Persian
Language
Geographical Research is Published in Persian Fulltext.
Volume 32, Issue 4 (2018)
GeoRes 2018, 32(4): 138-147 |
Back to browse issues page
Article Type:
Subject:
History
Received: 2017/10/11 | Accepted: 2017/11/7 | Published: 2018/03/17
Received: 2017/10/11 | Accepted: 2017/11/7 | Published: 2018/03/17
How to cite this article
Montazeri M, Kefayatmotlagh O R. Detection of Iran’s Vegetation Seasons Using NDVI. GeoRes 2018; 32 (4) :138-147
URL: http://georesearch.ir/article-1-371-en.html
URL: http://georesearch.ir/article-1-371-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rights and permissions
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Authors
Majid Montazeri *1, Omid Reza Kefayatmotlagh2
1- Department of Climatology, University of Isfahan, Isfahan, Iran , montazeri@geo.ui.ac.ir
2- Department of Climatology, University of Isfahan, Isfahan, Iran
2- Department of Climatology, University of Isfahan, Isfahan, Iran
Abstract (4177 Views)
Introduction and background: Awareness of vegetation and its health is representative of climate conditions for each place. To study and monitor the vegetation on a global and regional scale access to timely field data are usually difficult and limited. Estimation of vegetation on an ordinary method that includes an overall of vegetation is time-consuming and does not provide precise information. Thus remote sensing is a very helpful way that is in priority as provide a large overview and is repetitive and time can be saved using this method. Application of vegetation indices that is widely used can be exploited to quantify the net annual products in planetary and other scales. The present study is motivated to find out a methodology to detect and classify vegetation and also to detect Iran’s vegetation seasons. Methods: In this effort as the first step MODIS_Aqua 16 days of Normalized Difference Vegetation Index (NDVI) from 1381.4.13 to 1393.12.23 were downloaded from MODIS web site. And then nearly 10 billion codes were analyzed in matlab to calculate the mean 16 days of NDVI and then a Matrix of 23*7541502 was constructed. In the last step, the cluster analysis was applied to the data using Ward method. Results: The investigations indicated that according to the spatial values of NDVI five separate seasons are observable in the country as follow densely (February 26th to April 15th), very dense (April 15th to June 2nd), sparse (June 2nd to October 24th), transient (October 24th to November 25th) and very sparse (November 25th to February 26th) seasons. Discussion and conclusion: In general, vegetation is massive in the spring, sparse in summer and winter, and the autumn is the transition season.
Keywords:
References
1. Alawi-Panah, K. (2006), Application of Remote Sensing in Earth Sciences, Tehran University Press, Second Edition, (in Persian).
2. Alijani, B. (2010), Iran's Climate, Tehran, 10th Edition, Payam Noor University pub (in Persian).
3. Baaghideh, M., Alijani, B., Zeaiean, P. (2011), Investigating the Possibility of Using the NDVI Vegetation Index for Drought Analysis in Isfahan Province, Geographical Study of Arid Areas, No. 4, pp. 16-1 (in Persian).
4. Chuai, X. W., Huang, X. J., Wang, W. J., Bao, G. (2013), NDVI, Temperature and Precipitation Changes and Their Relationships with Different Vegetation Types During 1998–2007 in Inner Mongolia, China, Int. J. Climatol, No. 13, PP. 528–535.
5. Domroes, M., Kaviani, M., Schaefer, D. (1998), An Analysis of Regional and Intra-Annual Precipitation Variability Over Iran Using Multivariate Statistical Methods, Theor, Appl Climatol, Vol. 61, No. 3-4, pp. 151-159.
6. Farajzadeh, M., Fathnia, A., Alijani, A., Zeaiean, P. (2011), Evaluation of the Effect of Climatic Factors on Coating Growth in Dense Pastures in Iran Using AVHRR Images, Natural Geography Survey, No. 75, pp.14-1, (in Persian).
7. Ghayour, H.A, and Montazeri, M. (2004), The Regionalization of Iranian Temperature Regimes with Basic Components and Cluster Analysis, Geography and Development, No. 4, pp. 34-21, (in Persian).
