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Data

Climate Adjusted Seasonal Persistent Green Trend – Landsat, QLD DES Algorithm, QLD and NT Coverage 

Ver: 1.0
Status of Data: completed
Update Frequency: notPlanned
Security Classification: unclassified
Dataset Published: 2021-09-22
Dataset Last Modified: 2026-08-18
Metadata viewed 749 times
Dataset accessed 84 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). Climate Adjusted 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/cbd14835-7ab1-4828-a6d0-04cf183a17e8 
Data can be accessed from the following links:
HTTPCloud Optimised GeoTIFF - Climate Adjusted Seasonal Persistent Green Trend
WMSclimate_adjusted_seasonal_persistent_green_trend
HTTPLandscape Data Visualiser - Climate Adjusted Seasonal Persistent Green Trend
HTTPclimate_adjusted_seasonal_persistent_green_trend_la_1puKnon.txt
HTTPPoint-of-truth metadata URL
HTTPro-crate-metadata.json
The climate adjusted linear seasonal persistent green trend is derived from analysis of the linear seasonal persistent green trend, adjusted for rainfall. The current version is based on the 1987-2014 period.
Seasonal persistent green cover is derived from seasonal 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 summarised using simple linear regression, and the slope of the fitted line is captured in the linear seasonal persistent green product. This product is further processed to produce a climate-adjusted version. 
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
Changes in persistent green are the effects of many different drivers - climate, fire history, land use change, management change. The relationship between rainfall and vegetation growth in particular is widely accepted, with many remote sensing studies examining the relationship between various remotely sensed vegetation indices to rainfall. This product attempts to relate antecedent rainfall conditions to temporal patterns in vegetation cover in order to examine the residual trends, which we assume to be largely a product of other influences.
This climate adjusted seasonal persistent green trend product can be used to explain the residual changes in the persistent green product, which is expected to be a result of anthropogenic influences. For the impact of climate on persistent green, see the linear 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
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
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
Contact Us
Creative Commons Attribution 4.0 International Licence
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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/ 
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Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
Copyright 2010-2021. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP). 
It is not recommended that these data sets be used at scales more detailed than 1:100,000. 
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.