Analysis of the recent trends in vegetation dynamics and its relationship with climatological factors using remote sensing data for Caspian Sea watersheds in Iran
More details
Hide details
Department of Geography, Yazd University, Yazd 8915818411, Iran
Department of Remote Sensing, Yazd University, Yazd 8915818411, Iran
Department of Physics, Institute for Atmospheric Sciences-Weather and Climate, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Department of Environmental Science and Engineering Jiangwan campus, Fudan University, 2005 Songhu Road, Yangpu District Shanghai 200438, China
Department of Agricultural and Environmental Chemistry, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland
Iman Rousta   

Department of Geography, Yazd University, Assistant professor of climatology, Iran
Final revision date: 2022-05-05
Acceptance date: 2022-05-12
Publication date: 2022-06-22
Int. Agrophys. 2022, 36(3): 139–153
  • Using integrated GIS and RS to display the land surface biological dynamics
  • Using multiple satellite products and indices to identify the vegetation dynamic
  • A climate change point of view about atmosphere and ground phenomena
  • A comprehensive statistical analysis concerning CSW and its subregions separately
This study used NDVI, ET, and LST satellite images collected by moderate resolution imaging spectroradiometer and tropical rainfall measuring mission sensors to investigate seasonal and yearly vegetation dynamics, and also the influence of climatological factors on it, in the area of the Caspian Sea Watersheds for 2001-2019. The relationships have been assessed using regression analysis and by calculating the anomalies. The results showed that in the winter there is a positive significant correlation between NDVI and ET, and also LST (R = 0.46 and 0.55, p-value = 0.05, respectively). In this season, the impact of precipitation on vegetation coverage should not be significant when LST is low, as was observed in the analysed case. In spring, the correlation between NDVI and ET and precipitation is positive and significant (R = 0.86 and 0.55, p-value = 0.05). In this season, the main factor controlling vegetation dynamics is precipitation, and LST's impact on vegetation coverage may be omitted when precipitation is much higher than usual. In the summer, the correlation between NDVI and ET is positive and significant (R = 0.70, p-value = 0.05), while the correlation between NDVI and LST is negative and significant (R = –0.45, p-value = 0.05). In this season, the main factor that controls vegetation coverage is LST. In the summer season, when precipitation is much higher than average, the impact of LST on vegetation growth is more pronounced. Also, higher than usual precipitation in the autumn is the reason for extended vegetation coverage in this season, which is mainly due to increased soil moisture.
The authors declare that they have no conflict of interest
Alemu H., Senay G.B., Kaptue A.T., and Kovalskyy V., 2014. Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002-2011. Remote Sens., 6, 5885-908,
Allen R.G., Tasumi M., and Trezza R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. J. Irrig. Drain. Eng., 133, 380-94,
Bagherzadeh A., HoseiniA.V., and Totmaj L.H., 2020. The effects of climate change on normalized difference vegetation index (NDVI) in the Northeast of Iran. Mod. Earth Syst. Environ., 1-13,
Brutsaert W. and Stricker H., 1979. An advection‐aridity approach to estimate actual regional evapotranspiration. Water Resour. Res., 15, 443-50,
Cai D., Fraedrich K., Sielmann F., Guan Y., Guo S., Zhang L., and Zhu X., 2014. Climate and vegetation: An ERA-Interim and GIMMS NDVI analysis. J. Clim., 27, 5111-18,
Chanklan R., Suksut K., Chaiyakhan K., Kaoungku N., Kerdprasop K., and Kerdprasop N.. 2017. On applying regression and neural network to predict rainfall using satellite based index. In Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong kong, China.
Chao L., Zhang K., Wang J., Feng J., and Zhang M., 2021. A comprehensive evaluation of five evapotranspiration datasets based on ground and grace satellite observations: Implications for improvement of evapotranspiration retrieval algorithm. Remote Sens., 13, 2414,
Chen Z., Liu Z., Yin L., and Zheng W., 2022. Statistical analysis of regional air temperature characteristics before and after dam construction. Urban Clim., 41, 101085,
Cheng L., Xu Z., Wang D., and Cai X., 2011. Assessing interannual variability of evapotranspiration at the catchment scale using satellite‐based evapotranspiration data sets. Water Resour. Res., 47,
Chuvieco E., Cocero D., Riano D., Martin P., J. Martınez-Vega, De La Riva J., and Pérez F., 2004. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens. Environ., 92, 322-31,
Dhar S., Goswami S., Sarup J., and Matin S., 2020. Analysis of vegetation dynamics using remote sensing and GIS: a case study of Madhya Pradesh, India. Mod. Earth Sys. Environ., 7, 1039-1051,
Didan K., 2015. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land Processes DAAC.
