Data

Seasonal ground cover - Landsat, JRSRP algorithm, Australia Coverage

Terrestrial Ecosystem Research Network
Department of Environment and Science, Queensland Government
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/65878a57-f1b0-4e6b-8e7a-8a38ebe7960e&rft.title=Seasonal ground cover - Landsat, JRSRP algorithm, Australia Coverage&rft.identifier=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/65878a57-f1b0-4e6b-8e7a-8a38ebe7960e&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23884. The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of Landsat surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover > seasonal persistent green > seasonal composite of ground coverData CreationAlgorithm Summary 1: When viewed from above, as by the satellite, mid- and over-storey vegetation obscures the ground layer fractions (bare ground, green ground cover and dry ground cover). The seasonal fractional cover product, from which this ground cover product is derived, does not distinguish between these vegetation layers. As such, a high estimate in the green fraction may be due to green vegetation in any of the vegetation layers. To produce the ground cover product, the fractional cover product is adjusted using an estimate of the proportion of the pixel obscured by mid- and over-story foliage. This estimate of cover is an estimate of the combined persistent dry and persistent green layers. That is, all vegetation in the mid- and upper- stories. The mid and upper storage foliage is effectively removed from the estimates of cover and the ground cover estimate is therefore based only on the proportion of ground that was visible by the satellite.Algorithm Summary 2: We assume that fractional cover fractions in each vegetation layer are independent, that is that the presence of mid and over-storey vegetation does not influence the distribution of the ground cover fractions. The algorithm to create the ground cover layer relies on two satellite products (seasonal fractional cover and seasonal persistent green), as well as an estimate of persistent dry derived from field data. A simplified description of the algorithm follows: 1. Base imagery required is seasonal fractional cover and seasonal persistent green products. 2. Estimate of persistent dry is derived from field data relationship 3. The ‘gap-fraction’ is calculated (total pixel area - pixel area composed of persistent dry - pixel area composed of persistent green) 4. Adjust all three fractions for each pixel using persistent dry and persistent green estimatesBase Imagery: All seasonal ground cover images are derived directly from the equivalent date seasonal fractional cover image. See the seasonal fractional cover product metadata for details. Persistent green images can only be created retrospectively, so when the most current ground cover images are created using the most recent persistent green, whatever this may be. These images are tagged ‘_vinterim’ to indicate they will eventually be replaced, when the exact date persistent green becomes available. It is expected that the interim images will be almost identical in nature to the final products when they become available.Fractional Cover: Land cover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. The opening of the Landsat archive has provided an opportunity to composite imagery into representative seasonal images. