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Vegetation classification on Prathong Island, Phang Nga , Thailand

Vegetation classification on Prathong Island, Phang Nga , Thailand. Naiyana Srichai & Chanida Suwanprasit Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus. APAN 33 rd Meeting 13-17 February 2012. Introduction.

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Vegetation classification on Prathong Island, Phang Nga , Thailand

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  1. Vegetation classification on PrathongIsland, PhangNga, Thailand NaiyanaSrichai & ChanidaSuwanprasit Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus APAN 33rd Meeting 13-17 February 2012

  2. Introduction • vegetation type study date back to the Nineteenth Century : ecologists, plant geographers, vegetation scientists • three major determinants of vegetation-competition, stress and disturbance (Grime, 1974)

  3. Objectives • To classify vegetation on Prathong Island, PhangNga province, southern Thailand

  4. Study area: Prathong Island, PhangNga THAILAND 7th biggest island 1.5 km off the coast Size : width 9.7 km length 15.4 km Area : 92 sq.km Unseen Thailand 2002 deer, hornbill, adjutant stork, green turtle, dugong

  5. Source: Dept.of Marine and Coastal Resources, 2005

  6. Source: Dept.of Marine and Coastal Resources, 2005 11 Mammals spp. 86 Reptilesspp. 137 Birds > 20 Freshwater animals

  7. Koh Ra 19 households 109 people Pak Jok 87 households 134 people Koh Ra Tong Dab village 49 households 272 people ThaPaeyow 123 households 409 people KohPrathong Source: Dept.of Marine and Coastal Resources, 2005

  8. Source: Dept.of Marine and Coastal Resources, 2005 Koh Ra and Prathong Size71,000 Rais or 92 sq.km Mangrove32% (green) Beach forest7% (orange) Swamp forest13% (pink) Tropical forest13% (KohRa,purple) Grassland 8% (yellow) Beach26 km (orange) Seagrass 4,550 Rais (blue) Coral43 Rais (lighter green)

  9. Grassland Swamp forest Mangrove forest Beach forest

  10. Tsunami 26 Dec. 2004Area affected : 18.55 % (6.25% agricultural,92.88% others)

  11. Vegetation change after Tsunami Fragile land Salt tolerant tree invasion Casuarinaequisetifolia

  12. Data set: THEOS Multispectral Achieved on 19 Jan 2009 Spatial Resolution 15 m

  13. THEOS Spectral bands Band 0 (Blue) Band 1 (Green) Band 2 (Red) Band 3 (NIR)

  14. Process Outline THEOS image 2009 Pre-image processing • Classes • Grassland • Beach forest • Mangrove forest • Wetland (swamp forest) • Water • Other Image Classification Maximum Likelihood (MLC) Support Vector Machines (SVMs) Vegetation Mapping

  15. Support Vector Machines • SVMs : a supervised classifier, which requires training samples but SVMs are not relatively sensitive to training sample size (works with limited quantity and quality). • The SVM-based approach used a recursive procedure to generate prior probability estimates for known and unknown classes by adapting the Bayesian minimum-error decision rule (Mountrakis,et.at. 2011; Fauvel 2008).

  16. Support vector machines (SVMs) : numerousapplications in remote sensing . 108 relevant papers, published in 2007-2010. (G.Mountrakis, JunghoIm, C.Ogole, 2011)

  17. Unsupervised Classification: • K-Mean • 10 Classes

  18. ROI Separability

  19. Classification Results MLC SVMs Grassland Swamp Forest Beach Forest Mangrove Forest Sand Water Other

  20. RGB(0,1,2) SVMs MLC

  21. Class Confusion Matrix

  22. Conclusions • SVM classifier compared to the more conventional maximum likelihood approach gave slightly better accuracy using THEOS image for class : swamp forest of Prathong Island.

  23. Acknowledgement • Geo-Informatics and Space Technology Development Agency (Public Organization) • UniNet • Prince of Songkla University, Phuket campus

  24. References: • Department of Marine and Coastal Resources. 2005. Strategies for sustainable development of Koh Ra and KohPrathong with people participation. Unpublished report. • Fauvel, M.,  Benediktsson, J.A.,  Chanussot, J., Sveinsson, J.R.. 2008. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles.  Geoscience and Remote Sensing, 46 (11), 3804 - 3814  • GiorgosMountrakis, JunghoIm, Caesar Ogole. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247–259. • Grime, J.P. 1974. Vegetation classification by reference to strategies. Nature, 250 (5461), 26-31.

  25. KobKhun Ka : Thank You

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