Didan K., MunozA.B., Solano R., and Huete A., 2015. MODIS vegetation index user's guide (MOD13 series). University of Arizona: Vegetation Index and Phenology Lab, Version 3.00 (Collection 6),
Dutta D., Kundu A., Patel N., Saha S., and Siddiqui A., 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). Egypt. J. Remote Sens. Space Sci., 18, 53-63,
Fan K., Zhang Q., V.P. Singh, Sun P., Song C., Zhu X., Yu H., and Shen Z., 2019. Spatiotemporal impact of soil moisture on air temperature across the Tibet Plateau. Sci. Total Environ., 649, 1338-1348,
Fayech D. and Tarhouni J., 2020. Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area. Mod. Earth Sys. Environ., 7, 1667-1682,
Fonge B.A., Tabot P.T., Bakia M.-A., and Awah C.C., 2019. Patterns of land-use change and current vegetation status in peri-urban forest reserves: the case of the Barombi Mbo Forest Reserve, Cameroon. Geol., Ecol. Landsc., 3, 104-13,
Geerken R., Zaitchik B., and Evans J., 2005. Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. Int. J. Remote Sens., 26, 5535-5554,
Ghafarian Malamiri H., Rousta I., Olafsson H., Zare H., and Zhang H., 2018. Gap-filling of MODIS time series Land Surface Temperature (LST) products using Singular Spectrum Analysis (SSA). Atmosphere, 9, 334,
Goward S.N., Markham B., Dye D.G., Dulaney W., and Yang J., 1991. Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer. Remote Sens. Environ., 35, 257-77,
Hadian F., Hosseini S.Z., and Seydhasani M., 2014. Monitoring vegetation changes using precipitation data and satellite images in northwest of Iran. Iran. J. Range Des. Res., 21, 756-67.
Huffman G., Stocker E., Bolvin D., Nelkin E., and Jackson T.. 2019. "GPM IMERG final precipitation L3 half hourly 0.1 degree× 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC)." In.
Jelínek Z., Kumhálová J., Chyba J., Wohlmuthová M., Madaras M., and Kumhála F., 2020. Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic. Int. Agrophys., 34(3), 391-406,
Karam F., Breidy J., Stephan C., and Rouphael J., 2003. Evapotranspiration, yield and water use efficiency of drip irrigated corn in the Bekaa Valley of Lebanon. Agric. Water Manag., 63, 125-37,
Kim Y., Kimball J.S., Zhang K., Didan K., Velicogna I., and McDonald K.C., 2014. Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing. Int. J. Remote Sens., 35, 3700-3721,
Laio F., Porporato A., Fernandez-Illescas C., and Rodriguez-Iturbe I., 2001. Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress: IV. Discussion of real cases. Adv. Water Res., 24, 745-762,
Lan Z., Zhao Y., Zhang J., Jiao R., Khan M.N., Sial T.A., and Si B., 2021. Long-term vegetation restoration increases deep soil carbon storage in the Northern Loess Plateau. Sci. Rep., 11, 1-11,
Lereboullet A.-L., Beltrando G., and Bardsley D.K., 2013. Socio-ecological adaptation to climate change: A comparative case study from the Mediterranean wine industry in France and Australia. Agric. Ecos. Environ., 164, 273-85,
Loveland T.R., Zhu Z., Ohlen D.O., Brown J.F., Reed B.C., Yang L., 1999. An analysis of the IGBP global land-cover characterization process. Photogramm. Eng. Remote Sens., 65, 1021-1032,
Mansourmoghaddam M., Ghafarian Malamiri H.R., Rousta I., Olafsson H., and Zhang H., 2022a. Assessment of Palm Jumeirah Island's construction effects on the surrounding water quality and surface temperatures during 2001-2020. Water, 14, 634,
Mansourmoghaddam M., Rousta I., Zamani M.S., Mokhtari M.H., Karimi Firozjaei M., and Alavipanah S.K., 2022b. Investigating and modeling the effect of the composition and arrangement of the landscapes of Yazd City on the land surface temperature using machine learning and Landsat-8 and Sentinel-2 data. Iran. J. Remote Sens. GIS.
