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Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode / S.A. Soenen in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1007 - 1027 Titre : Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode Type de document : Article/Communication Auteurs : S.A. Soenen, Auteur ; Derek R. Peddle, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 1007 - 1027 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alberta (Canada) [Termes IGN] canopée [Termes IGN] classe d'objets [Termes IGN] correction du signal [Termes IGN] forêt tempérée [Termes IGN] image SPOT [Termes IGN] modélisation 3D [Termes IGN] Pinus (genre) [Termes IGN] précision de la classification [Termes IGN] réflectance végétale [Termes IGN] variance Résumé : (Auteur) In most forestry remote sensing applications in steep terrain, simple photometric and empirical (PE) topographic corrections are confounded as a result of stand structure and species assemblages that vary with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we present MFM-TOPO as a new physically-based modelling (PBM) approach for normalising topographically induced signal variance as a function of forest stand structure and sub-pixel scale components. MFM-TOPO uses the Li-Strahler geometric optical mutual shadowing (GOMS) canopy reflectance model in Multiple Forward Mode (MFM) to account for slope and aspect influences directly. MFM-TOPO has an explicit physical-basis and uses sun-canopy-sensor (SCS) geometry that is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. MFM-TOPO is compared against our recently developed SCS+C correction and a comprehensive set of other existing PE and SCS methods (cosine, C correction, Minnaert, statistical-empirical, SCS, and b correction) for removing topographically induced variance and for improving SPOT image classification accuracy in a Rocky Mountain forest in Kananaskis, Alberta Canada. MFM-TOPO removed the most terrain-based variance and provided the greatest improvement in classification accuracy within a species and stand density based class structure. For example, pine class accuracy was increased by 62% over shaded slopes, and spruce class accuracy was increased by 13% over more moderate slopes. In addition to classification, MFM-TOPO is suitable for retrieving biophysical parameters in mountainous terrain. Numéro de notice : A2008-007 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701311291 En ligne : https://doi.org/10.1080/01431160701311291 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29002 [article]

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