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An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 57 (May 2017) . - pp 36 - 48 Titre : An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands Type de document : Article/Communication Auteurs : Sahra Abdullahi, Auteur ; Mathias Schardt, Auteur ; Hans Pretzsch, Auteur Année de publication : 2017 Article en page(s) : pp 36 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande X [Termes IGN] Bavière (Allemagne) [Termes IGN] carte de Kohonen [Termes IGN] classification barycentrique [Termes IGN] classification non dirigée [Termes IGN] distance euclidienne [Termes IGN] forêt tempérée [Termes IGN] image radar moirée [Termes IGN] image TanDEM-X [Termes IGN] image TerraSAR-X [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data. Numéro de notice : A2017-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2016.12.010 En ligne : https://doi.org/10.1016/j.jag.2016.12.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85785 [article]