تهیه نقشه پوشش اراضی حوزه آبخیز قوری چای با پردازش تصاویر ماهواره ای
محورهای موضوعی :
آمایش سرزمین
مرضیه علی خواه اصل
1
,
داریوش ناصری
2
1 - استادیار، گروه کشاورزی و منابع طبیعی، دانشگاه پیام نور، تهران،
2 - گروه محیط زیست، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
تاریخ دریافت : 1395/02/18
تاریخ پذیرش : 1401/04/14
تاریخ انتشار : 1401/01/01
کلید واژه:
پوشش اراضی,
قوری چای,
طبقه بندی فازی,
طبقه بندی حداکثر احتمال,
چکیده مقاله :
زمینه: کاربری/پوشش اراضی، از دیرباز به منظور برنامه ریزی و مدیریت منابع طبیعی مد نظر قرار گرفته است و تکنیک‌های سنجش از دور، بهترین وسیله برای تهیه نقشه‌های بهنگام کاربری و پوشش اراضی می‌باشد و روش های مختلفی برای تهیه نقشه کاربری اراضی وجود دارد.
هدف: هدف از این تحقیق، تهیه نقشه پوشش اراضی حوزه آبخیز قوری چای با استفاده از پردازش و طبقه بندی تصاویر ماهواره ای می باشد که از مهمترین حوزه های استان اردبیل محسوب می شود.
روش بررسی: بدین منظور، تصاویر ماهواره‌ی لندست 8 مربوط به خرداد ماه سال 1394 با استفاده از روش های طبقه بندی نظارت شده ی حداکثر احتمال و فازی طبقه بندی شدند.
یافته ها: نتایج نشان داد که به ترتیب اراضی مرتعی، بایر، کشت دیم و مسکونی (روستا) بیشترین سطح از منطقه را تشکیل می دهند. برای ارزیابی صحت و دقت طبقه بندی‌های انجام شده، صحت کلی و ضریب کاپا تعیین گردید و نتایج نشان داد که روش طبقه بندی حداکثر احتمال با ضریب کاپا 82/0 و صحت کلی 88% نسبت به روش فازی با ضریب کاپا 81/0 و صحت کلی 87% از دقت بیشتری در فرایند طبقه بندی برخوردار است.
بحث و نتیجه گیری: بر اساس نتایج به دست آمده از این تحقیق، علی رغم قابلیت بالای تصاویر ماهواره ای در تهیه نقشه کاربری اراضی، بایستی به منظور افزایش دقت طبقه بندی، از پارامترهای جانبی نیز استفاده کرد.
چکیده انگلیسی:
Background: Land use/land cover has long been considered for natural resource planning and management and remote sensing techniques are the best tools to produce land use/cover maps. There are various methods for preparing land use maps.
Objective: The purpose of this study is to prepare a land cover map of Ghoorichay watershed using processing and classification of satellite images, which is one of the most important watersheds of Ardabil province.
Materials and Methods: For this purpose, Landsat 8 satellite images related to June 2015 were classified using supervised maximum likelihood and fuzzy classification methods.
Results: The results showed that rangelands, bare lands, dry lands, and residential lands (village) are the major land uses in the area, respectively. According to the results, maximum likelihood method with kappa coefficient of 0.82 and overall accuracy of %88 is more accurate than fuzzy classification method with kappa coefficient of 0.81 and overall accuracy of %87.
Discussion and Conclusion: Based on the results of this study, despite the high capability of satellite images in the preparation of land use map, in order to increase the accuracy of classification, peripheral parameters should be used.
منابع و مأخذ:
Ahmadi Nadooshan, M., Sefyanian, A., Khajeddin, S. J. (2009). Preparation of land cover map of Arak city using artificial neural network classification and maximum likelihood methods, Natural Geography Researches, No. 69 (In Persian).
Knorn, A., Radeloff, C.V., Kuemmerle,T., Kozak, J., Hoster, P., 2009. Land cover mapping of large areas using chain classification of neighboring landsat satellite images. Remote Sens. Environ, 113:957-964.
Ali Bakhshi, Z., Alikhah Asl, M., Rezvani, M. (2013), Preparation of land use map of Meighan wetland using supervised and fuzzy classification methods, Quarterly Journal of Human and Environment, No. 32, Spring (In Persian).
Alavi Panah, S. K., Matinfar, H., Sarmadian, F. (2004), Evaluation the use of satellite data in terms of saving time, National Productivity Conference. Iranian Academy of Sciences, (In Persan).
