Land Cover Mapping using Digital Earth Australia

Abstract

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy.

Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube

“…To overcome the issue of national yearly land cover maps, a valuable solution can be the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) model (Lucas & Mitchell, 2017;Lucas, Mitchell, Manakos, & Blonda, 2018). It is currently under implementation in Digital Earth Australia (Lucas et al, 2019). This model facilitates regular classification according to the Food and Agricultural Organization -Land Cover Classification System (FAO -LCCS) as well as translations to other taxonomies like the General Habitat Classification.…”

Digital Earth for Sustainable Development Goals

“…21), Geoscience Australia is leading an inter-institutional initiative to produce reliable, standardized, continental-scale maps of land cover and land-cover dynamics across Australia at 25 m spatial resolution using multi-scale time series of Landsat and Copernicus Sentinel datasets. This approach builds on the Earth Observation Data for Ecosystem Monitoring (EODESM; Lucas and Mitchell 2017), which is fully described in Lucas et al (2019a) and which provides multi-scale and temporal land-cover and evidence-based change maps by integrating environmental variables retrieved from EO data and utilizing the framework of the Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS; Version 2, Di Gregorio 2016). The approach is based on the requirement for information about land cover and its change over time, as both are essential input metrics to several SDG targets (Fig.…”

“…The DEA land cover product has been optimized for high-performance computing within the Open Data Cube (ODC) framework and is generating continental maps of land-cover datasets from environmental variables (thematic and continuous), with a focus on those that are generated at a national level within DEA's ODC environment (Lucas et al 2019a) and for multiple points in time. These include the vegetation cover fraction of the Joint Remote Sensing Research Program (Gill et al 2017), Water Observations from Space (WOfS) (Mueller et al 2016), surface reflectance Median Absolute Deviation (MAD) (Roberts et al 2018), and national mangrove distribution (Lymburner et al 2019) (Fig.…”

“…The key changes are NAV-NTV: denoting changes from Natural Aquatic Vegetation (2014) to Natural Terrestrial Vegetation (2016); NW-NAV: Natural Waterbodies to Natural Aquatic Vegetation; and NW-NTV: Natural Waterbodies to Natural Terrestrial Vegetation. b The corresponding change map indicating a progressive loss of open water area, the retreat of aquatic (wet) vegetation and a transition to drier vegetation on the outer margins of the lake basin(Lucas et al 2019a)…”

Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes

“…Poussin et al [42] demonstrated the benefits of Open and Reproducible Science using a snow detection algorithm, developed in Switzerland and shared as an open notebook, to monitor snow cover evolution for the last three decades in the Gran Paradiso National Park in Italy. Furthermore, Lucas et al [43] developed a conceptual framework to implement a Land Cover Change model, providing Australia and other countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually and using a consistent and internationally recognized taxonomy.…”