Thesis subject

MSc thesis topic: Monitoring ‘Regreening’ in the context of the ‘Regreening Africa’ project.

This thesis is in collaboration with the World Agroforestry centre (ICRAF) and aims to monitor tree regrowth from farmer managed natural regeneration (FMNR) and tree plantations (Regreening Africa). The Regreening Africa project is a 5-year EU-funded programme that started in 2017 and aims to reverse land degradation among 500.000 households, and across 1 million hectares in eight countries in Sub-Saharan Africa. Both FMNR as the regreening Africa project aim to reverse land degradation via land restoration and reforestation, and by doing this, build resilience to climate change and reconcile sustained food production.

In this research and as part of the regreening Africa project, the aim is explore the best available tools to monitor tree regrowth (both from plantations and FMNR). In the past many forest change detection algorithms were developed, but only a few also detect regrowth (Hansen et al., 2013; DeVries et al., 2015). One of the recently developed tools is the AVOCADO algorithm, a continuous forest monitoring tool that captures both deforestation/degradation and regrowth. The algorithm is based on the npphen function (Estay and Chávez, 2018).

Furthermore, the aim is to analyse if certain reforestation conditions explain the detections (or lack of detection) of regrowth. For this purpose we have field data (e.g. tree density) collected via the ‘regreening Africa app’.

Objectives

  • Test which model parameters and sensors can detect forest regrowth Combine the remote sensing results with the field data and analyse if the field data explains why (or why not) regrowth could be detected (e.g. minimum tree density, time since planting, etc.).

Literature

Requirements

  • Advanced modelling/programming skills (e.g Python, GEE, java script or R)
  • Advanced Earth Observation & Geo-scripting course

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