Abstract
Climate change is responsible for changes in the world’s vegetation. This study was aimed to investigate the effect of long-term variations in the air temperature, precipitation, and dust concentration on normalized difference vegetation index (NDVI) variations in the spring, summer, and growing season over arid regions of Iran. The results showed that the precipitation had a positive association with the NDVI in the spring and growing seasons (β > + 0.28; P < 0.05), while air temperature had a negative relationship with the NDVI changes in these seasons (β > − 0.34; P = 0.013). Our results also showed a negative relationship between the accumulated dust concentration (ADC) and NDVI in the spring (β = − 0.23; P = 0.09) and growing seasons (β = − 0.24; P = 0.003). Spatially, the strongest linkage between NDVI and climatic variables was observed in about two-thirds of the study region. In total, our findings indicate that the use of dust concentration, air temperature, and precipitation helps us to make a more accurate approach for evaluating the impacts of climate change on vegetation variations in the arid environments.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbas, F. M. (2019). Investigating role of vegetation in protection of houses during floods. Civil Engineering Journal., 5. https://doi.org/10.28991/cej-2019-03091436.
Abdollahi, A., et al. (2019). Investigation of the vegetation cover and the vulnerability of the Mashhad regions to desertification by using MODIS image and EVI. 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom). IEEE, pp. 46-49. https://doi.org/10.1109/CYBERNETICSCOM.2019.8875666.
Ackerman, T. L., Bamberg, S. A. (1974). Phenological studies in the Mojave Desert at Rock Valley (Nevada test site). Phenology and seasonality modeling. Springer, 215-226.
Adnan, S., et al. (2017). Meteorological impacts on evapotranspiration in different climatic zones of Pakistan. Journal of Arid Land., 9, 938–952. https://doi.org/10.1007/s40333-017-0107-2.
Ahmad, M., et al. (2020). Investigating the flow hydrodynamics in a compound channel with layered vegetated floodplains. Civil Engineering Journal, 6, 860–876. https://doi.org/10.28991/cej-2020-03091513.
Ahmed, S. S., et al. (2016). Effects of roadside dust pollution on fruit trees of miyyaghundi (Quetta) and ghanjdori (mastung), Pakistan. International Journal of Basic and Applied Sciences, 5, 38. https://doi.org/10.14419/ijbas.v5i1.5477.
Alijanian, M., et al. (2019). Evaluation of remotely sensed precipitation estimates using PERSIANN-CDR and MSWEP for spatio-temporal drought assessment over Iran. Journal of Hydrology., 579, 124189. https://doi.org/10.1016/j.jhydrol.2019.124189.
Alizadeh-Choobari, O., & Najafi, M. (2018). Extreme weather events in Iran under a changing climate. Climate dynamics., 50, 249–260. https://doi.org/10.1007/s00382-017-3602-4.
Althoff, T. D., et al. (2016). Climate change impacts on the sustainability of the firewood harvest and vegetation and soil carbon stocks in a tropical dry forest in Santa Teresinha Municipality, Northeast Brazil. Forest Ecology and Management., 360, 367–375. https://doi.org/10.1016/j.foreco.2015.10.001.
Alvarez, L. J., et al. (2012). Aeolian process effects on vegetation communities in an arid grassland ecosystem. Ecology and Evolution., 2, 809–821. https://doi.org/10.1002/ece3.205.
Bagherzadeh, A., et al. (2020). The effects of climate change on normalized difference vegetation index (NDVI) in the Northeast of Iran. Modeling Earth Systems and Environment. 1-13. https://doi.org/10.1007/s40808-020-00724-x.
Bao, L., et al. (2016). Effects of road dust on the growth characteristics of Sophora japonica L. seedlings. Journal of Environmental Sciences., 46, 147–155. https://doi.org/10.1016/j.jes.2015.08.031.
Bayat, R., et al. (2016). Studying the effect of dust on vegetation changes (case study: Shadegan Wetland, Khuzestan).
Bhatti, N. B., et al. (2019). Land covers change assessment after small dam’s construction based on the satellite data. Civil Engineering Journal., 5, 810–818. https://doi.org/10.28991/cej-2019-03091290.
