Data

Groundwater dependent ecosystems in the upper Darling River floodplain NSW, mapped using Landsat satellite data

Geoscience Australia
Kilgour, P. ; Symington, N.
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://pid.geoscience.gov.au/dataset/ga/148626&rft.title=Groundwater dependent ecosystems in the upper Darling River floodplain NSW, mapped using Landsat satellite data&rft.identifier=https://pid.geoscience.gov.au/dataset/ga/148626&rft.publisher=Commonwealth of Australia (Geoscience Australia)&rft.description=Groundwater dependent ecosystems (GDEs) rely on access to groundwater on a permanent or intermittent basis to meet some or all of their water requirements (Richardson et al., 2011). The Tasselled Cap percentile products created by Digital Earth Australia (2023) were used to identify potential GDEs for the upper Darling River floodplain study area. These percentile products provide statistical summaries (10th, 50th, 90th percentiles) of landscape brightness, greenness and wetness in imagery acquired between 1987 and present day. The 10th percentile greenness and wetness represent the lowest 10% of values for the time period evaluated, e.g. 10th greenness represents the least green period. In arid regions, areas that are depicted as persistently green and/or wet at the 10th percentile have the greatest potential to be GDEs. For this reason, and due to accessibility of the data, the 10th percentile Tasselled Cap greenness (TCG) and Tasselled Cap wetness (TCW) products were used as the basis for the assessment of GDEs for the upper Darling River floodplain study area. This data release is an ESRI geodatabase, with layer files, including:-      original greenness and wetness datasets extracted; -      classified 10th percentile greenness and wetness datasets (used as input for the combined dataset); -      combined scaled 10th percentile greenness and wetness dataset (useful for a quick glance to identify potential groundwater dependent vegetation (GDV) that have high greenness and wetness e.g. river red gums)-      combined classified 10th percentile greenness and wetness dataset (useful to identify potential GDV/GDE and differentiate between vegetation types)-      coefficient of variation of 50th percentile greenness dataset (useful when used in conjunction with the scaled/combined products to help identify GDEs)For more information and detail on these products, refer to https://dx.doi.org/10.26186/148545.ReferencesDigital Earth Australia (2023). Digital Earth Australia User Guide. Richardson, S., E. Irvine, R. Froend, P. Boon, S. Barber, and B. Bonneville. 2011a. Australian groundwater-dependent ecosystem toolbox part 1: Assessment framework. Waterlines Report 69. Canberra, Australia: Waterlines.Maintenance and Update Frequency: notPlannedStatement: For the upper Darling River floodplain study area, both TCG and TCW were extracted from Digital Earth Australia (DEA) using the Geoscience Australia Landsat Tasselled Cap Percentile Calendar Year Collection 3 product for all available years (1987–2022). Since there is currently no multi-year summary product based on the Collection 3 data, as there is for the Collection 2 data, a script (https://github.com/GeoscienceAustralia/GWB-GDE-DEA/blob/main/ExtractTCG_TCB_c3/get_TCG_TCB_images_c3.ipynb) was written in DEA Sandbox (https://app.sandbox.dea.ga.gov.au/) to extract all of the years and calculate the median of all the percentiles (10th, 50th, 90th) for the whole time period. For this study, a combination of the 10th percentiles of TCG and TCW were investigated, as they are more informative for identifying and characterising potential aquatic and terrestrial GDEs than each band individually. The 50th percentile greenness was also used to create the coefficient of variance (CV) dataset. The datasets created include:-      coefficient of variation of 50th percentile greenness dataset, created by modifying the above script and replacing ‘median’ with ‘std’ and then with ‘mean’ to acquire the standard deviation and mean for each pixel in the 50th percentile greenness datasets for the time period 1987 to 2022. These datasets were then loaded into a GIS and the CV dataset created by using the formula: CV = std / mean-      combined scaled 10th percentile greenness and wetness dataset, created by scaling both the greenness and wetness 10th percentile datasets using the scaling formula z = (x – min) / (max – min), which were then added together.-      classified 10th percentile greenness and wetness datasets (used as input for the combined classified dataset). These were created by capitalising on their normally distributed data and classified using the mean and 0.5 standard deviations into nine classes.-      combined classified 10th percentile greenness and wetness dataset was created by combining the classified greenness and wetness datasets using the ESRI ArcPro ‘Combine’ tool. This resulted in a dataset having a two-digit code with the first number of the code corresponding to greenness and the second digit correspond to wetness (refer to Table 2.1, Buckerfield et al., 2023).Reference:Buckerfield, S., Kilgour, P., Castellazzi, P., Dabovic, J., McPherson, A., Dixon-Jain, P., Symington, N., Buchanan, S. 2023. Groundwater dependent vegetation assessment using remote sensing, Exploring for the Future – Upper Darling Floodplain, New South Wales. Record 2023/575. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2023.575&rft.creator=Kilgour, P. &rft.creator=Symington, N. &rft.date=2023&rft.coverage=westlimit=142.8; southlimit=-32; eastlimit=148; northlimit=-29&rft.coverage=westlimit=142.8; southlimit=-32; eastlimit=148; northlimit=-29&rft_rights= https://creativecommons.org/licenses/by/4.0/&rft_rights=Creative Commons Attribution 4.0 International Licence&rft_rights=CC-BY&rft_rights=4.0&rft_rights=Any&rft_rights=Any&rft_rights=© Commonwealth of Australia (Geoscience Australia) 2023&rft_rights=Australian Government Security Classification System&rft_rights=https://www.protectivesecurity.gov.au/Pages/default.aspx&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Australian Government Security Classification System&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_subject=geoscientificInformation&rft_subject=EFTF – Exploring for the Future&rft_subject=Upper Darling&rft_subject=remote sensing&rft_subject=Landsat&rft_subject=Tasselled Cap&rft_subject=groundwater dependent ecosystem&rft_subject=GDE&rft_subject=floodplain&rft_subject=New South Wales&rft_subject=NSW&rft_subject=Groundwater hydrology&rft_subject=Published_External&rft.type=dataset&rft.language=English Access the data

