Brief description
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.
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 > climate adjusted seasonal persistent green trendData Creation
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.
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.
Climate Adjusted Seasonal Persistent Green Trend:
Using a Standardised Precipitation Index (SPI), which is a rainfall-based indicator, a relationship with the seasonal persistent green product was built, and the correlation was calculated and summarised using the 95th percentile. Where there was a poor relationship between the seasonal persistent green and rainfall based indicator, pixels were omitted from the adjusted trend product as the rainfall adjustment would not make sense.
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.
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.
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.
Data Quality Assessment Scope
local :
dataset
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. <br>
The fractional cover model was compared to samples drawn from 1500 field reference sites.
Data Quality Assessment Result
local :
Quality Result
The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail.<br>
The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites.
Created: 2013-11-25
Issued: 2021-09-22
Modified: 2024-09-23
Data time period: 1989-12-01 to 2014-12-31
text: Queensland and Northern Territory
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Point-of-truth metadata URL- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/cbd14835-7ab1-4828-a6d0-04cf183a17e8
- global : cbd14835-7ab1-4828-a6d0-04cf183a17e8
