• Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series

    Masolele RN, De Sy V et al.
    Remote Sensing of Environment

    In this paper, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-scale classification of land-use following tropical deforestation using dense satellite time series on the pan-tropical scale. Continental models performed better than the pan-tropical model, while overall spatio-temporal models performed better than spatial or temporal ones.

  • Integrated assessment of deforestation drivers and their alignment with subnational climate change mitigation efforts

    Bos AB, De Sy V et al.
    Environmental Science & Policy

    Our interdisciplinary approach revealed the complexities of local direct and indirect DD drivers, and the complementarity of remotely sensed, spatially modelled and locally reported methods for driver identification. Overall, REDD+ interventions were found to be aligned with deforestation drivers.

  • Remote sensing of land use and carbon losses following tropical deforestation

    De Sy V
    PhD dissertation, Wageningen University

    The need for data on drivers and activities causing forest carbon change have been highlighted as central components in REDD+ readiness efforts. Assessment of direct and indirect drivers on the national level is often lacking or incomplete. This thesis explores the role of remote sensing for monitoring tropical forests for REDD+ in general, and for assessing land use and related carbon emissions linked to drivers of tropical deforestation in particular.

  • An assessment of deforestation and forest degradation drivers in developing countries

    Hosonuma N, Herold M, De Sy V et al.
    Environmental Research Letters

    Countries are encouraged to identify drivers of deforestation and forest degradation (DD) in the development of national strategies and action plans for REDD+. In this letter we provide an assessment of proximate drivers of DD by synthesizing empirical data reported by countries as part of their REDD+ readiness activities and scientific literature.