Woody Age Since Disturbance - Sentinel-2, DES Algorithm, QLD Coverage
The woody vegetation extent for Queensland is attributed with an estimated age in years since the last significant disturbance. The method uses a sequential Conditional Random Fields classifier applied to Landsat time series starting 1988 to predict woody cover over the time period. A set of heuristic rules is used to detect and track regrowing woody vegetation in the time series of woody probabilities and record the approximate start and end dates of the most recent regrowth event. Regrowth detection is combined with the Statewide Land and Trees Study (SLATS) Landsat historic clearing data to provide a preliminary estimate of age since disturbance for each woody pixel in the woody extent. The 'last disturbance' may be due to a clearing event or other disturbance such as fire, flood, drought-related death etc. Note that not all recorded disturbances may result in complete loss of woody vegetation, so the estimated age since disturbance does not always represent the age of the ecosystem.
The age since disturbance product is derived from multiple satellite image sources and derived products which represent different scales and resolutions: Landsat (30 m), Sentinel-2 (10 m) and Earth-i (1 m).
Simple
Identification info
- Date (Creation)
- 2024-07-19
- Date (Publication)
- 2024-08-22
- Date (Revision)
- 2024-12-16
- Edition
- 1.0
Publisher
Custodian
Department of Environment, Science and Innovation (2023-2024), Queensland Government - Tindall, Dan ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
Dutton Park
Queensland
4102
Australia
Author
Department of Environment, Science and Innovation (2023-2024), Queensland Government - Collett, Lisa (Scientist (Remote Sensing))
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
Dutton Park
Queensland
4102
Australia
- Website
- https://www.tern.org.au/
- Purpose
- To provide an estimation of woody vegetation age for ongoing monitoring and reporting.
- 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.
- Status
- Completed
Point of contact
Department of Environment, Science and Innovation (2023-2024), Queensland Government - Collett, Lisa (Scientist (Remote Sensing))
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Spatial resolution
- Spatial resolution
- 10
- Topic category
-
- Environment
- Imagery base maps earth cover
Extent
- Description
- Queensland
N
S
E
W
))
Temporal extent
- Time period
- 1988-01-01 2022-12-31
- Maintenance and update frequency
- Annually
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Platform Vocabulary
- TERN Instrument Vocabulary
- TERN Parameter Vocabulary
- QUDT Units of Measure
- 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
- Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.
Resource constraints
- Classification
- Unclassified
Distribution Information
- Distribution format
-
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- OnLine resource
-
woody_age_since_disturbance
Woody Age Since Disturbance - Sentinel-2
- OnLine resource
- ro-crate-metadata.json
Data quality info
- Hierarchy level
- Dataset
- Other
- <br>Completeness (omission):<ul style="list-style-type: disc;"> <li>The dataset is unvalidated against field measures of vegetation age.</li> <li>There are some limitations to the age since disturbance estimation. It is assumed that the SLATS clearing data always records a transition from woody to non-woody, however, the Landsat clearing record does contain partial clearing. Additionally, the woody extent baseline contains sparse and/or young woody vegetation that may not have been detectable by Landsat - either by the Conditional Random Fields Landsat time series modelling or by SLATS in previous reporting periods and was therefore not able to be monitored at that scale. Missing data in the CRF fractional cover model inputs due to persistent cloud or topographic shadow as well as real but transient disturbance events such as fire or flood may cause woody probabilities to fall to 0, confounding regrowth age estimation.</li></ul> <br>Consistency (conceptual):<ul style="list-style-type: disc;"> <li>The dataset is unvalidated against field measures of vegetation age.</li> <li>The woody mapping specifications differ between the woody extent mapping data set and the SLATS Landsat clearing record. The 2018 woody extent baseline captures woody vegetation greater than 0.5 ha with a crown cover greater than ~ 10%, whereas the SLATS Landsat-based clearing data sets record clearing in woody vegetation > ~ 20% crown cover (minimum mapping unit of approx. 0.25 ha). This means that historic clearing events in some sparsely vegetated < 20% crown cover (~10% FPC) may not have been recorded by Landsat, and the age since disturbance therefore may be over-estimated in these regions. Integration of data sets of varying mapping scales and different resolutions derived from Landsat (30 m), Sentinel-2 (10 m) and Earth-i imagery result in some inconsistencies due to scale effects. The minimum mapping specifications of 0.5 ha in the woody extent means that small, nonwoody gaps (< 0.5 ha) and narrow linear features such as some roads and easements are labelled woody in the woody extent and may therefore be attributed with an age > 0. Conversely, small woody patches (< 0.5 ha) are labelled non-woody and will be assigned an age of 0 years.</li></ul>
