Land use mapping of Kaftareh Watershed of Ardabil using visual and digital processing of ETM+ image

Subject Areas : forest

ardavan ghorbani ¹ , farnoosh aslami ² , saeed ahmadabadi ³ , sahar ghaffari ⁴

1 - استادیار دانشکده فناوری کشاورزی و منابع طبیعی گروه مرتع و آبخیزداری دانشگاه محقق اردبیلی
2 - دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی
3 - دانش آموخته کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی
4 - دانشجوی دکترای علوم مرتع دانشگاه محقق اردبیلی

Received: 2015-01-02 Accepted : 2015-08-05 Published : 2016-02-20

Keywords: land use, Supervised classification, Accuracy assessment, Land cover, Visual interpretation, objected-based image analysis, Ardabil province,

Abstract :

Abstract The availability of land use information permits decision-makers to develop plans in short to long-term period for the conservation, sustainable use and development of natural resources and watersheds. In this study, ETM+ image (2006), GPS and GIS were used for image interpretation, field data collection and land use mapping. Preprocessing and required correction have conducted. Initially, field visit have been conducted and different land uses have been defined. In the second step, image was visually interpreted and then training area has selected and using the maximum likelihood algorithm image was classified. According to the lack of the capability for detecting river beds and residential areas in digital image processing, integration of visual and digital interpretation (supervised classification) and object-based image analysis were used. Results show that, in visual interpretation, there is almost no capability to discriminate rangeland from dry farming land uses; however garden, residential areas and riverbeds are discriminated. Results of supervised classification show that there are problems to detect and discriminate different land uses; however, by integration of digital and visual interpretation, it is possible to use Landsat data to discriminate different land uses in the areas such as Kaftareh watersheds and Arshagh region of Ardabil province. The results of the evaluation of object-based classification accuracy showed the highest overall accuracy, because the method parameters such as scale, shape, tone and texture, in addition to using pixel values ​​were used in classification, hence with appropriate segment creation, there is the possibility of precise discrimination of land uses such as residential areas from dryland farming. In the future studies, according to the importance of land use map in the studies such as natural resources, watershed managements and agriculture, it is better to use high spatial imagery and object-based methods.     

References:

1. Ahani, H., A.Ghorbani., Rastgar Moghadam,M.,Falah Shamsi,S., and M, Baghernejad.2009. Land use change detection using satellite imagery Tangeh-Sorkh watershed of Shiraz. Natural Resources and Agricultural Journal,16:242-252.


2. Ahmad Abadi,S. 2011. Spatial modeling of planting activities on range area using GIS & RS in Kaftareh Watershed of Ardabil. Msc Thesis of Watershed Management. University of Mohaghegh Ardabily.


3. Al-Ahmadi, F. S. and A. S. Hames. (2009) Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia, Journal of King Abdul Aziz Earth Science, 20(1): 167-191.


4. Alavipanah, K, M ,Masoudi.2001. Land use Mapping Preparation Using TM satellite imaging Data and GIS (A case Study: Mork region of Fars province Natural Resources and Agricultural Journal.


5. Arzani H, K.h , Mirakhorlou, S.Z ,Hosseini. 2009. Land use mapping using Landsat7 ETM data (Case study in middle catchment’s of Taleghan).Range and desert journal of Iran:16(2):150-160.


6. Baban, S. J. and M. Wan Yusof.  (2001) Mapping land use/ cover distribution on a mountainous tropical island using remote sensing and GIS, International Journal Remote Sensing, 22:10, 1909–1918.


7. Bastin, G.N., Pickup, G., Chewings, V.H., and G., Pearce. (1993) Land degradation assessment in central Australia using a grazing gradient method. Australian Rangeland Journal, 15: 190-216.


8. Chavez, P. S., Berlin, G. L. and L. B. Sowers. (1982) Statistical methods for selecting Landsat MSS ratio. Journal of Applied Photogrammetric Engineering, 8(1):


9. 23-30.


10. Congalton, R.G., and K. Green. (2009) Assessing the accuracy of remotely sensed data: principles and practices. 2^nd Ed, CRC Press Taylor & Francis Group, Boca Raton Florida. 179P.


11. Dellepiane, S.G. and P.C. Smith. (1999) Quality assessment of image classification algorithms for land cover mapping: A review and a proposal for a cost based approach. International Journal Remote Sensing, 20: 1461-1486.


12. Feysizadeh,b and H, Halali.2010. Comparision pixel basic methods and effective factors classification of vegetation and land use in West Azarbaijan province, Natural Geographic Research journal:71:73-84..


