Regional matters: On the usefulness of regional land-cover datasets in times of global change
Tulbure, M. G., Hostert, P., Kuemmerle, T., & Broich, M. (2021, December 28). REMOTE SENSING IN ECOLOGY AND CONSERVATION.
Unprecedented amounts of analysis-ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO-based land-cover and land-use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade-offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade-offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO-based land-cover and land-use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO-derived land-cover datasets can be preferable, with regional data providing the context-specificity that is often required for policy making and implementation (e.g., land-use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land-cover and land-use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high-quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land-cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.