This dataset contains processed satellite imageries of the Golf of Papua - Torres Strait (GP-TS) region. It includes:
- 12-year (mid 2008-mid 2019) of daily MODIS water type images (Wet season colour scale), and summaries (seasonal, annual,
long term, difference composite maps)
- 1 year (2019) of weekly Sentinel-3 water type images (Forel-Ule colour scale)
** This dataset is currently under embargo until 31/08/2021.
These outputs have been produced though the remote sensing components of the NESP Project 2.2.1 and NESP Project 5.14: Identifying
the water quality and ecosystem health threats to the high diversity Torres Strait and Far Northern GBR from runoff from
the Fly River (Waterhouse et al., 2018, in review and Petus et al., in prep.)
These studies used different sources and long-term databases of freely available satellite data to describe large-scale
turbidity patterns around the GP-TS region, map the Fly River plume and to identify instances and areas with likely plume
intrusion into the Torres Strait protected zone.
Multi-year datasets of medium-resolution satellite images (MODIS-Aqua and Sentinel-3) of the study area have been downloaded
and processed. Medium-resolution satellite data have been processed into daily colour class and water type maps of the
study area using two respective colour classification scales. Several spatial summaries have been produced (median, frequency,
difference composite maps) at different time scales (seasonal, annual, long term).
These spatial summaries provides a large scale baseline of the composition of coastal waters around the GP-TS region, as
well as a description of seasonal trends. This baseline is particularly important as field water quality data are scarce
and challenging to collect due to the remoteness of the study area, They provide a reference against which to compare future
changes, as well as spatially explicit information for when and where the influence from Fly River discharge is likely to
occur and which TS ecosystems are likely to be the most exposed.
In making this data publicly available for management, the authors from the TropWATER Catchment to Reef Research Group request
being contacted and involved in decision making processes that incorporate this data, to ensure its methodology and limitations
are fully understood.
MODIS-Aqua water type maps
Twelve years of water type maps (mid-2008 to mid-2019) were produced using daily MODIS-Aqua (MA) true colour satellite imagery
reclassified to 6 distinct ocean colour classes. The ocean colour is the result of interactions between sunlight and materials
in the water. It is co-determined by the absorption and scattering of various optically active water quality components:
the suspended sediment: SS, the coloured dissolved organic matter: CDOM and the chlorophyll-a: Chl-a. The ocean colour is
a simple indicator available to study the composition of our ocean and distinguish different surface water bodies and their
associated water quality characteristics (e.g., Petus et al., 2019, in prep.).The six colour classes (CC) were defined by
their colour properties across an Intensity-Hue-Saturation gradient (Alvarez-Romero et al., 2012) and were regrouped into
three optical water types: Primary (CC1-4), Secondary (CC5) and Tertiary (CC6). They were produced using the WSC scale classification
toolbox (Petus et al., 2019).
The WSC scale classification toolbox is a semi-automated toolbox using a suit of R and Python (ArcGIS) scripts that has been
developed originally for the Great Barrier Reef (GBR) through Marine Monitoring Program (MMP) funding (Alvarez-Romero et
al., 2013). The toolbox spectrally enhance (Red-Green-Blue, RGB to Intensity-Hue-Saturation, IHS) MODIS true colour imagery
and cluster the MODIS pixels into “cloud” (from the RGB image), “ambiant water” and six Wet Season Colour classes (from
the IHS image) through a supervised classification using typical apparent surface colour signatures of flood waters in the
GBR (Alvarez-Romero et al., 2013, Figure 1, right and Figure 2). Discrimination of colour classes has been based on the GBR
flood plume typology as defined originally in e.g., Devlin et al. (2011). It has been calibrated and validated with satellite
and in-situ water quality data, respectively (Alvarez-Romero et al., 2013; Devlin et al., 2015, Petus et al., 2016). Technical
details about the WSC scale classification have been published in e.g. Alvarez-Romero et al., 2013, Devlin et al., 2015;
Petus et al., 2016, 2019 and GBRMPA, 2020 and Waterhouse et al., in prep.
