Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province)

Behrang Manesh, Maliheh; Khosravi, Hassan; Azarnivand, Hossein; Senatore, Alfonso

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Volume 7, Issue 15 (2-2020)

PEC 2020, 7(15): 295-318

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Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province)

Maliheh Behrang Manesh¹

, Hassan Khosravi ^*

, Hossein Azarnivand¹

, Alfonso Senatore³

1- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran
University of Tehran, Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran , hakhosravi@ut.ac.ir
3- Department of Environmental and Chemical Engineering, University della Calabria, Rende, Italy

Abstract: (4003 Views)

Investigating the sustainability of vegetation changes is one of the most important issues of vegetation management in the direction of sustainable development. It is necessary to conduct studies in this regard to select appropriate management for greater compatibility with new conditions. In this research, changes in the vegetation dynamics trend of Fars province were studied using the NDVI index obtained from MOD13Q1 during the period 2001 to 2017. Mann-Kendall and Thiel-Sen tests in the Earth Trends Modeler of TerrSet software and the Hurst index in the ArcGIS software were calculated, then the results were combined with each other. Theil-Sen trend for statistical analysis of the changes slope, and Mann-Kendall test to determine the significance of these changes are a new approach to investigate the NDVI time series data analysis process. Overlaying the results of the trend analysis and Hurst index, the future sustainability in the NDVI changes was also calculated. According to the analysis of the Horst index, the trend of vegetation changes in the future is contrary to the changes that have taken place in the past.. In addition, the results of total trend analysis indicate that plant growth in mountainous areas continuously improves at a higher rate than the plains. Finally, the results of analyzing the Horst index and the process of vegetation changes showed that the highest number of pixels is for the month of May with 41.1% located in Class B. Class B represents that the rehabilitation trend has replaced the degradation trend in the region and this trend will not continue in the future. This month, the northern and southern strips of the province have a maximum value.

Keywords: Horst Index, Mann-Kendall, NDVI, Thiel-Sen

Full-Text [PDF 1828 kb] (1627 Downloads)

Type of Study: Research | Subject: Special
Received: 2019/03/19 | Accepted: 2019/06/10 | Published: 2020/03/17

References

1. Azizzadeh, M.R., Javan, K. 2018. Trends of Extreme Temperature over the Lake Urmia Basin, Iran, During 1987-2014. Journal of the Earth and Space Physics, 43 (4): 55-72.

2. Behrang Manesh, M., Heidary Alamdarlo, E., Ahmadi Jazi, N. 2018. Assessing the Reclamation and Destruction of Vegetation Using Remote Sensing (Case Study: Tehran Province). International Conference on Society and Environment, Tehran, University of Tehran.

3. Bonan, G. B. 2002. Ecological climatology: concepts and applications. Cambridge University Press.

4. Brink, A., Eva, H. 2009. Monitoring 25 years of land cover changes dynamics in Africa: a sample based remote sensing approach. Applied geography, 29: 501-512.

5. CAI, B.f, Rong, YU. 2009. Advance and evaluation in the long time series vegetation trends research based on remote sensing. . Journal of Remote Sensing, 13 (6): 1170-1186.

6. Cao, R., Jiang, W., Yuan, L., Wang, W., Lv, Z., Chen, Z. 2014. Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. Journal of Geographical Sciences, 24 (6): 963-979.

7. Charney, J.G., Quirk, W.J., Chow, S., Kornfield, J. 1977. A comparative study of the effects of albedo change on drought in the semiarid regions. Journal of Atmospheric Sciences, 34: 1366- 1385.

8. Colditz, R.R. Ressl, R.A. Bonilla-Moheno, M. 2015. Trends in 15-year MODIS NDVI time series for Mexico. In Analysis of Multitemporal Remote Sensing Images (Multi-Temp). Eighth International Workshop on the IEEE. 1-4.

9. Eltahir, E.A.B., Bras, R.L. 1996. Precipitation recycling, Reviews of Geophysics. 34 (3): 367–378.

10. Foley, J.A. et al. 2005. Global Consequences of Land Use. Science, 309 (5734): 570–574.

11. Fraedrich, K., Kleidon, A., Lunkeit, F. 1999. A Green Planet versus a Desert World: Estimating the Effect of Vegetation Extremes on the Atmosphere, Climatic Change, 12 (10): 3156–3163.

12. Heydari Alamdarloo, E., Behrang Manesh, M., Khosravi, H. 2018.Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environmental Monitoring and Assessment, 190:702.

