• صفحه اصلی
  • پایش تغییرات پوشش اراضی با استفاده از سنجش از دور و سامانه اطلاعات جغرافیایی (مطالعه موردی: حوضه آبخیز خوجین خلخال)

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-1612-3182 (R1) بازدید : 143 صفحه: 381 - 393

10.22034/jest.2020.22762.3182

نوع مقاله: پژوهشی

پایش تغییرات پوشش اراضی با استفاده از سنجش از دور و سامانه اطلاعات جغرافیایی (مطالعه موردی: حوضه آبخیز خوجین خلخال)

محورهای موضوعی : سیستم اطلاعات جغرافیایی

مهسا صفری پور 1 , داریوش ناصری 2

1 - استادیار، گروه کشاورزی و محیط زیست، دانشگاه پیام نور، تهران، ایران.* (مسوول مکاتبات)
2 - گروه محیط زیست، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران

تاریخ دریافت : 1395/09/14 تاریخ پذیرش : 1395/10/29 تاریخ انتشار : 1399/03/01

کلید واژه: حوضه آبخیز خوجین, تغییرات پوشش اراضی, سنجش از دور, تصاویر ماهواره‌ای,

چکیده مقاله :

زمینه و هدف: مطالعه میزان تغییرات و تخریب منابع در سال های گذشته می تواند در برنامه ریزی و استفادة بهینه از منابع و کنترل و مهار تغییرات غیر اصولی در آینده گام مهمی باشد و از آنجا که این تغییرات در سطوح وسیع و گسترده اتفاق می افتد، تکنولوژی سنجش از دور می تواند ابزاری ضروری و ارزش مند جهت پایش تغییرات باشد. هدف از تحقیق حاضر، پایش تغییرات پوشش اراضی در حوضه آبخیز خوجین خلخال در جنوب استان اردبیل با تکنیک سنجش از دور می باشد. روش بررسی: ابتدا تصاویر ماهواره لندست مربوط به تیر ماه سال های 1365، 1380 و 1395 تهیه و پس از تصحیح هندسی و ارتفاعی، طبقه بندی تصاویر به روش نظارت شده یا خوارزمیک حداکثر احتمال انجام پذیرفت. به منظور افزایش دقت طبقه بندی تصاویر نیز از شاخص NDVI، لایه مدل رقومی ارتفاعی و لایه شیب استفاده شد و دقت طبقه بندی نیز با شاخص کاپا و صحت کلی بررسی گردید. یافته ها: بر اساس نتایج در دوره اول (1380-1365) 36/22 درصد، در دوره دوم (1395-1380) 73/20 و در دوره آخر یعنی (1395-1365) 08/28 درصد از منطقه دچار تغییر کاربری شده است که در هر سه دوره، تبدیل کاربری مرتع به زراعت دیم بیش ترین تغییرات را دارد. بحث و نتیجه گیری: نتایج تحقیق حاضر نشان دهنده این امر است که علی رغم بالا بودن قدرت تصاویر لندست در تهیه نقشه کاربری اراضی، می توان با استفاده از شاخص ها و لایه های جانبی نظیر NDVI، ارتفاع و درصد شیب، قدرت تفکیک کاربری ها از یکدیگر را بهبود بخشید.

چکیده انگلیسی:

Background and Objective: Study of resources changes and destruction in previous can be useful in the planning and optimal use of resources to control inappropriate changes. Because of land use changes occur on large-scale, remote sensing techniques is a useful and valuable tool for monitoring the changes. The aim of this research is to monitoring of land cover changes in Khojin watershed located in the south of Ardabil province using remote sensing technique. Method: First TM, ETM+ and OLI images for the year 1986, 2001 and 2016 were collected respectively and after geometric and elevation corrections, images were classified using maximum likelihood classification method. To increase classification accuracy, NDVI, elevation and slope percent layers were used. Kappa and overall accuracy indexes were used to calculate classified images accuracy. Findings: Based on results, in the first period (1986-2001) 22.36%, in the second period (2001-2016) 20.73% and in the third period (1986-2016) 28.08% of area has changed and in all 3 periods, changing from rangeland to dry farming reached the most change.­ Discoution and Conclusion: Results showed despite high capability of Landsat images in mapping land use, using additional layers like NDVI, elevation and slope percent can improve classification accuracy of classified images.  

