• 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.

  • Tropical deforestation drivers and associated carbon emission factors derived from remote sensing data

    De Sy V, Herold M, Achard F et al.
    Environmental Research Letters

    This study quantified post-deforestation land use across the tropics for the period 1990–2000. This dataset was then combined with a pan-tropical AGB map at 30 m resolution to refine emission factor from forest conversion by matching deforestation areas with their carbon stock before and after clearing and to assess spatial dynamics by follow-up land use.

  • Impacts of intensifying or expanding cereal cropping in sub‐Saharan Africa on greenhouse gas emissions and food security

    van Loon MP, Hijbeek R, ten Berge HM, De Sy V et al.
    Global change biology

    This study assesses the impact of achieving cereal self‐sufficiency by the year 2050 for 10 sub-Saharan countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion.

  • Independent data for transparent monitoring of greenhouse gas emissions from the land use sector–What do stakeholders think and need?

    Romijn E, De Sy V, Herold M et al.
    Environmental Science & Policy

    Greenhouse gas emissions reduction from the land use sector requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Through an online survey, we investigated stakeholders’ data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. We also identify key elements for increasing overall transparency of data sources, definitions and methodologies.