Chappell, A., et al. (2018). Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters. Remote Sensing of Environment., 204, 756–768. https://doi.org/10.1016/j.rse.2017.09.026.
Chen, J., et al. (2019). Threshold effects of vegetation coverage on soil erosion control in small watersheds of the red soil hilly region in China. Ecological engineering., 132, 109–114. https://doi.org/10.1016/j.ecoleng.2019.04.010.
Chitsaz, N., & Azarnivand, A. (2017). Water scarcity management in arid regions based on an extended multiple criteria technique. Water Resources Management., 31, 233–250. https://doi.org/10.1007/s11269-016-1521-5.
Chu, H., et al. (2019). NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment., 650, 2051–2062. https://doi.org/10.1016/j.scitotenv.2018.09.115.
Chuai, X., et al. (2013). NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. International journal of climatology., 33, 1696–1706. https://doi.org/10.1002/joc.3543.
Ebrahimi Khusfi, Z., & Zarei, M. (2020). 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., 10, 204–216.
Ebrahimi Khusfi, Z., et al. (2020). Investigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran. Arid land research and management., 34, 239–263. https://doi.org/10.1080/15324982.2019.1694087.
Ebrahimi-Khusfi, Z., et al. (2020). Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2020.08.029.
Eckert, S., et al. (2015). Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113, 16–28. https://doi.org/10.1016/j.jaridenv.2014.09.001.
Emberson, L., et al. (2001). Impacts of air pollutants on vegetation in developing countries. Water, Air, and Soil Pollution., 130, 107–118. https://doi.org/10.1023/A:1012251503358.
Fakharizadehshirazi, E., et al. (2019). Long-term spatiotemporal variations in satellite-based soil moisture and vegetation indices over Iran. Environmental Earth Sciences., 78, 342. https://doi.org/10.1007/s12665-019-8347-4.
Fallah-Ghalhari, G., et al. (2019). Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology., 138, 1539–1562. https://doi.org/10.1007/s00704-019-02906-9.
Fatahi-Bafghi, M., et al. (2019). Diversity and antimicrobial activity of actinomycetes isolated from Lut Desert: the extremely arid climatic zones of Iran. International Journal of Peptide Research and Therapeutics., 25, 1201–1207. https://doi.org/10.1007/s10989-018-9767-9.
Frouz, J. (2020). Soil biodiversity conservation for mitigating climate change. In Climate Change and Soil Interactions (pp. 1–19). Elsevier. https://doi.org/10.1016/B978-0-12-818032-7.00001-1.
Fu, B., & Burgher, I. (2015). Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. Journal of Arid Environments., 113, 59–68. https://doi.org/10.1016/j.jaridenv.2014.09.010.
Gaur, M. K., Squires, V. R. (2018). Geographic extent and characteristics of the world’s arid zones and their peoples. Climate variability impacts on land use and livelihoods in drylands. Springer, 3-20.
Ghose, M. K., & Majee, S. (2007). Characteristics of hazardous airborne dust around an Indian surface coal mining area. Environmental Monitoring and Assessment., 130, 17–25. https://doi.org/10.1007/s10661-006-9448-6.
Gouveia, C., et al. (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. https://doi.org/10.1016/j.gloplacha.2016.06.011.
Hammad, B., et al. (2018). Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study. Renewable and Sustainable Energy Reviews., 82, 2218–2234. https://doi.org/10.1016/j.rser.2017.08.070.
Hatfield, J. L., & Prueger, J. H. (2010). Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing., 2, 562–578. https://doi.org/10.3390/rs2020562.
He, Y. (2014). The effect of precipitation on vegetation cover over three landscape units in a protected semi-arid grassland: temporal dynamics and suitable climatic index. Journal of Arid Environments., 109, 74–82. https://doi.org/10.1016/j.jaridenv.2014.05.022.
Hu, X., et al. (2019). Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agricultural and Forest Meteorology., 279, 107707. https://doi.org/10.1016/j.agrformet.2019.107707.
Hui, Y., et al. (2018). Vegetation’s role in controlling long-term response of near ground air temperature to precipitation change in a semi-arid region. Journal of Arid Environments., 152, 83–86. https://doi.org/10.1016/j.jaridenv.2018.01.015.
