Relationships between Meteorological Drought and Vegetation Degradation Using Satellite and Climatic Data in a Semi-Arid Environment in Markazi Province, Iran

Journal of Rangeland Science

مقاله 8، دوره 10، شماره 2، تیر 2020، صفحه 204-216   

نوع مقاله: Research and Full Length Article

نویسندگان

Zohre Ebrahimi Khusfi^{* 1}؛ Mahdi Zarei²

¹Assistant Professor, Department of Natural Science, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran

²Assistant Professor, Research Center of Social Studies and Geographical Sciences, Hakim Sabzevari University, Sabzevar, Iran

چکیده

The assessment of relationships between satellite-derived vegetation indices and meteorological drought improves our understanding of how these indices respond to climatic changes. The combination of climate data and the Normalized Difference Vegetation Index (NDVI) product of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery provided an opportunity to evaluate the impact of drought on land degradation over the growing seasons. The main goal of this study was to investigate the effect of drought on vegetation degradation in Meyghan plain, Arak, Iran. For this purpose, climatic and satellite data were used. The annual Standardized Precipitation Index (SPI) was calculated for 20 years (1998-2017). Then, the NDVI maps were classified into three classes according to the Tokunaga-Thug method. These classes are: Class 1) no vegetation; class 2) low-density or poor rangelands, and class 3) semi-dense and dense vegetation cover such as agricultural lands.The relationship between the percentage of vegetation cover classes (classes 2 and 3) and the drought index of the previous year was assessed using the Pearson correlation test. The results showed that the correlation between these variables was significantly dependent on vegetation degradation in the poor vegetation area (R=0.51; P-value<0.05). In contrast, there was a negative significant relationship between drought and the percentage of dense areas of vegetation (R=-0.46; P-value<0.06). Hence, it was concluded that the sensitivity of the low-density area (poor rangeland) to drought was more than dense vegetation covers (agricultural lands).Its reason is that the most important source of water supply for natural rangelands is the atmospheric precipitation that has been reduced due to the occurrence of droughts in recent years. 

کلیدواژه‌ها

Normalized Difference Vegetation Index؛ Drought Monitoring؛ MOD13A3؛ Tokunaga-Thug method؛ Semi-arid region

مراجع

Ahmed, Z. 2015. Determination and analysis of desertification process with satellite data Alsat-1 and Landsat in the Algerian Steppe. Engineering Geology for Society and Territory-Volume 2. Springer.

Amirataee, B. & Zeinalzadeh, K. 2016. Trends analysis of quantitative and qualitative changes in groundwater with considering the autocorrelation coefficients in west of Lake Urmia, Iran. Environmental Earth Sciences, 75, 371.

Ansari, A. & Golabi, M. H. 2019. Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands–A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7, 64-70.

Bahrami, M., Bazrkar, S. & Zarei, A. R. 2019. Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index. Journal of Water and Climate Change, 10, 181-196.

Barbosa, H., Kumar, T. L. & Silva, L. 2015. Recent trends in vegetation dynamics in the South America and their relationship to rainfall. Natural Hazards, 77, 883-899.

Birtwistle, A. N., Laituri, M., Bledsoe, B. & Friedman, J. M. 2016. Using NDVI to measure precipitation in semi-arid landscapes. Journal of arid environments, 131, 15-24.

Brede, B., Verbesselt, J., Dutrieux, L. P. & Herold, M. 2015. Performance of the enhanced vegetation index to detect inner-annual dry season and drought impacts on amazon forest canopies.  Proceedings of the 36th International Symposium on Remote Sensing of Environment, Berlin, Germany, 2015. 11-15.

Chao, N., Luo, Z., Wang, Z. & Jin, T. 2018. Retrieving Groundwater Depletion and Drought in the Tigris‐Euphrates Basin Between 2003 and 2015. Groundwater, 56, 770-782.

Chen, C., Son, N., Chen, C., Chiang, S., Chang, L. & Valdez, M. 2017. Drought monitoring in cultivated areas of Central America using multi-temporal MODIS data. Geomatics, Natural Hazards and Risk, 8, 402-417.

Cracknell, A. P. 2007. Introduction to remote sensing, CRC press.

Damavandi, A. A., Rahimi, M., yazdani, M. R. & noroozi, A. A. 2016. Assessment of drought severity using vegetation temperature condition index (VTCI) and Terra/MODIS satellite data in rangelands of Markazi Province, Iran. Journal of Rangeland Science, 6, 33-42.

Dubovyk, O., Landmann, T., Erasmus, B. F., Tewes, A. & Schellberg, J. 2015. Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. International Journal of Applied Earth Observation and Geoinformation, 38, 175-183.

Dutta, D., Kundu, A., Patel, N., Saha, S. & Siddiqui, A. 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science, 18, 53-63.

Farrokhzadeh, B., Mansouri, S. & Sepehri, A. 2018. Determining the correlation between NDVI and EVI vegetation indices and SPI drought index (Case Study: Golestan rangelands). Journal of Agricultural Meteorology, 5, 56-65.

Gouveia, C., Trigo, R., Beguería, S. & Vicente-serrano, S. M. 2017. Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators. Global and Planetary Change, 151, 15-27.

Gulácsi, A. & Kovács, F. 2015. Drought monitoring with spectral indices calculated from MODIS satellite images in Hungary. Journal of Environmental Geography, 8, 11-20.

