Analysis of Vegetation Indices Change in Sensors of Landsat Satellite (Case Study: Persian juniper fields of East Golestan National Park and Ghorkhod Protected Area)
Subject Areas : ENVIROMENT
Samereh Falahatkar
1
(دانشگاه تربیت مدرس)
Rahimeh Saberfar
2
(Department of environmental Science, Natural Resources Faculty, Tarbiat Modares University)
Seyed Hossein Kia
3
(Scientific Staff of Mahab-Ghodes Company)
Keywords: Change detection, NDVI, juniper, EVI and SAVI,
Abstract :
Today acquisition of knowledge regarding to the health of vegetation has an important role in the management of protected areas and conservation of plant and animal species. Remote sensing is a useful technology for investigation of vegetation since past to present that have priority over other method. Using vegetation indices in determination of the spatial distribution of vegetation is one of the ways for vegetation change detection. In this study, due to suitable atmospheric conditions and percentage of cloud cover less than 10%, the images of June 1987 and 2016 and May 2003, TM, ETM + and OLI sensors of Landsat and three vegetation indices, including NDVI, SAVI and EVI were used for the identification and classification vegetation cover. The cross-classification method was used to compare vegetation indices with land cover map which produced by hybrid classification and selecting the best vegetation index in classification. Kappa coefficient obtained from comparison of vegetation map using NDVI index and land cover map for 1987, 2003, and 2016 were 0.87, 0.82 and 0.87, respectively, which were higher than others. Also, the results of 30-year-old reveal a decrease of 9153.27 hectares of Juniperus in the studied area that 9092.43 ha were converted to rangelands and 60.84 ha to barren lands.
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References:
1. Adamchuk, V.I., Perk, R.L, & Schepers, J. S, 2003. Applications of remote sensing in site-specific management, University of Nebraska Cooperative Extension Publication EC, (2003): 03-702.
2. Ahmad, F., 2012. Detection of change in vegetation cover using multi-spectral and multi-temporal information for district Sargodha, Pakistan, Sociedade & Natureza, 24 (3): 557-571.
3. Ahmadpour, A., Soleimani, K, Shokri, M, & ghorbani, J, 2014. Comparison of the Efficiency of Three Common Methods of Satelliteized Satellite Monitoring in the Vegetation Study, Remote Sensing and Geographic Information System in Natural Resources, 5 (34): 77-89.
4. Akhani, h., 2004. Illustrated Flora Golestan National Park, Tehran Publishing and Printing Institute, 201 p.
5. Alavi Panah, S., 2013. Application of Remote Sensing in Earth Sciences (Soil Science), Fourth Edition, Tehran University Press, 500 p.
6. Ali Ahmad Khoruri, S., & Khoshnevis, M, 2000. Ecological and Ecological Studies of Iranian Ors of Iran, Research Institute for Forests and Rangelands, 208 p.
7. Arkhi, s., Graiy, p, & Erkhi, M, 2008. Evaluation of Land Use Change Process in Kabir Kouh Protected Area Using RS and GIS (Case Study: Ilam Province), Geomatics Conference 87, Iran Mapping Organization, Tehran. 10
8. Azizi Qalati, S., Rangraz, k, Taghizadeh, A, & Ahmadi, Sh, 2014. Modeling Land Use Change Using Logistic Regression Method in LCM Model (Case Study: Kohmareh Sorkhi Area of Fars province, Journal of Pistaciatics and Forestry Research, 22 (4): 596-585.
9. Bodily, J, 2004. portocol development at golden spike national historic site for soil survey updates, Applied remote senseing.
10. Chan, Z.O., & Elvidage, C.D, 1999. Vegatatio detection using high spectral resolution vegetation index, Remote sensing change detection Enviromental monitoring method and application, Sleeping bear press, INC.20.
11. Darwish, T., & Faour, G, 2008. Rangeland degradation in two watersheds of Lebnon. Lebanese Journal of Sci. 9: 71-80.
12. Esfandiari, A., Fattahi Moghadam, M, & Rangraz, k, 2016. Atmospheric Correction of Spectrometer Images and Empirical Line Calibration, National Conference on Geoinformatics, May-April 1395.