Jackson, M. W., & Jensen, J. R. (2005). An evaluation of remote sensing-derived landscape ecology metrics for reservoir shoreline environmental monitoring. Photogrammetric Engineering & Remote Sensing., 71, 1387–1397. https://doi.org/10.14358/PERS.71.12.1387.
Javan, K., & Teimouri, M. (2019). Spatial analysis of occurrence probability of dusty days in west and southwest of Iran. Arabian Journal of Geosciences., 12, 477. https://doi.org/10.1007/s12517-019-4627-2.
Javari, M. (2016). Trend and homogeneity analysis of precipitation in Iran. Climate., 4, 44. https://doi.org/10.3390/cli4030044.
Jha, S., & Srivastava, R. (2018). Impact of drought on vegetation carbon storage in arid and semi-arid regions. Remote Sensing Applications: Society and Environment., 11, 22–29. https://doi.org/10.1016/j.rsase.2018.04.013.
Kameswaran, S., et al. (2019). Dust pollution and its influence on vegetation–a critical analysis. https://doi.org/10.26479/2019.0501.31.
Khalili, K., et al. (2016). Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic environmental research and risk assessment., 30, 1205–1221. https://doi.org/10.1007/s00477-015-1095-4.
Khusfi, Z. E., et al. (2020). Spatial and seasonal variations of sand-dust events and their relation to atmospheric conditions and vegetation cover in semi-arid regions of central Iran. Geoderma., 365, 114225. https://doi.org/10.1016/j.geoderma.2020.114225.
Kim, D., et al. (2017). Role of surface wind and vegetation cover in multi-decadal variations of dust emission in the Sahara and Sahel. Atmospheric Environment., 148, 282–296. https://doi.org/10.1016/j.atmosenv.2016.10.051.
Leenders, J. K., et al. (2016). Wind erosion reduction by scattered woody vegetation in farmers’ fields in northern Burkina Faso. Land Degradation & Development., 27, 1863–1872. https://doi.org/10.1002/ldr.2322.
Li, W., et al. (2018). On the drying trends over Egypt using harmonic analysis of the enhanced vegetation index. AGU Fall Meeting Abstracts.
Liu, S., et al. (2019). Spatial-temporal changes in vegetation cover in a typical semi-humid and semi-arid region in China: changing patterns, causes and implications. Ecological indicators., 98, 462–475. https://doi.org/10.1016/j.ecolind.2018.11.037.
Lu, J., et al. (2019). Mapping the agricultural drought based on the long-term AVHRR NDVI and North American Regional Reanalysis (NARR) in the United States, 1981–2013. Applied geography., 104, 10–20. https://doi.org/10.1016/j.apgeog.2019.01.005.
Ma, B., et al. (2017). Vegetation index differencing for estimating foliar dust in an ultra-low-grade magnetite mining area using Landsat imagery. Ieee Access., 5, 8825–8834. https://doi.org/10.1109/ACCESS.2017.2700474.
MacKinnon, G., et al. (2011). Spatial and temporal variations in Pb concentrations and isotopic composition in road dust, farmland soil and vegetation in proximity to roads since cessation of use of leaded petrol in the UK. Science of the total environment., 409, 5010–5019. https://doi.org/10.1016/j.scitotenv.2011.08.010.
Mahmoudi, P., et al. (2019). Investigating the trend of average changes of annual temperatures in Iran. International Journal of Environmental Science and Technology., 16, 1079–1092. https://doi.org/10.1007/s13762-018-1664-4.
Mares, M. A. (2017). Encyclopedia of deserts. University of Oklahoma Press.
McTainsh, G., & Pitblado, J. (1987). Dust storms and related phenomena measured from meteorological records in Australia. Earth Surface Processes and Landforms., 12, 415–424. https://doi.org/10.1002/esp.3290120407.
Meili, N., et al. (2020). An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (ut&c v1. 0). Geoscientific Model Development., 13, 335–362. https://doi.org/10.5194/gmd-13-335-2020.
Mirhosseini, M., et al. (2011). Assessing the wind energy potential locations in province of Semnan in Iran. Renewable and Sustainable Energy Reviews., 15, 449–459. https://doi.org/10.1016/j.rser.2010.09.029.
