CHAPTER 1. Mangrove Ecology Key Points What is a Mangrove ...
Comparison of vegetation indices for mangrove mapping using THEOS data
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Transcript of Comparison of vegetation indices for mangrove mapping using THEOS data
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
APAN-33rd Meeting
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Outline
1. Introduction 2. Objectives3. Study area4. Methodology5. Result6. Conclusion7. 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
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(a) Rhizophora mucronata Poir I (b) Rhizophora apiculata Blume (c) Sonneratia ovata Backer
(d) Rhizophora Ceriops Decandra (e) Rhizophora Bruguiera s.
(Department of marine and coastal resource, 2011)
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Vegetation indices
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• 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
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• To classify mangrove and non-mangrove areas.
• To find out a suitable vegetation index for identifying mangrove area.
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Study area
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Pa Khlok sub-district, Phuket, Thailand
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Study area
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source: www.technicchan.ac.th, 2011
source: http://cccmkc.edu.hk 8
Methodology
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Input THEOS data
Pre-Image Processing
Image classification
Post classification
Compare Image
Output mapping dataROI
Training
Test
5 VIs• NDVI• SR• SAVI• PVI• TVI
unsupervised supervised
K-mean
Visual Interpretation
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THEOS Satellite
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Description MS
Spectral bands and resolution 4 multispectral (15 meters)
Spectral ranges B1 (blue) : 0.45 -0.52 µmB2 (green) : 0.53 – 0.60 µmB3 (red) : 0.62 – 0.69 µmB4 (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)10
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Band1: Blue0.45 -0.52 µm
Band2: Green0.53 – 0.60 µm
Band3: Red0.62 – 0.69 µm
THEOS Spectral bands
Band4: NIR0.77 – 0.90 µm11
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Selection of ROIs
ROIs Training pixels (50%)
Test pixels(50%)
Mangrove 691 691Non-mangrove• water• cloud on water• cloud on land• forest• agriculture•Others
1,36413
1321,118
38788
1,36413
1321,118
38788
Total 3,661 3,661
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ROIs Table
Class Mangrove Cloud (water)
Cloud(land Forest Agriculture water Others
Mangrove - 2.00 1.98 1.59 1.33 2.00 1.99Cloud water
2.00 - 2.00 2.00 2.00 1.99 1.99Cloud land 1.98 2.00 - 1.98 1.96 2.00 1.98Forest 1.61 2.00 1.99 - 1.72 1.99 1.99Agriculture 1.29 2.00 1.97 1.71 - 1.99 1.99water 2.00 1.99 2.00 1.99 1.99 - 1.99Others 1.99 1.99 1.98 1.99 1.99 1.99 -
Training Sample ROI Test Sample ROI
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Class Formulas Authors
Normalized Different Vegetation Index (NDVI) (Pearson and Miller, 1972)
Simple Ratio (SR) (Pearson and Miller, 1972)
Soil Adjusted Vegetation Index (SAVI) (Huete, 1998)
Perpendicular Vegetation Index (PVI) (Richardson and Wiegand,1977)
Triangular Vegetation Index (TVI) 0.5(120(NIR-G) )-200(R-G) (Broge & Leblanc, 2000)
REDNIR
R) (NIRR) - (NIR
L) R (NIRL)(1 R) - (NIR
2,,
2,, )()( NIRVNIRSRVRS
5 Vegetation Indices
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Vegetation Indices
NDVI SR SAVI PVI TVI
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Image Classification
K-mean MLC+NDVI MLC+SR MLC+SAVI MLC+TVIMLC MLC+PVI
Classification 2 classes : mangrove and non – mangrove areas
Unsupervised Supervised
Yellow = Non-mangrove
Blue = 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
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• 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. 19
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References
• 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/~kei-kph/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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THANK YOUFOR
YOUR ATTENTION
Jiraporn Kongwongjun, Chanida Suwanprasit and Pun Thongchumnum
Faculty of Technology and Environment, Prince of Songkla University,
Phuket Campus
APAN-33rd Meeting
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