of 30
i
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ii
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:
: .
: Ph.D. (Remote Sensing), International Institute for Aerospace Survey and Earth Science (ITC), Netherlands, 2006
: xxxxxxx
ii
2
2 2 91.94%
Abrtract
Mangrove forests are important for coastal ecosystems. The composition and distribution of
mangroves trees provide the useful information for management planning in the mangroves forest.
Previously, the satellite image-based analysis result showed the potential for species discrimination in the
area but the difficulty in dominant species, RM and RA, separation still remained. The very high resolution
satellite image was brought to this study which provides the better difference in texture of objects on the
image. As mentioned earlier, the texture analysis and object-based classification was performed to
dominant species discrimination in this study site. The research results showed the improvement of
accuracy at 91.94% when used the texture corporate with spectral reflectance. In conclusion, the
differences in mangroves trees canopies can be extracted by the very high resolution satellite image
which classified by the texture analysis and the object-based method.
iii
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1 ......................................................................................................................................................................................... 1
......................................................................................................................................................................................... 1
1.1 ................................................................................................................................... 3
1.2 ................................................................................................................................................................ 3
1.3 ................................................................................................................................................. 3
1.4 .................................................................................................................................................... 3
1.5 ........................................................................................................................................... 3
1.6 ................................................................................................................................................................... 3
2 ......................................................................................................................................................................................... 4
....................................................................................................................................................................... 4
2.1 .......................................................................................................................... 4
2.2 Spectral Angle Mapper (SAM) ............................................................................................................................... 5
2.3 ....................................................................................................................................................... 5
2.4 ............................................................................................................................................. 9
3 .....................................................................................................................................................................................10
.............................................................................................................................................................10
3.1 ...................................................................................................................................................... 10
4 .....................................................................................................................................................................................13
............................................................................................................................................................................13
4.1 Hyperion ........................................................................................................................... 14
4.2 Quickbird .............................................................................................................. 14
4.3 ........................................................................................................................................................... 16
5 .....................................................................................................................................................................................18
v
...............................................................................................................................................................18
5.1 ........................................................................................................................................................................... 18
5.2 ............................................................................................................................................................. 19
....................................................................................................................................................................... 200