Mo, K., et al. (2019). Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China. Journal of Hydrology., 574, 138–147. https://doi.org/10.1016/j.jhydrol.2019.04.044.
Modarres, R., & da Silva, V. d. P. R. (2007). Rainfall trends in arid and semi-arid regions of Iran. Journal of arid environments., 70, 344–355. https://doi.org/10.1016/j.jaridenv.2006.12.024.
Modarres, R., & Sadeghi, S. (2018). Spatial and temporal trends of dust storms across desert regions of Iran. Natural Hazards., 90, 101–114. https://doi.org/10.1007/s11069-017-3035-8.
Montandon, L., & Small, E. (2008). The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment., 112, 1835–1845. https://doi.org/10.1016/j.rse.2007.09.007.
Moradi, A., et al. (2017). Effects of dust on forest tree health in Zagros oak forests. Environmental monitoring and assessment., 189, 549. https://doi.org/10.1007/s10661-017-6262-2.
Mostafaeipour, A. (2013). Economic evaluation of small wind turbine utilization in Kerman, Iran. Energy Conversion and Management., 73, 214–225. https://doi.org/10.1016/j.enconman.2013.04.018.
Nabaei, S., et al. (2019). Copula based assessment of meteorological drought characteristics: regional investigation of Iran. Agricultural and Forest Meteorology., 276, 107611. https://doi.org/10.1016/j.agrformet.2019.06.010.
Nam, K.-P., & Kim, C.-H. (2013). Study on sensitivity of different standardization methods to climate change vulnerability index. Journal of Environmental Impact Assessment., 22, 677–693. https://doi.org/10.14249/eia.2013.22.6.677.
Nanzad, L., et al. (2019). NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. Journal of arid environments., 164, 69–77. https://doi.org/10.1016/j.jaridenv.2019.01.019.
Nasab, F. Q., & Rahnama, M. (2019). Developing restoration strategies in Jazmurian wetland by remote sensing. International Journal of Environmental Science and Technology, 1–16. https://doi.org/10.1007/s13762-019-02568-0.
Norouzian, R., Plonsky, L. (2018). Correlation and simple linear regression in applied linguistics. The Palgrave handbook of applied linguistics research methodology. Springer, 395-421. https://doi.org/10.1057/978-1-137-59900-1_19.
Olive, D. J.(2017). Multiple linear regression. Linear Regression. Springer, 17-83.
Ozer, P., et al. (2007). Estimation of air quality degradation due to Saharan dust at Nouakchott, Mauritania, from horizontal visibility data. Water, Air, and Soil Pollution., 178, 79–87. https://doi.org/10.1007/s11270-006-9152-8.
Pang, G., et al. (2017). Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012. Quaternary International., 444, 87–96. https://doi.org/10.1016/j.quaint.2016.08.038.
Pishgar-Komleh, S., & Akram, A. (2017). Evaluation of wind energy potential for different turbine models based on the wind speed data of Zabol region, Iran. Sustainable Energy Technologies and Assessments., 22, 34–40. https://doi.org/10.1016/j.seta.2017.05.007.
Pour, S. H., et al. (2020). Spatiotemporal changes in aridity and the shift of drylands in Iran. Atmospheric Research., 233, 104704. https://doi.org/10.1016/j.atmosres.2019.104704.
Przybysz, A., et al. (2014). Accumulation of particulate matter and trace elements on vegetation as affected by pollution level, rainfall and the passage of time. Science of the Total Environment., 481, 360–369. https://doi.org/10.1016/j.scitotenv.2014.02.072.
Rahimi, M., et al. (2018). Trends in indices of extreme temperature and precipitation in Iran over the period 1960-2014. Open Journal of Ecology., 8, 396. https://doi.org/10.4236/oje.2018.87024.
Rai, P. K., & Panda, L. L. (2014). Leaf dust deposition and its impact on biochemical aspect of some roadside plants of Aizawl, Mizoram, North East India. International Research Journal of Environment Sciences., 3, 14–19.
Rebello, V., et al. (2020). Spatiotemporal vegetation response to extreme droughts in eastern Brazil. Remote Sensing Applications: Society and Environment., 100294. https://doi.org/10.1016/j.rsase.2020.100294.
