Investigating the Trend of Desertification Changes in Different Land Uses of Gavkhoni Basin Using Change Vector Analysis Method

Authors

‎10.22052/deej.2021.10.33.31

Abstract

Introduction: Desertification refers to the decreased biological potentials in the ecosystem of hyper-arid, arid, semi-arid, and humid semi-arid regions because of climate change and human activities. The phenomenon occurs due to a combination of direct and indirect factors whose intensity varies according to time and place, making the scientific, replicable, and systematic evaluation of desertification an essential task. Remote sensing technology which is based on spatial information collected at regular intervals by aircraft and satellites plays a prominent role in assessing and monitoring land degradation and desertification on a local, regional and global scale. On the other hand, Change Detection is a process that evaluates spatial changes in various phenomena caused by natural and human factors, using multi-time satellite images. As an effective method for detecting and describing land cover changes, the change vector analysis method provides information on spectral changes in terms of magnitude and direction. Therefore, considering the significance of determining the intensity of desertification in different parts of Iran and evaluating methods for investigating the changes in desertification intensity, the present study sought to evaluate desertification using the change vector analysis for different land-uses of the Gavkhoni basin.

Materials and Methods: This study used the change vector analysis (CVA) method to determine desertification changes in the Gavkhooni basin based on algorithm-driven classification, producing two components of magnitude and the direction of change. Moreover, to evaluate the intensity of desertification via the change vector analysis method, EVI and BSI were used for examining the study area's vegetation and bare soil. Possessed with 13 layers to assess the land use, the MCD12Q1 product with annual temporal resolution and spatial resolution of 500 m was used as a Level-3 network product in the sine image system to evaluate the land use. In addition, the IGBP standard was also used to assess land cover and land use.

Results: The results of analyzing the changes made in the BSI during 2001-2005 and 2000-2016 indicated that throughout the latter period, BSI values decreased in central, western, northwestern, eastern, and southeastern regions of the study area. On the other hand, the results of analyzing the changes in the EVI revealed that during the same period, the index values increased in the west and northwest of the region, while the index value decreased in the eastern, southern, and southeastern parts of the region. Moreover, the results of analyzing the changes in desertification showed that the number of changes in some areas of the center, west, southwest, and southeast of the region was greater than other areas, which could be attributed to rehabilitation or destruction in the study area.
The results of analyzing desertification-related changes in terms of direction suggested that the intensity of destruction in the center, south, east, southeast, and northeast of the region was higher than that of other regions. The rehabilitation has occurred in the northern, northwestern, and southwestern regions. Among the areas that were under rehabilitation process, 14.15%, 12.04%, and 10.31% of the basin area were found to be in the low, medium, and high rehabilitation classes, respectively. On the other hand, in terms of the extent of destruction in the region, 12.02% of the study fell under the medium destruction class, while 8.24% and 8.91% of the study area were placed under the low and high degradation classes, respectively. However, 34.33% had remained unchanged in terms of desertification status.
According to the results of analyzing the intensity and direction of changes in each land use, 0.42% of agricultural lands were found to be in the high destruction class. Furthermore, the greatest percentage of high rehabilitation class belonged to grasslands, which covered 5.40% of the study area.  However, 28.5% of the area which comprised of barren lands was divided under the trend-free class. It was also found that 1.88% of non-dense shrubs and 0.36% of residential lands were under the high destruction class.

Discussion and Conclusion: As desertification is among the serious ecological crises in today's world, it is necessary to well identify and recognize the causes and processes involved in desertification on a regional and global scale. Therefore, this study used the vector analysis method to evaluate the desertification status in different land-uses of the Gavkhoni basin. The multivariate CVA technique was used in the pixel-by-pixel analysis of bands or spectral indices. The changes that occurred throughout two different periods (as mentioned earlier) were identified by placing the quantitative value of the pixels on the two axes of the Cartesian plane, out of which two componential elements, i.e., magnitude and direction were obtained.
In general, the results of the present study indicated that while the east and center of the Gavkhoni basin were in a state of destruction and desertification, the bare soil in the western and northwestern regions of the Gavkhoni basin had been replaced by vegetation due to agricultural activities and cultivation and that these regions were in a state of rehabilitation. Therefore, the vector analysis model is recommended to be used for analyzing changes in other basins. In fact, unless a more accurate and better evaluation model is introduced, this model could be used confidently to assess the severity of future desertification.

