RESEARCH PAPER
Investigation of vegetation dynamics with a focus on agricultural land cover and its relation with meteorological parameters based on the remote sensing techniques: a case study of the Gavkhoni watershed
 
More details
Hide details
1
Department of Geography, Yazd University, Yazd 8915818411, Iran
 
2
Institute for Atmospheric Sciences-Weather and Climate and Department of Physics, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
 
3
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
 
 
Final revision date: 2024-02-20
 
 
Acceptance date: 2024-03-12
 
 
Publication date: 2024-05-06
 
 
Corresponding author
Jaromir Radosław Krzyszczak   

Department of Metrology and Modelling of Agrophysical Processes, Instytut Agrofizyki PAN Lublin, Doświadczalna 4, 20-290, Lublin, Poland
 
 
Int. Agrophys. 2024, 38(3): 213-229
 
Data Availability Statement: The data presented in this study are available on request from the first author.
HIGHLIGHTS
  • A negative trend in VAC was observed for the central region of the Gavkhoni basin
  • A combination of climatic, social, and cultural factors was responsible for VAC reduction
  • A diminishing water storage in the Zayandeh-Roud dam resulting from reduced rainfall was a significant contributing factor to VAC reduction
KEYWORDS
TOPICS
ABSTRACT
Background and Aims: This research investigates vegetation dynamics in the Gavkhouni catchment from 2001 to 2021, focusing on the spring season. The aim is to analyse the relationship between aridity, vegetation, and rainfall. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover thereby contributing to our understanding of the environmental dynamics in the Gavkhouni catchment. Methods: The study made use of MODIS data, including the Enhanced Vegetation Index and Vegetation Condition Index, along with monthly rainfall statistics from Chirps. Analytical methods include time series analyses using correlation and regression analysis. Results: Throughout the study period, the average spring vegetation cover was 9276.33 km². The years 2001 and 2018 had the lowest degree of vegetation (15.53, and 17.3% of the watershed area). Conversely, 2013, 2019, and 2020 had the most coverage (27.4, 26.8, and 26.3%). The Enhanced Vegetation Index highlighted the arid years (2001, 2008, 2011, and 2018) and the years with the lowest drought prevalence (2006, 2007, 2010, 2013). Enhanced Vegetation Index correlated with spring rainfall. Cropland cover declined over the study period, and a close correlation was found between winter rainfall and spring agricultural coverage.
FUNDING
This work was supported by Vedurfelagid, Rannis and Rannsoknastofa i vedurfraedi.
CONFLICT OF INTEREST
The Authors do not declare any conflict of interest.
 
REFERENCES (52)
1.
Ashraf M. and Routray J.K., 2013. Perception and understanding of drought and coping strategies of farming households in north-west Balochistan. Int. J. Disaster Risk Reduction, 5, 49-60. https://doi.org/10.1016/j.ijdr....
 
2.
Belward A.S., Estes J.E., and Kline K.D., 1999. The IGBP-DIS global 1-km land-cover data set DISCover: A project overview. Photogrammetric Engineering and Remote Sensing, 65(9), 1013-1020.
 
3.
Bhuiyan C., Singh R., and Kogan F., 2006. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int. J. Appl. Earth Obs. Geoinf., 8(4), 289-302. https://doi.org/10.1016/j.jag.....
 
4.
Cancelliere A., Mauro G.D., Bonaccorso B., and Rossi G., 2007. Drought forecasting using the standardized precipitation index. Water Res. Manag., 21, 801-819. https://doi.org/10.1007/s11269....
 
5.
Chang C.-T., Wang S.-F., Vadeboncoeur M.A., and Lin T.-C., 2014. Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices. Int. J. Remote Sens., 35(2), 598-620. https://doi.org/10.1080/014311....
 
6.
Couteron P., Hunke P., Bellot J., Estrany J., Martínez-Carreras N., Mueller E.N., Papanastasis V.P., Parmenter R.R., and Wainwright J., 2014. Characterizing patterns. In: Patterns of Land Degradation in Drylands: Understanding Self-Organised Ecogeomorphic Systems. 211-245. Springer Science+Business Media Dordrecht. https://doi.org/10.1007/978-94....
 
7.
Dikici M., 2022. Drought analysis for the Seyhan Basin with vegetation indices and comparison with meteorological different indices. Sustainability, 14(8), 4464. https://doi.org/10.3390/su1408....
 
8.
Dorjsuren M., Liou Y.-A., and Cheng C.-H., 2016. Time series MODIS and in situ data analysis for Mongolia drought. Remote Sensing, 8(6), 509. https://doi.org/10.3390/rs8060....
 
9.
Farahani A., Eftekhari A., Mirdavoudi H., and Goudarzi G., 2022. The effect of exclosure and climate changes on vegetation characteristics in the saline habitats of Meyghan playa margin, Arak. Iran. J. Seed Res., 29(3), 201-210. https://doi.org/10.22092/ijrdr....
 
