P
Pierre Defourny
Researcher at Université catholique de Louvain
Publications - 306
Citations - 10975
Pierre Defourny is an academic researcher from Université catholique de Louvain. The author has contributed to research in topics: Land cover & Vegetation. The author has an hindex of 53, co-authored 291 publications receiving 9107 citations. Previous affiliations of Pierre Defourny include Forschungszentrum Jülich & Asian Institute of Technology.
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Forest change detection by statistical object-based method
TL;DR: High detection accuracy and overall Kappa were achieved by OB-Reflectance method in temperate forests using three SPOT-HRV images covering a 10-year period.
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Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
TL;DR: In this paper, the authors assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables-age, top height, circumference, stand density and basal area-in even-aged common spruce stands.
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The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables
Rainer Hollmann,Christopher J. Merchant,RA Saunders,C Downy,Michael Buchwitz,Anny Cazenave,Emilio Chuvieco,Pierre Defourny,G. de Leeuw,René Forsberg,Thomas Holzer-Popp,Frank Paul,Stein Sandven,Shubha Sathyendranath,M. Van Roozendael,Wolfgang Wagner +15 more
TL;DR: The Climate Change Initiative (CCI) as discussed by the authors provides a forum to bring the data and modeling communities together to provide a climate system perspective and a forum for bringing data and modelling communities together.
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Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts
TL;DR: In this article, the authors developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques.
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Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery
Jordi Inglada,Marcela Arias,Benjamin Tardy,Olivier Hagolle,Silvia Valero,David Morin,Gérard Dedieu,Guadalupe Sepulcre,Sophie Bontemps,Pierre Defourny,Benjamin Koetz +10 more
TL;DR: Assessing to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale shows that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites.