Black-Bridge data in the detection of forest area changes in the example of Sudety and Beskidy

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dc.contributor.authorHycza, Tomasz
dc.contributor.authorStereńczak, Krzysztof
dc.contributor.authorBałazy, Radomir
dc.contributor.organizationForest Research Institute, Department of Forest Resources Management, Sękocin Staryen
dc.date.accessioned2018-02-05T15:41:08Z
dc.date.available2018-02-05T15:41:08Z
dc.date.issued2018-12-15
dc.description.abstractTwo change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.en
dc.identifier.doi10.1515/ffp-2017-0029
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/14458
dc.language.isoen
dc.publisherThe Committee on Forestry Sciences and Wood Technology of the Polish Academy of Sciences and the Forest Research Institute in Sekocin Staryen
dc.rightsUznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/pl/*
dc.subjectremote sensingen
dc.subjectBlack-Bridgeen
dc.subjectchange detectionen
dc.subjectNDVIen
dc.subjectpost-classification analysisen
dc.titleBlack-Bridge data in the detection of forest area changes in the example of Sudety and Beskidyen
dc.typearticleen
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