10.
Farajzadeh M., Fathnia A., Alijani B., and Zeaiean P., 2011. Assessment of the effect of climatic factors on the growth of dense pastures of Iran, Using AVHRR Images. Physical Geography Research Quarterly, 43(75(1390)), 491307.
 
11.
Ghafarian Malamiri H.R., 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(9), 334. https://doi.org/10.3390/atmos9....
 
12.
Gupta A., Moniruzzaman M., Hande A., Rousta I., Olafsson H., and Mondal K.K., 2020. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh. SN Applied Sciences, 2(12), 1993. https://doi.org/10.1007/s42452....
 
13.
Hansen M.C., DeFries R.S., Townshend J.R., and Sohlberg R., 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21(6-7), 1331-1364. https://doi.org/10.1080/014311....
 
14.
Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., and Ferreira L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83(1-2), 195-213. https://doi.org/10.1016/S0034-....
 
15.
Huete A., Justice C., and Liu H., 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sens. Environ., 49(3), 224-234. https://doi.org/10.1016/0034-4....
 
16.
Huete A., Justice C., and van Leeuwen W., 1999. MODIS vegetation index (MOD 13) algorithm theoretical basis document, version 3. University of Arizona, 129p.
 
17.
Huete A., Liu H., Batchily K., and Van Leeuwen W., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environ., 59(3), 440-451. https://doi.org/10.1016/S0034-....
 
18.
Jafari R. and Bakhshandehmehr L., 2016. Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran. Land Degradation Development, 27(2), 108-119. https://doi.org/10.1002/ldr.22....
 
19.
Jafari R. and Hasheminasab S., 2017. Assessing the effects of dam building on land degradation in central Iran with Landsat LST and LULC time series. Environmental Monitoring and Assessment, 18, 1-15. https://doi.org/10.1007/s10661....
 
20.
Kafarski M., Majcher J., Wilczek A., Szyplowska A., Lewandowski A., Zackiewicz A., and Skierucha W., 2019. Penetration depth of a soil moisture profile probe working in time-domain transmission mode. Sensors, 19(24), 5485. https://doi.org/10.3390/s19245....
 
21.
Kogan F.N., 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Am. Meterol. Soc., 76(5), 655-668. https://doi.org/10.1175/1520-0...<0655:DOTLIT>2.0.CO;2.
 
22.
Kogan F.N., 1997. Global drought watch from space. Bull. Am. Meterol. Soc., 78(4), 621-636. https://doi.org/10.1175/1520-0...<0621:GDWFS>2.0.CO;2.
 
23.
Khosravi Yeganeh S., Karampour M., and Nasiri B., 2023. Evalua-tion of the effect of drought on vegetation in Iran using satellite images and meteorological data. Iranian J. Remote Sensing GIS. https://doi.org/10.48308/gisj.....
 
24.
Loveland T.R. and Belward A., 1997. The international geosphere biosphere programme data and information system global land cover data set (DISCover). Acta Astronautica, 41(4-10), 681-689. https://doi.org/10.1016/S0094-....
 
25.
Luan J., Liu D., Zhang L., Huang Q., Feng J., Lin M., and Li G., 2018. Analysis of the spatial-temporal change of the vegetation index in the upper reach of Han River Basin in 2000-2016. Proc. Int. Association of Hydrological Sciences, 379, 287-292. https://doi.org/10.5194/piahs-....
 
26.
Mahmood S.A.R., Rousta I., and Mazidi A., 2022. Investigating the sustainability of vegetation change trends using remote sensing (Case Study: Northern River Basin of Afghanistan). Geography and Environmental Sustainability, 12(2), 17-35. https://doi.org/10.22126/GES.2....
 
27.
Majcher J., Kafarski M., Wilczek A., Szypłowska A., Lewandowski A., Woszczyk A., and Skierucha W., 2021. Application of a dagger probe for soil dielectric permittivity measurement by TDR. Measurement, 178, 109368. https://doi.org/10.1016/j.meas....
 
28.
Manesh M., Alamdarlo E.H., and Jazi N.A., 2018. Assessing the reclamation and destruction of vegetation using remote sensing (Case Study: Tehran Province). Int. Conf. Society Environment, Tehran. https://civilica.com/doc/81587....
 
29.
Mansourmoghaddam M., Ghafarian Malamiri H.R., Rousta I., Olafsson H., and Zhang H., 2022. Assessment of Palm Jumeirah Island’s construction effects on the surrounding water quality and surface temperatures during 2001-2020. Water, 14(4), 634. https://doi.org/10.3390/w14040....
 
30.
Martiny N., Camberlin P., Richard Y., and Philippon, N., 2006. Compared regimes of NDVI and rainfall in semi‐arid regions of Africa. Int. J. Remote Sensing, 27(23), 5201-5223. https://doi.org/10.1080/014311....
 
31.
Mirahsani M., Salman Mahiny A., Soffianian A., Moddares R., Jafari R., and Mohammadi J., 2018. Evaluation of vegetation supply water index through time-series images of MODIS products in drought monitoring over Gavkhuni Basin. Iranian J. Applied Ecology, 6(4), 31-47. https://doi.org/10.29252/ijae.....
 