In the GBR WSC scale, the brownish to brownish-green turbid water masses (colour classes 1 to 4, or primary water type)
are typical for inshore regions of GBR river plumes or nearshore marine areas with high concentrations of resuspended sediments
found during the wet season. These water bodies in flood waters typically contain high nutrient and phytoplankton concentrations,
but are also enriched in sediment and dissolved organic matter resulting in reduced light levels. The greenish-to-greenish-blue
turbid water masses (colour class 5, or Secondary water type) is typical of coastal waters rich in algae and also containing
dissolved matter and fine sediment. This water body is found in the GBR open coastal waters as well as in the mid-water
plumes where relatively high nutrient availability and increased light levels due to sedimentation favour coastal productivity.
Finally, the greenish-blue water mass (colour class 6 or Tertiary water type) correspond to waters with above ambient water
quality concentrations. This water body is typical for areas towards the open sea or offshore regions of river flood plumes
(e.g. Petus et al., 2019).
Sentinel-3 OLCI water type maps
One year (2019) of water type maps was also produced using daily Sentinel-3 Ocean and Land Color Instrument (S3 OLCI) Level-2
(hereafter S3) satellite data reclassified to 21 distinct ocean colour classes. The 21 colour classes (CC) were defined by
their colour properties across a Hue gradient and were produced using the Forel-Ule colour (FU) scale classification toolbox.
The FU classification toolbox is a semi-automated toolbox using a suit of Python, .bat and xml scripts that have been developed
originally for the GBR through MMP funding. It allow processing multi-year databases of satellite images using the FU classification
algorithm recently developed through the European Citclops project and implemented in the Science Toolbox Exploitation
Platform (SNAP) (http://www.citclops.eu/home, Van der Woerd and Wernand, 2015, 2018). Technical details about the WSC scale
classification have been published in e.g., Petus et al., 2019, in prep. and the Appendix B of Gruber et al., 2019.
The FU satellite algorithm converts satellite normalised multi-band reflectance information into a discrete set of FU numbers
using uniform colourimetric functions (Wernand et al., 2012). The derivation of the colour of natural waters is based on
the calculation of Tristimulus values of the three primaries (X, Y, Z) that specify the colour stimulus of the human eye.
The algorithm is validated by a set of hyperspectral measurements from inland, coastal and marine waters (Van der Woerd
and Wernand 2018) and is applicable to global aquatic environments (lake, estuaries, coastal, offshore). Technical details
about FU satellite algorithm, including detailed mathematical descriptions, are presented in e.g., Van der Woerd and Wernand
(2015, 2016), Van der Woerd and Wernand (2018) and Wernand et al. (2013). A first comparative study in the GBR suggested
that FU4-5, FU6-9 and FU ? 10 are similar to the Primary, Secondary and Tertiary water types in the WS colour scale, respectively
(Petus et al., 2019).
Both satellites and colour scales provides qualitative estimation of water composition and spatial datasets that can used
in conjunction with in-situ field measurements, satellite estimations and/or hydrodynamic modelling assessments of water
quality concentrations (if available). By itself, they are particularly interesting in remote areas where in-situ water quality
and optical data are scarce to inexistent as they both relies only on the apparent colour of the ocean.
This datasets have been used in Petus et al., in prep. and Waterhouse et al., in review to: (i) map optical water masses
in the study area; including the turbid Fly River plume, (ii) document long-term turbidity trends in the Gulf of Papua –
Torres Strait region, (iii) determine seasonal changes in turbidity and seasonal plume patterns, and; (iii) assess the
presence of ecosystems likely exposed to the Fly River plume, as well as their frequency of exposure. These datasets does
not allow assessing the trace metals contaminants of the Fly River discharge or assessing the ecological impact of Fly River
discharges on TS ecosystems.
Daily datasets: MODIS-Aqua true colour images and six colour class maps:
Database name: Daily_MAWS.gdb,
Data format: a2008141 = year 2008, Julian day 141
Twelve years (mid-2008 to mid-2019) of daily MODIS true colour images of the GPTS region were downloaded from the NASA
Rapid Response and EOSDIS worldview websites. The true colour images were spectrally enhanced (from red-green-blue to hue-saturation-intensity
colour systems), and clustered into six colour class maps using methods described above (Álvarez-Romero et al., 2013) and
post-processed in ArcGIS 10.3.