1 EO-1 .................................................................................................................................. 4
2 ................................................................................................................................. 4
3 QUICKBIRD .......................................................................................... 5
4 TRAIN TEST .................................................................. 11
5 KAPPA ......................................................... 13
6 ....................................................... 13
7 SCALE 80 ................. 15
8 CONFUSION MATRIX ....................................................................................... 16
1 ................................................................................................................................................................... 3
2 ( ) ( ) 2 ....................................................................... 5
3 ...................................................................................................................... 6
4 .................................................................................................... 7
5 SCALE LEVEL = 50 ............................................................................... 8
6 K NEAREST NEIGHBOR ......................................................................................................... 8
7 ................................................................................................................... 10
8 SCALE 50-80 ................................................................................... 14
9 ATTRIBUTE ....................................................... 15
10 ......................................................................................................................................... 17
11 2 .................................................................................................. 17
1
1
1.1
(Heumann, 2011)
(Green et al. 1998)
(Green et al., 1998; Wang et al., 2004; Huang, Zhang, and Wang, 2009)
(Cost-
effective) (Remote Sensing)
(Mumby et al., 1999)
(Green et al., 1998; Wang et al., 2004; Wang and Sousa, 2009; Heumann, 2011)
(Giri et al., 2011) (Held et al., 2003; Wang, Sousa, and Gong, 2004; Gao et
al., 2004) (Muttitanon and Tripathi, 2005; Conchedda, Durieux and Mayaux, 2008;
Sirikulchayanon, Sun, and Oyana, 2008; Colditz et al., 2012) ( Wang et al., 2004;
Vaiphasa et al., 2005; Vaiphasa, Skidmore, and de Boer, 2006; Vaiphasa et al., 2007; Neukermans et al.,
2008; Wang and Sousa, 2009; Huang et al., 2009; Keodsin and Vaiphasa, 2013)
(Wang and Sousa, 2009)
(Wang, Sousa, and Gong, 2004)
(Heumann, 2011)
(Vaiphasa et al., 2005; Keodsin and Vaiphasa, 2013)
2
Vaiphasa (2005)
16
(Rhizophoraceae Family)
ASTER
(Rhizophora mucronata Rhizophora apiculata) (Post classifier)
(Vaiphasa et al.,
2006)
(Hyperion EO-1)
(Keodsin and Vaiphasa, 2013)
(canopy) (Stem density) (Franklin,
Maudie, and Lavigne, 2001)
IKONOS Quickbird
(Wang et al., 2004)
(Texture Analysis) (Object-based
classification) (Wang, Sousa, and
Gong, 2004; Wang et al., 2008)
(family)
Hyperion EO-1
(Neukermans et al., 2008)
Hyperion EO-1 (30)
(Keodsin and Vaiphasa, 2013)
3
1.2
1.3
1.4
1.5
1.6
. . 7
(Vaiphasa et al., 2006) 5 Avicennia alba
(Aa), Avicennia marina (Am), Bruguiera parviflora (Bp), Rhizophora apiculata (Ra)
Rhizophora mucronata (Rm) (Keodsin and Vaiphasa,2013)
1 3 (Vaiphasa
et al., 2006) 1
1 a) . . b) Quickbird 13 2009
a
)
b
)
N
4
2
2.1
Hyperion Quickbird
2.1.1 Hyperion
Hyperion (Narrow band)
Earth Observing 1 (EO-1) 1
357-2576 242 10
EO-1 (Multispectral) 10
2 (Moderate resolution)
1 EO-1
ALI Hyperion 10 242 10 (panchromatic), 30 30 16 16 37x42 37x185 . 7.5x185 .
(Detectors low responsivity)
242 0 2
198 (Beck, 2003)
2
VIS 1-7 356-426
8-35 427-702 IR 36-57 712-925
58-76 926-983 SWIR 77-224 984-2396
225-242 2397-2576
5
2.1.2 Quickbird
Quickbird 2 (Multispectal) 4 2.4 (Panchromatic) 1 1 3-7 3
3 Quickbird (: Satellite imaging corporation, 2001)
450 7 (Nadir)
16.5 x 115 16.5 x 16.5
2.4 (Nadir) 0.6 (Nadir)
450-520 : 520-600 : 630-690 : 760-900 : 450-900 :
2.2 Spectral Angle Mapper (SAM)
SAM
2 ( ) ( ) 2 ( Kruse et al., 1993)
Band 2
Band 1
6
2 (Origin)
1 2
(1)
(
)
n
(
)
2.3
(Pixel based) salt
and peppers
(Wang, Sousa, and Gong, 2004)
2
1. (Finding Object) 2. (Classification) 3
3
2.3.1 (Finding Object)
(Objects)
(Heumann, 2011)
(1)
(2)
Classification
Define Feature
Classification
Map
Finding Object
Segmenting
Refine Segment
Compute Attribute
7
(Over segmented) (Under segmented)
edge-based (Jin, 2009) scale
4 scale
0-100
(Vo, 2013; Veljanovski, 2012) Trial and error (Wang, 2004; Santiago,
2013)
() () ()
4 () () Scale level = 15 () Scale level = 50
5
(Attribute)
8
5 Scale level = 50
3 1. (Spatial)
2. (Spectral)
3. (Texture) 3
Haralick et al.(1973) Mean Variance Entropy
Heumannn (2011)
Al-Kofahi et al.(2012)
Wang (2004)
2.3.2 (Classification)
(supervised classification) K
Nearest Neighbor (KNN)
(Training data) KNN
Euclidean
Nearest Neighbor
K
6 K nearest neighbor
X
Class A Class B
K=3
K=7
9
2.4
confusion matrix
(Testing data)
(Overall accuracy)
confusion matrix (Producer
accuracy)
(User accuracy)
(Cohens kappa statistic)
10
3
3.1
3.1.1
Hyperion EO-
1
ENVI 4.7 7
7
3.1.2
Hyperion UTM Zone 47N,
WGS 1984 (Unsupervised classification) K-mean
stratified random sampling 1
Hyperion EO-1
Pre-processing
Classification
Map
Confusion
matrix Mixed class masked
Quickbird
Pre-processing
Object-based classification
Map Comparison
11
(30x30) transect (Bullock, 1999)
1 (30)
1 transect
1 transect 200
15 500
100
GPS 1.
GPS 2.
