mobile bay water quality assessment using nasa spaceborne data products

Mobile Bay Water Quality Assessment Using NASA Spaceborne Data Products

Jenny Q. Du

Mississippi State University

OutlineOutline

• Project Objectives

• Current Research Status

• Proposed Approaches– Images with higher resolution– Classification methods

• Preliminary Results

• Ongoing Efforts

Project ObjectivesProject Objectives

• To use NASA Spaceborne Imagery (i.e., Landsat, ASTER, Hyperion) in the study of water quality and sediment dynamics in Mobile Bay, AL.

• To compare with the NASA research products in Mobile Bay using MODIS imagery and assess the improvements.

This MODIS satellite image shows sediment plumes moving into the Gulf of Mexico from the main branch of the Mississippi River and through the bayous in its Delta region

(visibleearth.nasa.gov)

Landsat 11/27/1999

Landsat 11/27/1999 (Mobile bay)

Landsat 10/15/2001

Landsat 10/15/2001 (Mobile Bay)

Landsat 2/17/2001

Landsat 2/17/2001 (Mobile Bay)

Current Research StatusCurrent Research Status

• MODIS (Aqua/Terra) – Pros: wide spatial coverage, high temporal

resolution (covers the entire globe almost everyday)

– Con: low spatial resolution (250m-1000m)

• Hard classification– K-means clustering– ISODATA

Proposed ApproachesProposed Approaches

• Satellite Images with Higher Resolutions– LANDSAT

• 30 m spatial resolution (can be enhanced to 15m); 4 VNIR bands– ASTER

• 15m spatial resolution; 3 VNIR bands– Hyperion

• 30m spatial resolution; 220 bands

• Fine Classification– Statistical Classifiers

• Correlation Study with Ground Truth– In situ sampling (Nov. 2007 – Sep. 2008)– Historic data

Preliminary ResultsPreliminary Results

• Satellite Images with Higher Resolutions– LANDSAT

• Classification– ISODATA

• Correlation Study with Ground Truth– Historic data (e.g., Water Resources Database)

Landsat 9/26/1991 (Mobile Bay)

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

(Hard) Classification Result

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations1=5.1Stations2=4.7

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations3=20Stations4=25

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations5=9.8Stations6= 10.3

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations7=27

Satellite Image 09/26/1991Ground Truth(Turbidity) 09/27/1991Stations 1 2 3 4 5 6 7

Class 2 2 3 3 4 4 5

Turbidity 5.1 4.7 20 25 9.8 10.3 27

Landsat 11/27/1999 (Mobile Bay)

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

(Hard) Classification Result

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations1=5.1Stations2=5.5Stations3=3.3Stations4=4.4Stations5=4.0TSSStations1=24Stations2=22Stations3=17Stations4=19Stations5=16CHL-AStations1=5.5Stations2=7.0Stations3=6.8Stations4=4.4Stations5=7.9

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations6=7.9TSSStations6=43CHL-AStations6=37

Satellite Image 11/27/1999Ground Truth 11/27/1999

Stations 1 2 3 4 5 6

Class 4 4 4 4 4 2

Turbidity 3.3 4.4 5.5 5.1 4 7.9

TSS 17 19 22 24 16 43

Chl-A 6.8 4.4 7.0 5.5 7.9 37

Landsat 2/17/2001 (Mobile Bay)

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

(Hard) Classification Result

Land

Class 1

Class 2

Class 3

Class 4

Class 5

Observation Stations

TurbidityStations1=18

Satellite Image 02/17/2001Ground Truth(Turbidity) 02/08/2001Stations 1

Class 3

Turbidity 18

Proposed Approaches (Cont’d)Proposed Approaches (Cont’d)

• Classification Approaches

r = M a– Unsupervised Linear Mixture Analysis

• Endmember signature extraction

• Fully constrained linear unmixing

– Blind Source Separation• Independent Component Analysis

Original Image

Linear unmixing result (soft Classification)

The (soft) endmember classification map that can be used for detailed water quality mapping

ICA result (soft classification)

The independent component (soft classification map) that can be used for detailed water quality mapping

Ongoing EffortsOngoing Efforts

• More detailed correlation analysis– Images and ground truth data collected at the same

time.– Images collected during Nov. 2007 and Sep. 2008.