Under the United Nations Framework Convention on Climate Change (UNFCCC), negotiations are in progress to develop a mechanism to reduce emissions from deforestation and forest degradation, and enhancing forest carbon stocks in (sub)tropical non-annex 1 countries (REDD+). In order for REDD+ activities to be effective, accurate and robust methodologies to estimate emissions from deforestation and forest degradation are crucial. Therefore, a national measurement, reporting and verification (MRV) system is required which follows the international Good Practice Guidelines of the Intergovernmental Panel on Climate Change (IPCC). Remote sensing is commonly considered an essential REDD+ observation tool and in combination with ground measurements it provides an objective, practical and cost-effective solution for developing and maintaining REDD+ MRV systems.
The need for data on drivers and activities causing forest carbon change has been highlighted as a central component in REDD+ readiness efforts. Monitoring drivers (e.g. deforestation by agricultural expansion, fuel wood extraction etc.) for REDD+ puts an emphasis on monitoring and tracking human activities. Remote sensing can help to provide information on follow-up land use, type and intensity of land changes, and shape and pattern of deforestation and degradation; which can generate understanding about proximate causes and drivers of deforestation and forest degradation.
The general objective of this research is to evaluate the role and options of remote sensing technologies for national forest monitoring for REDD+ (De Sy et al, 2012), with particular emphasis on identifying drivers of deforestation and forest degradation. The 2010 global remote sensing survey of the FAO Forest Resource Assessment will be used to quantify and assess regionally specific deforestation and degradation drivers, by the interpretation of forest change patches and follow-up land use in deforestation areas (FAO & JRC, 2012). These improved estimates generates novel opportunities for analysing relationships among specific drivers of deforestation and national circumstances, and for identifying underlying causes of deforestation. The focus for this symposium will be on a comparative pan-tropical assessment of regionally specific drivers of deforestation and the development of appropriate remote sensing methodologies for the assessment of these drivers.
The need for data on drivers and activities causing forest carbon change has been highlighted as a central component in REDD+ readiness efforts. Monitoring drivers (e.g. deforestation by agricultural expansion, fuel wood extraction etc.) for REDD+ puts an emphasis on monitoring and tracking human activities. Remote sensing can help to provide information on follow-up land use, type and intensity of land changes, and shape and pattern of deforestation and degradation; which can generate understanding about proximate causes and drivers of deforestation and forest degradation.
The general objective of this research is to evaluate the role and options of remote sensing technologies for national forest monitoring for REDD+ (De Sy et al, 2012), with particular emphasis on identifying drivers of deforestation and forest degradation. The 2010 global remote sensing survey of the FAO Forest Resource Assessment will be used to quantify and assess regionally specific deforestation and degradation drivers, by the interpretation of forest change patches and follow-up land use in deforestation areas (FAO & JRC, 2012). These improved estimates generates novel opportunities for analysing relationships among specific drivers of deforestation and national circumstances, and for identifying underlying causes of deforestation. The focus for this symposium will be on a comparative pan-tropical assessment of regionally specific drivers of deforestation and the development of appropriate remote sensing methodologies for the assessment of these drivers.
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Année de publication
2013
Auteurs
de Sy, V.; Herold, M.; Wijaya, A.; Verchot, L.V.; Lindquist, E.; Achard, F.
Langue
English
Mots clés
remote sensing, monitoring, carbon, emission, degraded forests, deforestation