Mansourmoghaddam M., Rousta I., Zamani M., Mokhtari M.H., Karimi Firozjaei M., and Alavipanah S.K., 2021. Study and prediction of land surface temperature changes of Yazd city: Assessing the proximity and changes of land cover. J.. GIS RS for Natur. Res., 12, 1-27.
Martínez B. and Gilabert M.A., 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ., 113, 1823-42,
Miao R., Liu Y., Wu L., Wang D., Liu Y., Miao Y., Yang Z., Guo M., and Ma J., 2022. Effects of long-term grazing exclusion on plant and soil properties vary with position in dune systems in the Horqin Sandy Land. Catena, 209, 105860,
Moulin S., Kergoat L., Viovy N., and Dedieu G., 1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Clim., 10, 1154-70,<1154:GSAOVP>2.0.CO;2.
Oki T. and Kanae S., 2006. Global hydrological cycles and world water resources. Sci., 313, 1068-72,
Olafsson H. and Rousta I., 2021. Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing. Eur. J. Remote Sens., 54, 351-63,
Ondier G.O., Siebenmorgen T.J., and Mauromoustakos A., 2010. Low-temperature, low-relative humidity drying of rough rice. J. Food Engin., 100, 545-50,
Paraskevas C., Georgiou P., Ilias A., Panoras A., and Babajimopoulos C., 2013. Evapotranspiration and simulation of soil water movement in small area vegetation. Int. Agrophys., 27, 445-453,
Patel N., Chopra P., and Dadhwal V., 2007. Analyzing spatial patterns of meteorological drought using standardized precipitation index. Meteorol. Appl., 14, 329-336,
Picoli M.C.A., Machado P.G., Duft D.G., Scarpare F.V., Corrêa S.T.R., Hernandes T.A.D., and Rocha J.V., 2019. Sugarcane drought detection through spectral indices derived modeling by remote-sensing techniques. Mod. Earth Sys. Environ., 5, 1679-1688,
Poorzady M. and Bakhtiari F., 2009. Spatial and temporal changes of Hyrcanian forest in Iran. iForest, 2, 198-206,
Quaye-Ballard J.A., Okrah T.M., Andam-Akorful S.A., Awotwi A., Osei-Wusu W., Antwi T., and Tang X., 2020. Assessment of vegetation dynamics in Upper East Region of Ghana based on wavelet multi-resolution analysis. Mod.Earth Sys. Environ., 6, 1783-93,
Roerink G.J., Menenti M., Soepboer W., and Su Z., 2003. Assessment of climate impact on vegetation dynamics by using remote sensing. Phys. Chem. Earth, 28, 103-09,
Rousta I., Doostkamian M., Taherian A.M., Haghighi E., Ghafarian Malamiri H.R., and Ólafsson H., 2017. Investigation of the spatio-temporal variations in atmosphere thickness pattern of Iran and the Middle East with special focus on precipitation in Iran. Climate, 5, 82,
Rousta I., Javadizadeh F., Dargahian F., Ólafsson H., Shiri-Karimvandi A., Vahedinejad S.H., Doostkamian M., Monroy Vargas E.R., and Asadolahi A., 2018. Investigation of vorticity during prevalent winter precipitation in Iran. Adv. Meteorol., 2018,
Rousta I., Khosh Akhlagh F., Soltani M., and Modir Taheri Sh. S., 2014. "Assessment of blocking effects on rainfall in northwestern Iran." In COMECAP 2014, 127-32. Heraklion-Grecce: COMECAP.
Rousta I., Olafsson H., Moniruzzaman M., Ardö J., Zhang H., Mushore T.D., Shahin S., and Azim S., 2020a. The 2000-2017 drought risk assessment of the western and southwestern basins in Iran. Mod. Earth Sys. Environ., 6, 1201-1221,
Rousta I., Olafsson H., Moniruzzaman M., Zhang H., Liou Y.-A., Mushore T.D., and Gupta A., 2020b. Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens., 12, 2433,
Rousta I., Olafsson H., Nasserzadeh M.H., Zhang H., Krzyszczak J., and Baranowski P., 2021. Dynamics of daytime land surface temperature (LST) variabilities in the Middle East countries during 2001-2018. Pure Appl. Geophys., 178, 2357-2377,
Running S.W., Mu Q., Zhao M., and Moreno A., 2019. MODIS global terrestrial evapotranspiration (ET) product (MOD16A2/A3 and year-end gap-filled MOD16A2GF/A3GF) NASA Earth Observing System MODIS land algorithm (For Collection 6).
