Brief description
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).Lineage
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) (earthexplorer.usgs.gov) 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).
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.
Additional inputs include the historic Landsat clearing data (1988-2018) and the 2018 woody extent baseline data set.
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.
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.
Notes
CreditWe 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.
To provide an estimation of woody vegetation age for ongoing monitoring and reporting.
Data Quality Assessment Scope
local :
dataset
<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>
Created: 2024-07-19
Issued: 2024-08-22
Modified: 2024-09-23
Data time period: 1988-01-01 to 2022-12-31
text: Queensland
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Point-of-truth metadata URL
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
doi :
https://doi.org/10.3390/rs5010083
Flood N. (2013b). Seasonal composite Landsat TM/ETM+ images using the medoid (a multi-dimensional median). Remote Sensing, 5(12): 6481-6500
doi :
https://doi.org/10.3390/rs5126481
Zhu Z. and Woodcock C. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118 (15): 83-94
doi :
https://doi.org/10.3390/rs5126481
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
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/9e835aec-5314-42c4-a39a-c8ef5959c12c
- global : 9e835aec-5314-42c4-a39a-c8ef5959c12c
