Classification of Australian Waterbodies across a Wide Range of Optical Water Types
Abstract
:1. Introduction
Spectral Data | Classification | Dataset | Reference |
---|---|---|---|
SeaWiFS data | ED a Eigenvector | Northwest Atlantic | [42] |
Normalized SeaWiFS data | ISODATA b | Global | [43] |
In situ Rrs c | hierarchical | English Channel | [37] |
In situ Rrs | FCM d | Global | [44] |
In situ Rrs | FCM d | Chinese lakes | [45] |
In situ Rrs | FCM d | Global lakes | [46] |
In situ Rrs | thresholding | Yellow Sea | [47] |
Normalized In situ Rrs | hierarchical | Eastern English Channel North Sea French Guiana | [33] |
Normalized In situ Rrs | k-means | Global | [1] |
Simulated rrs e | WFCM f | Estonia Finland | [48] |
- Develop a method to define distinct OWTs.
- Create a set of synthetic generalized inherent optical properties (GIOPs), based on the key features of each unique OWT.
- Present a case study as an example of a potential application of implementing the GIOPs water quality monitoring at a drainage basin scale.
2. Materials and Methods
2.1. In Situ Data
2.1.1. Datasets
2.1.2. Spectral Clustering
2.2. Satellite Data
- Create a mask of permanent waterbodies over the current (spatial) window by thresholding the WOfS dataset at the specified percentage limit (80%).
- Erode the water mask (by 2 pixels) and then re-expand it (by 2 pixels) in order to remove thin features connecting several main waterbodies: this allows for the selection of more than one sampling location where several waterbodies are connected by, e.g., thin river channels (would otherwise be counted as a single waterbody in the next step).
- Identify and count all the spatially distinct waterbodies in the current window. Discard any waterbody whose boundary extends beyond the edge of the window. The window size (1.0 × 1.0 degree) and overlap (0.7 degree) are selected such that these split waterbodies are ultimately processed (as a whole) in a different (overlapping) window, resulting in an unbiased selection of sampling location for these waterbodies. The window overlap is selected such that the largest waterbodies over the region of interest are properly captured.
- For each identified waterbody, erode the water mask (by 2 pixels) to remove the potential influence of nearby vegetation on the edges of the waterbody.
- Further erode the water mask (by 4 pixels) to ensure that the selected pixel is at the centre of a window of at least 9 × 9 pixels.
- For the remaining pixels, gradually erode the mask further until it cannot be eroded any more without removing all pixels. The resulting pixel(s) represent the most central location(s) for the considered waterbody. If more than one pixel remains, select the location closest to centre of gravity of the remaining pixels.
2.3. Data Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Acronym | Description |
---|---|
AOI | Area of Interest |
ARD | Analysis ready data |
DEA | Digital Earth Australia |
ED | Euclidian Distance |
EO | Earth Observation |
FCM | Fuzzy-c Means |
GIOP | Generalized Inherent Optical Properties |
IOP | Inherent Optical Properties |
IOSODATA | Iterative Self-Organizing Data Analysis Techniques |
Kd | Vertical Attenuations |
MDB | Murray-Darling Basin |
NAP | Non-algal particulates |
NCI | National Computing Infrastructure |
NEC | North East Coastal |
nSSM | normalized Spectral Similarity Metric |
ODC | Open Data Cube |
OWT | Optical Water Types |
Rrs | Remote Sensing reflectance |
rrs | Subsurface reflectance |
SDG | Sustainable Development Goals |
SDz | Secchi Depth |
SEC | South East Coastal |
SIOP | Specific Inherent Optical Properties |
TOA | Top of Atmosphere |
TSS | Total Suspended Solids |
WFCM | Weighted Fuzzy-c Means |
WOfS | Water Observations from Space |
Parameter | Description | Unit |
---|---|---|
CCHL | Chlorophyll concentration (a proxy for phytoplankton) | μg L−1 |
CDOM | Coloured dissolved organic matter | |
CNAP | Non algal particulates concentration | mg L−1 |
