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Linear Seasonal Persistent Green Trend - Landsat, QLD DES Algorithm, QLD and NT Coverage 

Ver: 1.0
Status of Data: completed
Update Frequency: periodic
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 1440 times
Accessed 127 times
Dataset Created: 2013-11-25
Dataset Published: 2021-09-22
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Linear seasonal persistent green trendWMSlinear_seasonal_persistent_green_trendHTTPLandscape Data Visualiser - Linear seasonal persistent green trendHTTPlinear_seasonal_persistent_green_trend_landsat_file_J81hF9e.txtHTTPro-crate-metadata.json
How to cite this collection:
Department of the Environment, T. (2021). Linear Seasonal Persistent Green Trend - Landsat, QLD DES Algorithm, QLD and NT Coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/d48f5633-ca38-4e1f-9352-d7c191ceade9 
The linear seasonal persistent green trend is derived from analysis of the seasonal persistent green product over time. The current version is based on the 1987-2014 period.
Seasonal persistent green cover is derived from seasonal fractional cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarized using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled. 
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 in partnership with the Joint Remote Sensing Research Program using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI satellite data sourced from the US Geological Survey. 
Purpose
The trends in persistent green are considered to be indicators of change in woody vegetation cover. This linear seasonal persistent green trend product largely shows the effect of climate and rainfall on woody vegetation. To examine trends adjusted for rainfall, use the climate adjusted linear seasonal persistent green trend product. 
Lineage
Landsat surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover > seasonal persistent green product > linear seasonal persistent green trend 
Method DocumentationData not provided.
Procedure Steps

1. 

Persistent Green Fractional Cover: Smoothing splines are fitted in multiple iterations per pixel through the full time series of seasonal fractional cover (green fraction only). At each iteration, zero weight is given to observations that lie above the spline, and observation below the line are weighted proportion to the size of the residual. Observations greater than 3 standard deviations from the residual mean are given zero weight, and those between 2 and 3 standard deviations are given less weight, this avoids contamination by outliers. Persistent green fractional cover for each season is estimated from the final spline iteration at each seasonal time step. Values reported are as for fractional cover, ie. percentages of cover plus 100. Areas with frequent seasonal fractional cover data gaps due to cloud may produce unreliable estimates of persistent green cover. 

2. 

Linear Seasonal Persistent Green Trend: The seasonal persistent green product is summarised using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled. 

Queensland and Northern Territory
Temporal Coverage
From 1989-12-01 to 2014-12-31 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
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.
The fractional cover model was compared to samples drawn from 1500 field reference sites. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail.
The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites. 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
GCMD Sciences
BIOSPHERE - VEGETATION COVER
HUMAN DIMENSIONS - LAND USE/LAND COVER CLASSIFICATION
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
OLI
TM
Parameters
persistent green vegetation fraction
Platforms
LANDSAT-5
LANDSAT-7
LANDSAT-8
Temporal Resolution
Annual
Topic
environment
imageryBaseMapsEarthCover
Author
Department of the Environment, Tourism, Science and Innovation, Queensland Government
Contact Point
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
Armston, J. D., et al. (2002). Geometric correction of Landsat MSS, TM, and ETM+ imagery for mapping of woody vegetation cover and change detection in Queensland. Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference.
de Vries, C., Danaher, T., Denham, R., Scarth, P. & Phinn, S. (2007). An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites, Remote Sensing of Environment, vol. 107, no. 3, pp. 414-429.
Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118. doi:10.1016/j.rse.2011.10.028
Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications.
Danaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., and Gillingham, S. (2010). Ecosystem Function in Savannas: Measurement and Modelling at Landscape to Global Scales.
Flood, N., Danaher, T., Gill, T. and Gillingham, S. (2013) An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sens. 2013, 5(1), 83-109. doi:10.3390/rs5010083
Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500
Danaher, T. and Collett, L (2006). Development, Optimisation and Multi-temporal Application of a Simple Landsat Based Water Index. Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, Australia, November 2006.
Scarth, P., Röder, A., Schmidt, M., 2010b. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis. In: Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference (ARSPC)
Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
Supplemental Information
Filenames for the seasonal fractional cover product conforms to the AusCover standard naming convention. The standard form of this convention is:
___
For more information, see the file naming convention file provided with this dataset. 
Resource Specific Usage
Data not provided. 
Environment Description
Data not provided. 
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