Weekly datasets: Sentinel-3 Forel Ule (21) colour class maps:
Database name: Weekly_S3FU.gdb,
Data format: T2019w01 = week 1 of 2019
One year (2019) of daily S3 OLCI imagery of the study area was downloaded on the EUMETSAT Copernicus Online Data Access
website (https://coda.eumetsat.int/#/home). S3 data were atmospherically corrected and were processed with SNAP (Van der Woerd
et al., 2016, Van der Woerd and Wernand 2018) and clustered into 21 FUC class maps using methods described above. Processed
daily images were combined into weekly maximum FUC composites. Weekly composites were produced to minimise the amount of
area without data per image due to masking of dense cloud cover which is very common in the study area.
Summaries: median, mean, Standard Deviation, difference and frequency maps
Median, mean and standard deviation composite maps (2008-2016, produced as part of NESP TWQ 2.2.1) were created from the
MODIS daily colour class maps. These composites were obtained by overlaying the daily MODIS colour class maps and calculating
mean and standard deviation and/or the median colour class category per pixel over seasonal, annual (2008 to 2016) and
long-term time periods (8 years: 2008-2016). The seasons were defined in this study as: monsoon season: January – April,
trade wind season: May to October and transition period: November and December. *Note that in these summaries produced through
NESP 2.2.1, data from the 2nd of Feb 2011 to 8th of May 2012 were missing from the NASA websites due to a hardware failure
of a NASA disk array that contained the MODIS images. Missing image were made available later, downloaded and used to produce
the new summaries below.
Summaries: Long-term median, long-term seasonal median and long-term monthly median (decadal: 2009-2018, produced as part
of NESP TWQ 5.14)
Database name: Summaries_MAWS_medianLT.gdb,
Data format: Med_0918_Mo = long-term median monsoonal composite, Med0918TW = long-term median trade wind composite, Month0918m
= long-term median monthly composite (for example: Aug0918m)
These summaries were created from the daily MODIS colour class maps. These composites were obtained by overlaying the daily
MODIS colour class maps and calculating the median colour class category for each pixel of our study area using all daily
data from 2009 to 2018, or using daily data collected in each monsoon and trade wind seasons or in each month of the 2009
– 2018 period. The seasons were defined in this study as: monsoon season: January – April, trade wind season: May-December.
Long-term (i) seasonal and (ii) monthly difference maps
Database name: Summaries_MAWS_difference_,maps.gdb
Data format: Diff_Seasonal0918m = seasonal difference map, Diff_Month0918m = monthly difference maps (for example, Diff_Aug0918m)
These summaries were created to illustrate areas with an increase (positive anomaly) or decrease (negative anomaly) in turbidity:
(i) in the trade wind season against the monsoonal trends; or (ii) in each month against long-term trends. The seasonal
difference map was calculated by subtracting the long-term median monsoonal and trade wind maps. The monthly difference maps
was calculated by subtracting each long-term monthly map and the decadal median maps
Frequency maps (2009-2018): Annual frequency maps were generated from the daily colour class maps for each of the colour
classes 1 to 6, as well as 1-4 combined (primary water type), 1-5 (primary plus secondary water types) and 1-6 combined.
The water type frequency was defined as the total number of days per year exposed to a given water type divided by the number
of data days (non-cloud) recorded per year, resulting in a normalised frequency on a scale from 0 – 1. An annual ‘data quality’
(dq) raster was also produced, calculated as the number of non-cloud days per pixel divided by the total number of daily
rasters available for the year. Processing was performed using Python 2.7.3 and ArcGIS 10.7 (ESRI, 2019).
Mean multi-annual frequency map (2009-2018): For each of the colour class combinations described above, a corresponding multiannual
average was generated using cell statistics: mean (Spatial Analyst), performed using Python 2.7.3 and ArcGIS 10.7 (ESRI,
Limitations of the data:
In the absence of significant ground-truthing measurements, this study assumed that turbidity levels in the Torres Strait
region decreased from the Primary to the Tertiary waters types, as observed in the GBR (e.g., Petus et al., 2019). The assumption
supported by preliminary match-ups undertaken between available field and MODIS satellite data, as well as match-ups between
MODIS water type maps and Sentinel-3 Forel-Ule water type maps (Petus et al., in prep; Waterhouse et al., in review). More
in-situ water quality measurements should, however, be collected to fully validate this assumption.
The dense cloud cover in the study area, allowed to capture satellite information about 50% of the time and it is likely
that some large sediment plumes associated with rough weather may have been missed. Also, it is impossible to fully separate
direct riverine plume influence from sediment resuspension or the influence of calcareous sediments in the satellite maps.
