11 results on '"Achard, Frédéric"'
Search Results
2. Global Land-Cover Change: Recent Progress, Remaining Challenges
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Ramankutty, Navin, Graumlich, Lisa, Achard, Frédéric, Alves, Diogenes, Chhabra, Abha, DeFries, Ruth S., Foley, Jonathan A., Geist, Helmut, Houghton, Richard A., Goldewijk, Kees Klein, Lambin, Eric F., Millington, Andrew, Rasmussen, Kjeld, Reid, Robin S., Turner, Billie L., II, Lambin, Eric F., editor, and Geist, Helmut, editor
- Published
- 2006
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3. State and evolution of the African rainforests between 1990 and 2010
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Mayaux, Philippe, Pekel, Jean-François, Desclée, Baudouin, Donnay, François, Lupi, Andrea, Achard, Frédéric, Clerici, Marco, Bodart, Catherine, Brink, Andreas, Nasi, Robert, and Belward, Alan
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- 2013
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4. Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000
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Bodart, Catherine, Brink, Andreas B., Donnay, François, Lupi, Andrea, Mayaux, Philippe, and Achard, Frédéric
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- 2013
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5. Tropical Forest Cover Change in the 1990s and Options for Future Monitoring
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Mayaux, Philippe, Holmgren, Peter, Achard, Frédéric, Eva, Hugh, Stibig, Hans-Jürgen, and Branthomme, Anne
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- 2005
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6. The Land Cover component of the ESA Climate Change Initiative. Extending the series of global land cover maps to 2015 with PROBA-V: current achievements
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Defourny, Pierre, Bontemps, Sophie, Lamarche, Celine, Brockmann, Carsten, Achard, Frédéric, Boettcher, Martin, De Maet, Thomas, Gamba, Paolo, Hagemann, Stephan, PROBA-V Symposium, and UCL - SST/ELI/ELIE - Environmental Sciences
- Subjects
PROBA-V ,Global ,Climate Change Initiative ,Land Cover ,European Space Agency - Abstract
Essential Climate Variables (ECVs) were listed by the Global Climate Observing System (GCOS) as critical information to further understand the climate system and support climate modelling. In response, the European Space Agency (ESA) launched its Climate Change Initiative (CCI) in order to deliver global datasets matching the need for long-term satellite-based products for the climate domain. The ESA Land Cover CCI (LC_CCI) project, dedicated to the Land Cover ECV, built on the ESA-GlobCover experiences to revisit all algorithms required for the generation of global LC products from various Earth Observation (EO) instruments that meet the needs of key users of the climate modelling community. The first phase of the LC_CCI project delivered a new generation of satellite-derived global land cover products consisting in three maps at 300 m spatial resolution for three epochs centered on the years 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002). These maps were obtained from SPOT-Vegetation and ENVISAT-MERIS time series. Other significant outputs were (i) 7-day surface reflectance time series for the whole archive of MERIS Full and Reduced Resolution data (2002-2012), (ii) land surface seasonality products describing the seasonal variability of the land surface for the vegetation greenness, snow cover and burned areas and (iii) a global map of open water bodies at 300 m spatial resolution derived from Envisat ASAR and ancillary data. All products were delivered with an aggregation tool, enabling re-projection, re-sampling and translation from LC classes into Plant Functional Types for the different climate models. Three major Earth System models already investigated these new products as land surface information. During this second phase (2014-2016), one objective of the LC_CCI project is to generate new global LC products covering the 1990s and 2015. Extending the series of LC maps to 2015 will rely exclusively on PROBA-V time series. These time series are composited from daily to weekly, then to seasonal periods, using the mean compositing algorithm and used as input to the classification chain. The change detection algorithm, originally based on SPOT-Vegetation data, is updated to the specificities of the PROBA-V time series covering 2013 to 2015. The challenge is to ensure a consistency between the successive global LC maps while they are derived from different sensors.
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- 2016
7. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015.
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Keenan, Rodney J., Reams, Gregory A., Achard, Frédéric, de Freitas, Joberto V., Grainger, Alan, and Lindquist, Erik
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FOREST products ,LAND cover ,TROPICAL forests ,REMOTE sensing ,COMPARATIVE studies - Abstract
The area of land covered by forest and trees is an important indicator of environmental condition. This study presents and analyses results from the Global Forest Resources Assessment 2015 (FRA 2015) of the Food and Agriculture Organization of the United Nations. FRA 2015 was based on responses to surveys by individual countries using a common reporting framework, agreed definitions and reporting standards. Results indicated that total forest area declined by 3%, from 4128 M ha in 1990 to 3999 M ha in 2015. The annual rate of net forest loss halved from 7.3 M ha y −1 in the 1990s to 3.3 M ha y −1 between 2010 and 2015. Natural forest area declined from 3961 M ha to 3721 M ha between 1990 and 2015, while planted forest (including rubber plantations) increased from 168 M ha to 278 M ha. From 2010 to 2015, tropical forest area declined at a rate of 5.5 M ha y −1 – only 58% of the rate in the 1990s – while temperate forest area expanded at a rate of 2.2 M ha y −1 . Boreal and sub-tropical forest areas showed little net change. Forest area expanded in Europe, North America, the Caribbean, East Asia, and Western-Central Asia, but declined in Central America, South America, South and Southeast Asia and all three regions in Africa. Analysis indicates that, between 1990 and 2015, 13 tropical countries may have either passed through their forest transitions from net forest loss to net forest expansion, or continued along the path of forest expansion that follows these transitions. Comparing FRA 2015 statistics with the findings of global and pan-tropical remote-sensing forest area surveys was challenging, due to differences in assessment periods, the definitions of forest and remote sensing methods. More investment in national and global forest monitoring is needed to provide better support for international initiatives to increase sustainable forest management and reduce forest loss, particularly in tropical countries. [ABSTRACT FROM AUTHOR]
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- 2015
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8. Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps.