13. Hou, X.,Ting, W., Liangju, Y.,Song, Qian. 2012. Characteristics of multi-temporal scale variation of vegetation coverage in the Circum Bohai Bay Region, 1999–2009. Acta Ecologica Sinica, 32 (6): 297-304.

14. Hurst, H.E. 1951. Long-term storage capacity of reservoirs. Transactions of American Society of Civil Engineers, 116: 770.

15. Jiang, D.J., Fu, X.F., Wang, K. 2013. Vegetation dynamics and their response to freshwater inflow and climate variables in the Yellow River Delta, China. Quaternary International, 304: 75–84.

16. Jiang, L. Jiapaer, G. Bao, A. Guo, H. Ndayisaba, F. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment. 599-600: 967-980.

17. Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y., Shen, W. 2015. Spatio-temporal analysis of vegetation variation in the Yellow River basin. Ecological Indicators, 51: 117–126.

18. Kendall, M. 1975. Rank Correlation Methods. London: Charles Griffin.

19. Khajoei Nasab, F., Khosravi, A. R. 2014. Ethnobotanical study of medicinal plants of Sirjan in Kerman Province, Iran. Journal of ethno pharmacology, 154 (1): 190-197.‏

20. Kundu, A., Dwivedi, S., Dutta, D. 2016. Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arabian Journal of Geosciences, 9 (2): 144.

21. Li, S., Zhao,Z., GAO, Y., Wang, Y. 2008. Determining the predictability and the spatial pattern of urban vegetation using recurrence quantification analysis: A case study of Shenzhen City. Geographical Research, 27: 1243-1251.

22. Mandelbrot, B., Wallis, J.R. 1969. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 5: 967–988.

23. Mann, H B. 1945. Non–parametric tests again trend. Econometrica. 13: 245–259.

24. Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. 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 (Basel, Switzerland), 7(11): 2636-2651.

25. Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., Xia, G. 2018. Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology, 248: 215-227.‏

26. Peng, J., Liu, Zh.,Liu, Y., W, J., Han, H. 2012. Trend analysis of vegetation dynamics in Qinghai–Tibet Plateau using Hurst Exponent. Ecological Indicators, 14 (1): 28-39.

27. Pettorelli, N., Vik, 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. Trends in ecology and evolution, 9 (20): 503-510.

28. Potter, C., Boriah, S., Steinbach, M., Kumar, V., Klooster, S. 2008. Terrestrial vegetation dynamics and global climate controls. Climate Dynamics, 31 (1): 67–78.

29. Rasmus, F.,Rasmussen, K., Theis, Th-N.,Mbow, Cheikh. 2009. Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113 (9): 1886-1898.

30. 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 Sciences, 16: 111-117.

31. Rosenfeld, D., Rudich, Y., Lahav. R. 2001. Desert dust suppressing precipitation: A possible desertification feedback loop. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 98 (11): 5975–5980.

32. Rouse. Jr, J. Haas, R. H. Schell, J. A. & Deering, D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Technical Reports server, 1: 309-317.

33. Schucknecht, A., Erasmi, S., Niemeyer, I., Matschullat1, J. 2013. Assessing vegetation variability and trends in northeastern Brazil using AVHRR and MODIS NDVI time series, European Journal of Remote Sensing, 46: 40-59.

34. Teferi, E., Uhlenbrook, S., Bewket, W. 2015. Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis. Earth System Dynamics, 6 (2):617–636.

35. Thiel, H. 1950. A rank-invariant method of linear and polynomial regression analysis, Part 3. In Proceedings of Koninalijke Nederlandse Akademie, van Weinenschatpen A. 53: 1397-1412

36. Villegas, J.C., Breshears,D.D., Zou, C.B., Royer, P.D. 2010. Seasonally pulsed heterogeneity in microclimate: phenology and cover effects along deciduous grassland–forest continuum. Vadose Zone Journal Abstract, 9 (3):537–547.

37. Zhang, Y., Zhao, Z., Li, S., Meng, X. 2008. Indicating variation of surface vegetation cover using SPOT NDVI in the northern part of North China. Geographical Research, 27 (4): 745-754.

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Behrang Manesh M, Khosravi H, Azarnivand H, Senatore A. Quantifying the trend of vegetation changes using remote sensing (Case study: Fars Province). PEC 2020; 7 (15) :295-318
URL: http://pec.gonbad.ac.ir/article-1-565-en.html

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