The large H2020 project ECOPOTENTIAL (2015-2019, 47 partners, contributing to GEO and GEOSS http://www.ecopotential-project.eu/) is devoted to making best use of remote sensing and in situ data to improve future ecosystem benefits, adopting the view of ecosystems as one physical system with their environment, focusing on geosphere-biosphere interactions, Earth Critical Zone dynamics, Macrosystem Ecology and cross-scale interactions, the effect of extreme events and using Essential (Climate, Biodiversity and Ocean) Variables as descriptors of change. In ECOPOTENTIAL, remote sensing and in situ data are collected, processed and used for a betterunderstandingoftheecosystemdynamics,analysingandmodellingtheeffectsofglobalchangesonecosystem functions and services, over an array of different ecosystem types, including mountain, marine, coastal, arid and semi-arid ecosystems. The project focuses on a network of Protected Areas of international relevance, that is representative of the range of environmental and biogeographical conditions characterizing Europe. Some of the activitiesoftheprojectaredevotedtodetectandquantifythechangestakingplaceintheProtectedAreas,through the analysis of remote sensing observations, in-situ data and gridded climatic datasets. Likewise, the project aims atprovidingestimatesofthefutureecosystemconditionsindifferentclimateandenvironmentalchangescenarios. In all such endeavours, one is faced with cross-scale issues: downscaling of climate information to drive ecosystem response, and upscaling of local ecosystem changes to larger scales. So far, the analysis has been conducted mainly by using traditional methods, but there is wide room for improvement by using more refined approaches. In particular, a crucial question is how to upscale the information gained at single-site scale to larger, regional or continental scale, an issue that could benefit from using, for example, complex network analysis.
Geosphere-biosphere interactions in European Protected Areas: a view from the H2020 ECOPOTENTIAL Project
Antonello Provenzale;Carmela Marangi;Mariasilvia Giamberini;Simona Imperio
2017
Abstract
The large H2020 project ECOPOTENTIAL (2015-2019, 47 partners, contributing to GEO and GEOSS http://www.ecopotential-project.eu/) is devoted to making best use of remote sensing and in situ data to improve future ecosystem benefits, adopting the view of ecosystems as one physical system with their environment, focusing on geosphere-biosphere interactions, Earth Critical Zone dynamics, Macrosystem Ecology and cross-scale interactions, the effect of extreme events and using Essential (Climate, Biodiversity and Ocean) Variables as descriptors of change. In ECOPOTENTIAL, remote sensing and in situ data are collected, processed and used for a betterunderstandingoftheecosystemdynamics,analysingandmodellingtheeffectsofglobalchangesonecosystem functions and services, over an array of different ecosystem types, including mountain, marine, coastal, arid and semi-arid ecosystems. The project focuses on a network of Protected Areas of international relevance, that is representative of the range of environmental and biogeographical conditions characterizing Europe. Some of the activitiesoftheprojectaredevotedtodetectandquantifythechangestakingplaceintheProtectedAreas,through the analysis of remote sensing observations, in-situ data and gridded climatic datasets. Likewise, the project aims atprovidingestimatesofthefutureecosystemconditionsindifferentclimateandenvironmentalchangescenarios. In all such endeavours, one is faced with cross-scale issues: downscaling of climate information to drive ecosystem response, and upscaling of local ecosystem changes to larger scales. So far, the analysis has been conducted mainly by using traditional methods, but there is wide room for improvement by using more refined approaches. In particular, a crucial question is how to upscale the information gained at single-site scale to larger, regional or continental scale, an issue that could benefit from using, for example, complex network analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
