Investigation and Prediction of Vegetation Cover Variations in the Geomorphic Units of the Northern Desert Zone of Isfahan Province

Ganjali, Jafar; Halabian, Amirhossein; Karam, Amir; Hajehforoshnia, Shila

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Volume 12, Issue 25 (3-2025)

PEC 2025, 12(25): 18-35

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Investigation and Prediction of Vegetation Cover Variations in the Geomorphic Units of the Northern Desert Zone of Isfahan Province

Jafar Ganjali ^*

, Amirhossein Halabian²

, Amir Karam³

, Shila Hajehforoshnia⁴

Assistant Professor, Department of Geography, Payame Noor University, Tehran, Iran, Department of Geography, Payame Noor University, Tehran, Iran , j_ganjali@pnu.ac.ir
2- Department of Geography, Payame Noor University, Tehran, Iran
3- Physical Geography Department, Kharazmi University, Tehran
4- Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization, Tehran, Iran

Abstract: (1994 Views)

The analysis of vegetation cover variations serves as a key index for identifying the heterogeneity of landforms and biodiversity and determining important areas for protecting this fundamental environmental component. This research examines changes in vegetation cover within the landforms of the northern desert zone of Isfahan Province using Landsat 5 and 8 digital data (TM and OLI-TIRS sensors) from 1987 to 2020. During this period, 200 images were acquired over 27 warm months (June 1st to August 31st annually). First, necessary preprocessing was applied to the study area images. After landform classification using the Topographic Position Index (TPI), vegetation cover changes in these landforms were examined. The findings revealed that the highest Enhanced Vegetation Index (EVI) values were associated with prominent landforms, mountains, and highlands, while the lowest values were observed in flat terrains (playas, mud, and clay pans). Changes in EVI at varying elevations indicated a noticeable decrease in EVI values at an altitude of 1400 m (middle pediments). In contrast, EVI values in mountainous regions (elevations exceeding 1400 m) displayed a significant increase. The EVI index on slopes steeper than 65° exhibited a marked decline due to the presence of rocky and steep terrain. Spatial patterns of the EVI index for the year 2030 suggest that the Karkas Mountains, particularly from Mount Karkas to Mount Marshan and the areas between Kashan and Ardistan, will encounter severe environmental conditions.

Article number: 2

Keywords: Enhanced Vegetation Index (EVI), Topographic position index (TPI), Physiographic factors, Landform, Landsat images

Full-Text [PDF 2059 kb] (792 Downloads)

Type of Study: Applicable | Subject: Special
Received: 2023/11/28 | Accepted: 2024/11/11 | Published: 2025/04/22

References

1. Ardö, J., Tagesson, T., Jamali, S., Khatir, A. 2017. MODIS EVI-based net primary production in the Sahel 2000–2014, International Journal of Applied Earth Observation and Geoinformation, 65:35–45.

2. Chen, R., Yin, G., Liu, G., Li, J., Verger, A. 2020. Evaluation and normalization of topographic effects on vegetation indices, Remote Sensing, 12, 2290: 1-9.

3. Darwish, T., Faour, G. 2008. Rangeland degradation in two watersheds of Lebanon. Lebanese Science Journal. 9: 71-80.

4. Depew, J.J. 2004. Habitat selection and movement patterns of cattle and white-tailed deer in a temperate savanna, M.Sc. thesis, Texas A & M University, 71 pages.

5. Fang, X., Zhu, Q., Ren, L., Chen, H., Wang, K., Peng, C. 2018. Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada. Remote Sensing of Environment, 206: 391-402.

6. Guisan, A., Weiss, S.B., Weiss, A.D.1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143: 107–122.

7. Heydari Alamdarloo, E., Abolhasani, A., Behrang Manesh, M., Khosravi, H. 2024. Application of remote sensing techniques for evaluating land surface vegetation, Remote Sensing of Soil and Land Surface Processes, 2024: 199-216. doi.org/10.1016/B978-0-443-15341-9.00006-X.

8. Horn, B. K. 1981. Hill shading and the reflectance map. Proceedings of the IEEE, 69(1): 14-47.

9. Jenness, J. 2006. Topographic Position Index (tpi_jen.avx) Extension for ArcView 3.x, v. 1.3a. Jenness Enterprises.

10. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. 2002. Remote sensing for natural resources management and environmental monitoring: manual of remote sensing 3 ed. University of Arizona.

11. Lyon, J.G., Yuan, D., Lunetta, R.S., Elvidge, C.D. 1998. A change detection experiment using vegetation indices. Photogrammetric Engineering & Remote Sensing, 64: 143-150.

12. Magee T.K., Ringold, P.L., Bollman M.A. 2008. Alien species importance in native vegetation along wadeable streams, John Day River Basin, Oregon, USA. Plant Ecology, 195(2): 287-307.

13. 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. Trends in Ecology and Evolution. 20(9): 503-510.

14. Puri. P. 2023. Enhanced vegetation index and land use analysis for seven sister states of India (2000–2022). Sustainable Development Goals in Northeast India, 2023: 167-183.

15. 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 (1): 111-117.

16. Reu, J.D., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., Smedt, P.D., Chu, W., Antrop, M., Maeyer, P.D., Finke, P., Meirvenne, M.V., Verniers, J., Crombé, P. 2013. Application of the topographic position index to heterogeneous landscapes. Geomorphology, 186: 39–49.

17. Takaku, J., Tadono, T., Tsutsui, K. 2014. Generation of high-resolution global DSM from ALOS PRISM, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-4, 243-248, ISPRS.

18. Vanderpost, C., Ringrose, S., Matheson, W., Arntzen, J. 2011. Satellite based long-term assessment of rangeland condition in semi-arid areas: An example from botswana. Journal of Arid Environments. 75(4): 383-389.

19. Vaogen, T.G. 2006. Remote sensing of complex land use change trajectoriesa: case study from the hilghlands of madagascar, agriculture. Ecosystems and Environment, 115(1-4): 219-228.

20. Verbesselt. J., Hyndman. R., Newnham. R., Culvenor. D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106–115.

21. Weiss, A. 2001. Topographic position and landforms analysis. ESRI user Conference, San Diego, C.A.

22. Wilson, J.P., Gallant, J.C. 2000. Terrain analysis: principles and applications. New York, John Wiley.

23. Yorks, T. P., West, N. E., Capels, K. M. 1992. Vegetation differences in desert shrublands of western utah's pine valley between 1933 and 1989. Journal of Range Management, 45: 569-578.

24. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A. 2003. Monitoring vegetation phenology using modis. Remote Sensing of Environment. 84(3): 471-475.

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Ganjali J, Halabian A, Karam A, Hajehforoshnia S. Investigation and Prediction of Vegetation Cover Variations in the Geomorphic Units of the Northern Desert Zone of Isfahan Province. PEC 2025; 12 (25) : 2
URL: http://pec.gonbad.ac.ir/article-1-951-en.html

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