Schwingshackl C., Hirschi M., and Seneviratne S.I., 2017. Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. J. Clim., 30, 7105-24,
Shahbazi F., Jafarzadeh A.A., Sarmadian F., Neyshabouri M., Oustan S., Anaya Romero M., Lojo M., and Rosa D.d.l., 2009. Climate change impact on land capability using MicroLEIS DSS, Int. Agrophys., 23(3), 277-286.
Shen G. and Wang R., 2001. Review of the application of vegetation remote sensing. J. Zhejiang Univ. - Agric. Life Sci., 27, 682-690.
Song K.-B., Baek Y.-S., Hong D.H., and Jang G., 2005. Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans. Power Syst., 20, 96-101,
Tang Q., Vivoni E.R., Muñoz-Arriola F., and Lettenmaier D.P., 2012. Predictability of evapotranspiration patterns using remotely sensed vegetation dynamics during the North American monsoon. J. Hydrometeorol., 13, 103-121,
Tavazohi E. and Nadoushan M.A., 2018. Assessment of drought in the Zayandehroud basin during 2000-2015 using NDDI and SPI indices. Fresenius Environ. Bull., 27, 2332-2340.
Telak L.J., Pereira P., and Bogunovic I., 2021. Soil degradation mitigation in continental climate in young vineyards planted in Stagnosols. Int. Agrophys., 35, 307-317,
Vilček J., Koco Š., Torma S., Lošák T., and Antonkiewicz J., 2019. Identifying soils for reduced tillage and no-till farming using GIS. Pol. J. Environ. Stud., 28(4), 2407-2413,
Wan Z., Hook S., and Hulley G., 2015. MOD11 L2 MODIS/terra land surface temperature/emissivity 5-Min L2 Swath 1 km V006. NASA EOSDIS Land Processes DAAC.
Zandbergen P., 2008. Applications of shuttle radar topography mission elevation data. Geogr. Compass, 2, 1404-1431,
Zhan S., Song C., Wang J., Sheng Y., and Quan J., 2019. A global assessment of terrestrial evapotranspiration increase due to surface water area change. Earths Future, 7(3), 266-282,
Zhang K., Ali A., Antonarakis A., Moghaddam M., Saatchi S., Tabatabaeenejad A., Chen R., Jaruwatanadilok S., Cuenca R., and Crow W.T., 2019a. The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar‐derived estimates of root‐zone soil moisture. J. Geophys. Res. Biogeosci., 124(11), 3208-3231,
Zhang K., Chao L.-j., Wang Q.-q., Huang Y.-c., Liu R.-h., Hong Y., Tu Y., Qu W., and Ye J.-y., 2019b. Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Sci. Eng., 12(2), 85-97,
Zhang K., Wang S., Bao H., and Zhao X., 2019c. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Nat. Hazards Earth Syst. Sci., 19, 93-105,
Zhang W., Liu B., and Wu J., 2001. Monitoring of plant coverage of plots by visual estimation and overhead photograph. Bull. Soil Water Conserv., 21, 60-63.
Zhang X., 2020. The optimization of spatial art pattern of vegetation landscape in the Bay Area. J. Coastal Res., 103, 1051-1055,
Zhao T., Shi J., Entekhabi D., Jackson T.J., Hu L., Peng Z., Yao P., Li S., and Kang C.S., 2021. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sens. Environ., 257, 112321,
Zhao T., Shi J., Lv L., Xu H., Chen D., Cui Q., Jackson T.J., Yan G., Jia L., and Chen L., 2020. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sens. Environ., 240, 111680,
Zhao X., Xia H., Pan L., Song H., Niu W., Wang R., Li R., Bian X., Guo Y., and Qin Y., 2021. Drought monitoring over Yellow River basin from 2003-2019 using reconstructed MODIS land surface temperature in Google Earth Engine. Remote Sens., 13, 3748,
Zhong L., Ma Y., Salama M.S., and Su Z., 2010. Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Clim. Change, 103, 519-535,