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Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review / Margaret E. Andrew in GIScience and remote sensing, vol 52 n° 3 (2015)  

Public [article]  inGIScience and remote sensing > vol 52 n° 3 (2015) . - pp 344 - 373 Titre : Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review Type de document : Article/Communication Auteurs : Margaret E. Andrew, Auteur ; Michael A. Wulder, Auteur ; Trisalyn Nelson, Auteur ; Nicholas C. Coops, Auteur Année de publication : 2015 Article en page(s) : pp 344 - 373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données localisées [Termes IGN] modèle conceptuel de données localisées [Termes IGN] occupation du sol [Termes IGN] service écosystémique [Vedettes matières IGN] Economie forestière Résumé : (auteur) Operational use of the ecosystem service (ES) concept in conservation and planning requires quantitative assessments based on accurate mapping of ESs. Our goal is to review spatial assessments of ESs, with an emphasis on the socioecological drivers of ESs, the spatial datasets commonly used to represent those drivers, and the methodological approaches used to spatially model ESs. We conclude that diverse strategies, integrating both spatial and aspatial data, have been used to map ES supply and human demand. Model parameters representing abiotic ecosystem properties can be supported by use of well-developed and widely available spatial datasets. Land-cover data, often manipulated or subject to modeling in a GIS, is the most common input for ES modeling; however, assessments are increasingly informed by a mechanistic understanding of the relationships between drivers and services. We suggest that ES assessments are potentially weakened by the simplifying assumptions often needed to translate between conceptual models and widely used spatial data. Adoption of quantitative spatial data that more directly represent ecosystem properties may improve parameterization of mechanistic ES models and increase confidence in ES assessments. Numéro de notice : A2015--103 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15481603.2015.1033809 En ligne : http://dx.doi.org/10.1080/15481603.2015.1033809 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87237 [article]