A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data
dc.contributor.author | Chirici, Gherardo | |
dc.contributor.author | Mura, Matteo | |
dc.contributor.author | McInerney, Daniel | |
dc.contributor.author | Py, Nicolas | |
dc.contributor.author | Tomppo, Erkki O. | |
dc.contributor.author | Waser, Lars T. | |
dc.contributor.author | Travaglini, Davide | |
dc.contributor.author | McRoberts, Ronald E. | |
dc.contributor.department | Luke | - |
dc.contributor.departmentid | Luke | -] |
dc.date.accessioned | 2017-01-26T09:12:41Z | |
dc.date.accessioned | 2025-05-28T17:33:35Z | |
dc.date.available | 2017-01-26T09:12:41Z | |
dc.date.issued | 2016 | |
dc.description.abstract | The 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.vuosik | 2016 | - |
dc.format | Sekä painettu, että verkkojulkaisu | - |
dc.format.bitstream | false | |
dc.format.pagerange | 282-294 | - |
dc.identifier.elss | 1879-0704 | - |
dc.identifier.olddbid | 480075 | |
dc.identifier.oldhandle | 10024/538051 | |
dc.identifier.uri | https://jukuri.luke.fi/handle/11111/31415 | |
dc.language.iso | eng | - |
dc.okm.corporatecopublication | ei | - |
dc.okm.discipline | 1172 Ympäristötiede | - |
dc.okm.discipline | 4112 Metsätiede | - |
dc.okm.internationalcopublication | on | - |
dc.okm.openaccess | 0 = Ei vastausta | - |
dc.okm.selfarchived | ei | - |
dc.publisher | Elsevier Science Inc. | - |
dc.publisher.country | us | - |
dc.publisher.place | New York, NY | - |
dc.relation.doi | 10.1016/j.rse.2016.02.001 | - |
dc.relation.ispartofseries | Remote sensing of environment | - |
dc.relation.issn | 0034-4257 | - |
dc.relation.volume | 176 | - |
dc.rights | All rights reserved | - |
dc.rights.copyright | Copyright: Elsevier Inc. | - |
dc.source.identifier | https://jukuri.luke.fi/handle/10024/538051 | |
dc.subject.keyword | k-nearest neighbors | - |
dc.subject.keyword | forestry applications | - |
dc.subject.keyword | review | - |
dc.subject.keyword | meta-analysis | - |
dc.subject.keyword | aboveground biomass | - |
dc.subject.keyword | satellite imagery | - |
dc.subject.keyword | moderate resolution | - |
dc.subject.keyword | ancillary data | - |
dc.subject.keyword | Basal area | - |
dc.subject.keyword | landsat-TM | - |
dc.subject.keyword | inventory | - |
dc.subject.keyword | volume | - |
dc.subject.keyword | attributes | - |
dc.subject.keyword | variables | - |
dc.title | A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data | - |
dc.type.okm | fi=A2 Katsausartikkeli tieteellisessä aikakauslehdessä|sv=A2 Översiktsartikel i en vetenskaplig tidskrift|en=A2 Review article, Literature review, Systematic review| | - |