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Student Number 973310601
Author Laju Gandharum(°ª¼w°ö)
Author's Email Address No Public.
Statistics This thesis had been viewed 1215 times. Download 685 times.
Department International Master Program for Environment Sustainable Development
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Classification of Oil Palm in Indonesia Using FORMOSAT-2 Satellite Image
Date of Defense 2010-07-07
Page Count 80
Keyword
  • Classification Studies
  • FORMOSAT-2 Satellite¡AOli Palm
  • Indonesia
  • Abstract Indonesia is the biggest exporter crude palm oil (CPO) in the world since 2006. Total export of Indonesian¡¦s CPO and its derivatives in 2007 was about 11 million tons or equal to US$ 6.2 billion. It is a valuable sector that supports Indonesian economics, but it also causes environmental and social impacts. Deforestation is a sensitive issue related to oil palm plantation expansion. Sustainable oil palm development is needed to reduce environmental impacts and to meet economics purpose. Through the utilization of remote sensing (RS) technology, this study has tried to support sustainable oil palm development.
    Cimulang oil palm plantation that lies in district of Bogor, West Java Province, Indonesia was chosen as study area. High spatial resolution FORMOSAT-2 satellite image that has 4 multispectral bands (8 m spatial resolution) and 1 panchromatic band (2 m spatial resolution) was used in this study. The objectives of this study are to classify growing stages of oil palms using only multispectral bands and to classify growing stages of oil palms using multispectral bands plus texture information of FORMOSAT-2 data, to test the accuracy of both classification results, and to support sustainable palm development by providing more often updated oil palm land use map. Texture extraction through image matching by correlation and maximum likelihood supervised classification method has been applied in this study. The result shows that overall accuracy for multispectral image classification is 66.4%. Triangular oil palms planting pattern that has space 9 m apart between trees can be seen visually in 2 m panchromatic image of FORMOSAT-2 data and it also can be extracted automatically by texture analysis through image matching by correlation. This texture information then added to multispectral bands for classification. The overall accuracy result of multispectral bands with texture information is 76.8%. Image classification accuracy has improved (10.4 %) if the classification process employed not only multispectral bands but also added with the texture information.
    Table of Content Chinese Abstracti
    Abstractii
    Acknowledgementsiii
    Table of Contentsiv
    List of Figuresvi
    List of Tablesx
    1.Introduction1
    2.Background Information3
    2.1.General information of Indonesia3
    2.2.Oil palm4
    2.2.1.The oil palm5
    2.2.2.Land suitability7
    2.2.3.Agro-industry process8
    2.2.4.Uses8
    2.2.5.Oil palm plantation in Indonesia         9
    2.3.Brief description of remote sensing     11
    2.4.Type of remote sensing image     14
    2.5.Application of remote sensing for oil palm16
    2.6.Merging spectral and textural information for image classification              19
    3.Study Area and Data Collection     21
    3.1.Study area              21
    3.2.Data collection         23
    3.2.1.FORMOSAT-2 satellite data     23
    3.2.2.Ground truth              26
    4.Methodology              31
    4.1.Image preparation         32
    4.2.Multispectral image classification33
    4.2.1.Training fields         34
    4.2.2.Maximum likelihood classification35
    4.2.3.Generalization (regrouping classes and smoothing zone edges)              35
    4.3.Image classification with texture information37
    4.3.1.Applying High-pass filter     38
    4.3.2.Texture extraction through image matching by correlation              39
    4.4.Accuracy assessment         43
    4.4.1.Overall¡¦s, User¡¦s and Producer¡¦s accuracy43
    4.4.2.Kappa statistic         44
    5.Result and Discussion         46
    5.1.Geometric correction results     46
    5.2.Results of multispectral images classification47
    5.3.Results of multispectral and texture information images classification         53
    5.4.Comparison              57
    6.Conclusion and Recommendation    60
    6.1.Conclusion              60
    6.2.Recommendation              61
    References         63
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    2.Chang, K.T., 2008, Introduction to Geographic Information Systems, Fourth Edition, Mc. Graw Hill International Editions.
