logo
Data

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
Dataset Published: 2021-09-22
Dataset Last Modified: 2026-08-18
Metadata viewed 1446 times
Dataset accessed 127 times
Metadata Created: 2013-11-25
Metadata Last Modified: 2026-08-19
How to cite this collection:
Department of the Environment, Tourism, Science and Innovation, Queensland Government (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 
Data can be accessed from the following links:
HTTPCloud Optimised GeoTIFFs - Linear seasonal persistent green trend
WMSlinear_seasonal_persistent_green_trend
HTTPLandscape Data Visualiser - Linear seasonal persistent green trend
HTTPlinear_seasonal_persistent_green_trend_landsat_file_J81hF9e.txt
HTTPPoint-of-truth metadata URL
HTTPro-crate-metadata.json
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
Data not provided. 
Method DocumentationData not provided.
Procedure StepsData not provided.
Spatial Description
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
Data Stream
Satellite Remote Sensing
GCMD Sciences
ENVIRONMENTAL GOVERNANCE/MANAGEMENT - LAND USE/LAND COVER CLASSIFICATION
VEGETATION - VEGETATION COVER
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
OLI
TM
Parameters
persistent green vegetation fraction
Platforms
LANDSAT-5
LANDSAT-7
LANDSAT-8
Project
TERN Landscapes
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. 
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
Contact Us
Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/
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/ 
Copyright 2010-2021. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP). 
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.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
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.