The Knowledge-Based Analysis on Medium Resolution Images of Remote Sensing to Extraction Information Land use Type SCS-CN, the Case Study on Grompol Watershed

Authors

Puguh Dwi Raharjo, Karangsambung Information and Conservation Center for Earth, LIPI, Kebumen 54353, IndonesiaFollow
Totok Gunawan, Faculty of Geography, Universitas Gadjah Mada, Jogyakarta 55281, Indonesia
Mohammad Pramono Hadi, Faculty of Geography, Universitas Gadjah Mada, Jogyakarta 55281, Indonesia

Abstract

Remote sensing imagery Landsat-8 is one image that has a good temporal resolution; in addition to the availability of data, this image can be obtained free of charge. Land cover type SCS-CN is part of a unit of land that affects runoff. The use of medium resolution imagery in reducing the SCS-CN land use type is considered relatively difficult, and it yields less good accuracy. Limitations on multispectral classification only rely on facts derived from spectral reflection, so that the two data are the same since different characteristic results are not so good. This study aims to determine the accuracy of precision medium-resolution imagery in reducing parameter land use type SCS-CN by using the knowledge-based analysis. The importance of understanding the landscape-ecology can be used to assist the translation from land cover in the form of land use. Vegetation factors and ecosystems are often used to generate metrics-based landscape. Accuracy from the interpretation of remote sensing image medium-resolution is obtained by 85.17%. Therefore, Landsat-8, in addition to easy retrieval of data, can also be used to identify the type of land cover SCS-CN, which is useful for the interests of surface water resources.

Bahasa Abstract

Analisis Knowledge-based pada Citra Penginderaan Jauh Resolusi Menengah untuk Ekstraksi Informasi Penggunaan Lahan Tipe SCS-CN, Studi Kasus DAS Grompol. Citra penginderaan jauh Landsat-8 merupakan salah satu citra yang memiliki resolusi temporal baik, selain itu ketersediaan datanya dapat diperoleh secara gratis. Penutup lahan tipe SCS-CN merupakan bagian dari dari unit lahan yang mempengaruhi limpasan permukaan. Penggunaan citra resolusi menengah dalam menurunkan informasi penggunaan lahan tipe SCS-CN dirasa relatif sulit dan mendapatkan akurasi yang kurang baik. Keterbatasannya pada klasifikasi multispektral hanya mempercayakan pada fakta yang diturunkan dari spektral, sehingga pada dua data numerik yang sama dan berbeda karakteristik memberikan hasil yang tidak bagitu baik. Pada penelitian ini bertujuan untuk mengetahui akurasi ketelitian citra resolusi menengah dalam menurunkan parameter penutup/penggunaan lahan tipe SCS-CN dengan menggunakan analisis knowledge-based. Pentingnya pemahaman mengenai landscape-ecology dapat digunakan untuk membantu dalam menerjemahkan penutup lahan ke dalam penggunaan lahan atau penutup lahan yang spesifik pada metode tertentu. Faktor vegetasi dan ekosistem sering digunakan untuk menghasilkan metrik berbasis landscape. Akurasi yang diperoleh dari interpretasi resolusi menengah citra penginderaan jauh sebesar 85,17%. Oleh karena itu, dengan menggunakan Landsat-8, di samping memudahkan pengambilan data juga dapat digunakan untuk mengidentifikasi jenis jenis tutupan lahan SCS-CN berguna untuk kepentingan sumber daya air permukaan.

References

Y.I. Tekelý, S. Akgül, T. Aküzüm, International Congress on River Basin Management, Turki, 2007, p.527.

C.H. Tan, A.M. Melesse, S.S. Yeh, Asian Conference on Remote Sensing, Nepal., 2002, p.8.

C. Gangodagamage, the 22nd Asian Conference on Remote Sensing, Singapore, 2001, p.6

P. Danoedoro, J. Indonesian Geography, 35/2 (2003) 81.

T.M. Lillesand, R.W. Kiefer, Remote Sensing and Image Intepretation, John Wiley, New York, 1979, p.612.

C.P. Lo, Applied Remote Sensing, Longman Inc, New York, 1986.

D. Lindgren. Land use Planing and Remote Sensing, Martinus Nijoff Publishers, Boston, 1985, p.176.

A. Meijerink, H. Brouwer, C. Mannaerts, C. Valenzuela, Introduction to The Use of Geographic Information Systems for Practical Hydrology, ITC, The Netherlands, 1994.

