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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