Mapping ‘Life on Land’

Abstract

In 2015, the 193 United Nations (UN) member states adopted the Sustainable Development Goals (SDGs). The mid-point of the SDG Agenda was reached in 2023 and while important progress has been made, the world is not on track to achieve the goals by 2030. Competing claims for land and resources by the different goals are among the reasons for the limited progress. SDG 15, the so-called ‘Life on Land’ goal aims to protect and manage ecosystems sustainably to preserve the diverse life forms and ensure that current and future generations can benefit from ecosystem services. This is often not integrated in the other SDGs. To find solutions that minimize trade-offs and exploit synergies between the SDGs, it is crucial to understand the (spatial) consequences of their implementation. Land system models can support this understanding by exploring different future pathways. When spatially-explicit, they can account for location-specific factors that determine the probable occurrence or disappearance of certain land systems. Knowledge on spatial distribution of land system patterns in the starting/baseline year is usually a requirement. With new remote sensing techniques, spatial data for many different land cover types are now available. Forest management, which plays a key role in SDG 15, is often ignored or simplified in global land system models and assessments, due to the lack of data. This thesis explores and provides methodological advancements that support gaining a better understanding of the spatial implications of achieving SDG 15. In chapter 2, provides a consistent, systematic approach to map different forest classes and uses - a stepping stone towards better mapping of forest management. Spatial understanding on how forests are managed has been lacking until now and the produced maps can be used in global change studies. The new knowledge on the spatial distribution of different forest classes and uses is applied in chapter 3, where I demonstrated how the newly developed data can be used in global biodiversity assessments. Here, the impact of accounting for different forest classes and uses on biodiversity assessments is explored. In the chapter, the changes in the spatial distribution of forest classes and uses are estimated, driven by projected future wood demands. The chapter explores the interaction between biodiversity loss, forest management and deforestation. In chapter 4, the spatial implications of the land degradation neutrality target, alongside demands for food, living space and resources are simulated in a case study in Turkey. While the results show that land degradation neutrality can be achieved, while still providing resources and housing for the national population, a large extent of afforestation would be required to achieve such a future. As chapter 4 highlights the importance of afforestation for restoration, chapter 5 analyses the spatial probability of short-rotation woody plantations, advancing on the methodological approaches of chapter 3 and 4. The impact of climate change on their future distribution is estimated, following three emission scenarios. The results show that especially in high emission scenarios, short-rotation woody plantations might become less suitable in many areas of the Pan-Tropics. Altogether, this thesis provides more nuanced spatial representation of different forest management characteristics and describes two major methodological advancements to better estimate the spatial implications of SDG 15. The research shows that different land management strategies can support achieving the targets of SDG 15. However, they often come with trade-offs for other targets and SDGs. Spatial knowledge is required to support better tree-planting, reforestation and restoration initiatives and avoid the downsides caused by tree-planting at the wrong locations. Global commitments that are implemented without considering the spatial realities can fail and could do more harm than good along the way.