T. Gunawan, Ph.D Thesis, Bogor Institute of Agriculture, Indonesia, 1981.

L. Xianzhao, L. Jiazhu, J. Chinese Geographical Science, 18/3 (2008) 235.

M. Coskun, N. Musaoglu, ISPRS Proceedings XXth, Istanbul, 2004, p.4.

M. Ebrahimian, L.F. See, M.H. Ismail, A.M. Ismail, J. Eur. Sci. Res. 34/4 (2009) 575.

P. Sudiro, T. Purwadi, Sukresno, M. Nurlaili, J. Agritech, 25 (2002) 14.

K.D. Sharma, S. Singh, J. Hydrologica, 37/1 (1992) 52.

NASA, Landsat 7 Science Data Users Handbook, 1999.

USGS, Landsat 8 (LDCM) History, 2013b.

NASA, Landsat Data Continuity Mission, 2013.

P.M. Mather, Computer Processing of Remotely Sensed Images, John Wiley, England, 2004.

GLCF, Landsat Technical Guide, Global Land Cover Facility, 2004

J.B. Campbell, R.H. Wynne, Introduction to Remote Sensing, The Guilford Press, New York, 2011.

P. Danoedoro, Introduction to Remote Sensing Digital (Indonesia), Andi Offset, Yogyakarta, 2012.

S.H. Purwadhi, Digital Imagery Interpretation (Indonesia), Gramedia Widiasarana Indonesia, Jakarta, 2001.

T. Brandt., P.M. Mather, Classification Methods for Remotely Sensed Data, CTC Press Taylor & Francis Group, New York, 2009.

J. Gao, Digital Analysis of Remotely Sensed Imagery, McGraw-Hill Companies Inc, United States, 2009.

M. Avci, Z. Akyurek, ISPRS Proceedings, Tech. Com. IV, Vol 35 Part B4, Istanbul, 2004, p.516.

M. Judex, H.P. Thamand, G. Menz, Proceedings of the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover, Germany., 2006, p.191.

J. Richards, Remote Sensing Digital Image Analysis, an Introduction, Springer-Verlag, Germany, 1993.

J. Jensen, Introduction Digital Image Processing, A Remote Sensing Perspective, Pearson Prentice Hall, US, 2005, p.592.

Y. Cohen, M. Shoshany, ISPRS Proceedings, Volume XXXIII Part B7(/1-4), Amsterdam, 2000, p. 280.

C. Unsulan, K.L.Boyer, Multispectral Satellite Image Understanding, From Land Classification to Building and Road Detection, Springer-Verlag, London, 2001, p.186.

I.P.G. Ardhana, Ekologi Tumbuhan, Udayana University Press, Denpasar, 2012.

R.S. Lunetta, J.F. Knight, J. Ediriwickrema, J.G. Lyon, L.D. Worthy, J. Remote Sensing of Environment, 105/2 (2006) 154

J. Howard, Remote Sensing of Forest Resources Theory and Application, Chapman & Hall, London., 1991.

P. Thanapura, D.L. Helder, S. Burckhard, E. Warmath, M. O’Neill, D. Galster, J. Photogramm. Eng. Remote Sens. 73/1 (2007) 57.

M.A. Friedl, C.E. Brodley, A.H. Strahler, IEEE Trans. Geosci. Remote Sens. 37/2 (1999) 969.

S. Adsavakulchai, D. Minns, A. Chan, 2004. ISEIS Publication, Environmental Informatics Archives, 2 (2004) 499.

R.J. Schaetzl, S. Anderson, Soils Genesis and Geomorphology, Cambridge University Press, New York, 2005, p.827.

V. Zuidam, Z. Canelado, Terrain Analisys and Classification Using Aerial Photography, A Geomorphological Approach. ITC textbook of Photo-Interpretation, ITC, The Netherlands, 1978.

USGS, Landsat Processing Detail, Standars Parameter, 2013a.

USGS, Landsat Data Continuity Mission (LDCM) Level 1, 2012.

C. Gyanesh, B.L. Markham, J.A. Barsi, IEEE Geosci. Remote Sens. Lett. 4/3 (2007) 490.

USGS, Landsat Update, 2013e.

USGS, Using the USGS Landsat 8 Product, 2013d.

R. McCoy, Field Methods in Remote Sensing, The Guildford Publications, New York, 2005, p. 177.

Surono, B. Toha, I. Sudarno, Geological Map Sheet Surakarta-Giritontro, Geological Research and Development Center, Bandung, 1992.

Recommended Citation

Raharjo, Puguh Dwi; Gunawan, Totok; and Hadi, Mohammad Pramono (2016) "The Knowledge-Based Analysis on Medium Resolution Images of Remote Sensing to Extraction Information Land use Type SCS-CN, the Case Study on Grompol Watershed," Makara Journal of Technology: Vol. 20: Iss. 2, Article 2.
DOI: 10.7454/mst.v20i2.3057
Available at: https://scholarhub.ui.ac.id/mjt/vol20/iss2/2