. . 5
Avicennia alba (Aa), Avicennia marina (Am), Bruguiera
parviflora (Bp), Rhizophora apiculata (Ra) Rhizophora mucronata (Rm)
(Keodsin and Vaiphasa,2013) 1 3
(Vaiphasa et al., 2006)
4 2
(Train) (Test)
4 Train Test
Mangrove Species Train Test Avicennia alba (Aa) 44 44 Avicennia marina (Am) 30 30 Bruguiera parviflora (Bp) 38 38 Rhizophora apiculata (Ra) 51 51 Rhizophora mucronata (Rm) 38 38 Total 201 201
3.1.3 Hyperion
Hyperion 242
198 (Atmospheric
Correction) MODTRAN FLAASH
image to image
resampling Nearest Neighbor 1
12
Hyperion
Keodsin and Vaiphasa (2013) genetic algorithm
7 11 27 29 57 74 126 127
(Supervised classification) SAM
(rotation) 10
1
10
3.1.4 Quickbird
Quickbird multispectral 4
(Atmospheric Correction) MODTRAN
FLAASH
image to image resampling Nearest
Neighbor 1 Quickbird
Hyperion
Hyperion
Attribute
(training area) 10 Hyperion
(overall accuracy) trial & error
Scale 0-100 Scale
attribute Atrribute 6 1) Spatial+Spectral+Texture
2) Spatial 3) Spectral 4) Spatial+Texture 5) Spatial+Spectral 6) Spectral+Texture
Attribute
10 scale
13
4
4.1 Hyperion
5 SAM 10 5
91.54% Kappa 0.89 9 6
5 Kappa
Training set Overall accuracy Kappa
1 91.54% 0.89 2 87.06% 0.84 3 89.05% 0.86 4 89.05% 0.86 5 87.56% 0.84 6 90.55% 0.88 7 84.08% 0.80 8 89.55% 0.87 9 91.04% 0.89 10 88.06% 0.85
6
kappa
user accuracy
2
6
Training Set Ground Truth (Pixels)
1
Class RM RA AM AA BP Total Prod. accuracy User accuracy RM 33 5 1 1 1 41 86.84% 80.49% RA 5 44 0 0 1 50 86.27% 88.00% AM 0 0 43 0 0 43 97.73% 100.00% AA 0 2 0 29 1 32 96.67% 90.63% BP 0 0 0 0 35 35 92.11% 100.00% Total 38 51 44 30 38 201 OA: 91.54% Kappa: 0.89
14
2
2 10
Quickbird
4.2 Quickbird
4.2.1 Scale Attribute
Scale Attribute 8 Scale
Scale 0-50
scale 90 training area
Scale 50-80 10
8
8 scale 50-80
10
attribute scale scale
80
attribute 5
9 Texture
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
50 60 70 80
60.10% 60.75%
69.70%
85.11%
Acc
ura
cy
Scale
15
9 attribute
9 Attribute 10
Spectral+Texture 75.03% scale
8 9 scale 80
kappa
Rm Ra 10 7
7 Scale 80
Iteration OA-Test (%) Kappa 1 88.98 0.78
2 86.99 0.75 3 90.22 0.81 4 91.95 0.84 5 89.10 0.78
6 84.12 0.70 7 85.49 0.72 8 83.03 0.68 9 86.34 0.74
10 90.61 0.81
4 91.95%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Spatial+Spectral+Texture
Spatial
Spectral
Spatial+Texture
Spectral+Texture
Spatial+Spectral
70.09%
60.68%
73.13%
66.52%
75.03%
68.05%
Accuracy
16
4.3
2 Quickbird SAM Hyperion
8 (a) (b)
Quickbird 91.94% Hyperion
9.92% User Accuracy Rm Ra 80.00% 91.58% 77.78%
93.91% Producer Rm 18.81%
Ra 2 8 a) b) Rm
Ra
8 confusion matrix (a) Quickbird (b) SAM Hyperion
(a) Class Rm Ra Producer Accuracy User Accuracy
Rm 91.97 6.27 91.97% 91.58% Ra 8.03 91.93 91.93% 93.91% Miss classified 0 1.8 Total 100 100 Overall Accuracy = 91.94% Kappa = 0.84
(b) Class Rm Ra Producer Accuracy User Accuracy Rm 63.16 1.96 63.16% 80.00% Ra 36.84 96.08 96.08% 77.78% Others 0 1.96
Total 100 100 Overall Accuracy = 82.02% Kappa = 0.86
2 SAM Hyperion Objec-based
Quickbird 10 (a) (b)
2 11
Rm Ra
Ra Rm
10 (a) Ra
17
10 (a) Hyperion SAM (b) Quickbird Object-based
11 2
Rhizophora Apiculata (Ra) Rhizophora Mucronata (Rm)
Rm (SAM) to Ra (OBJ) Ra (SAM) to Rm (OBJ) No change
N
(a) (b)
N
18
5
5.1
Over
segment
Rm Ra
(Keodsin and Vaiphasa, 2013)
Quickbird
2 (Vaiphasa,
2006; Keodsin and Vaiphasa, 2013)
(Wang, 2004a)
training
80 training
(family)
19
5.2
20
Al-Kofahi, S., Steele, C., VanLeeuwen, D., and Hilaire, R. S. 2012, Mapping land cover in urban residential
landscapes using very high spatial resolution aerial photographs. Urban Forestry & Urban
Greening, 11, 291301
Baraldi, A., and Parmiggiani, F. 1995. An Investigation of the Textural Characteristics Associated with
Gray Level Cooccurrence Matrix Statistical Parameters. Geoscience and Remote Sensing, IEEE
Transactions On 33(2): 293304.
