Monitoring of Thirty Years of Land Cover Changes using Remote Sensing and GIS (Case study: Qaresu watershed, Ardabil)

Subject Areas : GIS

Fereshteh Namdar ¹ , Shahla Mahmoudi ² , Abazar Esmali Ouri ³ , Ebrahim Pazira ⁴

1 - Ph.D. student in Soil Science, Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Professor of Soil Science, Faculty of Agriculture, University of Tehran, Tehran, Iran. * (Corresponding Author)
3 - Associate Professor in Watershed Science & Engieering, Department of Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
4 - Professor of Soil Science, Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2019-06-30 Accepted : 2019-12-18 Published : 2021-02-19

Keywords: satellite images, Qaresu Watershed, land cover changes, Remote sensing,

Abstract :

Background and Objective: Studying the extent of change and destruction in resources in previous can contribute to efficient planning and utilization of these resources and limiting similar adverse changes in the future. Since changes in resources occur over large expanses of land, remote sensing technology can serve as a essential and valuable tool for monitoring these changes. The purpose of this study was to monitor the land cover changes in Qaresu watershed in Ardabil province using the remote sensing technique. Methods: In this study land cover changes was consulted in 30 years from 1985 to 2015. Landsat images of the study area pertaining to July 1985, 2000 and 2015 were acquired. After geometric and elevation corrections, the images were classified by the supervised approach using the maximum likelihood method. The NDVI index, DEM, and slope layers were used to enhance the image classification accuracy. Classification accuracy was assessed with Kappa index and overall accuracy indexes. Findings: The results showed 20.43% change in the watershed’s land uses over the period from 1985 to 2000, 41.426% over the period from 2000 to 2015, and 27.99% over the period from 1985 to 2015. In all three periods, the greatest changes were in dry farming. Discussion 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 separation accuracy of land using.  

References:

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. Garagozlo, Ali Reza, Nori kermani, Ali, Keshmiri, Zahra, 2009. Evaluation of changes and analysis of urban development using satellite datas with high separation and GIS/ RS (case study: 5 region Tehran municipality). Environment Science and Technology, Year 11, No. 1. (In Persian)

1. 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.


2. Mohammady, Majid, Moradi, Hamid Reza, Zeinivand, Hosein, 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.


3. 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: pp. 317-328.


4. Sangari, Saleh, Bromand, Naser, 2013. Investigating land use/ cover changes in past 30 years using RS (Case study: Zarand region in Kerman Provence). Journal of Application of RS and GIS in Natural Resources, Year 4, No .1, pp. 56-67. (In Persian)


5. Mathers, P., 2005. Computer Processing of Remotely- Sensed Images. John Wiley & Sons, 345 pp.


6. 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.


7. Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing, 25(12), pp. 2365-2407.


8. Kalarestagi, Ahmad, Ahmadi, Hasan, Jafari, Mohammad, Ghodosi, Jamal, 2008. Prediction of probable changes jungle to Dry Farming using Maximum Likelihood method in Ferim  Sahra watershed in Mazandaran provence. Journal of Search and  Development, year 21, No. 3, pp. 52-63. (In Persian)


9. 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.

5. Jensen, J.R., Cowen, D.C., 1999.  Remote sensing of urban suburban infrastructure and socio-economic attributes. Photogram Engineering, Remote Sensing, 65, pp. 611–622.


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. Fatemitalab, Seid Reza, Madanipour kermanshahi, Morteza, Hashemi Seid Armin, 2015. Estimate of cover changes in Rodsar Jungle using neural network techniques and maximum likelihood method. Remote Sensing and GIS in Natural Resources, Year 6, No .2. (In Persian)


8. Moradi, Ali Reza, Arzani, Mohammad, Ebrahimi, Hosein, 2016. Assessment change of Rangeland to Dry Farming using satellite images and GIS. Remote Sensing and GIS in Natural Resources, Year 7, No .1. (In Persian)


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. 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. 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. Jafarnia, Shahram, Oladi, Jafar, Hojati, Seid Mohammad, Mirakhorlo, Khosro, 2016. Assessment of situation and detection of changes in Mangro Jungle in Gheshm Island using satellite images in 1988- 2008. Environment Science and Technology, Year 18, No. 1. (In Persian)


13. Rahdari, Vahid, Safianian, Ali Reza, Maleki, Saeedeh, Khagealdin, Seid Jamaladin, Rahdari, Meisam, 2016. Preparation land use and land cover maps using satellite datas and GIS (Case study: Moteh wildlife shelter). Environment Science and Technology, Year 18, No. 1. (In Persian)


14. Bureau of Natural Resources and Watershed Management of Ardabil Provence. 2018. Reports on the master study of Qaresu watershed in Ardabil. (In Persian)