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Remote Sensing
KIMBERLEY MARINE RESEARCH PROGRAM NODE
PROJECT 1.4 – DR PETER FEARNS
AcknowledgmentsThe State Government of Western Australia and WAMSI partners for funding this research.
Professor David Antoine, Nick Hardman‐Mountford, Mark Broomhall
Alan Pearce, Rodrigo Garcia
WA Museum
WAMSI Dredge Node, Themes 2/3 Predicting and measuring the characteristics of sediment plumes due to dredging operations
KMRP
KMRP
Goal: Quantify the reliability of remotely sensed turbidity products for use in the Kimberley region
Objective 1: Analyse uncertainties of remotely sensed turbidity products by comparison of different algorithms and different resolution products with each other and with archived in situ data
Objective 2: Analyse time series of remotely sensed turbidity data to provide first‐stage pilot products that may be applicable for future use as marine management tools.
Deliverables• Analysis of ensemble variability between different algorithms
• Assessment of sub‐km scale variability from comparison with high‐resolution products
• Quantification of uncertainty from comparison with archived in situ data
• Maps of turbidity "hotspot" regions (i.e. regions of frequently occurring high turbidity events and regions of extreme variability).
• Alternative: Maps of different turbidity regimes (e.g. permanently high turbidity, frequent turbid events, infrequent turbid events, persistently clear water).
• Turbidity indicator products (e.g. days above a set turbidity threshold)
Review ProcessAssets
Finfish
Coral
Seagrass
Invertebrates
Intertidal
Mangroves
Turtles
Cetaceans
Water Quality
Coastal Biological
Wilderness
DPaW‐defined assets
Define condition/pressure metrics
Consider applicability of remote sensing. Data sources, spatial and temporal resolution, accuracy, confidence etc.
Example of feedback for coral asset
Turbidity
Issues•Change detection
• Accuracy, precision, spatial resolution, temporal frequency
•Spatial temporal patterns/processes• Variability, baselines, climatology, hotspots
•Accuracy, reliability• Algorithms, data processing, atmospheric correction, quality control, expert users
•Access to data and delivery of products• MODIS and Landsat data archives
• iVEC and NCI• Search• Process• Analyze• Deliver
Reference Location Data Type
Atmos. Corr.
Water Param.
DataRange
Stat. Technique
Bands/Algor.
Regression Coefficient
Error N Limits
TSS, TSM, SSC, SPM
MODISPetus et al. (2010) Bay of Biscay,
FranceMODIS MODIS level
2GTSS 0.3-145.6mg/l 2nd ord. polynomial TSM 12,450Rrs B
1 2 666.1Rrs B10.4
r2 0.97 RMSE=61% 74 Fails to retrieve TSS below 0.5mg/l
Doxaran et al. (2009) Gironde Estuary, France
MODIS Dark Pixel Method
SPM - Exponential SPM 12.996e RrsB2 Rrs B1 0.189
r2 0.89 Terra: 22%Aqua: 18%
204 Choice of atmospheric correction method has issues
Wang et al. (2008) Hangzhou Bay, China
MODIS 6S model SSC 0-2500mg/l Log Transformed Linear
lnSSC 50.171B1‐1.523lnSSC 43.233B2‐1.396
r2 0.73
r2 0.76
RMSE =501mg/lRMSE=424mg/l
25
Miller and McKee (2004)
Northern Gulf of Maxico, USA
MODIS Dark-object subtraction
TSM 1-55mg/l Linear TSM 1140.25RrsB1‐1.91
r2 0.89 RMSE=4.74mg/l
52
Landsat (13), SPOT (4), MERIS (4), OTHER (5)
Secchi Disk Depth, Turbidity
• Analysis of ensemble variability between different algorithms
Potential in situ data sets identified for comparison with remote sensing products.Availability and quality of those in italics has not yet been confirmed Data set Area Data types Source SS03 Cruise 2010 Kimberley
Shelf, King Sound
Optical profiles (IOPs, 35 stations), water column TSS, Chl and other water quality parameters (63 stations)
CSIRO
WAMSI KMRP 2.2.2 Collier Bay, Walcott Inlet
TSS and Chl at surface and depth (~20 stations)
AIMS/UWA
WAMSI KMRP 1.1.1 Collier Bay Chl, PAR, TSS?, Secchi disk?
CSIRO
Total Foundation KGR
King George River
Chl, PAR CSIRO
Curtin Collier Bay Optical survey
Collier Bay Optical profiles Curtin
WAMSI Dredging Theme 2/3
Onslow Optical profiles, TSS, particle size distributions
Curtin
NT coastal Darwin Optical profiles … CSIRO AIMS Kimberley Kimberley
coastal and offshore
Optical profiles, TSS, chl, NTU, DALEC
AIMS
Map of TSS samples collected in Collier Bay as part of WAMSI KMRP Biogeochemistry project (2.2.2). Courtesy of J. Greenwood (CSIRO).
• Quantification of uncertainty from comparison with archived in situ data
MODIS TSS
2‐band fixed AODWith coefficients from Onslow Dredge
2‐band NIR AODWith coefficients from Onslow Dredge
MODIS TSS
2‐band NIR AODWith coefficients from Barrow Island dredge
MODIS TSS
2‐band NIR AODWith coefficients from Onslow Dredge
MODIS TSS
2‐band SWIR AODWith coefficients from Onslow Dredge
Landsat 8 TSS30 m True colourimage
Derby
7th April, 2008 14th April, 2008 21st July, 2008 4th August, 2008
Patterns and Processes
MODIS SST Feb ‐ Dec
7 day average
MODIS SST Feb ‐ Dec
7 day average
MODIS SST Feb ‐ Dec
7 day average
MODIS SST Feb ‐ Dec
7 day average
MODIS SST Feb ‐ Dec
7 day average
MODIS SST Feb ‐ Dec
7 day average
Monthly SSTs near Cape Leveque (block 16‐17°S, 122‐123°E) from Reynolds OISST (red), Hadley (blue), and the Reynolds‐Hadley differences (green ‐‐right axis).
Image courtesy of Alan Pearce
(2015/3/30 19:00 Z) http://oceancurrent.imos.org.au/Broome/2015033019.gif
AcknowledgmentsThe State Government of Western Australia and WAMSI partners for funding this research.
Professor David Antoine, Nick Hardman‐Mountford, Mark Broomhall
Alan Pearce, Rodrigo Garcia
WA Museum
WAMSI Dredge Node, Themes 2/3 Predicting and measuring the characteristics of sediment plumes due to dredging operations
KMRP
40
Montgomery Island, Kimberley