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Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

Public [article]  inBaltic forestry > vol 25 n° 1 (2019) . - pp 113 - 123 Titre : Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images Type de document : Article/Communication Auteurs : Linda Gulbe, Auteur ; Aleksandrs Kozlovs, Auteur ; Janis Donis, Auteur ; Agris Tradkovs, Auteur Année de publication : 2019 Article en page(s) : pp 113 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Lettonie Résumé : (auteur) Countrywide up-to-date tree cover maps provide valuable information for planning and management purposes to investigate location of the resources and to identify afforestation and deforestation patterns. Landsat programme offers freely available satellite data with time span more than three decades and it can serve as bases for tree cover map calculation using satellite image classification; however, practical use of classification methods is limited due to lack of user-friendly solutions and complex interpretation of the results. The objective of this study is to evaluate user-friendly hybrid classification scheme for tree cover mapping in Latvia and to explore the nature of the spectral classes and consistency of the results when methodology is applied to images of different dates. Tree cover in this context means the area covered by crown of the tree, which may or may not be considered as forest according to local provisions. Tree cover is estimated using unsupervised fuzzy c-means methods with the stability check to ensure the presence of the same spectral classes in independent tests. Spectral classes are classified into two categories: tree cover and other by employing k-nearest neighbours. Such approach does not require high quality sample data and does not include user defined internal parameters of the algorithms (however, they can be specified if needed). The best overall accuracy achieved for year 2014 was 94.2% with producer's accuracy 98.7% (tree cover), 90.5% (other land cover), user's accuracy 90.0% (tree cover), 98.8% (other land cover) and kappa 0.89. Consistency studies showed high impact (within 10% of overall accuracy) of unique conditions during the image acquisition. Some of the spectral classes represent borderline case between relatively dense tree cover and other land cover types like sparse young stands. Those cases are the main threat to the consistency between the results of different dates and seasons. Numéro de notice : A2019-375 Thématique : FORET Nature : Article DOI : sans En ligne : https://www.balticforestry.mi.lt/bf/PDF_Articles/2019-25%5B1%5D/Baltic%20Forestr [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93417 [article]