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MSc thesis subject: Assessing forest emission sinks in Peru

Forests play an important role in the new Paris climate agreement, mainly through REDD+ in tropical developing countries: Reducing Emissions from Deforestation and forest Degradation and the enhancement of forest carbon stocks. REDD+ is thus not only about reducing emissions from forest loss but also about increasing the forest emission sink. Despite the importance of the forest sinks in the tropics, little larger scale quantification exists. Remote sensing can be used to quantify forest sinks in a spatially explicit way.

Both increases in forest area (forest cover gain) and carbon sequestration in existing forests contribute to the forest sink. Remote sensing products can help to identify forest cover gain (Hansen et al. 2013, Sexton et al. 2013), and the associated increase in forest biomass (Avitabile et al. 2016) to quantify carbon sequestration. The potential carbon sequestration of young regrowing forests can be assessed by combining a forest age map with known relationships between forest age and carbon accumulation rates (Chazdon et al. 2016). You will explore and combine these approaches to assess the size of the forest sink in Peru.

Objectives

  • Asses the forest emission sink (potential) in Peru from forest gain and forest (re)growth
  • Asses the usefulness, advantages and disadvantages of different existing remote sensing products for assessing forest sinks.

Literature

  • Avitabile V et al. (2016) An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology 22:1406-1420.
    Chazdon RL et al. (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Science Advances 2:e1501639.
  • Hansen MC et al. (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850-853.
  • Sexton JO et al. (2013) Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS Vegetation Continuous Fields with lidar-based estimates of error. International Journal of Digital Earth 6:427-448.

Requirements

  • Analytical skills
  • Interest in spatial analysis
  • Scripting skills (in R) would be useful

Theme(s): Sensing & measuring, Integrated Land Monitoring