13. Foody, G.M. (1992) On the compensation for chance agreement in image classification accuracy assessment. Photogrammeteric Engineering and Remote Sensing, 58: 1459-1460.


14. Ghorbani, A. and M. Pakravan. (2013) Land use mapping using visual vs. digital image interpretation of TM and Google earth derived imagery in Shrivan-Darasi watershed (Northwest of Iran), European Journal of Experimental Biology, 3(1): 576-582.


15. Ghorbani, A., Bruce, D., and F. Tiver. (2006) Stratification: a problem in rangeland monitoring. In Proceeding of the 1st International Conference on Object-based Image Analysis (OBIA), July 4-5, Salzburg, Austria.


16. Hossieni,S. 2002. Investigation on ability of ETM⁺ satellite image Data For land use mapping preparation (A case Study: Mazandaran province, Chamestan region).Msc Thesis,Tehran University.


17. Igué A.M., Houndagba C. J., Gaiser T. and K. Stahr. (2012) Accuracy of the Land Use/Cover classification in the Oueme Basin of Benin (West Africa), International Journal of AgriScience, 2(2): 174-184.


18. Iranmanesh, F., A, Charkhabi., and N, Jalali.2006. Measuring of morphometric characteristics gullies in south eastern of Iran Using ETM⁺ Image processing. Natural Resources and Agricultural Sciences Journal: 10(1):233-245.


19. Jaafari, Sh. and A.A., Nazarisamani. (2013) Comparison between land use/land cover mapping through Landsat and Google Earth imagery, American-Eurasian Journal of Agriculture & Environment Science, 13(6): 763-768.


20. Jansen L. J.M. and A. Di Gregorio. (2004) Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon, International Journal of Applied Earth Observation and Geoinformation, 5: 141–157.


21. Lefsky, M. A. and W. B. Cohen. (2003) Selection of  remotely sensed data. P13–46, in: M. A. Wulder and S. E. Franklin (eds.), Remote sensing of forest environments: concepts and case studies, Kluwer Academic Publishers, Boston, USA.


22. Lillesand, ‎T. M., Kiefer, ‎ R.W. and J. W., Chipman. (2008) Remote Sensing and Image Interpretation, John Wiley & Sons, Inc., 6^th Ed., 812p.


23. Lu. D. and Q. Weng. (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 28(5): 823–870.


24. Mather, P. M., (2005) Computer processing of remotely–sensed images, 3^rd Ed, John Wiley & Sons, Ltd. pp319.


25. Montserud, R. A. and R. Leamans. (1992) Comparing global vegetation maps statistic, Ecological Modeling, 62: 275-293.


26. Myint, S. W., Gober P., Brazel A., Grossman-Clarke, S., Q., Weng. (2012) Per-pixel vs. object-based classification of urban land covers extraction using high spatial resolution imagery. Remote Sensing of Environment, 115: 1145–1161.


27. Rasouli, E and H,Mohammadzadeh.2010. Remot sensing basic of Knowledg. Elmiran publition.190p.


28. Shataee, Sh and A, Abdi, 2007. Land use mapping preparation in Zagros mountain region using ETM⁺ satellite images Data (A case study: Sarkan Watershed of Lorestan).Natural Resources and Agricultural Sciences Journal: 1(4):129-138.


29. Singh, V. and A. Dubey. (2012) Land use mapping using remote sensing and GIS techniques in Naina - Gorma basin, part of Rewa district, M.P., India, International Tapiador, F.J. and J. L. Journal of Emerging Technology and Advanced Engineering, 2(11): 151-156.


30. Casanova. (2003) Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain Landscape and Urban Planning Journal, 62: 2, 103-115.


31. Stehman, S. V. (2004) A critical evaluation of the normalized error matrix in map accuracy assessment, Photogrammetric Engineering and Remote Sensing. 70: 743–751.


32. Walter, V. (2004) Object-based classification of remote sensing data for change detection, ISPRS Journal of Photogrammetry and Remote Sensing, 58: 225– 238.


33. Wolberg. G. (1990) Digital image warping. Los Alamitos, CA: IEEE Computer Society Press, p 318.

 

 

 

27.  Najarloo, S.2005. Investigation on Fars expansion area using areal photographic, Topo graphic maps, Satellite image ETM⁺ and IRS-ID, Msc Thesis Agricultural and Natural Resources Faculty.

28.  Rafieyan, A., A. Darvishsefat and M. Namiranian.2006. Evaluation of Landsat7 Data in Forest Mapping (Case study: Forest of Babol).Iranian Natural Resources Journal: 59(4): 843-852.