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Ecoclimap-ii/Europe: a Twofold Database of Ecosystems and Surface Parameters at 1 Km Resolution Based on Satellite Information for Use in Land Surface, Meteorological and Climate Models : Volume 6, Issue 2 (30/04/2013)

By Faroux, S.

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Book Id: WPLBN0003987014
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Reproduction Date: 2015

Title: Ecoclimap-ii/Europe: a Twofold Database of Ecosystems and Surface Parameters at 1 Km Resolution Based on Satellite Information for Use in Land Surface, Meteorological and Climate Models : Volume 6, Issue 2 (30/04/2013)  
Author: Faroux, S.
Volume: Vol. 6, Issue 2
Language: English
Subject: Science, Geoscientific, Model
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Moigne, P. L., Masson, V., Kaptué Tchuenté, A. T., Faroux, S., Martin, E., & Roujean, J. (2013). Ecoclimap-ii/Europe: a Twofold Database of Ecosystems and Surface Parameters at 1 Km Resolution Based on Satellite Information for Use in Land Surface, Meteorological and Climate Models : Volume 6, Issue 2 (30/04/2013). Retrieved from http://hawaiilibrary.net/


Description
Description: CNRM-GAME (Météo France, CNRS), UMR3589, 42 avenue Gaspard Coriolis, 31057 Toulouse CEDEX, France. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

Summary
ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

Excerpt
Bicheron, P., Leroy, M., Brockmann, C., Krämer, U., Miras, B., Huc, M., Nino, F., Defourny, P., Vancutsem, C., Arino, O., Ranéra, F., Petit, D., Amberg, V., Berthelot, B., and Gross, D.: GLOBCOVER: a 300 m global land cover product for 2005 using ENVISAT/MERIS time series, Proceedings of the Recent Advances in Quantitative Remote Sensing Symposium, Valencia, 2006.; Bonan, G., Levis, S., Sitch, S., Vertenstein, M., and Oleson, K.: A dynamic global vegetation model for use in climate models: concepts and description of simulated vegetation dynamics, Glob. Change Biol., 9, 1543–1566, 2003.; Boone, A., de Rosnay, P., Balsamo, G., Beljaars, A., Chopin, F., Decharme, B., Delire, C., Ducharne, A., Gascoin, S., Grippa, M., Guichard, F., Gusev, Y., Harris, P., Jarlan, L., Kergoat, L., Mougin, E., Nasonova, O., Norgaard, A., Orgeval, T., Ottle, C., Poccard-Leclercq, I., Polcher, J., Sandholt, I., Saux-Picart, S., Taylor, C., and Xue, Y. K.: The Amma Land Surface Model Intercomparison Project (ALMIP), B. Am. Meteorol. Soc., 90, 1865–1880, 2009.; Dawson, T., North, P., Plummer, S., and Curran, P.: Forest ecosystem chlorophyll content: implications for remotely sensed estimates of net primary productivity, Int. J. Remote Sens., 24, 611–617, 2003.; Defries, R., Hansen, M., and Townshend, J.: Global Discrimination of land cover types from metrics derived from AVHRR Pathfinder data, Remote Sens. Environ., 54, 209–222, 1995.; Di Gregorio, A. and Jansen, L.: Food & the United Nations, Land cover classifications system (LCCS), classification concepts and user manual, Rome, Italy, 2000.; EC: Regionalization and stratification of European forest ecosystems, European Commission, 1995.; EEA: Sustainable use and management of natural resources, European Environment Agency, 2005.; FAO: Climate change and food security: a framework document, Food and Agriculture Organisation of the United Nations, Italy, 2008.; Koeppe, C. E. and De Long, G. C.: Weather and climate, McGraw-Hill, 341 pp., 1958.; Avissar, R. and Pielke, R. A.: A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology, Mon. Weather Rev., 117, 2113–2136, 1989.; Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Nino, F., Weiss, M., Samain, O., Roujean, J., and Leroy, M.: LAI, FAPAR and FCover CYCLOPES global products derived from Vegetation, Part 1: principles of the algorithm, Remote Sens. Environ., 110, 305–316, 2007.; Bartholomé, E. and Belward, A.: GLC2000: A new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.; Friedl, M., Mciver, D., Hodges, J., Zhang, X., Muchoney, D., Strahler, A., Woodcock, C., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287–302, 2002.; Fritz, S. and See, L.: Quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications, Glob. Change Biol., 14, 1057–1075, 2008.; Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F., and Obersteiner, M.: Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover, Remote Sens., 1, 345–354, 2009.; Fritz, S., You, L., Bun, A., See, L., McCallum, Ian, Schill, C., Perger, C., Liu, J., Hansen, M., and Obersteiner, M.: Cropland for sub-Saharan Africa: A synergistic approach usingfive land cover data sets, Geophys. Res. Lett., 38, L04404, doi:10.1029/2010GL046213, 2011.; GEOSS: The global Earth Observation System of Systems GEOSS 10-Year Implementation Plan, 2005.; Gitay, H. and Noble, I.: Plant functional types: their relevance to ecosystem properties and global change, Part I: What are functional types and how should we seek them?, edited by: Smith, T., Shugart, H., and Woodward, F., Cambridge U

 

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