Beck, R. EO-1 User Guide [Online]. University of Cincinnati: Department of Geography University of
Cincinnati, 2003. Available From: eo1.usgs.gov/documents/EO1userguidev2pt320030715UC.pdf
[2012, March 15]
Belluco, E. et al. 2006. Mapping Salt-marsh Vegetation by Multispectral and Hyperspectral Remote
Sensing. Remote Sensing of Environment 105(1): 5467.
Bullock, J. M., 1999. Plants. In W. J. Sutherland (ed.), Ecological census techniques:A handbook, pp. 186-
213. UK: Cambridge University Press 1996.
Colditz, R.R. et al. 2012. Potential effects in multi-resolution post-classification change detection.
International Journal of Remote Sensing 33(20): 64266445.
Conchedda, G., Durieux, L. and Mayaux, P. 2008. An object-based method for mapping and change
analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing 63(5):
578589.
Franklin, S. E., Maudie, A. J., and Lavigne, M. B. 2001. Using spatial co-occurrence texture to increase
forest structure and species composition classification accuracy. Photogrammetric Engineering
and Remote Sensing 67(7): 849855.
Gao, J., Chen, H., Zhang, Y., and Zha, Y. 2004. Knowledge-based Approaches to Accurate Mapping of
Mangroves from Satellite Data. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 11(70):
12411248.
Giri, C. et al. 2011. Status and distribution of mangrove forests of the world using earth observation
satellite data. Global Ecology and Biogeography 20(1): 154159.
21
Green, E. P., Clark, C. D., Mumby, P. J., Edwards, A. J., and Ellis, A. C. 1998. Remote Sensing Techniques
for Mangrove Mapping. International Journal of Remote Sensing 19(5): 935956.
Haralick, R. M., Shanmugam, K. and Dinstein, H. 1973. Textural Features for Image Classification. IEEE
Transactions on Systems, Man and Cybernetics SMC 3(6): 610 621.
Held, A., Ticehurst, C., Lymburner, L., and Williams, N. 2003. High Resolution Mapping of Tropical
Mangrove Ecosystems Using Hyperspectral and Radar Remote Sensing. International Journal of
Remote Sensing 24(13): 27392759.
Heumann, B.W. 2011. Satellite remote sensing of mangrove forests: Recent advances and future
opportunities. Progress in Physical Geography 35(1): 87108.
Huang, X., Zhang, L., and Wang, L. 2009. Evaluation of Morphological Texture Features for Mangrove
Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery. IEEE Geoscience
and Remote Sensing Letters 6(3): 393397.
Jin, X. 2009.Segmentation-based image processing system. US Patent 20090123070, ed November 14,
2007, and issued May 14, 2009.
Koedsin, W. and Vaiphasa, C. 2013, Discrimination of Tropical Mangroves at the Species Level with EO-1
Hyperion Data. Remote Sensing, 5, 3562-3582
Kruse, F.A. et al. 1993. The Spectral Image Processing System (SIPS)interactive Visualization and
Analysis of Imaging Spectrometer Data. Remote Sensing of Environment 44(2-3): 145163.