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Bartalev, Svyatoslav S., Kissiyar, Ouns, Achard, Frédéric, Bartalev, Sergey A., and Simonetti, Dario
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FORESTS & forestry ,LAND cover ,LANDSAT satellites ,REGRESSION analysis ,FOREST maps ,REMOTE-sensing images - Abstract
The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level. The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation. First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover. A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale. [ABSTRACT FROM PUBLISHER]
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- 2014
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9. Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach.
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Beuchle, René, Grecchi, Rosana Cristina, Shimabukuro, Yosio Edemir, Seliger, Roman, Eva, Hugh Douglas, Sano, Edson, and Achard, Frédéric
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LAND cover , *CERRADO ecology , *BIOMES , *ECOLOGICAL zones , *LANDSAT satellites - Abstract
The main objective of our study was to provide consistent information on land cover changes between the years 1990 and 2010 for the Cerrado and Caatinga Brazilian seasonal biomes. These areas have been overlooked in terms of land cover change assessment if compared with efforts in monitoring the Amazon rain forest. For each of the target years (1990, 2000 and 2010) land cover information was obtained through an object-based classification approach for 243 sample units (10 km × 10 km size), using (E)TM Landsat images systematically located at each full degree confluence of latitude and longitude. The images were automatically pre-processed, segmented and labelled according to the following legend: Tree Cover (TC), Tree Cover Mosaic (TCM), Other Wooded Land (OWL), Other Land Cover (OLC) and Water (W). Our results indicate the Cerrado and Caatinga biomes lost (gross loss) respectively 265,595 km 2 and 89,656 km 2 of natural vegetation (TC + OWL) between 1990 and 2010. In the same period, these areas also experienced gain of TC and OWL. By 2010, the percentage of natural vegetation cover remaining in the Cerrado was 47% and in the Caatinga 63%. The annual (net) rate of natural vegetation cover loss in the Cerrado slowed down from −0.79% yr −1 to −0.44% yr −1 from the 1990s to the 2000s, while in the Caatinga for the same periods the rate increased from −0.19% yr −1 to −0.44% yr −1 . In summary, these Brazilian biomes experienced both loss and gains of Tree Cover and Other Wooded Land; however a continued net loss of natural vegetation was observed for both biomes between 1990 and 2010. The average annual rate of change in this period was higher in the Cerrado (−0.6% yr −1 ) than in the Caatinga (−0.3% yr −1 ). [ABSTRACT FROM AUTHOR]
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- 2015
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10. An automated approach for segmenting and classifying a large sample of multi-date Landsat imagery for pan-tropical forest monitoring
- Author
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Raši, Rastislav, Bodart, Catherine, Stibig, Hans-Jürgen, Eva, Hugh, Beuchle, René, Carboni, Silvia, Simonetti, Dario, and Achard, Frédéric
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FOREST monitoring , *FOREST mapping , *IMAGE analysis , *LAND cover , *ENVIRONMENTAL impact analysis , *REMOTE sensing - Abstract
Abstract: The TREES-3 project of the Joint Research Centre aims at assessing tropical forest cover changes for the periods 1990–2000 and 2000–2010 using a sample-based approach. This paper refers to the 1990–2000 assessment. Extracts of Landsat satellite imagery (20km×20km) are analyzed for these reference dates for more than 4000 sample sites distributed systematically across the tropical belt. For the processing and analysis of such a large amount of satellite imagery a robust methodological approach for forest mapping and change detection has been developed. This approach comprises two automated steps of multi-date image segmentation and object-based land cover classification (based on a supervised spectral library), followed by an intense phase of visual control and expert refinement. Image segmentation is done at two spatial scales, introducing the concept of a minimum mapping unit via the automated selection of a site-specific scale parameter. The automated segmentation of land cover polygons and the pre-classification of land cover types mainly aim at avoiding manual delineation and at reducing the efforts of visual interpretation of land cover to a reasonable level, making the analysis of 4000 sample sites feasible. The importance of visual control and correction can be perceived when comparing to the initial automatic classification result: about 20% of the polygon labels were changed through expert knowledge by visual interpretation. The component of visual refinement of the mapping results had also a notable impact on forest area and change estimates: for a set of sample sites in Southeast Asia (~90% of all sites of SE-Asia) the rate of change in tree cover (deforestation) was assessed at 0.9% and 1.6% before and after visual control, respectively. [Copyright &y& Elsevier]
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- 2011
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11. Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics
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Bodart, Catherine, Eva, Hugh, Beuchle, René, Raši, Rastislav, Simonetti, Dario, Stibig, Hans-Jürgen, Brink, Andreas, Lindquist, Erik, and Achard, Frédéric
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REMOTE sensing , *FOREST canopies , *FOREST management , *IMAGE processing , *CALIBRATION , *SPECTRORADIOMETER , *LAND cover - Abstract
Abstract: In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990–2000–2005. The paper presents the pre-processing steps applied in an operational and robust manner to this large amount of multi-date and multi-scene imagery: conversion to top-of-atmosphere reflectance, cloud and cloud shadow detection, haze correction and image radiometric normalization. The results show that the haze correction algorithm has improved the visual appearance of the image and significantly corrected the digital numbers for Landsat visible bands, especially the red band. The impact of the normalization procedures (forest normalization and relative normalization) was assessed on 210 image pairs: in all cases the correlation between the spectral values of the same land cover in both images was improved. The developed automatic pre-processing chain provided a consistent multi-temporal data set across the tropics that will constitute the basis for an automatic object-based supervised classification. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
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