Transcript Jiraporn_Kongwongjan

APAN-33rd Meeting
Comparison of
vegetation indices for
mangrove mapping
using THEOS data
Jiraporn Kongwongjun,
Chanida Suwanprasit
and Pun Thongchumnum
Faculty of Technology and Environment,
Prince of Songkla University,
Phuket Campus
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Outline
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Introduction
Objectives
Study area
Methodology
Result
Conclusion
Acknowledgement
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The importance of mangroves
Mangrove forests are useful as fishing areas, wildlife reserves, for recreation,
human habitation, aquaculture and natural ecosystem.
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Mangrove vegetations
(a) Rhizophora mucronata Poir I (b) Rhizophora apiculata Blume
(c) Sonneratia ovata Backer
(Department of marine and coastal
resource, 2011)
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(d) Rhizophora Ceriops Decandra (e) Rhizophora Bruguiera s.
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Vegetation indices
• The remote sensing is applicable for
mangrove mapping.
• The vegetation indices (VIs) in
forest areas have been widely used
and provide accurate classification.
• Different VIs is suitable for different
vegetation cover.
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Objectives
• To classify mangrove and non-mangrove
areas.
• To find out a suitable vegetation index for
identifying mangrove area.
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Study area
Pa Khlok sub-district,
Phuket, Thailand
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Study area
source: www.technicchan.ac.th, 2011
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source: http://cccmkc.edu.hk
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Methodology
Input THEOS data
Pre-Image Processing
Visual Interpretation
Image classification
Post classification
unsupervised
supervised
Compare Image
K-mean
Output mapping data
5 VIs
• NDVI
•SR
•SAVI
•PVI
•TVI
Training
ROI
Test
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THEOS Satellite
Description
MS
Spectral bands and resolution
4 multispectral (15 meters)
Spectral ranges
B1 (blue) : 0.45 -0.52 µm
B2 (green) : 0.53 – 0.60 µm
B3 (red) : 0.62 – 0.69 µm
B4 (NIR) : 0.77 – 0.90 µm
Imaging swath
90 km.
Image dynamics
8 bits -12 bits
Absolute localization accuracy (level
1B)
< 300 m (1 s)
Off-nadir viewing
±50° (roll and pitch)
Signal to Noise Ratio
>100
(Pitan, 2008)
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THEOS Spectral bands
Band1: Blue
0.45 -0.52 µm
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Band2: Green
0.53 – 0.60 µm
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Band3: Red
0.62 – 0.69 µm
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Band4: NIR
0.77 – 0.90 µm
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Selection of ROIs
Training pixels
(50%)
ROIs
Test pixels
(50%)
691
691
Non-mangrove
• water
• cloud on water
• cloud on land
• forest
• agriculture
•Others
1,364
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132
1,118
387
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1,364
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132
1,118
387
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Total
3,661
3,661
Mangrove
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ROIs Table
Training Sample ROI
Test Sample ROI
Mangrove
Cloud
(water)
Cloud
(land
Forest
Agriculture
water
Others
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2.00
1.98
1.59
1.33
2.00
1.99
Cloud
water
2.00
-
2.00
2.00
2.00
1.99
1.99
Cloud land
1.98
2.00
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1.98
1.96
2.00
1.98
Forest
1.61
2.00
1.99
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1.72
1.99
1.99
Agriculture
1.29
2.00
1.97
1.71
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1.99
1.99
water
2.00
1.99
2.00
1.99
1.99
-
1.99
Others
1.99
1.99
1.98
1.99
1.99
1.99
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Class
Mangrove
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5 Vegetation Indices
Class
Formulas
Normalized Different Vegetation Index
(NDVI)
(NIR - R)
Authors
(Pearson and Miller, 1972)
(NIR  R)
NIR
Simple Ratio (SR)
(Pearson and Miller, 1972)
RED
Soil Adjusted Vegetation Index (SAVI)
(NIR - R) (1  L)
(Huete, 1998)
(NIR  R  L)
Perpendicular Vegetation Index (PVI)
(  S , R   V , R )  (  S , NIR   V , NIR )
Triangular Vegetation Index (TVI)
0.5(120(NIR-G) )-200(R-G)
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(Richardson and
Wiegand,1977)
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(Broge & Leblanc, 2000)
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Vegetation Indices
NDVI
SR
SAVI
PVI
TVI
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Image Classification
Supervised
Unsupervised
K-mean
MLC
MLC+NDVI
MLC+SR
MLC+SAVI
MLC+PVI
MLC+TVI
Classification 2 classes : mangrove and non – mangrove areas
Blue =
Mangrove
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Yellow = Non-mangrove
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Overall accuracy
Classified
Overall accuracy
Kappa coefficient
Maximum Likelihood (MLC)
96.46%
0.9522
MLC+ NDVI
96.78%
0.9565
MLC+ SR
96.78%
0.9565
MLC + SAVI
96.78%
0.9565
MLC + PVI
95.67%
0.9417
MLC + TVI
95.30%
0.9364
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Conclusion
• NDVI, SR and SAVI are the best indices between
mangrove and non-mangrove forests with 96.78% overall
accuracy.
• THEOS with 15 m resolution is appropriate for visual
interpretation. However, spectral resolution of 4 bands
seems to give limited vegetation classification.
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Acknowledgement
• Faculty of Technology and Environment, Prince of Songkla
university, Phuket campus, providing invaluable assistance
during work
• Geo-Informatics and Space Technology Development
Agency organization (GISTDA)
• UniNet
• Adviser and co-adviser in particular to Dr.Chanida
Suwanprasit and Dr.Pun Thongchumnum who give suggestion
and Dr.Naiyana Srichai and my graduate friends for
encouragement.
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References
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Pitan Singhasneh (2011). " THEOS Satellite Data Service "
<http://www.gisdevelopment.net/technology/rs/mwf09_theos.htm> ( 10 February 2012)
Cccmkc University (2011). "Mangrove in Phuket, Thailand" <http://cccmkc.edu.hk/~keikph/Mangrove/mangrove_page%201.htm> ( 10 February 2012)
Huete A. (1988). “A soil-adjusted vegetation index (SAVI).” Remote Sensing of Environment, 25
(3), 295-309.
Richardson A. J. and Wiegand C. L. (1977). “Distinguishing vegetation from soil background
information(by gray mapping of Landsat MSS data” Photogrammetric Engineering and Remote
Sensing., 43(12), 1541-1552.
Pearson, R. L. and Miller, L. D. (1972). “Remote mapping of standing crop biomass for estimation
of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado” Proceedings
of the 8th International Symposium on Remote Sensing of the Environment II., 1355-1379.
Broge, N. H., & Leblanc, E. (2000). “Comparing prediction power and stability of
broadband and hyperspectral vegetation indices for estimation of green leaf area
index and canopy chlorophyll density”. Remote Sensing of Environment, 76,
156−172.
Department of marine and coastal resource. (2011). " Research Paper 14th Mangrove National
Seminar" < http://issuu.com/mffthailand/docs/mangrove14th > ( 10 February 2012)
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APAN-33rd Meeting
THANK YOU
FOR
YOUR ATTENTION
Jiraporn Kongwongjun,
Chanida Suwanprasit
and Pun Thongchumnum
Faculty of Technology and Environment,
Prince of Songkla University,
Phuket Campus
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