    3.Chen, D., Stow, D.A., and Gong, P., 2004, Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case, International Journal of Remote Sensing 25, p2177-2192.
    4.Coburn, C.A. and Roberts, A.C.B., 2004, A Multiscale Texture Analysis Procedure for Improved Forest Stand Classification, International Journal Remote Sensing, Vol. 25, No. 20, p4287-4308.
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    6.Corley, R.H.V. and Tinker, P.B., 2003, The Oil Palm, Fourth edition, Blackwell Science Ltd., Oxford.
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    9.Gibson, P.J., 2000, Introductory Remote Sensing: Principles and Concepts, Routledge, London.
    10.Gonzales R. C., and Woods, R. E., 2002, Digital Image Processing, Second Edition, Prentice Hall, New Jersey.
    11.Ibrahim, S., Hasan, Z. A., and Khalid, M., 2000, Application of Optical Remote Sensing Technology for Oil Palm Management, The 21st Asian Conference on Remote Sensing, Taipei - Taiwan.
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    17.National Portal of Republic of Indonesia, Profile of Indonesia, Cited: April 18, 2010, URL: http://www.indonesia.go.id/
    18.Nordin, L., Shahruddin, A., and Mariamni, H., 2002, Application of AIRSAR Data to Oil Palm Tree Characterization, MACRES Bulletin, ISSN No: 1511-7748, Kuala Lumpur.
    19.Perkebunan Nusantara VIII, Profile, Cited: April 20, 2010, URL: http://www.pn8.co.id/pn8_eng/index.php?option=com_content&task=category¡±ionid=4&id=13&Itemid=28
    20.Sukamto, 2008, 58 Kiat Meningkatkan Produktivitas dan Mutu Kelapa Sawit (58 Techniques to Increasing and Quality of Oil Palm), Penebar Swadaya, Jakarta.
    21.Sunarko, 2009, Budi Daya dan Pengelolaan Kebun Kelapa Sawit dengan Sistem Kemitraan (Cultivation and Management of Oil Palm through Partnership System), Agromedia Pustaka, Jakarta.
    22.Taiwan¡¦s NSPO (National Space Organization), Space Programs, FORMOSAT-2, Program Description,  Cited: April 25, 2010, URL: http://www.nspo.org.tw/2008e/projects/project2/intro.htm
    23.The IDL Astronomy User's Library, Image Filtering, NASA, Cited on May 25, 2010, URL: http://idlastro.gsfc.nasa.gov/idl_html_help/Filtering_an_Imagea.html#wp1022814
    24.Tsai, F., Chung, C.K., and Liu, G.R., 2008, Texture Analysis for Three Dimensional Remote Sensing Data by 3d GLCM.
    25.Tutorial: Fundamental Remote Sensing, Canadian Center of Remote Sensing, Cited: May 6, 2020, URL: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php
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    27.Wahid, B.O., Nordiana, A.A., and Tarmizi, A.M., 2005, Satellite Mapping of Oil Palm Land Use, MPOB Information Series, MPOB TT No. 255, ISSN 1511 ¡V 7871.
    28.Weng, Q., 2010, Remote Sensing and GIS Integration: Theories, Methods, and Applications, Mc Graw Hill.
    29.WikiPedia, Multi-spectral image, Cited: June 8, 2010, URL: http://en.wikipedia.org/wiki/Multi-spectral_image
    30.WikiPedia, The Free Encyclopedia, Indonesia, Cited: April 18, 2010, URL: http://en.wikipedia.org/wiki/Indonesia
    31.WikiPedia, The Free Encyclopedia, Oil Palm, Cited: April 19, 2010, URL: http://en.wikipedia.org/wiki/Oil_palm
    Advisor
  • Chi-Farn Chen(³¯Ä~ÿ)
  • Files
  • 973310601.pdf
  • approve immediately
    Date of Submission 2010-07-22

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