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Integration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia / Oumer S. Ahmed in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 953 - 961 Titre : Integration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia Type de document : Article/Communication Auteurs : Oumer S. Ahmed, Auteur ; Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur Année de publication : 2014 Article en page(s) : pp 953 - 961 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse des mélanges spectraux [Termes IGN] canopée [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] feuillu [Termes IGN] image Landsat [Termes IGN] occupation du sol [Termes IGN] Pinophyta Résumé : (auteur) Airborne Light Detection and Ranging (lidar) data provide useful measurements of forest canopy structure but are often limited in spatial coverage. Satellite remote sensing data from Landsat can provide extensive spatial coverage of generalized forest information. A forest survey approach that integrates airborne lidar and satellite data would potentially capitalize upon these distinctive characteristics. In this study in coastal forests of British Columbia, the main objective was to determine the potential of Landsat imagery to accurately estimate forest canopy cover measured from small-footprint airborne lidar data in order to expand the lidar measurements to a larger area. Landsat-derived Tasseled Cap Angle (TCA) and spectral mixture analysis (SMA) endmember fractions (i.e., sunlit canopy, non-phofosynthetic vegetation (NPV), shade and exposed soil) were compared to lidar-derived canopy cover estimates. Pixel-based analysis and object-based area-weighted error calculations were used to assess regression model performance. The best canopy cover estimate was obtained (in the object-based deciduous forest models) with a mean object size (MOS) of 2.5 hectares (adjusted R2 = O.86 and RMSE = 0.28). Overall, lower canopy cover estimation accuracy was obtained for coniferous forests compared to deciduous forests in both the pixel and object-based approaches. Numéro de notice : A2014-672 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.953 En ligne : https://doi.org/10.14358/PERS.80.10.953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75152 [article]

Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 963 - 970 Titre : Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation Type de document : Article/Communication Auteurs : Davood Akbari, Auteur ; Abdolreza Safari, Auteur ; Saeid Homayouni, Auteur Année de publication : 2014 Article en page(s) : pp 963 - 970 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification orientée objet [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] segmentation hiérarchique [Termes IGN] zone urbaine Résumé : (auteur)An effective approach to spectral-spatial classification has been achieved using Hierarchical SEGmentation (HSEG) by Tarabalka et al. (2009 and 2010). Our goal is to improve this approach to the classification of hyperspectral images in urban areas. The first step of our proposed method is to segment the spectral images using a novel marker-based HSEG, method. The spatial features from segmented images are then extracted. Spatial information such as the area, entropy, shape, adjacency, and relation features constitute the components of feature space. Last, using both spectral and spatial features, the image objects are classified by a support vector machine (SVM) classifier. Three image data-sets were used to test this method. The results of our experiment indicate that the main advantage of the proposed method, compared to the previous HSEG-based approach, is that it increases classification accuracy by selecting the appropriate contextual features of different objects. Numéro de notice : A2014-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.963 En ligne : https://doi.org/10.14358/PERS.80.10.963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75153 [article]