Global Land-Use Dynamics
In this research field, we address a long-standing gap in Global Change and Sustainability-related sciences by developing consistent gridded data products on the recent global dynamics in multiple land-use variables. To this end, we integrate and harmonize heterogeneous data sources through advanced statistical modelling techniques, while addressing various data-quality issues throughout the data life cycle.
This work rests on the shoulders of an international collaborative network, which we coordinate at iDiv. The founding of the Land Use Change Knowledge Integration Network (LUCKINet, www.luckinet.org) was motivated by a shared vision of free, quality-assured, highly detailed and interoperable data products that depict global dynamics in different facets of land use over the past decades. Through different projects, the network develops a series of data products and tools that contribute to longer-lasting, collaborative, and open-science solutions for the continuous monitoring and analysis of global land-use dynamics. This will enable manifold applications in science, policy, and management for which currently available data products are woefully insufficient. The Macroecology and Society lab contributes to the LUCKINet program with several focused research projects (see project descriptions at the end of this page). All data products will be made openly available using FAIR principles.
LUCKINet emphasizes data quality. The accuracy of any modelled data product crucially hinges on the availability of detailed, high quality, and unbiased primary data that are available for model training and validation. Therefore, we invest substantially in the mobilization and integration of primary observational data on land-use from hundreds of disparate sources. We also take great care assessing the uncertainties in those input data and making them visible in the final gridded land-use maps.
LUCKINet emphasizes applicability. Gridded land-use data are vitally needed in many different fields, but different applications place very distinct demands on such data. We cater for these different needs by developing alternative versions of data products that are optimized for a range of typical downstream applications. Our computational routines can, for example, produce gridded data that are consistent with the official countrywide statistics published by national agencies and are thus ready to be used in global assessments to support international policy-making processes. On the other hand, applications tailored to environmental resource managers can provide highest-possible accuracy of the individual pixel values in their focal region by including local expert knowledge and richer covariate information, even if the results deviate from official numbers. Yet other applications make minimal use of model covariates and assumptions to support causal analysis between variables without the risk of circular reasoning.
LUCKINet builds on the power of collaboration. We fully embrace the need to address more challenges than would be feasible for a single research group over the course of a few years. Our vision further acknowledges that not all aspects can be addressed at the same speed or in parallel. Using technologies that support new forms of collaboration, we can address many of these challenges via distributed efforts. We develop workflows that capture standardized metadata that allow tracing data provenance, to also enable future projects to easily pick up where we left off. The open and modular design of our computational workflows facilitates both successive improvements and retrospective updates of the developed data products, as our collaborative network grows and better input data become available.
Our aim is to engage a growing network of contributors from the wider community of land system scientists, remote sensing experts, and geoinformaticians in this endeavor. Together, we will successively increase the list of covered land-use variables, consolidate and improve the employed techniques and, of course, we will collectively make use of these data!
Current projects contributing to this research field at iDiv:
