Comparison of Multispectral and Hyperspectral Signatures

of

Coral Reef Substrate

Ankita Acharya (MG-0109)

Guide:-

Nandini Ray Chaudhury

Sci/ Engr-SD.

END/ABHG/EPSA.

SAC (ISRO)

Introduction

Coral Reefs

Marine ecosystem

Coral polyps + zooxanthallae

*Temperature 220C to 280C, not < 150C

*Depth 50 m (shallow); 100 m

*Salinity 32-40 ppt

*Found 300N and 300S latitude

Importance

Storage bank

Food

Medicine

Protect coast

Habitat/ nursery

Feed area

Fishing

Tourism

* source: Encyclopedia Britanica

Cont...

• Threats

• Sea water temperature

• Sea water transperancy

•Run off sediments

• Pollutants

• Population pressure

• Multispectral and Hyperspectral

•The term "multi" is derived from the Latin word for "many" and "hyper" is

the Greek word for "over", "above", or an "exaggerated amount". The

science of multispectral and hyperspectral remote sensing is based on

taking a portion of the electromagnetic spectrum and dividing it into

number of bands for the purpose of analytical computation.

Aim

Pixel level Comparison of Multispectral and Hyperspectral Signatures of Coral Reef

Substrates.

To generate a reef specific classification system to build a theoritical approach.

Generation of spectral signatures of selected coral reefs using multispectral data.

Generation of spectral signatures of selected coral reefs using hyperspectral data

To compare multispectral and hyperspectral signatures of coral reef substrates.

Objective

LISS-III sensor Hyperion Sensor

Data Used

Multispectral

RESOURCESAT-1 (IRS-P6)

LISS-III

Hyperspectral

Earth Observation – 1

Hyperion

LISS-III Hyperion

Platform Resourcesat EO1

Orbital altitude 817 km 705 km

Swath 141 km 7.5 km

Swath width 140 km 7.7 km

Spatial resolution 23.5 m 30 m

Repetivity 24 days Data aquisition as per

request

Revisit time 5 days Data aquisition as per

request

Spectral resolution

(no. of bands with

bandwidth)

520-590 nm

620-680 nm

770-860 nm

1550-1700 nm

10 nm

400-2500 nm

220

Radiometric resolution 7 bit 16 bit

Kind of sensor (Spatial

scanning technique)

Pushbroom Pushbroom

Comparison of Two Sensors

Methodology

Multispectral Hyperspectral

Preprocessing Preprocessing

• Data Downloading

• FCC Generation

• Radiance conversion

• Subsetting

• Enhancement

Data generation Data generation

• Data Downloading

• FCC Generation

• Radiance conversion

• Subsetting

• Enhancement

Comparison

• AOI

• ROI

• ACII

• AOI

• ROI

• ACII

1. Gulf of Kachchh

2. Malvan

3. Lakshadweep

4. Gulf of Mannar

5. Andaman

6. Nicobar

1

2

3

4

5

6

Rationale

Reef Regions Reef Type Geomorphological

Zone

Geomorphological

Sub Zone

Ecological

Zone

Total

Gulf of Kachchh 4 4 20 16 44

Malvan 1 3 2 1 7

Lakshadweep 3 6 15 2 26

Gulf of Mannar 3 5 12 5 25

Andaman 3 7 13 3 26

Nicobar 1 2 3 0 6

Comparison of Indian Coral Reef Regions: Inter and Intra Reef Classes

Interview island

Port Mount Bay

Malay island

Test sites

Malay island viewed from IRS P6 - LISS-III

Port Mount viewed from IRS P6 - LISS-III

North

South

Interview island viewed from IRS P6 - LISS-III

showing north and south study area

Level-1 Level-2 Level-3 Level-4

Biogeographic region Reef Type Geomorphological Zone

Geomorphological sub

zone

Indo-west Pacific Faunal

Province

Fringing

Reef

Reef Front

Reef Slope (Deeper)

Reef Slope

Reef Crest

Moat

Reef Flat

Bare Reef Flat

Sanded Reef Flat

Benthic Sub Zone

Depression Zone on the

Reef Flat

Sand Patch

Sand Vegetation

Beach

Dry Sand

Sand with overlaying

water column

Classification Scheme for Coral Reef Habitat

Results

Data Source: IRS-P6 LISS-III

Dated: 22 February 2004

Data Source: EO1 Hyperion

Dated: 2004

Port Mount Bay

Interview

North

Interview

south

Conclusion

Hyperspectral data enhances the potential of substrate level discrimination of coral

reef targets.

Higher in spectral Channel 2 (Green band: 520-590nm) and 3 (Red Band : 620-680

nm).

A secondary peak response of the substrates is observed for all pixel groups in

between 1000 to 2000 nm in the hyperspectral signatures which is not present for the

multi-spectral data.

Hyperspectral data thus helps to understand the substrate response in the infra-red

region of the spectrum which becomes difficult in the multi-spectral data because of

point data in two discrete spectral channels representing NIR and SWIR regions.

Sand in general gives the highest spectral response in the entire spectrum

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http://www.and.nic.in/

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Thank you …