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Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas / Ram Avtar in Geocarto international, vol 32 n° 8 (August 2017)  

Public [article]  inGeocarto international > vol 32 n° 8 (August 2017) . - pp 874 - 885 Titre : Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas Type de document : Article/Communication Auteurs : Ram Avtar, Auteur ; Pankaj Kumar, Auteur ; Akiko Oono, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 874 - 885 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aide à la décision [Termes IGN] forêt [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] image satellite [Termes IGN] mangrove [Termes IGN] service écosystémique [Termes IGN] surveillance écologique [Termes IGN] zone urbaine Résumé : (Auteur) The application of remote sensing (RS) techniques to monitor ecosystem services has increased in recent years. Nevertheless, the potential application of RS to monitor some of ecosystem services is still challenging. The paper reviews the applications of RS to monitor ecosystem services of forests, mangroves and urban areas. Satellite data provide substantial information about dynamics of environmental changes over time from local to global scale. These information are useful data sources for the people who are involved in the on-going evaluation and decision-making process to manage ecosystem. Many recent research papers on the topic were reviewed to find new applications and limitations of RS for monitoring ecosystem services. Advanced RS techniques have high potential to monitor ecosystem services with the advancement of sensors ranging from aerial photography to high and medium resolution optical RS and from hyperspectral RS to microwave RS. Numéro de notice : A2017-456 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1206974 Date de publication en ligne : 15/08/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1206974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86381 [article]

An algorithm for automated estimation of road roughness from mobile laser scanning data / Pankaj Kumar in Photogrammetric record, vol 30 n° 149 (March - May 2015)  

Public [article]  inPhotogrammetric record > vol 30 n° 149 (March - May 2015) . - pp 30 - 45 Titre : An algorithm for automated estimation of road roughness from mobile laser scanning data Type de document : Article/Communication Auteurs : Pankaj Kumar, Auteur ; Paul Lewis, Auteur ; Conor P. Mcelhinney, Auteur ; Alias Abdul-Rahman, Auteur Année de publication : 2015 Article en page(s) : pp 30 - 45 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données localisées 3D [Termes IGN] indicateur de qualité [Termes IGN] lasergrammétrie [Termes IGN] réseau routier [Termes IGN] rugosité de la route [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile Résumé : (Auteur) Road roughness is the deviation of a road surface from a designed surface grade that influences safety conditions for road users. Mobile laser scanning (MLS) systems provide a rapid, continuous and cost-effective way of collecting highly accurate and dense 3D point-cloud data along a route corridor. In this paper an algorithm for the automated estimation of road roughness from MLS data is presented, where a surface grid is fitted to the lidar points associated with the road surface. The elevation difference between the lidar points and their surface grid equivalents provides residual values in height which can be used to estimate roughness along the road surface. Tests validated the new road-roughness algorithm by successfully estimating surface conditions on multiple road sections. These findings contribute to a more comprehensive approach to surveying road networks. Numéro de notice : A2015-362 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12090 Date de publication en ligne : 22/02/2015 En ligne : https://doi.org/10.1111/phor.12090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76799 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
106-2015011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An automated algorithm for extracting road edges from terrestrial mobile LiDAR data / Pankaj Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 44 - 55 Titre : An automated algorithm for extracting road edges from terrestrial mobile LiDAR data Type de document : Article/Communication Auteurs : Pankaj Kumar, Auteur ; Conor P. Mcelhinney, Auteur ; Paul Lewis, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 44 - 55 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] extraction du réseau routier [Termes IGN] semis de points [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) Terrestrial mobile laser scanning systems provide rapid and cost effective 3D point cloud data which can be used for extracting features such as the road edge along a route corridor. This information can assist road authorities in carrying out safety risk assessment studies along road networks. The knowledge of the road edge is also a prerequisite for the automatic estimation of most other road features. In this paper, we present an algorithm which has been developed for extracting left and right road edges from terrestrial mobile LiDAR data. The algorithm is based on a novel combination of two modified versions of the parametric active contour or snake model. The parameters involved in the algorithm are selected empirically and are fixed for all the road sections. We have developed a novel way of initialising the snake model based on the navigation information obtained from the mobile mapping vehicle. We tested our algorithm on different types of road sections representing rural, urban and national primary road sections. The successful extraction of road edges from these multiple road section environments validates our algorithm. These findings and knowledge provide valuable insights as well as a prototype road edge extraction tool-set, for both national road authorities and survey companies. Numéro de notice : A2013-606 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.08.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32742 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013111	RAB	Revue	Centre de documentation	En réserve L003	Disponible