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Error assessment in two lidar-derived TIN datasets / M.H. Peng in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 933 - 947 Titre : Error assessment in two lidar-derived TIN datasets Type de document : Article/Communication Auteurs : M.H. Peng, Auteur ; T.Y. Shih, Auteur Année de publication : 2006 Article en page(s) : pp 933 - 947 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] pente [Termes descripteurs IGN] point de vérification [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] rugosité [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] Triangulated Irregular Network [Termes descripteurs IGN] variabilité [Termes descripteurs IGN] végétation Résumé : (Auteur) An accuracy assessment of two lidar-derived elevation datasets was conducted in areas of rugged terrain (average slope 26.6°). Data from 906 ground checkpoints in various land-cover types were collected in situ as reference points. Analysis of the accuracy of lidar-derived elevation as a function of several factors including terrain slope, terrain aspect, and land-cover types was conducted. This paper attempts to characterize vegetation information derived from lidar data based on variables such as canopy volume, local roughness of point clouds, point spacing of lidar ground returns, and vegetation angle. This information was used to evaluate the accuracy of elevation as a function of vegetation type. The experimental results revealed that the accuracy of elevation was considerably correlated with five factors: terrain slope, vegetation angle, canopy volume, local roughness of point clouds, and point spacing of lidar ground returns. The results show a linear relationship between the elevation accuracy and the combination of vegetation angle and the point spacing of ground returns (r2 > 0.9). The combination of vegetation angle and point spacing of ground returns explains a significant amount of the variability in elevation accuracy. Elevation accuracy varied with different vegetation types. The elevation accuracy was also linearly correlated with the product of the point spacing of ground returns and the tangent of the slope (r2 = 0.9). A greater product value implies a greater elevation error. In addition, with regard to terrain aspect, one dense dataset with extra cross-flight data revealed a lesser impact of aspect on elevation accuracy. Copyright ASPRS Numéro de notice : A2006-312 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28036 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-06081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
105-06082	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods / João M.B. Carreiras in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 897 - 910 Titre : Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur ; Y.E. Shimabukuro, Auteur Année de publication : 2006 Article en page(s) : pp 897 - 910 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] cartographie numérique [Termes descripteurs IGN] classification ascendante hiérarchique [Termes descripteurs IGN] image SPOT-Végétation [Termes descripteurs IGN] Mato Grosso [Termes descripteurs IGN] occupation du sol Résumé : (Auteur) The main objective of this study is to evaluate the feasibility of deriving a land-cover map of the state of Mato Grosso, Brazil, for the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT) sensor. For this purpose we used a VGT temporal series of 12 monthly composite images, which were further transformed to physical-meaningful fraction images of vegetation, soil, and shade. Classification of fraction images was implemented using several recent machine learning developments, namely, filtering input training data and probability bagging in a classification tree approach. A 10-fold cross validation accuracy assessment indicates that filtering and probability bagging are effective at increasing overall and class-specific accuracy. Overall accuracy and mean probability of class membership were 0.88 and 0.80, respectively. The map of probability of class membership indicates that the larger errors are associated with cerrado savonna and semi-deciduous forest. Copyright ASPRS Numéro de notice : A2006-313 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28037 [article]

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Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-06081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
105-06082	RAB	Revue	Centre de documentation	En réserve 3L	Disponible