ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project

Tchuenté, Armel Thibaut Kaptué; Roujean, Jean‐Louis; Faroux, Stéphanie

doi:10.1016/j.rse.2009.12.008

Cited by 53 publications

(26 citation statements)

References 50 publications

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“…To fill in the gaps caused by cloud contamination, a 4-degree polynomial function is used. This approach is similar to that previously used by Mayaux et al (2004) and Kaptué et al (2010). NDVI time series are the central parameter used to process the automatic classification, as will be seen later (Set.…”

Section: Ndvi Data From Spot/vegetationmentioning

confidence: 99%

“…An example is the improvement of the description of biomes for south-western France, with which winter and summer crops could be separated, thereby leading to relevant detailed simulations of the atmospheric carbon dioxide in the CarboEurope Regional Experiment Strategy (CERES) (Sarrat et al, 2007). More recently, Kaptué et al (2010) developed a new ecosystem classification within the ECOCLIMAP-II programme, with 37 distinct types over West Africa. This database was developed over the AMMA (African Monsoon Multidisciplinary Analysis) zone to provide upgraded information on the land surface properties of the West Africa region.…”

Section: Principles Of the Ecoclimap Databasementioning

confidence: 99%

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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

et al. 2013

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Abstract. 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.

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Section: Ndvi Data From Spot/vegetationmentioning

confidence: 99%

Section: Principles Of the Ecoclimap Databasementioning

confidence: 99%

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

et al. 2013

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“…Potential evapotranspiration was estimated with the Penman-Monthieth equation based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (Uppala et al, 2005). The Ecoclimap data set (Tchuente et al, 2010), provided by Meteo France, was also used to identify seasonal and spatial variations of leaf area index.…”

Section: Model Application To the Chao Phraya River Basinmentioning

confidence: 99%

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

Sayama

Tatebe

Iwami

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. The Thailand floods in 2011 caused unprecedented economic damage in the Chao Phraya River basin. To diagnose the flood hazard characteristics, this study analyses the hydrologic sensitivity of flood runoff and inundation to rainfall. The motivation is to address why the seemingly insignificant monsoon rainfall, or 1.2 times more rainfall than for past large floods, including the ones in 1995 and 2006, resulted in such devastating flooding. To quantify the hydrologic sensitivity, this study simulated longterm rainfall-runoff and inundation for the entire river basin (160 000 km 2 ). The simulation suggested that the flood inundation volume was 1.6 times more in 2011 than for the past flood events. Furthermore, the elasticity index suggested that a 1 % increase in rainfall causes a 2.3 % increase in runoff and a 4.2 % increase in flood inundation. This study highlights the importance of sensitivity quantification for a better understanding of flood hazard characteristics; the presented basin-wide rainfall-runoff-inundation simulation was an effective approach to analyse the sensitivity of flood runoff and inundation at the river basin scale.

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“…The mixture with savanna leads to the largest underestimation (0.87, −43.7%) for evergreen needleleaf forest. The large errors related to shrubs and savanna for the evergreen forests could be due to the selection of wrong LUTs [26]. Evergreen broadleaf forest can be confused with deciduous broadleaf forest, which causes an underestimation of LAI up to 0.41 (−9.8%).…”

Section: Misclassification Induced Lai Errors (Mies)mentioning

confidence: 99%

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

Fang

Myneni

2013

Remote Sensing

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Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (<0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31).Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes.

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ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project

Cited by 53 publications

References 50 publications

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

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

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

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