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Brief description

Groundwater dependent ecosystems (GDEs) rely on access to groundwater on a permanent or intermittent basis to meet some or all of their water requirements (Richardson et al., 2011). The Tasselled Cap percentile products created by Digital Earth Australia (2023) were used to identify potential GDEs for the upper Darling River floodplain study area. These percentile products provide statistical summaries (10th, 50th, 90th percentiles) of landscape brightness, greenness and wetness in imagery acquired between 1987 and present day. The 10th percentile greenness and wetness represent the lowest 10% of values for the time period evaluated, e.g. 10th greenness represents the least green period. In arid regions, areas that are depicted as persistently green and/or wet at the 10th percentile have the greatest potential to be GDEs. For this reason, and due to accessibility of the data, the 10th percentile Tasselled Cap greenness (TCG) and Tasselled Cap wetness (TCW) products were used as the basis for the assessment of GDEs for the upper Darling River floodplain study area.

This data release is an ESRI geodatabase, with layer files, including:

-      original greenness and wetness datasets extracted;

-      classified 10th percentile greenness and wetness datasets (used as input for the combined dataset);

-      combined scaled 10th percentile greenness and wetness dataset (useful for a quick glance to identify potential groundwater dependent vegetation (GDV) that have high greenness and wetness e.g. river red gums)

-      combined classified 10th percentile greenness and wetness dataset (useful to identify potential GDV/GDE and differentiate between vegetation types)

-      coefficient of variation of 50th percentile greenness dataset (useful when used in conjunction with the scaled/combined products to help identify GDEs)

For more information and detail on these products, refer to https://dx.doi.org/10.26186/148545.

References
Digital Earth Australia (2023). Digital Earth Australia User Guide.
Richardson, S., E. Irvine, R. Froend, P. Boon, S. Barber, and B. Bonneville. 2011a. Australian groundwater-dependent ecosystem toolbox part 1: Assessment framework. Waterlines Report 69. Canberra, Australia: Waterlines.

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Maintenance and Update Frequency: notPlanned
Statement:
For the upper Darling River floodplain study area, both TCG and TCW were extracted from Digital Earth Australia (DEA) using the Geoscience Australia Landsat Tasselled Cap Percentile Calendar Year Collection 3 product for all available years (1987–2022). Since there is currently no multi-year summary product based on the Collection 3 data, as there is for the Collection 2 data, a script (https://github.com/GeoscienceAustralia/GWB-GDE-DEA/blob/main/ExtractTCG_TCB_c3/get_TCG_TCB_images_c3.ipynb) was written in DEA Sandbox (https://app.sandbox.dea.ga.gov.au/) to extract all of the years and calculate the median of all the percentiles (10th, 50th, 90th) for the whole time period. For this study, a combination of the 10th percentiles of TCG and TCW were investigated, as they are more informative for identifying and characterising potential aquatic and terrestrial GDEs than each band individually. The 50th percentile greenness was also used to create the coefficient of variance (CV) dataset. 

The datasets created include:

-      coefficient of variation of 50th percentile greenness dataset, created by modifying the above script and replacing ‘median’ with ‘std’ and then with ‘mean’ to acquire the standard deviation and mean for each pixel in the 50th percentile greenness datasets for the time period 1987 to 2022. These datasets were then loaded into a GIS and the CV dataset created by using the formula: CV = std / mean

-      combined scaled 10th percentile greenness and wetness dataset, created by scaling both the greenness and wetness 10th percentile datasets using the scaling formula z = (x – min) / (max – min), which were then added together.

-      classified 10th percentile greenness and wetness datasets (used as input for the combined classified dataset). These were created by capitalising on their normally distributed data and classified using the mean and 0.5 standard deviations into nine classes.

-      combined classified 10th percentile greenness and wetness dataset was created by combining the classified greenness and wetness datasets using the ESRI ArcPro ‘Combine’ tool. This resulted in a dataset having a two-digit code with the first number of the code corresponding to greenness and the second digit correspond to wetness (refer to Table 2.1, Buckerfield et al., 2023).

Reference:
Buckerfield, S., Kilgour, P., Castellazzi, P., Dabovic, J., McPherson, A., Dixon-Jain, P., Symington, N., Buchanan, S. 2023. Groundwater dependent vegetation assessment using remote sensing, Exploring for the Future – Upper Darling Floodplain, New South Wales. Record 2023/575. Geoscience Australia, Canberra.
http://dx.doi.org/10.11636/Record.2023.575

Notes

Purpose
To assist in the identification and characterisation of groundwater dependent ecosystems.

Created: 04 07 2023

Issued: 15 08 2023

This dataset is part of a larger collection

Click to explore relationships graph

148,-29 148,-32 142.8,-32 142.8,-29 148,-29

145.4,-30.5

text: westlimit=142.8; southlimit=-32; eastlimit=148; northlimit=-29

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Other Information
Download the ESRI geodatabase (zip) [3.1 GB]

uri : https://d28rz98at9flks.cloudfront.net/148626/148626_00_1.zip

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