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Land cover and land use are often (and erroneously) used interchangeably. One of the reasons for these is that certain land uses, such as agriculture or urban, are associated with characteristic land cover categories. However, other land uses, such as nature conservation, do not have a one-on-one relationship with a particular land cover type. For example the land cover category ‘woodland’ can correspond to multiple land use categories, including ‘nature conservation’, ‘recreation’, ‘grazing’, and ‘timber production’. Conversely, the land use category ‘recreation’ could be applicable to different land cover categories, such as ‘woodland’, ‘lake’, or ‘urban’. The confusion between these two concepts is reviewed in Fisher et al. (2005).
Fractional Cover
Fractional Cover typically refers to the fraction of an area, usually a pixel in the remote sensing context, that is covered by each of three specific types of cover: photosynthetic vegetation, non-photosynthetic vegetation (e.g. senescent vegetation, leaf litter, and stubble), and bare landscape (e.g. soil, rocks, and recently burn areas). These three fractions are commonly and respectively referred as ‘green cover’, ‘brown cover’, and ‘bare cover’ fractions. In this type of remote sensing products is also common for the green and brown fraction to appear combined in what is known as the ‘total cover’ fraction. Sometimes fractional cover data products do not include all fractions, but only a subset of them.
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Table 1. Cover data products available from TERN Landscape Assessment (AusCover) facility, including their type (i.e. fractional cover, ground cover, land cover, or ‘other’ cover data products; see above), their source, spatial coverage, and temporal coverage.
Cover Type | Data Product | Source | Spatial Coverage | Temporal Coverage | |
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| Fractional Cover - Landsat, JRSRP algorithm | Landsat | Australia | 2000 – 2011 | |
Seasonal Fractional Cover - Landsat, JRSRP algorithm | Landsat | Australia | 1986 – Ongoing | ||
Fractional Cover - MODIS, CSIRO algorithm | MODIS | Australia | 2000 – Ongoing | ||
Fractional Cover - MODIS, Monthly Median Composites | MODIS | Australia | 2000 – Ongoing | ||
Fractional Cover Metrics – MODIS | MODIS | Australia | 2000 – Ongoing | ||
Fractional: Green | Seasonal Persistent Green Cover - Landsat, JRSRP algorithm | Landsat | Australia | 1990 – Ongoing | |
Fractional: Green, Total | Seasonal Cover Deciles - Landsat, JRSRP algorithm | Landsat | Australia | 1986 – Ongoing | |
Fractional: Total | Total Cover - MODIS, Land Condition Index (LCI) algorithm | MODIS | Australia | 2000 – 2011 | |
Fractional: Total at different Heights | Vegetation Height and Structure | Laser (ICESat/GLAS & ALOS PALSAR) + Landsat | Australia | 2003 – 2009 | |
Fractional & Ground | SLATS Star Transects | Field (for validation) | Australia | 1995 – Ongoing | |
Ground | Seasonal Ground Cover - Landsat, JRSRP algorithm | Landsat | Australia | 1990 – Ongoing | |
Australian Ground Cover Reference Sites Database - ABARES | Field? (for validation) | Australia | |||
Land | Dynamic Land Cover Dataset – MODIS | MODIS | Australia | v.1.: 2000-2008 v.2.1.: 2001–Ongoing | |
Land Cover Type - MODIS, MCD12Q1(c5.1) mosaic | MODIS | Australia | 2001 – Ongoing | ||
‘Other’: Woody Green | Persistent Green-Vegetation Fraction and Wooded Mask | Field + Airborne + Satellite | Australia | 2000 – 2010 | |
‘Other’: Woody Green & Foliage Projective | Western NSW Refugia - Collection, JRSRP algorithm | Landsat + Shuttle Radar Topography Mission | Western NSW | 1988 – 2012 | |
Woody Extent and Foliage Projective Cover - SPOT, OEH algorithm, NSW | SPOT-5 HRG | NSW | 2011 | ||
Water | Water Count and Prevalence - Landsat, JRSRP algorithm, NSW | Landsat | NSW | 1988 - 2012 |
Table 2. Description of the temporal and spatial coverage and resolution of the datasets in the ‘Cover’ Data Products in Landscape Assessment (AusCover) facility, as well as the description of the files containing the datasets. In pink data products that currently can be downloaded. Acronyms: CRS = Coordinate Reference System, nc = NetCDF.
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