Resource lineage
- Statement
- <br>The long time series of Landsat imagery was used as the primary image data to derive woody vegetation age estimates. Landsat imagery from Landsat-7 and -8 is downloaded from the United States Geological Survey (USGS website) (<a href="https://earthexplorer.usgs.gov/">earthexplorer.usgs.gov</a>) and processed to surface reflectance as described in Flood (2013a). All Landsat imagery is geometrically corrected by the USGS. Cloud and cloud shadow masks are computed using the Fmask methods of Zhu and Woodcock (2012). Three monthly seasonal fractional cover composites are produced for Queensland from surface reflectance using the medoid method described in Flood (2013b). </br> <br>Thirty-three years (1988 to 2021) of seasonal Landsat fractional cover components (Scarth, 2008) were used in the woody likelihood modelling and regrowth event detection. The fractional cover data consisted of bare, green, and non-green sub-pixel components.</br> <br>Additional inputs include the historic Landsat clearing data (1988-2018) and the 2018 woody extent baseline data set.</br> <br>The 2019 woody extent data set was used as sample strata to generate woody/non-woody training sequences for a Conditional Random Field (CRF) classifier. The 2019 woody extent data were resampled from 10 m to 30 m spatial resolution to align spatially with the Landsat time series. A temporal augmentation method of sampling was used to generate sequences of woody training labels. For each tile in a regular grid of 150X150 km tiles across Queensland, a model was fit between the fractional cover data and the training data sequences. The CRF model produces a sequence of annual woody probabilities (0 to 1). This release involved a refit of the CRF model as the previously published 2018 Age Since Disturbed data was based on the 2017 woody extent and fractional cover time series. A simple set of heuristic rules are then applied to detect patterns in the woody probability sequence that might characterise a typical woody regrowth response curve and track that regrowth event over time. Regrowth detection is combined with the SLATS Landsat clearing record to provide a preliminary estimation of age since last disturbance for all woody pixels recorded in the 2018 baseline woody extent.</br> <br>Each data file includes data from the beginning of the data collection (1988) to the year when the data were processed and published in the Statewide Landcover and Trees Study (SLATS) report.</br>
- Hierarchy level
- Dataset
- Title
- Flood N. (2013a). An operational scheme for deriving standardised surface reflectance from Landsat TM/ETM+ and SPOT HRG imagery for Eastern Australia. Remote Sensing, 5(1): 83-109
- Website
-
https://doi.org/10.3390/rs5010083
Method documentation
- Title
- Flood N. (2013b). Seasonal composite Landsat TM/ETM+ images using the medoid (a multi-dimensional median). Remote Sensing, 5(12): 6481-6500
- Website
-
https://doi.org/10.3390/rs5126481
Method documentation
- Title
- Zhu Z. and Woodcock C. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118 (15): 83-94
- Website
-
https://doi.org/10.3390/rs5126481
Method documentation
- Title
- Scarth P., Gillingham S. and Muir J. (2008). Assimilation of spectral information and temporal history into a statewide woody cover change classification. Proceedings of 14<sup>th</sup> Australasian Remote Sensing and Photogrammetry Conference, Darwin, Northern Territory, Australia, vol. 29. 2008
Reference System Information
- Reference system identifier
- EPSG/EPSG:3577
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/9e835aec-5314-42c4-a39a-c8ef5959c12c
- 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/9e835aec-5314-42c4-a39a-c8ef5959c12c
Point-of-truth metadata URL
- Date info (Creation)
- 2023-10-09T00:00:00
- Date info (Revision)
- 2024-12-16T00:00:00
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
thumbnail
Spatial extent
N
S
E
W
Provided by
Associated resources
Not available