17 May 2022 12:40
The project aims to develop annual global land cover maps with a 100m resolution consistent over time, and has been tracking land cover changes since 2015.
The Copernicus Global Land Service (CGLS) continuously provides a set of biophysical variables describing the vegetation conditions, the energy budget at the continental surface, as well as the cryosphere and water cycle across the globe. The Dynamic Land Cover (LC) map at 100m resolution is a new product in the portfolio of the CGLS, which aims to deliver a yearly global land cover map at 100m spatial resolution with an overall accuracy of higher than 85%.
The CGLS LC map provides primary land cover information on the spatial distribution of land cover classes such as evergreen closed forest, evergreen open forest, deciduous closed forest, deciduous open forest, mixed forest, shrubs, herbaceous vegetation, croplands, urban/built-up, bare land/space vegetation, snow and ice, permanent water bodies, temporal water bodies and herbaceous wetlands. Apart from these classes, the map also provides a set of four vegetation continuous fields that provide proportional estimates for vegetation cover types, namely trees, herbaceous vegetation, shrub and bare ground. These continuous classifications may depict areas of heterogeneous LC better than the standard (categorical) classifications and as such can be tailored for application use (e.g. forest monitoring, crop monitoring, biodiversity and conservation, environmental monitoring and security in Africa, climate modelling, etc.).
The C-GLOPS global LC map could also directly or indirectly support some of the UN Sustainable Development Goals (SDGs). SDG 15: “Life on land”, for example, focuses on “protecting, restoring and promoting sustainable use of terrestrial ecosystems, sustainably managing forests, combating desertification, and halting and reversing land degradation and biodiversity loss”. This requires high-quality information on land cover and land cover change.
IIASA’s main role within the project is to provide high quality reference data on LC at a global scale. This includes the development of tools and web-applications via Geo-Wiki to collect training data at a 10m resolution; the development of tools to detect potential land cover changes by analyzing time-series of remote sensing data and historical photos; training and forming a group of experts in visual interpretation of high resolution imagery; and quality assessment of intermediate mapping products.
IIASA also leads reference data collection efforts by the group of trained experts and ensures that the quality of the collected data is of high standard. These reference data will serve (1) machine learning techniques for classification of remote sensing data at a resolution from 10m to 100m, and (2) evaluation of land cover/land cover change products at a resolution finer than 100m.
The team is currently working on improving the LC 100m map for 2015 and developing the LC 100m maps for 2016 and 2017 over Africa. Their goal is to also produce a global LC 100m map for 2015 before the end of 2018.
Schepaschenko D, Fritz S, See L, Laso Bayas JC, Lesiv M, Kraxner F, & Obersteiner M (2017). Comment on “The extent of forest in dryland biomes”. Science 358 (6362): eaao0166. DOI:10.1126/science.aao0166.
Laso Bayas JC, Lesiv M, Waldner F, Schucknecht A, Duerauer M, See L, Fritz S, Fraisl D, et al. (2017). A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform. Scientific Data 4: e170136. DOI:10.1038/sdata.2017.136.
Lesiv M, Fritz S, McCallum I, Tsendbazar N, Herold M, Pekel J-F, Buchhorn M, Smets B, et al. (2017). Evaluation of ESA CCI prototype land cover map at 20m. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-17-021
See L, Laso Bayas JC, Schepaschenko D, Perger C, Dresel C, Maus V, Salk C, Weichselgartner J, et al. (2017). LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya. Remote Sensing 9 (7): e754. DOI:10.3390/rs9070754.
Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, Duerauer M, Karner M, et al. (2017). A global dataset of crowdsourced land cover and land use reference data. Scientific Data 4: p. 170075. DOI:10.1038/sdata.2017.75.