Finally this project also assumes that the RGB and IHS signals recorded over turbid, sediment-dominated waters such as the
GBR flood waters mainly results from the water contribution and that fully accurate atmospheric corrections of MODIS-Aqua
data are not crucial for this type of turbid waters and assessment.
1) Alvarez-Romero, J., Devlin, M., da Silva, E., Petus, C., Ban, N., Pressey, R., Kool, J., Roberts, J., Cerdeira-Estrada,
S., Wenger, A., Brodie, J., 2013. A novel approach to model exposure of coastal-marine ecosystems to riverine flood plumes
based on remote sensing techniques. Journal of Environmental Management, 119. pp. 194-207
2) Devlin, M., Schroeder, T., McKinna, L., Brodie, J., Brando, V., & Dekker, A., 2011. Monitoring and mapping of flood plumes
in the Great Barrier Reef based on in situ and remote sensing observations. Advances in Environmental Remote Sensing to
Monitor Global Changes, 147-191.
3) Devlin, M., Petus, C., Teixeira da Silva, E., Tracey. D., Wolff, N., Waterhouse, J., Brodie, J., 2015. Water Quality
and River Plume monitoring in the Great Barrier Reef: An Overview of Methods Based on Ocean Colour Satellite Data. Remote
Sensing 7, 12909-12941; doi:10.3390/rs71012909
4) Great Barrier Reef Marine Park Authority 2020, Marine Monitoring Program quality assurance and quality control manual
2018-19, Great Barrier Reef Marine Park Authority, Townsville
5) O'Brien, D., Mellors, J., Petus, C., Martins, F., Wolanski, E. and Brodie, J. (2015) Monitoring and assessment of water
quality threats to Torres Strait marine waters and ecosystems. Progress Report August 2015 Report No. 15/43. Townsville,
James Cook University.
6) Petus, C., Devlin, M., Thompson, A., McKenzie, L., Collier, C., Teixeira da Silva, E., Tracey, D. Estimating the Exposure
of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating
a Simple Satellite Risk Framework with Environmental Data, 2016. Remote Sensing, 8(3), 210; doi:10.3390/rs8030210
7) Petus et al., in prep. Monitoring sediment distribution and potentially polluted riverine runoff in a data-limited environment:
insights from satellite images in the remote Torres Strait.
8) Van der Woerd, H.J., Wernand, M.R., 2015. True colour classification of natural waters with medium-spectral resolution
satellites: SeaWiFS, MODIS, MERIS and OLCI. 2015. Sensors 15, 25663–25680. https://doi.org/10.3339/s151025663 .
9) Van der Woerd, J.H., Wernand, R.M., 2018. Hue-angle product for low to medium spatial resolution optical satellite sensors.
Remote Sensing 10, 180.
10) Waterhouse, J., Petus, C., Brodie J., Bainbridge, S., Wolanski, E., Dafforn, K.A., Birrer, S.C., Lough, J., Tracey,
D., Johnson, J.E., Chariton, A.C., Johnston, E.L., Li, Y., Martins, F., O’Brien, D. (2018) Identifying water quality and ecosystem
health threats to the Torres Strait and Far Northern GBR from runoff of the Fly River. Report to the National Environmental
Science Program. Reef and Rainforest Research Centre Limited, Cairns (162pp.).
11) Waterhouse, J., Apte, S., Petus, C., Bainbridge, S., Wolanski, E., Tracey, D., Angel, B.M., Jarolimek, C, V., Brodie Jon.,
in review. NESP Project 5.14. Identifying water quality and ecosystem health threats to the Torres Strait from Fly River
runoff. Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (162pp.).
12) Waterhouse et al., in prep. Waterhouse, J., Gruber, R., Logan, M., Petus, C., Howley, C., Lewis, S., Tracey, D., James,
J., Mellors, J., Tonin, H., Skuza, M., Costello, P., Davidson, J., Gunn, K., Lefevre, C., Moran, D., Robson, B., Shanahan,
M., Zagorskis, I., Shellberg, J., 2021. Marine Monitoring Program: Annual Report for Inshore Water Quality Monitoring 2019-20.
Report for the Great Barrier Reef Marine Park Authority, Great Barrier Reef Marine Park Authority, Townsville.
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.14_TS-water-quality