Centre de documentation
scientifique

Geostatistical methods for predicting soil moisture continuously in a subalpine basin / Katherine E. Williams in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 4 (April 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 4 (April 2014) . - pp 333 - 341 Titre : Geostatistical methods for predicting soil moisture continuously in a subalpine basin Type de document : Article/Communication Auteurs : Katherine E. Williams, Auteur ; Sharolyn Anderson, Auteur Année de publication : 2014 Article en page(s) : pp 333 - 341 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Colorado (Etats-Unis) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] géostatistique [Termes IGN] humidité du sol [Termes IGN] hydrographie de surface [Termes IGN] image Ikonos [Termes IGN] pente [Termes IGN] régression géographiquement pondérée Résumé : (Auteur) This study presents spatial statistical methods for examining the distribution of soil moisture in a sub-alpine environment. The high local variability of soil moisture is not well characterized by spatial interpolation from dispersed data points. Interpolation using only field samples from Loch Vale, Rocky Mountain National Park, Colorado produced coarse estimates that followed mean soil moisture trends, but failed to capture local mid-slope variation. A properly specified regression model was identified by using dispersed field samples and ancillary data derived from Ikonos-2 and lidar data. This model predicted soil moisture patterns at a much finer spatial resolution. An intensive field campaign provided independent soil moisture measurements that were used to assess the model's accuracy. The modeled soil moisture estimates captured local variability associated with topographic terrain differences along mid-slope areas. Numéro de notice : A2014-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.4.333 En ligne : https://doi.org/10.14358/PERS.80.4.333 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33113 [article]