8. Halebian, A. H. (2008), Investigating the Effect of Azores High Temperature on Iran's Temperature and Precipitation, Ph. D Thesis, by Dr. S.A Masoudian, Natural History Geography, Climatology, University of Isfahan, (in Persian).
9. Heli, L., Quangin, Sh., Jiyuan, L., Junbang, W., Shenbin, Ch., Zhuogi, Ch. (2008), Cluster Analysis on Summer Precipitation Field over Qinghai, Tibet Plateau from 1961 to 2004, Geogr. No. 18, pp. 121-132.
10. http://glossary.ametsoc.org/wiki/Main_Page.
11. https://modis.gsfc.nasa.gov/data/dataprod/
12. https://www.unenvironment.org/resources/evaluation-synthesis-reports/unep-annual-evaluation-report-2005, pp. 1-60.
13. Huete, A. (2004), Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing3 ed., University of Arizona, Vol. 4.
14. Kaufman, L., Rousseuw, P.J. (1990), Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
15. Khosravi, M., Dostkamian, M., Mirmosavi, H., Bayat, A., Beyg Rezaei, E. (1393), Classification of Temperature and Precipitation in Iran by Land Surveying and Cluster Analysis, Regional Planning, No. 13, pp. 132-121, (in Persian).
16. Kumar, J., Jon W., William, W.H., Steven, P.N., Forrest, M.H., Doug, N. (2015), Characterization and Classification of Vegetation Canopy Structure and Distribution within the Great Smoky Mountains National Park Using LiDAR, IEEE 15th International Conference on Data Mining Workshops, No. 178, pp. 1478-1485.
17. Masoudian, S.A. (2003), Iranian Climate Zones, Geography and Development, No. 2, pp. 184-171, (in Persian).
18. Masoudian, S.A. (2011), Iran's climate, Mashhad, 1st Edition, Sharia Toos Pub. (in Persian).
19. Masoudian, S.A., Darand, M., Karsaz, S. (2011), The Zoning of West and Northwest Rain of Iran by Cluster Analysis, Natural Geography, No. 11, pp. 44-35 (in Persian).
20. Matsushita, B., Wei, Y., Jin, Ch., Yuyichi, O., Guoyu, 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 No. 7, pp. 2636-2651.
21. Mirmosavi, S.H., Karimi, H. (2013), Study of the Effect of Drought on Vegetation Using MODIS Sensor Images in Kurdistan Province, Geography and Development, No. 31, pp. 76-57 (in Persian).
22. Seibert, P., Frank, A., Formayer, H. (2007), Synoptic and Regional Patterns of Heavy Precipitation in Austria, Theoretical and Applied Climatology, No. 87, pp. 139-153.
23. Wang, J, Rich, P.M., Rich, K.P. (2003), Temporal Responses of NDVI to Precipitation and Temperature in the Central Great Plains, USA, International Journal of Remote Sensing, No. 11, pp. 2345-2364.
24. Wang, J., Price, K.P., Rich, P.M. (2001), Spatial Patterns of NDVI in Response to Precipitation and Temperature in the Central Great Plains, International Journal of Remote Sensing, No. 18, pp. 3827–3844.
25. Wang, Q., Samuel, A., John, T., Andre, G. (2005), On the Relationship of NDVI with Leaf Area Index in a Deciduous Forest Site, Remote Sensing of Environment, No. 94, pp. 244–255.
26. Wang, X., Xie, H. (2009), New Methods for Studying the Spatiotemporal Variation of Snow Cover Based on Combination Products of Modis Terra and Aqua, Jornal of Hydrology, Vol. 371, pp. 192-200.
27. Xu, W., Gu, S., Zhao, X.Q., Xiao, J., Tang, Y., Fang, J., Zhang, J., Jiang, Sh. (2011), High Positive Correlation Between Soil Temperature and NDVI from 1982 to 2006 in Alpine Meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau, International Journal of Applied Earth Observation and Geoinformation, No. 13, pp. 528–535.
28. Yurdanur, U., Tayfunkindapb, A., Mehmet, K.C. (2003), Redefining the Climate Zones of Turkey Using Cluster, International Journal of Climatology, No. 23, pp. 1045-1055.