Remote sensing of land use and carbon losses following tropical deforestation

The new Paris Agreement, approved by 195 countries under the auspice of the United Nations Framework Convention on Climate Change (UNFCCC), calls for limiting global warming to “well below" 2°Celsius. An important part of the climate agreement relates to reducing emissions from deforestation and forest degradation, and enhancing carbon stocks (REDD+) in non-Annex I (mostly developing) countries. Over the last decades the growing demand for food, fibre and fuel has accelerated the pace of forest loss. In consequence, tropical deforestation and forest degradation are responsible for a large portion of global carbon emissions to the atmosphere, and destroy an important global carbon sink that is critical in future climate change mitigation. Within the REDD+ framework, participating countries are given incentives to develop national strategies and implementation plans that reduce emissions and enhance sinks from forests and to invest in low carbon development pathways. For REDD+ activities to be effective, accurate and robust methodologies to estimate emissions from deforestation and forest degradation are crucial. Remote sensing is an essential REDD+ observation tool, and in combination with ground measurements it provides an objective, practical and cost-effective solution for developing and maintaining REDD+ monitoring systems. The remote sensing monitoring objective for REDD+ is not only to map deforestation but also to support policy formulation and implementation. Identifying and addressing drivers and activities causing forest carbon change is crucial in this respect. Despite the importance of identifying and addressing drivers, quantitative information on these drivers, and the related carbon emissions, is scarce at the national level. The main objective of this thesis is to explore 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. To achieve this, this thesis investigates the following research questions: What is the current role and potential of remote sensing technologies and methodologies for monitoring tropical forests for REDD+ and for assessing drivers of deforestation? What is the current state of knowledge on drivers of deforestation and degradation in REDD+ countries? What are land use patterns and related carbon emissions following deforestation, capitalising on available land use and biomass remote sensing data? The research conducted in this PhD thesis contributes to the understanding of the role of remote sensing in forest monitoring for REDD+ and in the assessment of drivers of deforestation. In addition, this thesis contributes to the improvement of spatial and temporal quantification of land use and related carbon emissions linked to drivers of tropical deforestation. The results and insights described herein are valuable for ongoing REDD+ forest monitoring efforts and capacity development as REDD+ moves closer to becoming an operational mitigation mechanism.