Centre de documentation
scientifique

Mapping a priori defined plant associations using remotely sensed vegetation characteristics / Hans D. Rölofsen in Remote sensing of environment, vol 140 (January 2014)  

Public [article]  inRemote sensing of environment > vol 140 (January 2014) . - pp 639 - 651 Titre : Mapping a priori defined plant associations using remotely sensed vegetation characteristics Type de document : Article/Communication Auteurs : Hans D. Rölofsen, Auteur ; Lammert Kooistra, Auteur ; Peter M. van Bodegom, Auteur ; Jochem Verrelst, Auteur ; Johan Krol, Auteur ; Jan-Philip M. Witte, Auteur Année de publication : 2014 Article en page(s) : pp 639 - 651 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation [Termes IGN] classification [Termes IGN] humidité du sol [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] nutriment végétal [Termes IGN] Pays-Bas [Termes IGN] phytosociologie [Termes IGN] placette d'échantillonnage [Termes IGN] répartition géographique [Termes IGN] salinité [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has only recently been confronted by the remote sensing community. We present an approach to map the spatial distribution of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate between spectral reflectance and association occurrences. For a 12 km2 study site in the Netherlands, the relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared (SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R2 0.73, 0.64 and 0.76 for salinity, moisture and nutrients, respectively). These relations were applied to map IVs for the complete study site. Association occurrence probabilities were modelled as function of IVs using a large database of vegetation plots with known association and IVs. Using the mapped IVs, we calculated occurrence probabilities of 19 associations for each pixel, resulting in both a crisp association map with the most likely occurring association per pixel, as well as occurrence probability maps per association. Association occurrence predictions were assessed by a local vegetation expert, which revealed that the occurrences of associations situated at frequently predicted indicator value combinations were over predicted. This seems primarily due to biases in the GPR predicted IVs, resulting in associations with envelopes located in extreme ends of IVs being scarcely predicted. Although the results of this particular study were not fully satisfactory, the method potentially offers several advantages compared to current vegetation classification techniques, like site-independent calibration of association probabilities, site-independent selection of associations and the provision of IV maps and occurrence probabilities per association. If the prediction of IVs can be improved, this method may thus provide a viable roadmap to bring a priori defined plant associations into the domain of remote sensing. Numéro de notice : A2014-796 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2013.09.030 En ligne : https://doi.org/10.1016/j.rse.2013.09.030 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81015 [article]