A new 300 m vegetation map for Central Africa based on multi-sensor times series
(2010) Third recent advances in quantitative remote sensing — Location: Valencia, Spain (27.September.2010)
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- Abstract
- An operational characterization of the extent and location of vegetation types of the humid tropics is essential for studies concerning habitat and biodiversity monitoring, forest resources management, Carbon accounting, biogeochemical and climatic cycles. Central Africa contains the second largest and the least degraded area of contiguous tropical forest of the world. Estimating vegetation cover in this remote region would be a major challenge without the use of satellite imagery. However, in spite of recent progress in earth observation, vegetation mapping in tropical areas still represents an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. In the present study, the vegetation types of 8 countries in Central Africa have been mapped thanks to a semi-automatic processing method based on temporal and spectral information from 19 months of ENVISAT MERIS FRS observation and 8 years of SPOT VEGETATION time series. The approach is based on a previous 1-km mapping effort for the Democratic Republic of Congo and on the lessons learnt from the ESA-GlobCover project. A land cover map with 20 vegetation classes was produced in five major steps: data compositing, seasonal stratification of the study zone, unsupervised classifications, automatic labelling and manual editing. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. This mapping exercise will be a reference document to deliver area estimates of the different forest types in a consistent way for DRCongo, Gabon, Cameroon, Equatorial Guinea, Central African Republic, Congo, Rwanda and Burundi.
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Verhegghen, A., & Defourny, P. (2010). A new 300 m vegetation map for Central Africa based on multi-sensor times series. Third recent advances in quantitative remote sensing. Third recent advances in quantitative remote sensing, Valencia, Spain. https://hdl.handle.net/2078.5/150167