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High-resolution forest canopy height estimation in an African blue carbon ecosystem / David Lagomasino in Remote sensing in ecology and conservation, vol 1 n° 1 (October 2015)    

Public [article]  inRemote sensing in ecology and conservation > vol 1 n° 1 (October 2015) . - pp 51 - 60 Titre : High-resolution forest canopy height estimation in an African blue carbon ecosystem Type de document : Article/Communication Auteurs : David Lagomasino, Auteur ; Temilola Fatoyinbo, Auteur ; Seung-Kuk Lee, Auteur ; Marc Simard, Auteur Année de publication : 2015 Article en page(s) : pp 51 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] biomasse [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] hauteur des arbres [Termes IGN] mangrove [Termes IGN] MNS SRTM [Termes IGN] Mozambique Résumé : (auteur) Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereo-photogrammetric techniques on high-resolution spaceborne imagery (HRSI) for southern Mozambique. A mean-weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18-1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three-dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications. Numéro de notice : A2015--101 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.3 En ligne : http://doi.org/10.1002/rse2.3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87170 [article]

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