Rezazadeh, M., et al. (2013). Climatology of the Middle East dust events. Aeolian Research., 10, 103–109. https://doi.org/10.1016/j.aeolia.2013.04.001.
Rishmawi, K., et al. (2016). Vegetation responses to climate variability in the northern arid to sub-humid zones of Sub-Saharan Africa. Remote Sensing., 8, 910. https://doi.org/10.3390/rs8110910.
Roodposhti, M. S., et al. (2017). Drought sensitivity mapping using two one-class support vector machine algorithms. Atmospheric research., 193, 73–82. https://doi.org/10.1016/j.atmosres.2017.04.017.
Salmerón, R., et al. (2018). Variance inflation factor and condition number in multiple linear regression. Journal of Statistical Computation and Simulation., 88, 2365–2384. https://doi.org/10.1080/00949655.2018.1463376.
Sedighifar, Z., et al. (2019). Investigating spatiotemporal relationship between EVI of the MODIS and climate variables in northern Iran. International Journal of Environmental Science and Technology, 1–12. https://doi.org/10.1007/s13762-019-02374-8.
Shahraki, A. S., et al. (2020). An integrated model for economic assessment of environmental scenarios for dust stabilization and sustainable flora–fauna ecosystem in international Hamoun wetland. Environment, Development and Sustainability, 1–21. https://doi.org/10.1007/s10668-020-00616-1.
Shamsipour, A., et al. (2008). An analysis of drought events for central plains of Iran through an employment of NOAA-AVHRR data. Desert., 13, 105–115.
Shanker, M., et al. (1996). Effect of data standardization on neural network training. Omega., 24, 385–397. https://doi.org/10.1016/0305-0483(96)00010-2.
Shao, Y., et al. (2003). Northeast Asian dust storms: real-time numerical prediction and validation. Journal of Geophysical Research: Atmospheres., 108. https://doi.org/10.1029/2003JD003667.
Sharafati, A., et al. (2020). Spatial assessment of meteorological drought features over different climate regions in Iran. International Journal of Climatology., 40, 1864–1884. https://doi.org/10.1002/joc.6307.
Shi, Q., et al. (2016). A Bayesian ridge regression analysis of congestion’s impact on urban expressway safety. Accident Analysis & Prevention., 88, 124–137. https://doi.org/10.1016/j.aap.2015.12.001.
Soltani, M., et al. (2016). Assessment of climate variations in temperature and precipitation extreme events over Iran. Theoretical and Applied Climatology., 126, 775–795. https://doi.org/10.1007/s00704-015-1609-5.
Son, N., et al. (2012). Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation., 18, 417–427. https://doi.org/10.1016/j.jag.2012.03.014.
Song, H., et al. (2019). Soil organic carbon and nutrient losses resulted from spring dust emissions in Northern China. Atmospheric Environment., 213, 585–596. https://doi.org/10.1016/j.atmosenv.2019.06.043.
Speranza, C. I., et al. (2010). Indigenous knowledge related to climate variability and change: insights from droughts in semi-arid areas of former Makueni District, Kenya. Climatic change., 100, 295–315. https://doi.org/10.1007/s10584-009-9713-0.
Sruthi, S., & Aslam, M. M. (2015). Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia., 4, 1258–1264. https://doi.org/10.1016/j.aqpro.2015.02.164.
Sun, Y., et al. (2020). Dust deposition on vegetation leaves in Shanghai, China. International Journal of Environmental Health Research., 1–14. https://doi.org/10.1080/09603123.2020.1714559.
Swain, S., et al. (2017). Future changes in summer MODIS-based enhanced vegetation index for the South-Central United States. Ecological informatics., 41, 64–73. https://doi.org/10.1016/j.ecoinf.2017.07.007.
Tehrani, E. N., et al. (2019). Trend analysis of hydro-climatic variables in the north of Iran. Theoretical and applied climatology., 136, 85–97. https://doi.org/10.1007/s00704-018-2470-0.
Touré, A. A., et al. (2019). Dynamics of wind erosion and impact of vegetation cover and land use in the Sahel: a case study on sandy dunes in southeastern Niger. Catena., 177, 272–285. https://doi.org/10.1016/j.catena.2019.02.011.
