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Auteur Mathias Neumann |
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Optimal resolution for linking remotely sensed and forest inventory data in Europe / Adam Moreno in Remote sensing of environment, vol 183 (15 September 2016)
[article]
Titre : Optimal resolution for linking remotely sensed and forest inventory data in Europe Type de document : Article/Communication Auteurs : Adam Moreno, Auteur ; Mathias Neumann, Auteur ; Hubert Hasenauer, Auteur Année de publication : 2016 Article en page(s) : pp 109 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale
[Termes IGN] Autriche
[Termes IGN] Belgique
[Termes IGN] Corine Land Cover
[Termes IGN] Finlande
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Union Européenne
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forests provide critical ecosystem services that ensure the sustainability of the environment and society. To manage forests on large scales, spatially explicit gridded data that describes the characteristics of these forests over the entire study area are required. There have been multiple efforts to create such data on regional and global scales. This type of gridded spatially explicit data on forest characteristics are typically done by integrating terrestrial forest inventory (NFI) and satellite-based remotely sensed data. Many studies that incorporate remotely sensed data and forest inventory data often directly compare pixels to inventory plots. The standard resolution of 0.0083° is typically used to integrate these two types of data sets. There is an assumption that, when producing gridded data sets incorporating forest inventory data, the finer the resolution the better the information. This assumption may seem intuitive, however at this resolution, in Europe, each 0.0083° cell has on average 1 NFI plot, which results in a sample with 0 degrees of freedom that represents 0.02% of the cell area. In this study, we challenge this assumption and we quantify the optimal resolution with which to compare and combine remotely sensed and NFI data from the largest collated and harmonized NFI data set in Europe including 196,434 plots. We determined that aggregating data with an original resolution of 0.0083° to between 0.0664° and 0.266° (or × 8 to × 32) produces the best agreement between these two forest inventory and remotely sensed data sets, and the lowest standard error in NFI data, and maintains the majority of the local-level spatial heterogeneity. Numéro de notice : A2016-707 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.05.021 En ligne : http://dx.doi.org/10.1016/j.rse.2016.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82079
in Remote sensing of environment > vol 183 (15 September 2016) . - pp 109 - 119[article]