Thesis Committee Dr. Nitin Kumar Tripathi Chairperson Prof. Seishiro Kibe Member Dr. Wenresti Gallardo 14-May-2008 MAPPING CHANGES IN THE MARINE ENVIRONMENT OF PHU QUOC ISLAND, VIET NAM Ton Binh Minh Remote sensing and GIS FoS
Thesis Committee Dr. Nitin Kumar Tripathi ChairpersonProf. Seishiro Kibe MemberDr. Wenresti Gallardo Member
14-May-2008
MAPPING CHANGES IN
THE MARINE ENVIRONMENT OF PHU QUOC ISLAND, VIET NAM
Ton Binh MinhRemote sensing and GIS FoS
- Area 593 km2, - 26 small islands - Phu Quoc Island; An Thoi- Tho Chau archipelagoes- Coastal Hydrologic condition for ecotourism
Phu Quoc Island
Natural Resources
Phu Quoc Island not only has potential for marine resources but also for ecotourism and relax
- Remote sensing and GIS are power tools for mapping and management of resources
- Surface- Under Water
- Marine resource are: Over exhausted and none plan exploited Fish, turtles, dugong, dolphins are on the verge of
extinction. - Unsustainable fishing techniques
(small mesh fishnets, cyanide, dynamite, flying raking, and Increasing number of fishermen, fishing vessels with close shore fishery activities).
- Detailed maps of marine habitats, changing rule of environment for monitoring marine environment management, conservation coastal ecosystems and sustainable development
Objective
- To detect the changes in sea grass beds, coral reefs during 2000-2004 and 1992-2007.
- To apply remote sensing and GIS to monitor and evaluate coastal resources in Phu Quoc Island.
Detail objectives- To map benthic communities: sea grass, coral (live and
dead), sand, rubble around Phu Quoc Island,
- To provide data for environmental and management of seagrass beds and coral reefs,
Bai BonAster 2004
An ThoiLandsat 2007
Study area- An Thoi- Bai Bon
Data base- Ancillary data,- Remote sensing data, - Field data.
- Ancillary data:
+ Previous reports.
+ Administration, transportation
+ Bathymetric depth+ Sediment maps
Data Surveillance and Collection tools- GPS, - Tape measurement (30m), - Digital camera, - Data form, - Secchi disk,- Diving equipment- Boat
