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Mapping ecosystem services at the regional scale: the validity of an upscaling approach
Authors:Solen Le Clec’h  Sean Sloan  Valéry Gond  Guillaume Cornu  Thibaud Decaens  Simon Dufour
Affiliation:1. Agricultural Economics and Policy, ETH Zürich, Zürich, Switzerland;2. Laboratoire LETG Rennes – COSTEL (UMR CNRS 6554), Université Européenne de Bretagne -Rennes 2, Rennes, France;3. School of Science and Engineering, Center for Tropical Environmental and Sustainability Science, James Cook University, Cairns, Australia;4. CIRAD, TA C-105 / D, Campus international de Baillarguet, Montpellier, France;5. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175 CNRS/Univ of Montpellier/Univ of Montpellier 3/EPHE/SupAgro Montpellier/INRA/IRD, Montpellier, France;6. Laboratoire LETG Rennes – COSTEL (UMR CNRS 6554), Université Européenne de Bretagne -Rennes 2, Rennes, France
Abstract:Mapping ecosystem services (ES) over large scales is important for environmental monitoring but is often prohibitively expensive and difficult. We test a hybrid, low-cost method of mapping ES indicators over large scales in Pará State, Brazil. Four ES indicators (vegetation carbon stocks, biodiversity index, soil chemical quality index and rates of water infiltration into soil) were measured in the field and then summarized spatially for regional land-cover classes derived from satellite imagery. The regionally mapped ES values correlated strongly with independent and local measures of ES. For example, regional estimates of the vegetation carbon stocks are strongly correlated with actual measures derived from field samples and validation data (significant anova test – p-value = 4.51e?9) and differed on average by only 20 Mg/ha from the field data. Our spatially-nested approach provides reliable and accurate maps of ES at both local and regional scales. Local maps account for the specificities of an area while regional maps provide an accurate generalization of an ES’ state. Such up-scaling methods infuse large-scale ES maps with localized data and enable the estimation of uncertainty of at regional scales. Our approach is first step towards the spatial characterization of ES at large and potentially global scales.
Keywords:Ecosystem services mapping  up-scaling  remote sensing  soil chemical quality  soil infiltrability  biodiversity  carbon stocks  Brazilian Amazon
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