Ibrahimi, A. & Baali, A. 2018. Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saiss Plan (Northern Morocco). International Journal of Intelligent Engineering & Systems, 11, 267-275.

Keshavarz, M., Maleksaeidi, H. & Karami, E. 2017. Livelihood vulnerability to drought: A case of rural Iran. International Journal of Disaster Risk Reduction, 21, 223-230.

Khosravi, H., Haydari, E., Shekoohizadegan, S. & Zareie, S. 2017. Assessment the effect of drought on vegetation in desert area using landsat data. The Egyptian Journal of Remote Sensing and Space Science, 20, S3-S12.

Ko, D. W., Kim, D., Narantsetseg, A. & Kang, S. 2017. Comparison of field-and satellite-based vegetation cover estimation methods. Journal of Ecology and Environment, 41, 5.

Kundu, A., Denis, D., Patel, N. & Dutta, D. 2018. A Geo‐spatial study for analysing temporal responses of NDVI to rainfall. Singapore Journal of Tropical Geography, 39, 107-116.

Lian, J., Zhao, X., Li, X., Zhang, T., Wang, S., Luo, Y., Zhu, Y. & Feng, J. 2017. Detecting sustainability of desertification reversion: Vegetation trend analysis in part of the Agro-Pastoral Transitional Zone in Inner Mongolia, China. Sustainability, 9, 211.

Marengo, J. A., Torres, R. R. & Alves, L. M. 2017. Drought in Northeast Brazil—past, present, and future. Theoretical and Applied Climatology, 129, 1189-1200.

Mariano, D. A., Dos santos, C. A., Wardlow, B. D., Anderson, M. C., Schiltmeyer, A. V., Tadesse, T. & Svoboda, M. D. 2018. Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment, 213, 129-143.

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, 7, 2636-2651.

Mckee, T. B. Drought monitoring with multiple time scales.  Proceedings of 9th Conference on Applied Climatology, Boston, 1995, 1995.

Medellín-azuara, J., Macewan, D., Howitt, R. E., Sumner, D. A., Lund, J. R., Scheer, J., GAILEY, R., HART, Q., Alexander, N. D. & Arnold, B. 2016. Economic analysis of the 2016 California drought on agriculture. Center for Watershed Sciences. Davis, CA: UC Davis.

Morid, S., Smakhtin, V. & Bagherzadeh, K. 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27, 2103-2111.

Mu, Q., Zhao, M., Running, S. W., Kimball, J. S. & Mcdowell, N. G. Using MODIS weekly evapotranspiration to monitor drought.  Remote Sensing and Modeling of Ecosystems for Sustainability XIII, 2016. International Society for Optics and Photonics, 997502.

Nazari, M., Razzaghi, F., Khalili, D., Kamgar-haghighi, A. & Zarandi, S. 2017. Regionalization of dryland farming potential as influenced by droughts in western Iran. International Journal of Plant Production, 11, 315-332.

Park, S., Im, J., Jang, E. & Rhee, J. 2016. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology, 216, 157-169.

Patil, B., Pawar, K. & Babu, A. 2014. Studies on induction of drought tolerance by seed hardening in Bt cotton. Plant Archives, 14, 357-362.

Rajabi, A. M. & Ghorbani, E. 2016. Land subsidence due to groundwater withdrawal in Arak plain, Markazi province, Iran. Arabian Journal of Geosciences, 9, 738.

Ribeiro, A. F., Russo, A., Gouveia, C. M. & Páscoa, P. 2019. Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theoretical and Applied Climatology, 136, 203-220.

Rouse, J. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFG, Type III. Final Report, 371.

Safari shad, M., Ildoromi, A. & Akhzari, D. 2017. Drought Monitoring Using Vegetation Indices and MODIS Data (Case Study: Isfahan Province, Iran). Journal of Rangeland Science, 7, 148-159.

Tokunaga, M. & Thug, V. 2002. Finding the relationship between vegetation index and coherence signature to utilize the product of radar interferometry in land cover application. Asian Association on Remote Sensing.

Wang, Q. & Tenhunen, J. D. 2004. Vegetation mapping with multitemporal NDVI in North Eastern China transect (NECT). International Journal of Applied Earth Observation and Geoinformation, 6, 17-31.

Yan, H., Wang, S. Q., Wang, J. B., Lu, H. Q., Guo, A. H., Zhu, Z. C., Myneni, R. B. & Shugart, H. H. 2016. Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data. Journal of Geophysical Research: Atmospheres, 121, 2283-2298.

Yang, P., Xia, J., Zhang, Y., Zhan, C. & Qiao, Y. 2018. Comprehensive assessment of drought risk in the arid region of Northwest China based on the global palmer drought severity index gridded data. Science of the Total Environment, 627, 951-962.

Yao, J., Chen, Y., Zhao, Y., Mao, W., Xu, X., Liu, Y. & Yang, Q. 2018. Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China. Theoretical and applied climatology, 131, 1503-1515.

Zhang, Q., Han, L., Jia, J., Song, L. & Wang, J. 2016. Management of drought risk under global warming. Theoretical and applied climatology, 125, 187-196.

Zhang, X., Yamaguchi, Y., Li, F., He, B. & Chen, Y. 2017. Assessing the impacts of the 2009/2010 drought on vegetation indices, normalized difference water index, and land surface temperature in Southwestern China. Advances in Meteorology, 2017.