Vegetation classification on Prathong Island, Phang Nga, Thailand

Naiyana Srichai & Chanida Suwanprasit

Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus

APAN 33rd Meeting 13-17 February 2012

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•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)

Introduction

Objectives

•To classify vegetation on Prathong Island, Phang Nga province, southern Thailand

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Study area: Prathong Island, Phang Nga THAILAND

7th biggest island 1.5 km off the coastSize : width 9.7 kmlength 15.4 kmArea : 92 sq.km

Unseen Thailand 2002 deer,

hornbill, adjutant stork, green turtle, dugong

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Wild orchids

79 spp.

Local plants

96 spp.

Local vegetables

65 spp.

Source: Dept.of Marine and Coastal Resources, 2005

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11 Mammals spp.

86 Reptiles spp.

137 Birds

> 20 Freshwater animals

Source: Dept.of Marine and Coastal Resources, 2005

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Koh Ra

Koh Prathong

Source: Dept.of Marine and Coastal Resources, 2005

Koh Ra19 households109 people

Tong Dab village49 households272 people

Tha Paeyow123 households409 people

Pak Jok87 households134 people

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Koh Ra and Prathong Size 71,000 Rais or 92 sq.km

Mangrove 32% (green)

Beach forest 7% (orange)

Swamp forest 13% (pink)

Tropical forest 13% (Koh Ra,purple)

Grassland 8% (yellow)

Beach 26 km (orange)Seagrass 4,550 Rais (blue)

Coral 43 Rais (lighter green)

Source: Dept.of Marine and Coastal Resources, 2005

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Swamp forest Grassland

Beach forestMangrove forest

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

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Vegetation change after Tsunami

Fragile landSalt tolerant tree invasion Casuarina equisetifolia

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

Spectral Band Wavelength (m)

Band 0 (Blue) 0.45-0.52

Band 1 (Green) 0.53-0.60

Band 2 (Red) 0.62-0.69

Band 3 (NIR) 0.77-0.90

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THEOS Spectral bands

Band 0 (Blue) Band 1 (Green) Band 2 (Red) Band 3 (NIR)

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Classes

• Grassland• Beach forest• Mangrove forest• Wetland (swamp forest)• Water• Other

THEOS image 2009

Image Classification

Maximum Likelihood (MLC)

Support Vector Machines (SVMs)

Pre-image processing

Vegetation Mapping

Process Outline

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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).

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

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Unsupervised Classification:• K-Mean• 10 Classes

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ROI Separability

Classes Grassland

Beach Forest

Mangrove

Forest

Swamp forest

Sand Water

Grassland - 1.982 2.000 1.610 1.959 2.000Beach Forest - 1.766 1.881 2.000 2.000

Mangrove Forest - 1.996 2.000 2.000

Swamp Forest - 1.648 1.997

Sand - 2.000

Water -

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Classification Results

GrasslandSwamp ForestBeach ForestMangrove ForestSandWaterOther

MLC SVMs

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MLC SVMsRGB(0,1,2)

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Class Confusion Matrix

ClassMLC SVMs

Prod. Acc. (%) User Acc. (%) Prod. Acc. (%) User Acc. (%)

Grassland 98.68 100.00 96.71 100.00

Beach Forest 97.26 97.06 100.00 97.14

Mangrove Forest 97.20 100.00 99.15 99.39

Swamp 46.55 56.84 61.21 83.53

Water 97.58 70.35 97.58 82.31

Sand 98.21 100.00 99.40 98.82

Over all Accuracy 94.29 % (Kappa Co. = 0.921) 96.72 % (Kappa Co.= 0.954)

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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.

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Acknowledgement• Geo-Informatics

and Space Technology Development Agency (Public Organization)

• UniNet

• Prince of Songkla University, Phuket campus

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References:• Department of Marine and Coastal Resources. 2005.

Strategies for sustainable development of Koh Ra and Koh Prathong 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 

• Giorgos Mountrakis, Jungho Im, 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.

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Kob Khun Ka : Thank You