منابع و مأخذ:


  1. Chen, X., Vierling, L., Deering, D., 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98(1), Pp 63-79.
    2.
  2. Gharagozlou, A., Nouri Kermani, A., Keshmiri, Z., 2009, Assessment of physical changes and analysis of urban development using remote sensing with high resolution & GIS/RS(case study: District 5 of Tehran), Journal of Environmental Science and technology, Vol. 11, No.1. (In Persian)
    3.
  3. Xian, G., Homer, C., Fry, J., 2009.  Updating the 2001 National Land Cover Databaseland cover classification to 2006 by using Landsat imagery change detectionmethods. Remote Sensing of Environment, 113, Pp 1133–1147.
    4.
  4. Mohammady, M., Moradi, HR., Zeinivand, H., 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environment Science and technology, 12: Pp 1515–1526.
    5.
  5. Jensen, J.Rand Cowen, D.C., 1999.  Remote sensing of urban suburban infrastructure and socio-economic attributes. Photogram Engineering, Remote Sensing, 65, Pp 611–622.
    6.
  6. Hathout, S., 2002.  The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada.  Journal of Environmental Management, 66, Pp 229–238.
    7.
  7. Fatemi talab, S.R., Maadani pour, M., Hashemi, S.A., 2014, Estimation of changes in the cover plant of Rudsar forests using classification of artificial neural network & Maximum probability, Journal of Rs & GIS in natural resources, the sixth year, No.2. (In Persian)
    8.
  8. Moradi, A., Jafari, M., Arzani, H., Ebrahimi, M., 2016, Assessment of land use changes into dry land using satellite images and Geographical information system (GIS), Journal of Rs & GIS in natural resources, the seventh year, No.1. (In Persian)
    9.
  9. Waseem, M., Paul, H., Jeffrey, G., Boshra, H., Salem, B., 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63 (2015), Pp 101-112.
    10.
  10. Maimaitijiang, m., Ghulam, a., Onésimo Sandoval, j.s., 2015. Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geo information, 35 (2015), Pp 161–174.
    11.
  11. Xiao, T.Y., Huiping, L.X., 2015. Land cover changed object detection in remote sensing data with medium spatial resolution. International Journal of Applied Earth Observation and Geo information, 38, (2015), Pp 129–137.
    12.
  12. Jafarnia, Sh., Oladi, J., Hoojati, S.M., Mirakhorloo, Kh., 2016, Status and change detection of Mangrove forest in Qeshm Island using satellite imagery from 1988 to 2008, J. Env. Sci. Tech., Vol 18, No.1. (In Persian)
    13.
  13. Rahdari, V., Soffianian, A., Maleki Najfabdai, S., Khajeddin, S.J., Rahdari, M., 2017, Land use/cover mapping usig satellite data and geographic information system (GIS) (Case study: Mouteh wild life sanctuary), J. Env. Sci. Tech., Vol 18, No.1. (In Persian)
    14.
  14. Detailed plan studies, Khojin watershed in Khalkhal, General Administration of Natural Resources and Watershed Management, Engineering and Studies Unit, 2016. (In Persian)
    15.
  15. Yuan, F.K.E., Sawaya, B.C., Loeffelholz, M. E., 2005. Land cover classification and change analysis of the Twin (Minnesota) Metropolitan Area by multi temporal Landsat remote sensing. Remote sensing of Environment, 95: 317-328.
    16.
  16. Rasooli, A., & Mahmoodzadeh, H., 2010, Basics of Remote sensing, Basic knowledge, 1th edition Alimardan publication. (In Persian)
    17.
  17. Nazari Samani, A.A., Ghorbani, M., Koohbani, H.R., 2010, Assessing the trend of land use change of Taleghan watershed between 1987-2001, Journal of Rangeland Research, 4th year, No.3. pp.442-451. (In Persian)
    18.
  18. Di Gregoria, A., 2005.Landcover classification system (LCCS), classification concepts and user manual, software version2. Rome: Food and Agriculture Organization (FAO) of the United Nations.
    19.
  19. 20- Soltanian, S., Rahimi, E., Sabzghabayei, Gh., Rostami, K., Zeidi,A., 2014, Evaluating the trend of land use changes in Oshtrankooh protected area between 1989- 2005 using Landsat images, journal of new technologies in environmental engineering & renewable sorces, No.1. (In Persian)
    20.
  20. Yousefi, S., Tazeh, M., Mirzaei, S., Moradi, H.R., Tavangar, Sh., 2011, Comparison of different algorithms for classifying satellite images in land use mapping (Case study: Noor city), Journal of RS & GIS application in natural resources, No., 2, pp15-25. (In Persian)
    21.
  21. Sanjari, S., Boroumand, N., 2013, Monitoring land use / land cover changes over the past three decades using RS (case study: Zarand city in Kerman province), Journal of RS & GIS application in natural resources, 4th year, No.1., pp. 56-67. (In Persian)
    22.
  22. Elcavy, O., Rod, J., Ismail, H., Suliman, A., 2011. Land use and land cover changes detections in the western Nile delta of Egypt using remote sensing data. Applied geography, 31(2011), pp. 483-494.
    23.
  23. Mathers, P., 2005. Computer Processing of Remotely- Sensed Images. John Wiley & Sons, 345 pp.
    24.
  24. Roosta, Z., Darvish, S.Y., Falahati, F., 2012, Application of remote sensing and geographic information system in extracting land use map of Shiraz suburbs, Landuse planning, 4th year, No.6., pp149-163. (In Persian)
    25.
  25. kelarestaghi, A., Ahmadi, H., Jafari, M., Ghodoosi, J.,2008, Predicting Possible Changes in Forest Use to Deforestation Using Probabilistic Modeling in Frame Sahra Watershed-Mazandaran Province, Journal of Research and Construction, 21th year, No.3., pp. 52-63. (In Persian)
    26.
  26. Rajesh, B., Yuji, M., 2006. Land use change analysis using remote sensing and GIS: A Case Study of Kathmandu Metropolitan, Nepal. Research Abstracts on Spatial Information Science CSIS DAYS: 1.
    27.
  27. Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing, 25(12), Pp 2365-2407.
    28.