Jensen, j., 2005. Introductory digital image processing: A remote sensing perspective (3rd ed) Upper Saddle River, NJ: Prentice Hall. 526 pp.
Huang, C., Davis, L.S., Townshend, J.R., 2002. An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23 (4): 725–749.
Rajesh, B.T., Yuji, M., 2009. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography 29, 135-144.
Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28 (5): 823-870.
Akbarpour, A., Sharifi, M. B., Memarian Khalilabad, H. (2006), Comparison of fuzzy and maximum likelihood methods for preparing land use layer with the help of ETM + data (Case study: Kameh watershed), Iranian Journal of Range and Desert Research, V. 13, N. 1. (In Persian).
Alikhah Asl, M., Forootan, E., (2013), Using Fuzzy classification method to prepare land use map (Case study: Hablehroud Watershed), Quarterly Journal of Human and Environment, No. 24 (In Persian).
Akbari, E., Majidi, I., Amir Ahmadi, A. (2014), Preparation of Sabzevar land use map using maximum likelihood and artificial multilayer perceptron neural network methods, Quarterly journal of environmental based territorial planning, No. 23, pp. 148-128 (In Persian).
Elizabeth, A. W., William, L., Stefanov, C.G., Diane, H., 2006. Land use and land cover mapping from diverse data sources for an arid urban environments. Computers, Environment and Urban Systems 30 (3): 320–346.
Seto, K. c., Woodcock C.E., Song, C., Huang, X., Lu, J., kaufmann R. K., 2002. Monitoring land-use change in the pearl River Delta using Landsat TM, International 1 Journal of Remote Sensing, ISSN 0143-1161 print/ISSN 1366-590 1 online ©2002 Taylor& Francis Ltd.
Billah, M., Rahman, G.A., 2004. Land cover Mapping of Khulna city Applying Remote sensing Technique, 12.conf.on.Geoinformation Research, Bridging the Pacific and Atlantic, University of Gavel, Swen,7-9 june 2004.
Al-Ahmadi, F. S., Hames, A.S., 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. JKAU, Earth Science, 20 (1): 167-191.
Billah, M., Rahman, G.A., 2004. Land cover Mapping of Khulna city Applying Remote sensing Technique, 12.conf.on.Geoinformation Research, Bridging the Pacific and Atlantic, University of Gavel,Swen,7-9 june 2004.
Lu, , Mausel, P., Brondizio, E., Moran, E,. 2004. Change Detection Techniques. International Journal of Remote Sensing, 25(12): 2365-2401.
Mir Akhorloo, H. (2003), Preparation of land use map in the forests of the north of the country using LANDSAT 7 ETM + data satellites, Iranian Journal of Forest and Poplar Research, N. 3, V. 14, Pp. 325-358, (In Persian).
Shataee, Sh., Abdi, O. (2007), Land use mapping in the mountainous areas of Zagros using TM data (study area: Sorkhab Khorram Basin), Journal of Agriculture and Natural Resources Sciences, N. 1, V. 14, Pp. 129-138, (In Persian).
Sarouee, S., Darvish Sefat, A. A. (1999), Investigation of the possibility of forest density classification in Zagros forests with the help of satellite data, University of Tehran, Department of Forestry, MSc thesis, (In Persian).
Saei Jamalabad, M., Darvish Sefat, A. A. (2003), Change detection of forest area and density using remote sensing technology, Khajeh Nasireddine Tusi University, Department of Remote Sensing, MSc thesis, (In Persian).
Scott, G. B. and Mark, R. G. 2001. Classification of Land Cover Types for the Fort Bening Ecoregion Using Enhanced Thematic Mapper Data. Strategic Envirenmental Research and Development program (SERDP). ERDC/ET TNECMI- 01 -01 .9 pp.
Shalaby, A. and Tateishi, R. 2007. Remote Sensing and GIS for mapping and monitoring land cover and land use changes in the Northwestern coastalzone of Egypt. Applied Geography 27 (2007), 28-41.
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Ahmadi Nadooshan, M., Sefyanian, A., Khajeddin, S. J. (2009). Preparation of land cover map of Arak city using artificial neural network classification and maximum likelihood methods, Natural Geography Researches, No. 69 (In Persian).
Knorn, A., Radeloff, C.V., Kuemmerle,T., Kozak, J., Hoster, P., 2009. Land cover mapping of large areas using chain classification of neighboring landsat satellite images. Remote Sens. Environ, 113:957-964.
