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Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images / Andrew L. Fleming in European Journal of Forest Research, vol 134 n° 1 (January 2015)  

Public [article]  inEuropean Journal of Forest Research > vol 134 n° 1 (January 2015) . - pp 125 - 137 Titre : Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images Type de document : Article/Communication Auteurs : Andrew L. Fleming, Auteur ; Guangxing Wang, Auteur ; Ronald E. McRoberts, Auteur Année de publication : 2015 Article en page(s) : pp 125 - 137 Langues : Anglais (eng) Descripteur : [Termes IGN] carte thématique [Termes IGN] Etats-Unis [Termes IGN] Illinois (Etats-Unis) [Termes IGN] image Landsat-TM [Termes IGN] inventaire forestier étranger (données) [Termes IGN] prédiction [Termes IGN] puits de carbone [Termes IGN] régression linéaire [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. Numéro de notice : A2015-190 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-014-0838-y Date de publication en ligne : 05/08/2014 En ligne : https://doi.org/10.1007/s10342-014-0838-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75966 [article]