Linda See
Linda See is a Principal Research Scholar in the Novel Data Ecosystems for Sustainability (NODES) research group at the International Institute for Applied Systems Analysis (IIASA). Her main research interests include artificial intelligence-based methods, geographic information systems (GIS), land cover, crowdsourcing, and citizen science. She is currently an editor of Environment and Planning B.
Sessions
There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how forests, cropland and grassland are intensively and extensively managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys and National Forest Inventories) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/).
One of the main inputs to the development of a land use management map is Corine land cover, which is a remotely sensed product developed by the Copernicus Land Monitoring Service every six years. First, we produced an annual time series of Corine from 2000 to 2018 by using the high-resolution land cover times series produced by OpenGeoHub and the BFAST algorithm applied to MODIS data to determine the year of change between the six-year production cycle of CORINE. Any remaining changes that were unaccounted for had the year of change selected randomly. Transition rules were also applied to ensure that the land cover/land use transitions were reasonable.
Land use management classes for forest, cropland, grassland and urban areas were then devised in collaboration with the modelers in the LAMASUS project as well as around 30 stakeholders who participated in the first LAMASUS stakeholder workshop. Using different input data sets from remote sensing, in-situ data (from LUCAS), modelled data from CAPRI, and statistical information from agricultural censuses, surveys and other sources, rules were developed to allocate the Corine land cover classes to more detailed land use management classes. Here we will present the results of this mapping along with a method for how the map has been fit to official area statistics so that this information can be used by the economic land use models in LAMASUS.