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013). Further details of the product can be found on the seasonal fractional cover product page.Persistent Green: The persistent green model estimates persistent green by investigating the long term green fraction of the fractional cover product and determining the minimum green that is present regardless of seasonality. The underlying premise of the persistent green product is the separation of the fractional cover product green fraction into variable and trend components. The trend component is the green fraction which is always present regardless of season and is therefore assumed to be associated with perennial vegetation and therefore ‘persistent’.Persistent Dry: The adjustments made rely both on satellite estimates of persistent green and persistent dry, as the calculation of total gap depends on this. While we currently have satellite estimates of persistent green vegetation, we do not have satellite estimates of persistent dry. We can, however, derive a relationship between persistent dry and persistent green from the field data. This relationship can then be used to estimate persistent dry from the satellite persistent green product.Canopy Gap Fraction: The visible mid FPC is the proportion of total mid-level green measurements multiplied by the proportion of the site you can see because it is not excluded by canopy leaves, branches or dead vegetation (canopy gap). The canopy gap is calculated from the field data as the total number of points observed in the over-storey divided by the total number of observations taken at the site. The mid FPC is the total mid storey green observations divided by the total number of observations at the site.Data Storage: A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent) + 100. band 2 - green vegetation fraction (in percent) +100. band 3 – non-green vegetation fraction (in percent) + 100Progress Code: supersededMaintenance and Update Frequency: notPlanned&rft.creator=Department of Environment and Science, Queensland Government &rft.date=2021&rft.edition=1.0&rft.relation=https://doi.org/10.3390/rs5126481&rft.relation=https://doi.org/10.3390/rs5010083&rft.relation=https://www.publications.qld.gov.au/dataset/ad30c88c-185d-4d9a-9971-69968f958a70/resource/350aeffd-ff8f-4876-a12f-7eb8d4e6f991/download/coverundertreesreportrp64gfinal.pdf&rft.relation=https://www.researchgate.net/publication/236022381_Field_measurement_of_fractional_ground_cover&rft.coverage=northlimit=-9.5; southlimit=-44.5; westlimit=112.5; eastLimit=154.5; projection=EPSG:4326&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=&rft_rights=Copyright 2010-2021. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).&rft_rights=While every care is taken to ensure the accuracy of this information, the Joint Remote Sensing Research Project (JRSRP) makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.&rft_rights=It is not recommended that these data sets be used at scales more detailed than 1:100,000.&rft_rights=TERN services are provided on an “as-is” and “as available” basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.&rft_subject=environment&rft_subject=imageryBaseMapsEarthCover&rft_subject=VEGETATION COVER&rft_subject=LAND USE/LAND COVER&rft_subject=EARTH SCIENCE&rft_subject=LAND SURFACE&rft_subject=SOILS&rft_subject=ENVIRONMENTAL SCIENCE AND MANAGEMENT&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=ECOLOGICAL APPLICATIONS&rft_subject=LANDSAT-5&rft_subject=LANDSAT-7&rft_subject=LANDSAT-8&rft_subject=TM&rft_subject=ETM+&rft_subject=OLI&rft_subject=bare soil fraction (Percent)&rft_subject=Percent&rft_subject=photosynthetic vegetation fraction (Percent)&rft_subject=non-photosynthetic vegetation fraction (Percent)&rft_subject=vegetation area fraction (Percent)&rft_subject=30 meters - < 100 meters&rft_subject=Weekly - < Monthly&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