 

_||_

  1. Chen, X., Vierling, L., Deering, D., 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98(1), Pp 63-79.
    2.
  2. Gharagozlou, A., Nouri Kermani, A., Keshmiri, Z., 2009, Assessment of physical changes and analysis of urban development using remote sensing with high resolution & GIS/RS(case study: District 5 of Tehran), Journal of Environmental Science and technology, Vol. 11, No.1. (In Persian)
    3.
  3. Xian, G., Homer, C., Fry, J., 2009.  Updating the 2001 National Land Cover Databaseland cover classification to 2006 by using Landsat imagery change detectionmethods. Remote Sensing of Environment, 113, Pp 1133–1147.
    4.
  4. Mohammady, M., Moradi, HR., Zeinivand, H., 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environment Science and technology, 12: Pp 1515–1526.
    5.
  5. Jensen, J.Rand Cowen, D.C., 1999.  Remote sensing of urban suburban infrastructure and socio-economic attributes. Photogram Engineering, Remote Sensing, 65, Pp 611–622.
    6.
  6. Hathout, S., 2002.  The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada.  Journal of Environmental Management, 66, Pp 229–238.
    7.
  7. Fatemi talab, S.R., Maadani pour, M., Hashemi, S.A., 2014, Estimation of changes in the cover plant of Rudsar forests using classification of artificial neural network & Maximum probability, Journal of Rs & GIS in natural resources, the sixth year, No.2. (In Persian)
    8.
  8. Moradi, A., Jafari, M., Arzani, H., Ebrahimi, M., 2016, Assessment of land use changes into dry land using satellite images and Geographical information system (GIS), Journal of Rs & GIS in natural resources, the seventh year, No.1. (In Persian)
    9.
  9. Waseem, M., Paul, H., Jeffrey, G., Boshra, H., Salem, B., 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63 (2015), Pp 101-112.
    10.
  10. Maimaitijiang, m., Ghulam, a., Onésimo Sandoval, j.s., 2015. Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geo information, 35 (2015), Pp 161–174.
    11.
  11. Xiao, T.Y., Huiping, L.X., 2015. Land cover changed object detection in remote sensing data with medium spatial resolution. International Journal of Applied Earth Observation and Geo information, 38, (2015), Pp 129–137.
    12.
  12. Jafarnia, Sh., Oladi, J., Hoojati, S.M., Mirakhorloo, Kh., 2016, Status and change detection of Mangrove forest in Qeshm Island using satellite imagery from 1988 to 2008, J. Env. Sci. Tech., Vol 18, No.1. (In Persian)
    13.
  13. Rahdari, V., Soffianian, A., Maleki Najfabdai, S., Khajeddin, S.J., Rahdari, M., 2017, Land use/cover mapping usig satellite data and geographic information system (GIS) (Case study: Mouteh wild life sanctuary), J. Env. Sci. Tech., Vol 18, No.1. (In Persian)
    14.
  14. Detailed plan studies, Khojin watershed in Khalkhal, General Administration of Natural Resources and Watershed Management, Engineering and Studies Unit, 2016. (In Persian)
    15.
