accessioned2017-01-26T09:12:41Z
dc.date.accessioned2025-05-28T17:33:35Z
dc.date.available2017-01-26T09:12:41Z
dc.date.issued2016
dc.description.abstractThe k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN.-
dc.description.vuosik2016-
dc.formatSekä painettu, että verkkojulkaisu-
dc.format.bitstreamfalse
dc.format.pagerange282-294-
dc.identifier.elss1879-0704-
dc.identifier.olddbid480075
dc.identifier.oldhandle10024/538051
dc.identifier.urihttps://jukuri.luke.fi/handle/11111/31415
dc.language.isoeng-
dc.okm.corporatecopublicationei-
dc.okm.discipline1172 Ympäristötiede-
dc.okm.discipline4112 Metsätiede-
dc.okm.internationalcopublicationon-
dc.okm.openaccess0 = Ei vastausta-
dc.okm.selfarchivedei-
dc.publisherElsevier Science Inc.-
dc.publisher.countryus-
dc.publisher.placeNew York, NY-
dc.relation.doi10.1016/j.rse.2016.02.001-
dc.relation.ispartofseriesRemote sensing of environment-
dc.relation.issn0034-4257-
dc.relation.volume176-
dc.rightsAll rights reserved-
dc.rights.copyrightCopyright: Elsevier Inc.-
dc.source.identifierhttps://jukuri.luke.fi/handle/10024/538051
dc.subject.keywordk-nearest neighbors-
dc.subject.keywordforestry applications-
dc.subject.keywordreview-
dc.subject.keywordmeta-analysis-
dc.subject.keywordaboveground biomass-
dc.subject.keywordsatellite imagery-
dc.subject.keywordmoderate resolution-
dc.subject.keywordancillary data-
dc.subject.keywordBasal area-
dc.subject.keywordlandsat-TM-
dc.subject.keywordinventory-
dc.subject.keywordvolume-
dc.subject.keywordattributes-
dc.subject.keywordvariables-
dc.titleA meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data-
dc.type.okmfi=A2 Katsausartikkeli tieteellisessä aikakauslehdessä|sv=A2 Översiktsartikel i en vetenskaplig tidskrift|en=A2 Review article, Literature review, Systematic review|-

Tiedostot

Kokoelmat