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Modelling forest canopy trends with on-demand spatial simulation / Gordon M. Green in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 1-2 (January - February 2016) . - pp 61 - 73 Titre : Modelling forest canopy trends with on-demand spatial simulation Type de document : Article/Communication Auteurs : Gordon M. Green, Auteur ; Sean C. Ahearn, Auteur Année de publication : 2016 Article en page(s) : pp 61 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] champ aléatoire de Markov [Termes IGN] forêt [Termes IGN] image Terra-MODIS [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] service web géographique [Termes IGN] simulation numérique Résumé : (auteur) Understanding trends in forest canopy cover at local, national, and global scales is important for many applications, including policymaking related to forest carbon sequestration. Globally consistent land-cover data sets derived from MODerate-resolution Imaging Spectroradiometer (MODIS) are now available for a period of more than 10 years, long enough to detect trends both in deforestation and in afforestation. However, methods of modelling land-cover change normally require specialized software and expertise, limiting the availability of this information. This barrier to access can be eliminated through the use of web services that construct models on demand based on user-specified regions of interest, so that parameters are inferred from, and relevant to, local conditions. In this paper we present a proof-of-concept system for building and running spatial Markov chain models of forest-cover change on demand, and demonstrate how the on-demand approach may be implemented for similar applications. Numéro de notice : A2016-010 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1066791 En ligne : http://dx.doi.org/10.1080/13658816.2015.1066791 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79331 [article]

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A multi-scale approach to mapping canopy height / Gordon M. Green in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 2 (February 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 2 (February 2013) . - pp 185 - 194 Titre : A multi-scale approach to mapping canopy height Type de document : Article/Communication Auteurs : Gordon M. Green, Auteur ; C. Ahearn, Auteur ; W. Ni-Meister, Auteur Année de publication : 2013 Article en page(s) : pp 185 - 194 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse multiéchelle [Termes IGN] base de données d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] données altimétriques [Termes IGN] données ICEsat [Termes IGN] données laser [Termes IGN] Etats-Unis [Termes IGN] fusion de données [Termes IGN] hauteur de la végétation [Termes IGN] occupation du sol [Termes IGN] service web géographique Résumé : (Auteur) Mapping vegetation height over large areas presents a problem of scale: height varies with the individual tree or stand, but the resolution of available datasets is too low to characterize this variability sufficiently for many applications. We address this problem by fusing 1 km resolution canopy height data derived from satellite-based laser altimetry with higher-resolution land-cover data, resulting in 30 m resolution estimates of canopy height. These are downscaled further to 1 m resolution by simulating individual trees. A web service architecture is used, which allows processing to occur on demand without preprocessing large datasets. We compared the resulting canopy volumes to reference airborne lidar data from 262 randomly located 1 km2 areas within nine study sites. Results at 30 m resolution show an RMSE of 33 percent of the mean reference volume and an R2 of 0.77; at 1 m the RMSE is 66 percent and the R2 is 0.38. Numéro de notice : A2013-078 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.2.185 En ligne : https://doi.org/10.14358/PERS.79.2.185 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32216 [article]