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Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 795 - 807 Titre : Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation Type de document : Article/Communication Auteurs : Alyssa Endres, Auteur ; Giorgos Mountrakis, Auteur ; Huiran Jin, Auteur ; Wei Zhuang, Auteur ; Ioannis Manakos, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 795 - 807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] biomasse aérienne [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] feuillu [Termes IGN] fusion de données [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat Résumé : (auteur) Aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health. LiDAR (Light Detection And Ranging), specifically NASA’s Laser Vegetation Imaging Sensor (LVIS), PALSAR (Phased Array type L-band Synthetic Aperture Radar), and Landsat data have been previously used in biomass estimation with promising results when used individually. In this manuscript, all three products are jointly utilized for the first time to assess their importance for deciduous biomass estimation. Results indicate that LVIS inputs are ranked as most important followed by PALSAR inputs. Particularly for PALSAR, scenes acquired in May and August were ranked higher compared to other months. Numéro de notice : A2016-827 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164942 En ligne : http://dx.doi.org/10.5721/EuJRS20164942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82707 [article]

Interurban visibility diagnosis from point clouds / Oscar Iglesias in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 673 - 690 Titre : Interurban visibility diagnosis from point clouds Type de document : Article/Communication Auteurs : Oscar Iglesias, Auteur ; Lucia Diaz-Vilarino, Auteur ; Higinio González-Jorge, Auteur ; Henrique Lorenzo, Auteur Année de publication : 2016 Article en page(s) : pp 673 - 690 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] sécurité routière [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser terrestre [Termes IGN] visibilité Résumé : (auteur) We present an approach for automatic visibility analysis in interurban roads from point clouds. The methodology is based on a ray-tracing algorithm followed by an occlusion detection to identify potential obstacles between the driver and the theoretical position of pedestrians and cyclists. As a result, the area of visibility from each driver position is obtained. The method compares the performance and suitability of point clouds acquired from both Airborne and Mobile Laser Scanning. The methodology is tested in six real case studies. In most cases, results obtained from MLS are more accurate since the point clouds are acquired from a perspective similar to driver and they have higher resolution. Numéro de notice : A2016-828 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164935 En ligne : http://dx.doi.org/10.5721/EuJRS20164935 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82708 [article]

Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns / Kaja Kandare in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 337 - 359 Titre : Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns Type de document : Article/Communication Auteurs : Kaja Kandare, Auteur ; Hans Ole Ørka, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; Michele Dalponte, Auteur Année de publication : 2016 Article en page(s) : pp 337 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Alpes [Termes IGN] délimitation [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt alpestre [Termes IGN] houppier [Termes IGN] Italie [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical merging based on overlapping among clusters. On an Alpine forest dataset, we analysed the effects of different forest structures and point cloud densities on tree crown delineation. Forest structure affects mainly the omission error, which eases with homogeneous tree spacing and sizes, while on the commission error forest structure has only slight effect. Delineation accuracy increases with higher point densities where Mann-Whitney-Wilcoxon test shows that accuracy differences between thinned data and original data are statistically significant. Numéro de notice : A2016-829 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164919 En ligne : http://dx.doi.org/10.5721/EuJRS20164919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82709 [article]

An intensity recovery algorithm (IRA) for minimizing the edge effect of LIDAR data / Fabiane Bordin in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 301 - 315 Titre : An intensity recovery algorithm (IRA) for minimizing the edge effect of LIDAR data Type de document : Article/Communication Auteurs : Fabiane Bordin, Auteur ; Fabrício Galhardo Müller, Auteur ; Elba Calesso Teixeira, Auteur ; Sílvia Beatriz Alves Rolim, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 301 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effet de bord [Termes IGN] intensité lumineuse Résumé : (auteur) The terrestrial laser scanner is an equipment developed for surveying applications and is also used for many other purposes due to its ability to acquire 3D data quickly. However, before intensity data can be analyzed, it must be processed in order to minimize the edge or border effect, one of the most serious problems of LIDAR's intensity data. Our research has focused on characterizing the edge effect behavior as well as to develop an algorithm to minimize edge effect distortion automatically (IRA). The IRA showed to be effective recovering 35.71% of points distorted by the edge effect, providing significant improvements and promising results for the development of applications based on TLS data intensity to many studies. Numéro de notice : A2016-830 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164917 En ligne : http://dx.doi.org/10.5721/EuJRS20164917 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82713 [article]

