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Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 15 - 25 Titre : Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics Type de document : Article/Communication Auteurs : Jasper A. Slingsby, Auteur ; Glenn R. Moncrieff, Auteur ; Adam M. Wilson, Auteur Année de publication : 2020 Article en page(s) : pp 15 - 25 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] approche hiérarchique [Termes IGN] biodiversité [Termes IGN] classification bayesienne [Termes IGN] détection de changement [Termes IGN] écosystème [Termes IGN] incendie [Termes IGN] internet interactif [Termes IGN] Le Cap [Termes IGN] milieu naturel [Termes IGN] modèle dynamique [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] surveillance de la végétation [Termes IGN] surveillance écologique Résumé : (auteur) Managing fire, water, biodiversity and carbon stocks can greatly benefit from early warning of changes in the state of vegetation. While near-real time tools to detect forest change based on satellite remote sensing exist, these ecosystems have relatively stable natural vegetation dynamics. Open (i.e. non-forest) ecosystems like grasslands, savannas and shrublands are more challenging as they show complex natural dynamics due to factors such as fire, postfire recovery, greater contribution of bare soil to observed vegetation indices, as well as high sensitivity to rainfall and strong seasonality. Tools to aid the management of open ecosystems are desperately required as they dominate much of the globe and harbour substantial biodiversity and carbon. We present an innovative approach that overcomes the difficulties posed by open ecosystems by using a spatio-temporal hierarchical Bayesian model that uses data on climate, topography, soils and fire history to generate ecological forecasts of the expected land surface signal under natural conditions. This allows us to monitor and detect abrupt or gradual changes in the state of an ecosystem in near-real time by identifying areas where the observed vegetation signal has deviated from the expected natural variation. We apply our approach to a case study from the hyperdiverse fire-dependent African shrubland, the fynbos of the Cape Floristic Region, a Global Biodiversity Hotspot and UNESCO World Heritage Site that faces a number of threats to vegetation health and ecosystem function. The case study demonstrates that our approach is useful for identifying a range of change agents such as fire, alien plant species invasions, drought, pathogen outbreaks and clearing of vegetation. We describe and provide our full workflow, including an interactive web application. Our approach is highly versatile, allowing us to collect data on the impacts of change agents for research in ecology and earth system science, and to predict aspects of ecosystem structure and function such as biomass, fire return interval and the influence of vegetation on hydrology Numéro de notice : A2020-349 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.017 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95231 [article]

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