PHY | Phytoplankton | |
a*PHY(440 nm) | Chlorophyll-a specific absorption at 440 nm | m2mg−1 |
a*PHY(676 nm) | Chlorophyll-a specific absorption at 676 nm | m2mg−1 |
γ aCDOM | Spectral slope constant of CDOM absorption coefficient | nm−1 |
aCDOM(440 nm) | Absorption of CDOM at 440 nm | m−1 |
a*NAP(440 nm) | Specific absorption of NAP at 440 nm | m2g−1 |
γ aNAP | Spectral slope constant of NAP absorption coefficient | nm−1 |
b*bNAP(555 nm) | Specific backscattering due to NAP at 555 nm | m2g−1 |
γ bbNAP | Spectral slope constant of NAP backscattering coefficient | nm−1 |
Basin | Area (km2) | Mean Rainfall (mm) | Mean Elevation (m) | Climate | Hydrogeology | Land Use |
---|---|---|---|---|---|---|
MDB | 1,061,000 | 458 | 260 | Range of climatic conditions: cool and humid eastern uplands; temperate southeast; subtropical northeast with monsoonal rain; hot, dry semi-arid; arid western plains. | Basinal aquifers in sedimentary deposits within the flatter landscapes; fractured rock aquifers and valley-fill alluvium in the highlands bordering the basin. | Dryland pasture, dryland and irrigated cropping, and urban land use. |
NEC | 451,000 | 827 | 173 | Subtropical to tropical with hot, wet summers and cooler, dry winters. Monsoonal summer rainfall in the north, winter rainfall the south. | Topographically diverse terrain with high relief in coastal ranges and tablelands and coastal alluvial plains. Outcropping fractured basement rock, alluvial valley systems, and coastal sand deposits. | Native pasture, dryland and irrigated agriculture, and urban land use. |
SEC | 129,500 | 995 | 323 | Warm temperate climate with moderate rainfall. | Outcropping fractured basement rock, alluvial valley, and coastal sand aquifers. | Nature conservation, dryland pasture, irrigated and dryland cropping, and urban land use |
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Max | Min | Mean | Median | SD | |
---|---|---|---|---|---|
CCHL | 12.78 | 0.03 | 0.86 | 0.48 | 1.50 |
603.60 | 0.92 | 25.98 | 9.87 | 67.26 | |
CNAP | 90.85 | 0.10 | 4.57 | 2.69 | 7.17 |
82.98 | 0.51 | 9.52 | 4.08 | 14.14 | |
a*PHY(440 nm) | 0.4838 | 0.0192 | 0.0875 | 0.0728 | 0.0598 |
0.1953 | 0.0023 | 0.0365 | 0.0331 | 0.0249 | |
a*PHY(676 nm) | 0.1907 | 0.0072 | 0.0293 | 0.0260 | 0.0148 |
0.1286 | 0.0013 | 0.0203 | 0.0190 | 0.0135 | |
γ aCDOM | 0.0373 | 0.0002 | 0.0143 | 0.0147 | 0.0045 |
0.0211 | 0.0073 | 0.0170 | 0.0178 | 0.0026 | |
aCDOM(440 nm) | 5.3877 | 0.0053 | 0.2452 | 0.0851 | 0.5299 |
4.4714 | 0.2023 | 1.0740 | 0.8383 | 0.7586 | |
a*NAP(440 nm) | 0.2457 | 0.0010 | 0.0245 | 0.0170 | 0.0271 |
0.3342 | 0.0030 | 0.1009 | 0.0948 | 0.0480 | |
γ aNAP | 0.0153 | 0.0044 | 0.0098 | 0.0111 | 0.0031 |
0.0158 | 0.0042 | 0.0103 | 0.0107 | 0.0027 | |
b*bNAP(555 nm) | 0.1984 | 0.0005 | 0.0112 | 0.0075 | 0.0183 |
0.3767 | 0.0062 | 0.0564 | 0.0212 | 0.0821 | |
γ bbNAP | −0.0220 | −3.3386 | −1.0197 | −0.9000 | 0.5804 |
−0.2862 | −3.1108 | −1.1317 | −1.0313 | −0.4703 |
N | Sum Sq | Mean Sq | F Value | Pr(>F) | |
---|---|---|---|---|---|
CCHL | 32 | 289.60 | 9.05 | 6.50 | <0.001 |
17 | 16796 | 988 | 0.19 | 1.000 | |
CNAP | 32 | 1196 | 37.38 | 0.70 | 0.884 |
17 | 5159 | 303.45 | 3.42 | <0.001 | |
a*PHY(440 nm) | 32 | 7.81E−02 | 2.44E−03 | 0.66 | 0.925 |
17 | 5.46E−03 | 3.21E−03 | 0.48 | 0.958 | |
a*PHY(676 nm) | 32 | 2.82E−03 | 8.81E−05 | 0.37 | 0.999 |
17 | 5.64E−04 | 3.32E−05 | 0.157 | 1.000 | |
γ aCDOM | 32 | 06.00E−04 | 1.87E−05 | 0.93 | 0.576 |
17 | 1.08E−04 | 6.36E−06 | 0.96 | 0.509 | |
aCDOM(440 nm) | 32 | 19.62 | 0.6132 | 2.57 | <0.001 |
17 | 15.33 | 0.9016 | 1.75 | 0.047 | |
a*NAP(440 nm) | 32 | 1.53E−02 | 4.79E−04 | 0.63 | 0.944 |
17 | 5.74E−02 | 3.38E−03 | 1.52 | 0.105 | |
γ aNAP | 32 | 6.67E−4 | 2.09E−05 | 2.60 | <0.001 |
17 | 3.03E−4 | 1.78E−05 | 3.20 | <0.001 |
Cluster# | N | Reflectivity | Rrs Characteristics 1 | SIOP Characteristics 2 | Dominant Absorber 3 | Description |
---|---|---|---|---|---|---|
