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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 397–402, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-397-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 397–402, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-397-2015

  29 Apr 2015

29 Apr 2015

Tropical Forest Remote Sensing Services for the Democratic Republic of Congo inside the EU FP7 ReCover Project (Final Results 2000-2012)

J. Haarpaintner1, D. de la Fuente Blanco2, F. Enßle3, P. Datta3, A. Mazinga4, C. Singa4, and L. Mane4 J. Haarpaintner et al.
  • 1Norut, Tromsø, Norway
  • 2GMV, Madrid, Spain
  • 3Albert-Ludwigs University Freiburg, Freiburg, Germany
  • 4OSFAC, Kinshasa, Democratic Republic of Congo

Keywords: REDD+, Forest, Forest Change, SAR, Congo Basin

Abstract. ‘ReCover’ was a 3-year EU-FP7 project (Nov. 2010 – Dec. 2013), aiming to develop and improve science based remote sensing services to support tropical forest management and activities to reduce emission from deforestation and forest degradation (REDD) in the tropical region (Häme et al., 2012). This is an overview of the final ReCover service delivery of 2000-2012 single-year optical (Landsat, ALOS AVNIR-2, RapidEye) and C-and L-band SAR (Envisat ASAR and ALOS Palsar, respectively) image mosaics, their derived forest/non-forest maps, a multi-sensor forest change map (2000-2010) and a biomass map (based on 2003-2009 ICESat GLAS) o he user of he De ocr ic Repub ic of Congo DRC), he Observatoir Satellitale des Forê s d’Afrique Cen r e OSFAC). The results are an improvement from a first iteration service delivery in 2012 after a critical review and validation process by both, the user and service providers, further method development and research, like a prior statistical data analysis considering temporal/seasonal variability, improved data pre-processing, and through the use of ground reference data collected in March 2013 for classification training. Validation with Kompsat-2 VHR data for the 2010 forest/non-forest maps revealed accuracies of 87% and 88% for optical and radar sensors, respectively.