Copyright 2010-2021. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).

While every care is taken to ensure the accuracy of this information, the Joint Remote Sensing Research Project (JRSRP) makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.

It is not recommended that these data sets be used at scales more detailed than 1:100,000.

TERN services are provided on an “as-is” and “as available” basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Access:

Open view details

unclassified

Contact Information

Street Address:
Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd
QLD 4068
Australia
Ph: +61 7 3365 9097

esupport@tern.org.au

Brief description

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/23884. The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, this is an experimental product which has not been fully validated.

Notes

Supplemental Information

band 1 - bare ground fraction (in percent) + 100

band 2 - green ground cover fraction (in percent) +100

band 3 - non-green ground cover fraction (in percent) + 100

band 4 - Error Layer representing the RMSE between the predicted pixel value and the actual pixel value on a nominal scale of 100 (no error) to 200 (very large error).

The standard form of the file naming convention:seasonal_ground_cover_landsat_filenaming_convention.txt

Lineage

Landsat surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover > seasonal persistent green > seasonal composite of ground cover

Data Creation
Algorithm Summary 1: When viewed from above, as by the satellite, mid- and over-storey vegetation obscures the ground layer fractions (bare ground, green ground cover and dry ground cover). The seasonal fractional cover product, from which this ground cover product is derived, does not distinguish between these vegetation layers. As such, a high estimate in the green fraction may be due to green vegetation in any of the vegetation layers. To produce the ground cover product, the fractional cover product is adjusted using an estimate of the proportion of the pixel obscured by mid- and over-story foliage. This estimate of cover is an estimate of the combined persistent dry and persistent green layers. That is, all vegetation in the mid- and upper- stories. The mid and upper storage foliage is effectively removed from the estimates of cover and the ground cover estimate is therefore based only on the proportion of ground that was visible by the satellite.
Algorithm Summary 2: We assume that fractional cover fractions in each vegetation layer are independent, that is that the presence of mid and over-storey vegetation does not influence the distribution of the ground cover fractions. The algorithm to create the ground cover layer relies on two satellite products (seasonal fractional cover and seasonal persistent green), as well as an estimate of persistent dry derived from field data. A simplified description of the algorithm follows: 1. Base imagery required is seasonal fractional cover and seasonal persistent green products. 2. Estimate of persistent dry is derived from field data relationship 3. The ‘gap-fraction’ is calculated (total pixel area - pixel area composed of persistent dry - pixel area composed of persistent green) 4. Adjust all three fractions for each pixel using persistent dry and persistent green estimates
Base Imagery: All seasonal ground cover images are derived directly from the equivalent date seasonal fractional cover image. See the seasonal fractional cover product metadata for details. Persistent green images can only be created retrospectively, so when the most current ground cover images are created using the most recent persistent green, whatever this may be. These images are tagged ‘_vinterim’ to indicate they will eventually be replaced, when the exact date persistent green becomes available. It is expected that the interim images will be almost identical in nature to the final products when they become available.
Fractional Cover: Land cover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. The opening of the Landsat archive has provided an opportunity to composite imagery into representative seasonal images. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013). Further details of the product can be found on the seasonal fractional cover product page.
Persistent Green: The persistent green model estimates persistent green by investigating the long term green fraction of the fractional cover product and determining the minimum green that is present regardless of seasonality. The underlying premise of the persistent green product is the separation of the fractional cover product green fraction into variable and trend components. The trend component is the green fraction which is always present regardless of season and is therefore assumed to be associated with perennial vegetation and therefore ‘persistent’.
Persistent Dry: The adjustments made rely both on satellite estimates of persistent green and persistent dry, as the calculation of total gap depends on this. While we currently have satellite estimates of persistent green vegetation, we do not have satellite estimates of persistent dry. We can, however, derive a relationship between persistent dry and persistent green from the field data. This relationship can then be used to estimate persistent dry from the satellite persistent green product.
Canopy Gap Fraction: The visible mid FPC is the proportion of total mid-level green measurements multiplied by the proportion of the site you can see because it is not excluded by canopy leaves, branches or dead vegetation (canopy gap). The canopy gap is calculated from the field data as the total number of points observed in the over-storey divided by the total number of observations taken at the site. The mid FPC is the total mid storey green observations divided by the total number of observations at the site.
Data Storage: A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent) + 100. band 2 - green vegetation fraction (in percent) +100. band 3 – non-green vegetation fraction (in percent) + 100

Progress Code: superseded
Maintenance and Update Frequency: notPlanned

Notes

Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey.
Purpose
This product captures variability in ground cover at seasonal (ie three-monthly) time scales, forming a consistent time series from 1989 - present. It is useful for investigating inter-annual changes in ground cover and analysing regional comparisons. The green and non-green fractions may include a mix of woody and non-woody vegetation. For applications that focus on all vegetation, the fractional cover product may be more suitable. For applications investigating rapid change during a season, monthly composite or single-date (available on request) fractional cover products may be more appropriate. Note: A new fractional cover algorithm will be implemented during 2021, based on additional field validation and a new machine learning approach. This will lead to a new version of the ground cover products.
Data Quality Information

Data Quality Assessment Scope
local : dataset
1) The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points. 2) The fractional cover model was compared to samples drawn from 1500 field reference sites.

Data Quality Assessment Result
local : Quality Result
1) The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail. 2) The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites.

Created: 2014-05-01

Issued: 2021-03-31

Modified: 2014-07-14

Data time period: 1989-12-01

This dataset is part of a larger collection

Click to explore relationships graph

154.5,-9.5 154.5,-44.5 112.5,-44.5 112.5,-9.5 154.5,-9.5

133.5,-27