Data base- Field data
Quadrats
20M
20M +
Field work techniquesLinear transect
Integration linear transect and quadrat for collecting data in seagrass area
=
1m
1m
Analyzing Image
Mean DN value of coral, seagrass, sand in four bands
Landsat 2007
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10
20
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60
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80
90
1 2 3 4
Band
DN
val
ue
Coral
Seagrass
Sand
0
20
40
60
80
100
120
1 2 3Band
DN
va
lue
Coral
Seagrass
Sand
Mean DN value of coral, seagrass, sand in band 1, 2, 3
Aster 2004Landsat 2007
1. Image Registration - Image-to-image registration- Aster image in 2000. - WGS 84, UTM 48 N.
Digital image processing
2. Image masking - Segregating land and sea area - Near-infrared band (0.78-0.98μm) was used for masking
Water Column Correction
Landsat TM, Aster images (atmospheric correction)
Training site selection (uniform pixel, various in depth)
Band pairs selection
Extracting pixel value
Calculating VAR and COVARtraining site’s pixel in band i, j
Calculating coefficients attenuation (ki/kj)
Implementation depth-invariance to whole image(Water column correction image)
Landsat 2007 High OIF, as FCC (4,3,1)
Landsat 2007 Low OIF as true color composite (321)
Color composite - Optimum Index factor
Landsat 2007 low OIF as true color composite (3,2,1)
Landsat 2007 High OIF as FCC (4,3,1)
Optimum Index Factor
Accuracy Assessment - Site specific error matrix and Kappa analysis
Image Classification- Maximum Likelihood classifier, field data , WCC image- Class hierarchies and definition
0
500
1000
1500
2000
2500
3000
3500
2007
He
cta
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500
1000
1500
2000
2500
3000
3500
2004
He
cta
0
50
100
150
200
250
300
350
400
450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
0
50
100
150
200
250
300
350
400
450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
0
50
100
150
200
250
300
350
400
450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
0
50
100
150
200
250
300
350
400
450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
Classification in Bai Bon
0
50
100
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450
500
2004H
ec
ta
0
50
100
150
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250
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450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
Classification results in An Thoi using Aster 2004
0
50
100
150
200
250
300
350
400
450
500
2007
He
cta
0
50
100
150
200
250
300
350
400
450
500
2004
Sand zone
Dead coral
Live coral cover 01-10%
Live coral cover 10-20%
Rubble
Seagrass dens 70-80%
Seagrass dens 80-90%
Seagrass dens 90-100%
Classification results in An Thoi using Landsat 2007
Map 2007:
Overall accuracy = 70%
KHAT = 64%,
Map accuracy
Map 2004
Overall accuracy = 77%
KHAT = 74%
Change detection
- Non site specific error matrix,
- Map overlay
Landsat imageDec -1992
Landsat image Jan 2007
Aster imageDec- 2000
Aster imageNov- 2004
Reefs, seagrass habitat map
1992
Reefs, seagrass habitat map
2007
Reefs, seagrass habitat map
2000
Reefs, seagrass habitat map
2004
Overlay Overlay
Reef habitat change in 2000-2004
Seagrass habitat change in 2000-2004
Reef habitat change in 1992-2007
Seagrass habitat change in 1992-2007
Non Site Post Classification Comparison
The overall accuracy = 42 % map 2007 agree 42%, with map 1992 Khat value = 29% category of map 2007 is same with map 1992 29%
Change detection analysis
Degeneration of live coral, seagrass in period 2000-2004, An Thoi.
Decrease quality of live coral, seagrass in period 2000-2004 in An Thoi
Degeneration of live coral, seagrass in period 1992-2007, An Thoi
Decrease quality of live coral, seagrass in period 1992-2007 in An Thoi
Comparing a part of Duong Dong town by using Landsat 25m, Aster 15m and Quick Bird 2.4m imageries
Benthic Habitats Mapping Using Medium and High Resolution Image
Extending Quick Bird Imagery to Detect Bottom Type
-The Gram Schmidt Spectral Sharpening- Sharpen multispectral data of QB at 2.4 m to panchromatic band at 0.6 m resolution.
- Gram-Schmidt techniques:
+ Not limited to the number of bands that can be processed at one time.
+ Preserved the spectral characteristics of lower spatial resolution multispectral data in the higher spatial resolution
Data fusion technique for Quick bird imagery
Fused image with 0.6m resolution. Using Gram-Schmidt Spectral Sharpening band combination band 3,2,1.
Fused image with 0.6m resolution. Using Gram-Schmidt Spectral Sharpening, band combination 4,3,2.
Extending Quick Bird Imagery to Detect Bottom TypeData fusion technique for Quick bird imagery
Quick bird data potential for detail mapping benthic community
Comparing fusion image (2,4,3) with Aster(1,2,3)fusion image and original image
Applying enhance technique to Quick bird data
True colour combine, square root enhance, various information
Aster imagery because it lacks blue band.
Band ratio: composite of 3/1, 3/2, 2/1 (RGB)
•. Several light yellow areas and light green appeared in reefs and terrestrial rock. Possible these have developed of marine algae or seagrass.
Conclusion1. Remote sensing and GIS are powerful tools for
conservation and management marine resource.• as they provide data of difficult location and powerful analysis
tools.
2. The coral reefs and seagrass beds map in 2007 and 2004 has been created using bands 1, 2 and 3 of Landsat and bands 1, and 2 of Aster.
3. RS techniques and image processing were used to enhance image reflectance and spectral characteristics.
4. Water column correction was employed to remove suspended matter effect.
5. Maximum likelihood classification yielded high overall accuracy =70% (Landsat 2007) and = 77 % (Aster 2004)
6. Live coral and seagrass changes were detected which can be very useful for managing marine resources
7. The comparison of the change area between 1992-2007 and 2000-2004 was not significant.
8. Degeneration and regeneration were successfully processed. Seagrass and coral reefs in study area are degenerating. Possible, sea environment of PQ is changing to worse status.
9. Quick Bird imagery with high resolution is better for mapping benthic communities in detail;
• it is optical imagery thus, one scene covers small area, • cloud effect,• Image is more expensive than moderate resolution
satellite image.
Conclusion