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