Large scale forest change monitoring using satellite data

In the last decade, time-series analysis of satellite data has been established as a tool for detecting and monitoring forest change at small land scale. This thesis aims to develop methods that advance time series forest change detection techniques from local, to regional and national scale usage, to the point where they can be used as tools for in international reporting such as that required by the United Nations Framework Convention on Climate Change (UNFCCC).

The research will focus on identifying and managing the problems associated with larger scale forest loss detection using time series analysis, and will evaluate possible options to improve forest change detection at large scale. Specifically,  the effect of different time-series algorithms, differences in forest type, forest type variation, data availability, and deforestation drivers, will be evaluated on the accuracy of the deforestation detection, while multi-sensor image time series will be tested as solutions to improve forest loss detection at large scale.