Ali Bakhshi, Z., Alikhah Asl, M., Rezvani, M. (2013), Preparation of land use map of Meighan wetland using supervised and fuzzy classification methods, Quarterly Journal of Human and Environment, No. 32, Spring (In Persian).
Alavi Panah, S. K., Matinfar, H., Sarmadian, F. (2004), Evaluation the use of satellite data in terms of saving time, National Productivity Conference. Iranian Academy of Sciences, (In Persan).
Jensen, j., 2005. Introductory digital image processing: A remote sensing perspective (3rd ed) Upper Saddle River, NJ: Prentice Hall. 526 pp.
Huang, C., Davis, L.S., Townshend, J.R., 2002. An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23 (4): 725–749.
Rajesh, B.T., Yuji, M., 2009. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography 29, 135-144.
Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 28 (5): 823-870.
Akbarpour, A., Sharifi, M. B., Memarian Khalilabad, H. (2006), Comparison of fuzzy and maximum likelihood methods for preparing land use layer with the help of ETM + data (Case study: Kameh watershed), Iranian Journal of Range and Desert Research, V. 13, N. 1. (In Persian).
Alikhah Asl, M., Forootan, E., (2013), Using Fuzzy classification method to prepare land use map (Case study: Hablehroud Watershed), Quarterly Journal of Human and Environment, No. 24 (In Persian).
Akbari, E., Majidi, I., Amir Ahmadi, A. (2014), Preparation of Sabzevar land use map using maximum likelihood and artificial multilayer perceptron neural network methods, Quarterly journal of environmental based territorial planning, No. 23, pp. 148-128 (In Persian).
Elizabeth, A. W., William, L., Stefanov, C.G., Diane, H., 2006. Land use and land cover mapping from diverse data sources for an arid urban environments. Computers, Environment and Urban Systems 30 (3): 320–346.
Seto, K. c., Woodcock C.E., Song, C., Huang, X., Lu, J., kaufmann R. K., 2002. Monitoring land-use change in the pearl River Delta using Landsat TM, International 1 Journal of Remote Sensing, ISSN 0143-1161 print/ISSN 1366-590 1 online ©2002 Taylor& Francis Ltd.
Billah, M., Rahman, G.A., 2004. Land cover Mapping of Khulna city Applying Remote sensing Technique, 12.conf.on.Geoinformation Research, Bridging the Pacific and Atlantic, University of Gavel, Swen,7-9 june 2004.
Al-Ahmadi, F. S., Hames, A.S., 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. JKAU, Earth Science, 20 (1): 167-191.
Billah, M., Rahman, G.A., 2004. Land cover Mapping of Khulna city Applying Remote sensing Technique, 12.conf.on.Geoinformation Research, Bridging the Pacific and Atlantic, University of Gavel,Swen,7-9 june 2004.
Lu, , Mausel, P., Brondizio, E., Moran, E,. 2004. Change Detection Techniques. International Journal of Remote Sensing, 25(12): 2365-2401.
Mir Akhorloo, H. (2003), Preparation of land use map in the forests of the north of the country using LANDSAT 7 ETM + data satellites, Iranian Journal of Forest and Poplar Research, N. 3, V. 14, Pp. 325-358, (In Persian).
Shataee, Sh., Abdi, O. (2007), Land use mapping in the mountainous areas of Zagros using TM data (study area: Sorkhab Khorram Basin), Journal of Agriculture and Natural Resources Sciences, N. 1, V. 14, Pp. 129-138, (In Persian).
Sarouee, S., Darvish Sefat, A. A. (1999), Investigation of the possibility of forest density classification in Zagros forests with the help of satellite data, University of Tehran, Department of Forestry, MSc thesis, (In Persian).
Saei Jamalabad, M., Darvish Sefat, A. A. (2003), Change detection of forest area and density using remote sensing technology, Khajeh Nasireddine Tusi University, Department of Remote Sensing, MSc thesis, (In Persian).
Scott, G. B. and Mark, R. G. 2001. Classification of Land Cover Types for the Fort Bening Ecoregion Using Enhanced Thematic Mapper Data. Strategic Envirenmental Research and Development program (SERDP). ERDC/ET TNECMI- 01 -01 .9 pp.
Shalaby, A. and Tateishi, R. 2007. Remote Sensing and GIS for mapping and monitoring land cover and land use changes in the Northwestern coastalzone of Egypt. Applied Geography 27 (2007), 28-41.