Vali, A., et al. (2019). Investigating the topographic and climatic effects on vegetation using remote sensing and GIS: a case study of Kharestan region, Fars Province, Iran. Theoretical and Applied Climatology, 1–18. https://doi.org/10.1007/s00704-019-03073-7.
Vecchio, A., et al. (2020). Effect of vegetation on the temperatures of TRAPPIST-1 planets. The Astrophysical Journal., 891, 24. https://doi.org/10.3847/1538-4357/ab6d75.
Wamelink, W., et al. (2018). The role of abiotic soil parameters as a factor in the success of invasive plant species. Emerging Science Journal, 2, 308–365. https://doi.org/10.28991/esj-2018-01155.
Wang, J., et al. (2015). Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecological Engineering., 81, 451–461. https://doi.org/10.1016/j.ecoleng.2015.04.022.
Weiss, J. L., et al. (2004). Seasonal and inter-annual relationships between vegetation and climate in central New Mexico, USA. Journal of Arid Environments., 57, 507–534. https://doi.org/10.1016/S0140-1963(03)00113-7.
Wen, X., et al. (2019). Scale effects of vegetation restoration on soil and water conservation in a semi-arid region in China: resources conservation and sustainable management. Resources, Conservation and Recycling., 151, 104474. https://doi.org/10.1016/j.resconrec.2019.104474.
WMO. (1974). Manual on Codes (Vol. 1) International Codes.
Wu, S.-Y., et al. (2019). Climate change impact on vegetation growth in the humid-arid transition zone in northern China during 1982-2013. AGUFM. 2019, GC21D-1321.
Xu, Y., et al. (2016). NDVI-based vegetation responses to climate change in an arid area of China. Theoretical and Applied Climatology., 126, 213–222. https://doi.org/10.1007/s00704-015-1572-1.
Yoo, W., et al. (2014). A study of effects of multicollinearity in the multivariable analysis. International journal of applied science and technology., 4, 9.
Youssef, F., et al. (2012). The effect of vegetation patterns on wind-blown mass transport at the regional scale: a wind tunnel experiment. Geomorphology., 159, 178–188. https://doi.org/10.1016/j.geomorph.2012.03.023.
Zhang, G., et al. (2019). Impact of near-surface wind speed variability on wind erosion in the eastern agro-pastoral transitional zone of Northern China, 1982–2016. Agricultural and Forest Meteorology., 271, 102–115. https://doi.org/10.1016/j.agrformet.2019.02.039.
Zhang, P., et al. (2020). Contributions of climatic and anthropogenic drivers to vegetation dynamics indicated by NDVI in a large dam-reservoir-river system. Journal of Cleaner Production., 256, 120477. https://doi.org/10.1016/j.jclepro.2020.120477.
Zheng, K., et al. (2019). Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Science of the Total Environment., 660, 236–244. https://doi.org/10.1016/j.scitotenv.2019.01.022.
Zhou, J., et al. (2018). Effects of lead stress on the growth, physiology, and cellular structure of privet seedlings. PloS one., 13. https://doi.org/10.1371/journal.pone.0191139.
Zhu, Y., et al. (2019). Responses of vegetation to climatic variations in the desert region of northern China. Catena., 175, 27–36. https://doi.org/10.1016/j.catena.2018.12.007.
Zia-Khan, S., et al. (2015). Effect of dust deposition on stomatal conductance and leaf temperature of cotton in northwest China. Water., 7, 116–131. https://doi.org/10.3390/w7010116.
Ziyaee, A., et al. (2018). Spatial and temporal variations of airborne dust fallout in Khorasan Razavi Province, Northeastern Iran. Geoderma., 326, 42–55. https://doi.org/10.1016/j.geoderma.2018.04.010.
Acknowledgments
The authors would like to acknowledge the National Aeronautics and Space Administration (NASA) and Iran Meteorological Organization for making respectively the MODIS land products and meteorological data.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ebrahimi-Khusfi, Z., Mirakbari, M. & Khosroshahi, M. Vegetation response to changes in temperature, rainfall, and dust in arid environments. Environ Monit Assess 192, 691 (2020). https://doi.org/10.1007/s10661-020-08644-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-020-08644-0