Decision making at regional, national and international scales can be greatly improved with the availability of regular, consistent, and reliable maps of the land cover and how it changes over time and space. With modern improvements in data accessibility and the advancement of computational resources, operationalizing the production of these products at a large scale is now achievable. The next challenge comes with building systems which are not only just meeting today’s needs, but also have the ability to easily incorporate future anticipated improvements.
Geoscience Australia’s Digital Earth Australia (DEA) in collaboration with Aberystwyth University (Wales, UK) and Plymouth Marine Laboratory (PML) have built a globally applicable method for generating consistent, large-scale land cover maps from satellite imagery. The approach builds on the Earth Observation Data for Ecosystem Monitoring (EODESM) system (Lucas and Mitchell, 2017), which constructs and describes land cover classifications based on environmental descriptors derived from Earth observation (EO) data. The system’s land cover structure is based on the globally applicable United Nations Food and Agriculture Organisation Land Cover Classification System (UN FAO LCCS) taxonomy). This new land cover classification system has been built to be adaptable and modular, allowing for its application to a range of different landscapes, at different spatial and temporal resolutions and with a variety of data sources. One major feature of this new system is the inclusion of native support for the Open Data Cube environment. The system is open source, meaning that the algorithms and code are openly available to researchers anywhere in the world. The structure of the code enables researchers to utilise their own, regionally specific methods to build land cover products tailored to their own needs, while still adhering to the overall UN FAO LCCS framework. It can be run using data from a range of sensors including multispectral optical, radar, LIDAR, as well as custom georeferenced datasets.
This method is currently being used operationally by both DEA and Aberystwyth University to create national land cover products. For Australia, DEA has generated DEA Land Cover, a high resolution (25 m) continental, annual land cover map for each year from 1988 to 2020 by utilising over 30 years of Landsat sensor data. This data is being utilised in Australia’s environmental economic accounting, and is providing valuable insights to researcher and decision-makers. Similarly, Aberystwyth University has worked with DEA and Welsh Government through the Living Wales project to generate national land cover maps for Wales over for four years (https://wales.livingearth.online/) using multiple sensors including Sentinel 1 and Sentinel 2 and is currently extending the time-series back to the mid 1980s using Landsat sensor data (Lucas et al., 2018). In addition, several research bodies across the globe have begun a community of practice to share ideas, algorithms and support each other in implementing this land cover methodology in their own Open Data Cube environments.
Lucas, R.M.; Mitchell, A. Integrated Land Cover and Change Classifications. In The Roles of Remote Sensing in Nature Conservation: A Practical Guide and Case Studies; Díaz-Delgado, Lucas, R., Hurford, C., Eds.; Springer: Cham, Switzerland, 2017; pp. 295–308.
Lucas, R., Bunting, P., Horton, C., 2018. Living WALES — National Level Mapping and Monitoring Though Earth Observations, Ground Data and Models. IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium. Presented at the IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 6608–6610. https://doi.org/10.1109/IGARSS.2018.8519452
Owers, CJ, Lucas, RM, Clewley, D, Planque, C, Punalekar, S, Tissott, B, Chua, SMT, Bunting, P, Mueller, N & Metternicht, G 2021, 'Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development', Big Earth Data. https://doi.org/10.1080/20964471.2021.1948179