The Use of ALOS AVNIR-2 and GIS tools for mapping tropical Mangroves in Iriomote Island 西表島- South Japan Plan I. Iriomote Island II. Mangroves III. Problems and Objective IV. Materials and Methods V. Nakama’s river Mangrove map VI. Conclusion I. Iriomote Island • The second largest in Okinawa. • Area = 289 km², Population < 2,000 • Visitors > 150,000 • Tropical rainforest climate. • Typhoon season (June to September). • 80% is a protected state land. • 90% dense jungle and mangrove swamps. • 34.3% forms the Iriomote National Park. • Mt. Komi (古見岳 Komidake) 470 m, is the highest point. • The Iriomote Cat (Prionailurus iriomotensis) 西表山猫. II. Mangroves – The primary coastal ecosystem in the tropical and subtropical region of the world (Mahfud, 1999). – They thrive in salty environments because they are able to obtain fresh water from saltwater. – They trap and cycle various organic materials, chemical elements, and important nutrients. They provide attachment surfaces for various marine organisms. – They provide protected nursery areas and shelters for fishes, crustaceans, and shellfish. Bruguiera gymnorrhiza Bruguiera Rhizophora stylosa Sonneratia alba Kandelia candel III. Problematic and Objective - Recognize and detect changes of Mangrove ecosystems in Iriomote Island. - High accuracy maps using remote sensing and GIS tools: ALOS AVNIR, PRISM, PALSAR - Apply this methodology on South of Sulawesi in Indonesia. Pangkep Sulawasi Makassar Makassar IV. Materials and Methods 1. ALOS AVNIR-2 – Advanced Visible and Near Infrared Radiometer type 2- ALOS AVNIR2 is for observing land and coastal zones. – It provides better spatial land-coverage maps and land-use classification maps for monitoring regional environments. Bands 4 Band 1 : 0.42 to 0.50 micrometers Band 2 : 0.52 to 0.60 micrometers Wavelength Band 3 : 0.61 to 0.69 micrometers Band 4 : 0.76 to 0.89 micrometers Spatial 10m (at Nadir) Resolution Number of 7000/band Detectors Pointing - 44 to + 44 degree Angle Bit Length 8 bits ALOS AVNIR-2 of Ishigaki and Iriomote Islands Observation Date= 2009- 05- 31 SceneID= ALAV2A178403110 R = Band 3 G = Band 2 B = Band 1 ALOS AVNIR-2 : SR: 10 m False color combination R = Band 4 G = Band 2 B = Band 1 Unsupervised classification ISODATA • Group multiband spectral response patterns into clusters that are statistically separable. • ISODATA algorithm allows the number of clusters to be automatically adjusted during the iteration by merging similar clusters and splitting clusters with large standard deviations Deep water Shallow water Coral reef Sand Urban area Mangrove Forest Sawayama ‘s area Soumaya ‘s area Nakama river Distance = 20 km = 12.4 Miles Taketomi Selected area of study : Mangrove of Nakama river 2. Field work : From 3 to 15 February 2011 High performance wireless GPS – M 241 Canoes and boat Waterproof notes, Map, Camera and spectometer, Guide book of Mangrove species • Overlaying GPS data 50 stations • Subsetting of the Region of interest - ROI どうも有賀とございました。 Unsupervised Classification - ISODATA V. Mangrove map of Nakama’s river Supervised classification – Maximum likelihood: identifying spectrally similar areas on an image by identifying ‘training’ sites of known targets and then extrapolating them. Mangrove ML is based on statistics (mean; species in variance/covariance), a (Bayesian) Probability detail Function calculated from the inputs for classes established from training sites. Supervised classification – ML : Median filter 3x3 CLASS DISTRIBUTION FOR SELECTED AREA Number Class Samples Percent Area (Hectares) 1 Rhizophora stylosa 6,213 3.07 62.130 2 Sonneratia alba 6,887 3.40 68.870 3 Bruguieria gymnorrhiza 11,589 5.73 115.890 4 B.gymnorrhiza and R. stylosa 1,874 0.93 18.740 5 Water 15,676 7.74 156.760 6 Forest 130,556 64.50 1,305.560 7 Urban area 22,106 10.92 221.060 8 Agriculture 7,503 3.71 75.030 Total 202,404 100.00 2,024.040 Accuracy classification Cl_name Nb Acc% Nb. Rh So Br B&R Wa Fo UA Ag samp 1 2 3 4 5 6 7 8 Rh 1 65.7 102 67 12 17 5 0 1 0 0 So 2 57.5 40 8 23 2 4 0 3 0 0 Br 3 37.9 623 138 105 236 115 13 16 0 0 B&R 4 87.5 8 0 1 0 7 0 0 0 0 Wa 5 76.9 26 0 0 0 0 20 0 3 3 Fo 6 97.3 41367 180 381 152 0 1 40242 376 35 UA 7 60.4 4858 1 1 1 0 548 8 2933 1366 Ag 8 73.8 2472 2 0 20 0 207 28 391 1824 TOTAL 49496 396 523 428 131 789 40298 3703 3228 Re_Acc. % 16.9 4.4 55.1 5.3 2.5 99.9 79.2 56.5 OVERALL CLASS PERFORMANCE (45352 / 49496 ) = 91.6% Kappa Statistic (X100) = 72.9%. Kappa Variance = 0.000011. Good classification Supervised classification – ML : Mangrove Supervised classification – ML : Median filter 3x3 Accuracy classification Project Reference Number of Samples in Class Class Class Accuracy+ Number 1 2 3 4 5 Name Number (%) Samples Water Forest Urban area Agriculture Mangrove Water 1 76.9 26 20 0 3 3 0 Forest 2 97.5 41367 1 40336 376 21 633 Urban area 3 60.4 4858 548 7 2933 1366 4 Agriculture 4 73.8 2472 207 28 390 1824 23 Mangrove 5 94.7 936 12 38 0 0 886 TOTAL 49659 788 40409 3702 3214 1546 Reliability Accuracy (%)* 2.5 99.8 79.2 56.8 57.3 OVERALL CLASS PERFORMANCE (45999 / 49659 ) = 92.6% Kappa Statistic (X100) = 76.3%. Kappa Variance = 0.000010. Good Classification CLASS DISTRIBUTION FOR SELECTED AREA Number Class Samples Percent Area (Hectares) 1 Water 15,881 7.8 158.810 2 Forest 131,428 64.9 1,314.280 3 Urban area 22,055 10.9 220.550 4 Agriculture 7,358 3.6 73.580 5 Mangrove 25,682 12.7 256.820 Total 202,404 100.0 2,024.040 VI. Conclusion • Preliminary study of Mangroves, case of Iriomote island • Misclassification of Mangrove and forest • Step 2: Develop a new classification method able to make the difference between these classes • Change detection of Nakama river’s Mangrove 2006 and 2009 : Damage caused by Typhoon • Apply this methodology on Sulawasi Mangroves THANK YOU Sawayama SHUHEI Soumaya LAHBIB 12 Feb 2011 in Nakama River.