32.
Mirahsani M.S., Salman Mahiny A., Soffianian A., Mohamadi J., Modarres R., Modares R., and Pourmanafi S., 2019. Evaluation of trend in vegetation variations using time series images and Mann-Kendall test over Gavkhuni Basin. J. Environmental Studies, 45(1), 99-114. https://doi.org/10.22059/jes.2....
 
33.
Olafsson H. and Rousta I., 2021. Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing. Remote Sens. Environ., 54(1), 351-363. https://doi.org/10.1080/227972....
 
34.
Pei F., Wu C., Liu X., Li X., Yang K., Zhou Y., Wang K., Xu L., and Xia G., 2018. Monitoring the vegetation activity in China using vegetation health indices. Agric. Forest Meteorology, 248, 215-227. https://doi.org/10.1016/j.agrf....
 
35.
Peng J., Liu Z., Liu Y., Wu J., and Han Y., 2012. Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent. Ecological Indicators, 14(1), 28-39. https://doi.org/10.1016/j.ecol....
 
36.
Peters E., 2003. Propagation of drought through groundwater systems: illustrated in the Pang (UK) and Upper-Guadiana (ES) catchments. Ph.D. Thesis, Wageningen University, The Netherlands.
 
37.
Rannow S. and Neubert M., 2014. Managing protected areas in central and eastern Europe under climate change. In: Springer, Cham., https://doi.org/10.1007/978-94....
 
38.
Raynolds M.K., Comiso J.C., Walker D.A., and Verbyla D., 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sens. Environ., 112(4), 1884-1894. https://doi.org/10.1016/j.rse.....
 
39.
Razipoor M.E., 2019. Assessing the vegetation condition of herat province, Afghanistan Using GIS. Appl. Geol. Geophysics, 7(4), 92-97. https://doi.org/10.9790/0990-0....
 
40.
Rousta I., Javadizadeh F., Dargahian F., Olafsson 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. 2018, 6941501. https://doi.org/10.1155/2018/6....
 
41.
Rousta I., Khosh Akhlagh F., Soltani M., and Modir Taheri Sh S., 2014. Assessment of blocking effects on rainfall in northwestern Iran. Proceedings of COMECAP 2014, p.291.
 
42.
Rousta I., Mahmood S.R., and Saberi M.A., 2020a. Investigation of vegetation change using NDVI index and MODIS sensor in Balkh province of Afghanistan (in Persian). 2nd Nat. Conf. New Thoughts and Technologies in Geographical Sciences, Zanjan: Zanjan University.
 
43.
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(15), 2433. https://doi.org/10.3390/rs1215....
 
44.
Rousta I., Nasserzadeh M.H., Jalali M., Haghighi E., Olafsson H., Ashrafi S., Doostkamian M., and Ghasemi, A., 2017. Decadal spatial-temporal variations in the spatial pattern of anomalies of extreme precipitation thresholds (Case Study: Northwest Iran). Atmosphere, 8(8), 135. https://doi.org/10.3390/atmos8....
 
45.
Saraei S., Afrakhteh H., Riahi V., and Jalalian H., 2017. Evaluate the usage of information and communication technology in agricultural water use optimization using soft system approach. Interdisciplinary Studies in Humanities, 9(4), 49-70. https://doi.org/10.22631/isih.....
 
46.
Shah R., Bharadiya N., and Manekar V., 2015. Drought index computation using standardized precipitation index (SPI) method for Surat District, Gujarat. Aquatic Procedia, 4, 1243-1249. https://doi.org/10.1016/j.aqpr....
 
47.
Syed A., Liu X., Moniruzzaman M., Rousta I., Syed W., Zhang J., and Olafsson H., 2021. Assessment of climate variability among seasonal trends using in situ measurements: A case study of Punjab, Pakistan. Atmosphere, 12(8), 939. https://doi.org/10.3390/atmos1....
 
48.
Tate E., and Gustard A., 2000. Drought definition: a hydrological perspective. In: Drought and drought mitigation in Europe, 23-48. Springer. https://doi.org/10.1007/978-94....
 
49.
Wan Z., Wang P., and Li X., 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens., 25(1), 61-72. https://doi.org/10.1080/014311....
 
50.
Weishou S., Di J., Hui Z., Shouguang Y., Haidong L., and Naifeng L., 2011. The response relation between climate change and NDVI over the Qinghai-Tibet plateau. J. World Academy of Science, Eng. Technology, 59, 2216-2222. https://doi.org/10.5281/zenodo....
 
51.
Zhang N., Hong Y., Qin Q., and Zhu L., 2013. Evaluation of the visible and shortwave infrared drought index in China. Int. J. Disaster Risk Science, 4, 68-76. https://doi.org/10.1007/s13753....
 
52.
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. Climatic Change, 103(3-4), 519-535. https://doi.org/10.1007/s10584....
 
eISSN:2300-8725
ISSN:0236-8722
Journals System - logo
Scroll to top