01 | 135 | low | broad plateau peaking between 400 nm and 500 nm | lowest Chl | CDOM | oligotrophic coastal waters with low amounts of suspended material |
02 | 39 | moderate | maximum peak between 480 nm and 500 nm smaller peak around 545 nm | highest aCDOM(440 nm) of coastal waters | CDOM | coastal waters with a strong estuarine influence |
03 | 55 | low | broad plateau peaking between 475 nm and 575 nm | lowest NAP of coastal waters | CDOM | open coastal waters with higher amounts of suspended organic material than c3 |
04 | 6 | moderate | broad plateau peaking between 500 nm and 600 nm | highest chl and a*PHY (440 nm) of coastal waters | PHY | eutrophic tropical coastal waters |
05 | 6 | moderate | steep increase from 350 nm to 560 nm followed by a sharp decrease to 600 nm | highest γ aNAP of coastal waters | NAP and CDOM | relatively turbid tropical coastal waters containing organic particulate material |
06 | 42 | low | broad plateau peaking between 500 nm and 600 nm | lowest b*bNAP (555 nm) of inland waters | CDOM | clear inland lake waters |
07 | 5 | high | steep increase from 350 nm to a peak at 570 nm, followed by a decrease to an absorption peak at 680 nm with a smaller peak around 700 nm | highest chl and very low NAP | PHY | eutrophic waters with high phytoplankton content |
08 | 6 | high | steep increase from 350 nm to a peak at 570 nm, followed by a gradual decrease to around 700 nm | low NAP and relatively high Chl | CDOM | CDOM rich waters |
09 | 27 | moderate | steep increase from 350 nm to a peak at 570 nm, followed by a gradual decrease to around 700 nm | relatively high NAP and low b*bNAP (555 nm) | CDOM and NAP | relatively clear inland waters with small suspended particles |
10 | 4 | high | steep increase from 350 nm to a peak at 580 nm, followed by a more gradual decrease to around 700 nm | high NAP, high aCDOM(440 nm) and largest γNAP | CDOM | sediment laden waters containing organic particulate material |
11 | 21 | moderate | steep increase from 350 nm to a peak at 590 nm, followed by a broad shoulder between 590–700 nm | Highest NAP, relatively low b*bNAP (555 nm) | NAP and CDOM | Sediment laden waters with small suspended particles |
01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | Accuracy (%) | Sensitivity | Precision (%) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
01 | 102 | 0 | 18 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 89 | 0.91 | 76 |
02 | 0 | 25 | 1 | 0 | 11 | 3 | 0 | 0 | 2 | 0 | 0 | 79 | 0.64 | 60 |
03 | 4 | 7 | 39 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 78 | 0.63 | 70 |
04 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 75 | 0.50 | 67 |
05 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0.16 | 50 |
06 | 6 | 0 | 3 | 0 | 0 | 30 | 0 | 0 | 2 | 0 | 0 | 76 | 0.56 | 73 |
07 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 75 | 0.50 | 50 |
08 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 2 | 0 | 68 | 0.38 | 50 |
09 | 0 | 6 | 1 | 2 | 4 | 1 | 0 | 0 | 13 | 0 | 0 | 82 | 0.68 | 48 |
10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 49 | 0.00 | 0 |
11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 19 | 100 | 1.00 | 95 |
Overall Accuracy: | 69% | |||||||||||||
Kappa: | 62% |
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Botha, E.J.; Anstee, J.M.; Sagar, S.; Lehmann, E.; Medeiros, T.A.G. Classification of Australian Waterbodies across a Wide Range of Optical Water Types. Remote Sens. 2020, 12, 3018. https://doi.org/10.3390/rs12183018
Botha EJ, Anstee JM, Sagar S, Lehmann E, Medeiros TAG. Classification of Australian Waterbodies across a Wide Range of Optical Water Types. Remote Sensing. 2020; 12(18):3018. https://doi.org/10.3390/rs12183018
Chicago/Turabian StyleBotha, Elizabeth J., Janet M. Anstee, Stephen Sagar, Eric Lehmann, and Thais A. G. Medeiros. 2020. "Classification of Australian Waterbodies across a Wide Range of Optical Water Types" Remote Sensing 12, no. 18: 3018. https://doi.org/10.3390/rs12183018