  15. Yuan, F.K.E., Sawaya, B.C., Loeffelholz, M. E., 2005. Land cover classification and change analysis of the Twin (Minnesota) Metropolitan Area by multi temporal Landsat remote sensing. Remote sensing of Environment, 95: 317-328.
    16.
  16. Rasooli, A., & Mahmoodzadeh, H., 2010, Basics of Remote sensing, Basic knowledge, 1th edition Alimardan publication. (In Persian)
    17.
  17. Nazari Samani, A.A., Ghorbani, M., Koohbani, H.R., 2010, Assessing the trend of land use change of Taleghan watershed between 1987-2001, Journal of Rangeland Research, 4th year, No.3. pp.442-451. (In Persian)
    18.
  18. Di Gregoria, A., 2005.Landcover classification system (LCCS), classification concepts and user manual, software version2. Rome: Food and Agriculture Organization (FAO) of the United Nations.
    19.
  19. 20- Soltanian, S., Rahimi, E., Sabzghabayei, Gh., Rostami, K., Zeidi,A., 2014, Evaluating the trend of land use changes in Oshtrankooh protected area between 1989- 2005 using Landsat images, journal of new technologies in environmental engineering & renewable sorces, No.1. (In Persian)
    20.
  20. Yousefi, S., Tazeh, M., Mirzaei, S., Moradi, H.R., Tavangar, Sh., 2011, Comparison of different algorithms for classifying satellite images in land use mapping (Case study: Noor city), Journal of RS & GIS application in natural resources, No., 2, pp15-25. (In Persian)
    21.
  21. Sanjari, S., Boroumand, N., 2013, Monitoring land use / land cover changes over the past three decades using RS (case study: Zarand city in Kerman province), Journal of RS & GIS application in natural resources, 4th year, No.1., pp. 56-67. (In Persian)
    22.
  22. Elcavy, O., Rod, J., Ismail, H., Suliman, A., 2011. Land use and land cover changes detections in the western Nile delta of Egypt using remote sensing data. Applied geography, 31(2011), pp. 483-494.
    23.
  23. Mathers, P., 2005. Computer Processing of Remotely- Sensed Images. John Wiley & Sons, 345 pp.
    24.
  24. Roosta, Z., Darvish, S.Y., Falahati, F., 2012, Application of remote sensing and geographic information system in extracting land use map of Shiraz suburbs, Landuse planning, 4th year, No.6., pp149-163. (In Persian)
    25.
  25. kelarestaghi, A., Ahmadi, H., Jafari, M., Ghodoosi, J.,2008, Predicting Possible Changes in Forest Use to Deforestation Using Probabilistic Modeling in Frame Sahra Watershed-Mazandaran Province, Journal of Research and Construction, 21th year, No.3., pp. 52-63. (In Persian)
    26.
  26. Rajesh, B., Yuji, M., 2006. Land use change analysis using remote sensing and GIS: A Case Study of Kathmandu Metropolitan, Nepal. Research Abstracts on Spatial Information Science CSIS DAYS: 1.
    27.
  27. Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing, 25(12), Pp 2365-2407.
    28.

 

مقالات مرتبط

حقوق این وب‌سایت متعلق به سامانه مدیریت نشریات دانشگاه آزاد اسلامی است.
حق نشر © 1403-1400

صفحه اصلی| عضویت/ ورود| درباره سند| تماس با ما|
[English] [العربية] [en] [ar]