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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 waterfrom saltwater.
– They trap and cycle various organic materials, chemical elements, and importantnutrients. They provide attachment surfaces for various marine organisms.
– They provide protected nursery areas and shelters for fishes, crustaceans, andshellfish.
Rhizophora stylosa Sonneratia albaB
rug
uie
ra g
ymn
orr
hiz
aKandelia 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.
Sulawasi
Makassar
Makassar
Pangkep
IV. Materials and Methods1. 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 mapsfor monitoring regional environments.
Bands 4
Wavelength
Band 1 : 0.42 to 0.50 micrometersBand 2 : 0.52 to 0.60 micrometersBand 3 : 0.61 to 0.69 micrometersBand 4 : 0.76 to 0.89 micrometers
Spatial Resolution
10m (at Nadir)
Number of Detectors
7000/band
Pointing Angle
- 44 to + 44 degree
Bit Length 8 bits
SceneID= ALAV2A178403110
Observation Date= 2009- 05- 31
ALOS AVNIR-2 of Ishigaki and Iriomote Islands
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
Deep water
Shallow water
Coral reef
Sand
Urban area
Mangrove
Forest
• 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
Selected area of study : Mangrove of Nakama river
Taketomi
Sawayama ‘s area
Soumaya ‘s area
Distance = 20 km = 12.4 MilesNakama 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
50 stations
• Overlaying GPS data
どうも有賀とございました。
• Subsetting of the Region of interest - ROI
Unsupervised Classification - ISODATA
V. Mangrove map of Nakama’s river
Mangrovespecies indetail
Supervised classification – Maximum likelihood: identifying spectrally similar areas on an image by identifying ‘training’ sites of known targets and then extrapolating them.
ML is based on statistics (mean;variance/covariance), a (Bayesian) ProbabilityFunction calculated from the inputs for classesestablished 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
OVERALL CLASS PERFORMANCE (45352 / 49496 ) = 91.6%
Kappa Statistic (X100) = 72.9%. Kappa Variance = 0.000011. Good classification
Accuracy classificationCl_name Nb Acc% Nb.
sampRh1
So2
Br3
B&R4
Wa5
Fo6
UA7
Ag8
Rh 1 65.7 102 67 12 17 5 0 1 0 0So 2 57.5 40 8 23 2 4 0 3 0 0Br 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 0Wa 5 76.9 26 0 0 0 0 20 0 3 3Fo 6 97.3 41367 180 381 152 0 1 40242 376 35UA 7 60.4 4858 1 1 1 0 548 8 2933 1366Ag 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
Supervised classification – ML : Mangrove
Supervised classification – ML : Median filter 3x3
Project Reference Number of Samples in ClassClass Class Accuracy+ Number 1 2 3 4 5Name Number (%) Samples Water Forest Urban area Agriculture Mangrove
Water 1 76.9 26 20 0 3 3 0Forest 2 97.5 41367 1 40336 376 21 633Urban area 3 60.4 4858 548 7 2933 1366 4Agriculture 4 73.8 2472 207 28 390 1824 23Mangrove 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
Accuracy classification
NumberClass Samples Percent Area (Hectares)
1 Water 15,881 7.8 158.8102 Forest 131,428 64.9 1,314.2803 Urban area 22,055 10.9 220.5504 Agriculture 7,358 3.6 73.5805 Mangrove 25,682 12.7 256.820
Total 202,404 100.0 2,024.040
CLASS DISTRIBUTION FOR SELECTED AREA
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
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