TERN Geospatial Catalogue
Woody vegetation cover - Landsat, JRSRP, Australian coverage, 2000-2010
Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat.
Simple
Identification info
- Date (Creation)
- 2012-03-21
- Date (Publication)
- 2021-10-07
- Date (Revision)
- 2014-07-14
- Edition
- 1.0
Publisher
Author
Department of Environment, Energy and Science, New South Wales Government - Gill, Tony ()
4 Parramatta Square, 12 Darcy St, Parramatta, NSW, 2150, Australia
Parramatta
NSW
2150
Australia
- Website
- https://www.tern.org.au/
- Purpose
- We realised that there was no easily accessible map of woody-vegetation cover of Australia, produced consistently across the continent, for land managers and ecologists to use at a local-scale. Researchers and governments have opened access to their field, airborne and satellite image data, making the task of creating such a map possible. We built on these efforts to create a map of woody-vegetation cover of Australia for the decade from 2000 to 2010.
- 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.
- Credit
- This dataset was produced using data sourced from the US Geological Survey.
- Status
- Completed
Point of contact
Department of Environment, Energy and Science, New South Wales Government - Gill, Tony ()
4 Parramatta Square, 12 Darcy St, Parramatta, NSW, 2150, Australia
4 Parramatta Square, 12 Darcy St
Parramatta
NSW
2150
Australia
- Topic category
-
- Imagery base maps earth cover
- Environment
Extent
- Description
- Australia
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S
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W
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Temporal extent
- Time period
- 2000-01-01 2010-12-31
- Title
- Gill, T., et al, 2017. A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38(3), pp 679-705.
- Website
-
Gill, T., et al, 2017. A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38(3), pp 679-705.
Related documentation
- Title
- Guerschman, JP., et al, 2015. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data. Remote Sensing of Environment.
- Website
-
Guerschman, JP., et al, 2015. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data. Remote Sensing of Environment.
Related documentation
- Title
- Flood, N., Danaher, T., Gill, T., Gillingham, S. 2013. An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing
- Website
-
Flood, N., Danaher, T., Gill, T., Gillingham, S. 2013. An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing
Related documentation
- Title
- Zhu, Z. and Woodcock, C.E., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94
- Website
-
Zhu, Z. and Woodcock, C.E., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94
Related documentation
- Maintenance and update frequency
- Not planned
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Platform Vocabulary
- TERN Instrument Vocabulary
- TERN Parameter Vocabulary
- GCMD Horizontal Resolution Ranges
- GCMD Temporal Resolution Ranges
Resource constraints
- Use limitation
- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
- File name
- 88x31.png
- File description
- CCBy Logo from creativecommons.org
- File type
- png
- Title
- Creative Commons Attribution 4.0 International Licence
- Alternate title
- CC-BY
- Edition
- 4.0
- Access constraints
- License
- Use constraints
- Other restrictions
- Other constraints
- 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. <br />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. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
- Other constraints
- It is not recommended that these data sets be used at scales more detailed than 1:100,000.
Resource constraints
- Classification
- Unclassified
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd, Indooroopilly, QLD, Australia, 4068
Indooroopilly
QLD
4068
Australia
- OnLine resource
- Download via HTTP
Data quality info
- Hierarchy level
- Dataset
- Other
- 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.
Report
Result
- Statement
- The overall classification accuracy of the woody vegetation extent is 81.9%. The user's and producer's accuracy for the woody class were 85.6% and 90.6%, respectively. The user's and producer's accuracies for areas mapped as forest were high at 92.2% and 95.9% respectively. The user's and producer's accuracies for other wooded lands is 75.7% and 61.3%, respectively. Validation of woody foliage projective cover with field-measurements gave a coefficient of determination, R2 of 0.918 and a RMSE of 0.70.
Resource lineage
- Statement
- Data produced following the method described in http://dx.doi.org/10.1080/01431161.2016.1266112
- Hierarchy level
- Dataset
Process step
- Description
- Data: The foliage projective cover product is derived from an inter-annual time series of the green layer of the Landsat fractional cover product. The Landsat fractional cover product provides an estimates of the vertically-projected fraction of green vegetation, not-green vegetation and bare ground for each pixel. Landsat 5 TM and Landsat 7 ETM+ images were obtained for 374 world wide reference system 2 (WRS2) scenes covering Australia. One dry-season image per year was acquired between 2000 and 2010 for each scene except those where cloud or wet conditions precluded image acquistion for a year. The imagery were processed to BRDF and topographically adjusted reflectance; fractional cover estimates produced; and masks for cloud, cloud shadow, water, topographic shadow, incidence and exitance angle greater than 80 degrees, and snow created.
Process step
- Description
- Statistics: A robust regression of the form Y~b0 + b1*X, where Y is the green fraction and X is time, was fit to the masked time-series of green vegetation fractions. The following statistics were derived from the regression modelling for each pixel: 1) fitted fraction from the model at 30 June 2005. 2) number of observations in the time series 3) minimum green fraction in the time series once outliers are removed, where an outlier is defined as a point whose residual (observed-fitted) is greater than MAD/0.6745 where MAD is the median absolute deviation of observations from the fitted line. 4) a measure of the standard error of the robust regression fit calculated as sqrt( chisqd/(N-2) ) where N is the number of observations in the time series and chisqd is the weighted sum of squares of residuals. 5) a measure of the normalised standard error of the robust regression fit calculated as standard error divided by the minimum.
Process step
- Description
- Statistics 2: 6) The slope of the regression line in units of percent green fraction per day. 7) The standard deviation of negative residuals, i.e. those observations below the fitted line. A random forest classifier, using the minimum fraction and standard error was trained on 6597 field or image interpreted observations of woody vegetation presence or absence. The woody foliage projective cover was calculated using P = F - (A*V*tanh(B-F)). F is the robust-regression fitted fraction on 30 June 2005. V is the standard deviation of the negative residuals. A and B were parameters that were optimised and were A=7.93 and B=0.66.
Process step
- Description
- foliage projective cover (dma): 0 - null pixels <br /> 100-200 - scaled foliage projective cover. To convert to units use: cover_fraction = pixel*0.01 - 1.
Process step
- Description
- forest cover (dm7): 0 - null pixels <br /> 100 - not forest <br /> 110-200 - scaled foliage projective cover. To convert to units use: cover_fraction = pixel*0.01 - 1.
Process step
- Description
- Accuracy classes for persistent-green extent (dmb): 0 - null pixels <br /> 1 - other wooded lands. That is, classified as woody with a foliage projective cover < 0.1 <br /> 2 - not woody and a foliage projective cover < 0.1 <br /> 3 - forest. That is, classified as woody with a foliage projective cover >= 0.1 <br /> 4 - not woody and fpc >= 0.1.
Process step
- Description
- Accuracy: The user's and producer's accuracies, respectively, for each class are: 1 - 72.9% and 79.8% [40.4% and 100% after these pixels were reclassified to not persistent-green because their cover fractions were less than 0.1] 2 - 65.4% and 56.3% 3 - 92.2% and 95.5% 4 - 75.7% and 61.3%
Reference System Information
- Reference system identifier
- EPSG/EPSG:4326
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/e4de7f56-f1a5-418e-9118-3220f6f365f8
- Title
- TERN GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
Point of contact
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/e4de7f56-f1a5-418e-9118-3220f6f365f8
Point-of-truth metadata URL
- Date info (Creation)
- 2012-03-21T00:00:00
- Date info (Revision)
- 2023-02-01T00:00:00
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0
Identifier
Overviews
Spatial extent
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E
W
Provided by
Associated resources
Not available