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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

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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

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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

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Montgomery Island, Kimberley