13. Fatemi Talab, S.R., Maadani Pour Kermanshahi, M, & Hashemi, S.A, 2015. Estimation of Roudsar Forest Coverage by Using Artificial Neural Network Classification and Maximum Likelihood, Remote Sensing and Geographic Information System in Natural Resources, 6 (2): 33-44.
14. Farajollahi, A., Asgari, H, Onagh, M, & Salman Mahini, A.R, 2015. Remote Sensing and Geographic Information System in Natural Resources, 6 (4): 1-14.
15. Huete, A., 1998. A soil- Adjusted Vegetation Index (SAVI), Remote Sensing of Enviroment, 25: 295-309.
16. Huete, A., 2002. Remote Sensing for Natural Resources Management and Enviromental Monitoring: Manual of remote sensing 3 ed, Vol 4, Univercity of Arizona.
17. Keshtkar, H., 2007. Investigating IRS-1D Satellite Capability for Land Cover Mapping (Study Area: Ghorkhod Protected Area in North Khorasan Province), Master's Degree, Faculty of Natural Resources, University of Tehran, 141 p.
18. Keshtkar, H., Yeganeh, H, & Jabar Zare, A, 2011. Fluorescence and biological forms of plants in the protected area of Ghorkhod, Iranian Journal of Biology, 24 (3): 421-431.
19. Luo, J., Li, X, Ma, R, Li, F, Duan, H, Hu, W, & Huang, W, 2016. Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China, Ecological Indicators, 60: 503-513.
20. Matsushita, B., Wei, Y, Jin, C, Yuyichi, O, & Guoyn, Q, 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density Cypress forest, Sensors, www.mdpi.org/sensors.
21. Ming Lee, T., & Chung Yeh, H, 2009. Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological Engineering, 35:487-496.
22. Omo-Irabor, O.O., & Oduyemi, K, 2007. A hybrid image classification approach for the systematic analysis of land cover (LC) changes in the Niger Delta region. In Proceedings of the I5th International Symposium on Spatial Data Quality, Enschede, The Netherlands.
23. Pettorelli, N., Vik, J.O, Mysterud, A, Gaillard, J.M, Tucker, C.J, & Stenseth, N.C, 2005. Using the satellite –derived NDVI to assess ecological responses to environmental change.J, Trends in ecology and evolution. Vol.٢٠ No.٩.
24. Rawat, J.S., Biswas, V, & Kumar, M, 2013. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India, The Egyptian Journal of Remote Sensing and Space Science, 16 (2013): 111-117.
25. Rouse, J.W., Haas, R.H, Schell, J.A, & Deerin, D.W, 1973. Monitoring vegetation systems in the great plains with ERTS. N. SP-351. Ed Third ERTS Symposium. 1,309-317. Whastington. NASA.
26. Sabin, F.F., 1996. Remote sensing: Principal and interpretation 3rd Ed, W.H.Freman and company,New York, 494pp.
27. Salman Mahini, A., Nadali, A, Feghhi, J, & Riazi, B, 2012. Classification of Forest Areas in Golestan Province Using Maximum Likelihood Method Using ETM + Satellite Images 2001, Environmental Sciences and Technology, 14 (3): 47-56.
28. Sannie Nejad, S.C., Astarai, A, Mirhosseini, P, Keshavarzi, A, & Ghaemi, M, 2008. Using Satellite Images for Vegetation Studies (Comparison of Different Vegetation Indices - Case Study of Neishabour Area), National Congress of Agricultural Machinery and Mechanization, Ferdowsi University of Mashhad.
29. Tasviri, M.R., 1998. Representation of user and vegetation changes in Kashan's desert region using remote sensing data analysis, Faculty of Humanities, Tarbiat Modares University.
30. Tommervik, H., Hogda, K.A, & Solheim, I, 2003. Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data, Remote Sensing of Environment, 85(3): 370-388.
31. Vaogen, T.G., 2006. Remote sensing of complex land use change trajectoriesa: case study from the hilghlands of Madagascar, Agriculture, Ecosystems and Enviroment, 115: 219-228.
32. Van Beek, E., & Meijer, K, 2006. Integrated water resources management for the sistan closed inland delta. Water research institute, Iran.