Keywords


  1. Ataiee, H. and Fanaei, R., 2013. Identifying the trend of monthly and annual changes in the average temperature of Gavkhoni catchment during the statistical period of 1961-2010. Wetland Ecobiology, 5(17), 31-46. (in Farsi).
  2. Basirpour, A. and Moghadas, M., 2016. Challenges and problems of water supply in the agricultural sector of Isfahan province and Zayandehrud basin. National Congress of Irrigation and Drainage of Iran, (2). (in Farsi).
  3. Becerril-Piña, R., Díaz-Delgado, C., Mastachi-Loza, C.A. and González-Sosa, E., 2016. Integration of remote sensing techniques for monitoring desertification in Mexico. Human and Ecological Risk Assessment: An International Journal, 22(6), 1323-1340.
  4. Bhavani, M., Hanifar Sangeetha, V., Kalaivani, K., Ulagapriya, K. and Saritha, A., 2018. Change detection algorithm for multi-temporal satellite images: A review, Engineering and Technology (UAE), 7(2), 206-209.
  5. Civco, D.L., Hurd, J.D., Wilson, E.H., Song, M. and Zhang, Z., 2002. A Comparison of Land Use and Land Cover Change Detection Methods. American Congress on Surveying & Mapping – American Society for Photogrammetry and Remote Sensing 2002 Annual Conference Proceedings, 22-26.
  6. Dawelbait, M. and Morari, F., 2012. Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. Arid Environment, 80, 45-55.
  7. Faizi, Z., Mashhadi, N., Mohebzadeh, F. and Nouraie, , 2014. Land Degradation. Fourth International Conference on Environmental Challenges and Tree Botany., Sari, https://civilica.com/doc/788372. (in Farsi).
  8. Farajzadeh, M. and Kavousi, M., 2014. Evaluation and analysis of vegetation change trends using linear regression and change vector analysis (CVA) methods; Case Study: Dust Storms Center of Western Iran, Geography and Environmental Planning, 25(4), 69-82. (in Farsi).
  9. Ghadimi, A.R., Papzen, A. and Amini, , 2018. Investigation of the trend of agricultural land use change and its impact on the components of sustainable development (Case study: Zayandehrud basin of Isfahan province). Agricultural Extension and Education Research, 11(3), 41-58. (in Farsi).
  10. Gichenje, H., Pinto-Correia, T. and Godinho, S., 2019. An analysis of the drivers that affect greening and browning trends in the context of pursuing land degradation-neutrality. Remote Sensing Applications: Socity and Environment, 15, 100251.
  11. Hadiyan, F., Jaefari, R., Boshra, H. and Soltani, , 2013. Monitoring the effect of rainfall on vegetation changes using remote sensing techniques over a 12-year period (Case study: Semirom and Lordegan). Rangeland and Watershed Management, 66(4), 621-632. (in Farsi).
  12. Halabian, A.H. and Soltanian, M., 2016. Evaluation and forecasting of desertification changes in the east and south of Isfahan with CA-Markov model. Spatial Analysis of Environmental Hazards, 3(4), 71-88. (in Farsi).
  13. Kashkoulian, E., Sheikholeslami, A. and Naghavi, M., 2019. Environmental Impact Evaluation of Isfahan Steel Company and Preventive Strategies: A Case Study. Bioethics Journal, 9(33), 55-63. (in Farsi).
  14. Kheiry, M.A., Csaplovic, E. and Mahmoud, T.E., 2015. Change Vector Analysis for Analyzing and Mapping Desertification Processes in Arid and Semi-arid Region, North Kordofan State, Sudan. American Association for Science and Technology, 2(6), 2375-3803.
  15. Kussul, N., Kolotii, A., Shelestov, A., Yailymov, B. and Lavreniuk, M., 2017. Land degradation estimation from global and national satellite based datasets within UN program. LEEE, 17320251.
  16. Lamchin, M., Lee, J.Y., Lee, W.K., Lee, E.J., Kim, M., C.H., Lim, Choi, H.A. and Kim, S.R., 2016. Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Advances in Space Research, 57(1), 64-77.
  17. Masoudi, M., Vahedi, M., Nematollahi, A. and Fallah Shamsi, R. Comparison of land degradation in two semi-wet to wet areas (Sepidan city) and dry (Lamerd city) in Fars province based on the proposed RALDE model. Iranian Journal of Range and Desert Research, 22 (4): 820- 802. (in Farsi).
  18. Shao, Q., Shi, Y., Xiang, Z., Shao, H., Xian, W., Peng, P. and Li, Q. 2018. Monitoring the Grassland Change in the Qinghai-Tibetan Plateau: A Case Study on Aba County. Journal of the Indian Society of Remote Sensing, 46(4): 569-580.
  19. Polykretis, Ch., Grillakis, M.G. and Dimitrios, A., 2020. Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece. Remote Sensing, 12(319), 2-25.
  20. Vorovencii, I., 2017. Applying the change vector analysis technique to assess the desertification risk in the southwest of Romania in the period 1984–2011. Environmental monitoring Monitoring and Assessm 189(10), 524 p.
  21. Rahimi, M., Damavandi, A. and Jafarian, and. Investigation of applications of remote sensing in the assessment and monitoring of land degradation and desertification. Geographic Information, 22: 128-115. (in Farsi).
  22. Xu, D., Song, A., Li, D., Ding, X. and Ziyu, W., 2019. Assessing the relative role of climate change and human activities in desertification of North China from 1981 to 2010. Frontiers of Earth Science, 43-54.