Mumby, P.J., Green, E.P., Edwards, A.J., and Clark, C.D. 1999. The Cost-effectiveness of Remote Sensing
for Tropical Coastal Resources Assessment and Management. Journal of Environmental
Management 55(3): 157166.
Muttitanon, W. and Tripathi, N.K. 2005. Land use/land cover changes in the coastal zone of Ban Don Bay,
Thailand using Landsat 5 TM data. International Journal of Remote Sensing 26(11): 23112323.
Neukermans, G., Guebas, F. D., Kairo, J. G., and Koedam, N. 2008. Mangrove Species and Stand Mapping
in Gazi Bay (Kenya) Using Quickbird Satellite Imagery. Journal of Spatial Science 53(1): 7586.
22
Ouma, Y.O., Tetuko, J., and Tateishi, R. 2008. Analysis of Co-occurrence and Discrete Wavelet Transform
Textures for Differentiation of Forest and Non-forest Vegetation in Very-high-resolution Optical-
sensor Imagery. INTERNATIONAL JOURNAL OF REMOTE SENSING 29(12): 34173456.
Richards, J. A. and Jia, X., 2006. Remote Sensing Digital Image Analysis. 4th ed. Germany: Springer-Verlag.
Santiago, F. F. D., Kovacs, J. M. and Lafrance, P. An object-oriented classication method for mapping
mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data. International
Journal of Remote Sensing, 34, 2013, 563586
Satellite Imaging Corporation. Quickbird Satellite Sensor [Online]. 2001. Available from:
http://www.satimagingcorp.com/satellite-sensors/Quickbird.html
Sirikulchayanon, P., Sun, W. and Oyana, T. J. 2008. Assessing the impact of the 2004 tsunami on
mangroves using remote sensing and GIS techniques. International Journal of Remote Sensing
29(12): 35533576.
Vaiphasa, C., Ongsomwang, S., Vaiphasa, T., and Skidmore, A. K. 2005. Tropical Mangrove Species
Discrimination Using Hyperspectral Data: A Laboratory Study. Estuarine, Coastal and Shelf Science
65(1-2): 371379.
Vaiphasa, C., Skidmore, A.K. and de Boer, W.F. 2006. A post-classifier for mangrove mapping using
ecological data. ISPRS Journal of Photogrammetry and Remote Sensing 4(75): 110.
Vaiphasa, C., Skidmore, A. K., de Boer, W. F., and Vaiphasa,T. 2007. A Hyperspectral Band Selector for
Plant Species Discrimination. ISPRS Journal of Photogrammetry and Remote Sensing 62(3): 225
235.
Veljanovski, T., Kanjir, U., Pehani, P., Otir, K. and Kovai, P. (2012). Object-Based Image
Analysis of VHR Satellite Imagery for Population Estimation in Informal Settlement Kibera-Nairobi,
Kenya, Remote Sensing - Applications, Dr. Boris Escalante (Ed.), ISBN: 978-953-51-0651-7, InTech,
Available from:http://www.intechopen.com/books/remote-sensing-applications/object-based-
image-analysis-of-vhr-satellite-imagery-for-population-estimation-in-informal-settl
Vo, Q. T., Oppelt, N., Leinenkugel, P. and Kuenzer, C. 2013, Remote Sensing in Mapping Mangrove
Ecosystems An Object-Based Approach. Remote Sensing. 5, 183-201
23
Wang, L., Sousa, W. P. and Gong, P. 2004. Integration of object-based and pixel-based classification for
mapping mangroves with IKONOS imagery. International Journal of Remote Sensing 25(24): 5655
5668.
Wang, L., Sousa, W. P., Gong, P., and Biging, G. S. 2004. Comparison of IKONOS and Quickbird Images for
Mapping Mangrove Species on the Caribbean Coast of Panama. Remote Sensing of Environment
91(3-4): 432440.
Wang, L. and Sousa, W. P. 2009. Distinguishing mangrove species with laboratory measurements of
hyperspectral leaf reflectance. International Journal of Remote Sensing 30(5): 12671281.
Yu, Q., Gong, P., Tian, Y. Q., Pu, R. and Yang, J. 2008. Factors Affecting Spatial Variation of Classification
Uncertainty in an Image Object-based Vegetation Mapping, Photogrammetric Engineering &
Remote Sensing ,74,2008, 10071018.
