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 1

Outline 1.Introduction 2.Objectives 3.Study area 4.Methodology 5.Result 6.Conclusion 7.Acknowledgement 2

The importance of mangroves Mangrove forests are useful as fishing areas, wildlife reserves, for recreation, human habitation, aquaculture and natural ecosystem

Mangrove vegetations (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) 4

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. 5

Objectives To classify mangrove and non-mangrove areas. To find out a suitable vegetation index for identifying mangrove area. 6

Study area Pa Khlok sub-district, Phuket, Thailand 7

Study area source: source: 8

Methodology Input THEOS data Pre-Image Processing Image classification Post classification Compare Image Output mapping data ROI Training Test 5 VIs NDVI SR SAVI PVI TVI unsupervisedsupervised K-mean Visual Interpretation 9

THEOS Satellite DescriptionMS Spectral bands and resolution4 multispectral (15 meters) Spectral rangesB1 (blue) : µm B2 (green) : 0.53 – 0.60 µm B3 (red) : 0.62 – 0.69 µm B4 (NIR) : 0.77 – 0.90 µm Imaging swath90 km. Image dynamics8 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

Band1: Blue µm Band2: Green 0.53 – 0.60 µm Band3: Red 0.62 – 0.69 µm THEOS Spectral bands Band4: NIR 0.77 – 0.90 µm 11

Selection of ROIs ROIs Training pixels (50%) Test pixels (50%) Mangrove691 Non-mangrove water cloud on water cloud on land forest agriculture Others 1, , , , Total3,661 12

ROIs Table Class Mangrove Cloud (water) Cloud (land ForestAgriculturewaterOthers Mangrove Cloud water Cloud land Forest Agriculture water Others Training Sample ROITest Sample ROI 13

ClassFormulasAuthors 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) 5 Vegetation Indices 14

Vegetation Indices NDVISRSAVIPVITVI 15

Image Classification K-mean MLC+NDVI MLC+SRMLC+SAVIMLC+TVI MLC MLC+PVI Classification 2 classes : mangrove and non – mangrove areas Unsupervised Supervised Yellow = Non-mangrove Blue = Mangrove 16

Overall accuracy ClassifiedOverall accuracyKappa coefficient Maximum Likelihood (MLC)96.46% MLC+ NDVI96.78% MLC+ SR96.78% MLC + SAVI96.78% MLC + PVI95.67% MLC + TVI95.30%

Conclusion 18 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.

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

References Pitan Singhasneh (2011). " THEOS Satellite Data Service " ( 10 February 2012) Cccmkc University (2011). "Mangrove in Phuket, Thailand" ( 10 February 2012) Huete A. (1988). “A soil-adjusted vegetation index (SAVI).” Remote Sensing of Environment, 25 (3), 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), 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., 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" ( 10 February 2012) 20

THANK YOU FOR YOUR ATTENTION Jiraporn Kongwongjun, Chanida Suwanprasit and Pun Thongchumnum Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus APAN-33rd Meeting 21