Automatic segment-level tree species recognition using high resolution aerial winter imagery / Anton Kuzmin in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 239 - 259 Titre : Automatic segment-level tree species recognition using high resolution aerial winter imagery Type de document : Article/Communication Auteurs : Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Terhikki Manninen, Auteur ; Matti Maltamo, Auteur Année de publication : 2016 Article en page(s) : pp 239 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse discriminante [Termes IGN] betula pubescens [Termes IGN] composition floristique [Termes IGN] forêt boréale [Termes IGN] hélicoptère [Termes IGN] hiver [Termes IGN] image à ultra haute résolution [Termes IGN] image aérienne [Termes IGN] neige [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris Résumé : (auteur) Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73. Numéro de notice : A2016-831 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164914 En ligne : http://dx.doi.org/10.5721/EuJRS20164914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82714 [article]

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 157 - 169 Titre : Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests Type de document : Article/Communication Auteurs : Nicola Puletti, Auteur ; Nicola Camarretta, Auteur ; Piermaria Corona, Auteur Année de publication : 2016 Article en page(s) : pp 157 - 169 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] feuillu [Termes IGN] forêt [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] matrice de confusion [Termes IGN] Pinophyta [Termes IGN] régression multivariée par spline adaptative Résumé : (auteur) The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic. Numéro de notice : A2016-832 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164909 En ligne : http://dx.doi.org/10.5721/EuJRS20164909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82716 [article]

Accuracy of tree geometric parameters depending on the LiDAR data density / Edyta Hadas in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 73 - 92 Titre : Accuracy of tree geometric parameters depending on the LiDAR data density Type de document : Article/Communication Auteurs : Edyta Hadas, Auteur ; Javier Estornell, Auteur Année de publication : 2016 Article en page(s) : pp 73 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] Olea europaea [Termes IGN] précision géométrique (imagerie) Résumé : (auteur) The aim of this study was to compare geometric parameters of olive trees (tree height,crown base height, crown diameters, crown area), using LiDAR data of different densities: 0.5, 3.5 and 9 points m-2. Two strategies were proposed and verified with a focus on raster and raw data analysis. Statistical tests have shown, that for the tree height and crown base height estimation, the choice of strategy was irrelevant, but denser LiDAR data provided more accurate results. The raster analysis strategy applied for sparse and dense LiDAR datasets allowed crown shape to be determined with a similar accuracy which means raster data are useful for estimating other indirect tree parameters. The quality of results was independent from the tree size. Numéro de notice : A2016-833 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164905 En ligne : http://dx.doi.org/10.5721/EuJRS20164905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82718 [article]

Influence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 15 - 37 Titre : Influence of tree species complexity on discrimination performance of vegetation indices Type de document : Article/Communication Auteurs : Azadeh Ghiyamat, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Abdul Rashid Mohamed Shariff, Auteur Année de publication : 2016 Article en page(s) : pp 15 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] analyse discriminante [Termes IGN] espèce végétale [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] indice de végétation [Termes IGN] information complexe [Termes IGN] Pinus nigra corsicana [Termes IGN] Pinus sylvestris [Termes IGN] test de performance Résumé : (auteur) Performance of different vegetation indices (VIs) in combination with single- and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%. Numéro de notice : A2016-834 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164902 En ligne : http://dx.doi.org/10.5721/EuJRS20164902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82723 [article]