Study the effect of meteorological variables variation on phenology of temperate forest in Gorgan region using MODIS sensor

Document Type : Research Paper

Authors

1 Dept of Irrigation and Reclamation Engineering, Faculty of Agriculture, University of Tehran Karaj, Iran

2 Dept of Irrigation and Reclamation Eng, Faculty of Agriculture,University of Tehran, Karaj,Iran

Abstract

In this research, the temporal changes of three phenological events of the start (SOS), End (EOS) and Length (LOS) of the growing season of the temperate forest located in the south of Gorgan city, north of Iran, in response to meteorological variables variations was investigated. Temperature and rainfall data and the time series of the Vegetation Index (EVI) of the MODIS sensor, were collected and analyzed using three smoothing approaches. Results showed that in all three smoothing methods, withn an increase in Tmax and Tmin in the 10-day period prior to SOS phenology stage, it would be delayed.Similarlyany decrease in T ̅max and Tmin in a 10-day period before the EOS, would delay its onset. In Gaussian and Savitzky-Golay (p < 0.05) and Double Logistic (p < 0.01) methods, correlation between LOS and Tmin and Tmax in spring; and in all three smoothing methods, the correlation of SOS and EOS with T ̅_max in the summer season was significantly positive (p < 0.05) .A weak positive correlation of SOS and EOS with Tmin in the summer season was observed. Also, SOS, EOS and LOS phenological stages have not shown any significant relationship with precipitation in different seasons. According results, temperature is the most important factor in the variability of SOS, EOS, and LOS occurrence time in study forest. Any increase and decrease of Tmax and Tmin cause shifts in the time of occurrence of these phenology stages.

Keywords

Main Subjects

Introduction

Phenology is the study of the annual life cycles of plants, animals, and other living organisms, particularly the timing of cyclical patterns of major events driven by changes in weather and climate. Phenology is both biological and meteorological, thus providing a key perspective for investigating biosphere–atmosphere interdependence in spatial, temporal, and specific ecological contexts. The timing behaviors of plant and animal species are coordinated with the annual changes of sunlight, temperature and precipitation in different weather conditions. Land Surface phenology is the study of plant phenology at a regional to global scale, derived from data obtained from space-based optical sensors. The most common vegetation indices are NDVI and EVI index, which are examples of reflectance-based greenness information. The time of SOS, LOS and EOS of plant growth season are the most important phenological parameters that can be extracted from the time series of satellite vegetation indicators. In this study, using TIMESAT software and EVI index, the phenology stages of Gorgan city forest have been extracted and the sensitivity of these stages to climate fluctuations was investigated.

Methods and Materials

In this article, the land use map of Golestan province was retrieved using the global land cover product of the European Space Agency (ESA) with a resolution of 10 meters  in the Google Earth Engine software, and drawn in 9 classes. To obtain temperature and precipitation data, agrometeorological data of AgERA5 database  were obtained  using a Python programming code. Then these data (,  and P) were prepared seasonally and during the statistical period of 2001-2022 in the four seasons of winter, spring, summer and autumn. To extract the time series of the EVI index, the 16-day data of the MODIS sensor with a resolution of 250 meters was called in the Google Earth Engine system, and the SOS, EOS and LOS phenology stages of vegetation growth were calculated using TIMESAT software version 3.3 and they were prepared with three smoothing methods of Gaussian, Double Logistic and Savitzky-Golay. Then the relationship between EVI index and meteorological variables of temperature and precipitation in winter, spring, summer and autumn seasons was calculated by Pearson correlation method.

Results and Discussion

Results showed that in all three smoothing methods, an increase in  and  in the 10-day period prior to SOS phenology stage, it would be delayed, similarity any decrease in  and  in a 10-day period before the EOS, this stage is also delayed. In Gaussian and Savitzky-Golay (p < 0.05) and Double Logistic (p < 0.01) methods, correlation between LOS and  and  in spring; and in all three smoothing methods, the correlation of SOS and EOS with  in the summer season was significantly positive (p < 0.05), and the correlation of SOS and EOS with  in the summer season was weak and positive. Also, SOS, EOS and LOS phenological stages have not shown any significant relationship with precipitation in different seasons. According results, temperature is the most important factor in the variability of SOS, EOS, and LOS occurrence time in study forest. Any increase and decrease of  and  cause daily shifts in the time of occurrence of these phenology stages.

Conclusion

The results indicate that the annual shift in of SOS and EOS occurrence date has an increasing trend in all three smoothing methods, which indicates the high sensitivity of these phenology stages to meteorological variables and climate variations during the study period. Satellite LSP monitoring is directly driven by meteorological variables, and among them, temperature plays the most important role. It should be noted that some limitations such as the distinct phenology of plant species in a pixel, affect the change in timing of LSP criteria. Therefore, it is necessary to perform frequent field monitoring of plant species in the region. Availability of high-quality meteorological data within the forest region and finer spatial resolution of satellite images are also quite important to achieve reliable results. Therefore, it is suggested to use other satellite images with higher resolution in order to increase the accuracy in the time series of satellite spatial greenness information and consider the asymmetric effects of  and  in satellite vegetation phenology simulations.

Author Contributions

Conceptualization, N.G. and H.M.; methodology, N.G and H.M.; software, H.M.; validation, H.M.and N.G.; formal analysis, H.M.; investigation, H.M.; resources, H.M.; data curation, H.M.; writing—original draft preparation, H.M.; writing—review and editing, N.G.; visualization, H.M.; supervision, N.G.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.”

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article

Acknowledgements

The authors would like to thank Iran Meteorological Organization and Iran Forest and rangeland organization for providing some of required datasets

Ethical considerations

The study was approved by the Ethics Committee of the University of Tehran (Ethical code: IR.UT.RES.2024.500). The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

References

Bay, N., & Davoudi, M. (2010). Analysis and prediction of some climatic elements of Gorgan city. The Quarterly Journal of Zagros Geographical Landscape, 2(4), 99-114. SID. https://sid.ir/paper/175733/fa. (in Persian)  
Bonhomme, R. (2000). Bases and limits to using ‘degree.day’ units. European Journal of Agronomy, 13(1), 1-10. https://doi.org/10.1016/S1161-0301(00)00058-7.
Clerici, N., Weissteiner, C.J., Halabuk, A., Hazeu, G.W., Roerink, G.J., & Mücher, S. (2012). Phenology related measures and indicators at varying spatial scales. Investigation of phenology information for habitat classification using SPOT VGT and MODIS NDVI data. Applied Spatial Research Earth Observation and Environmental Informatics, Wageningen, Alterra, Alterra-Report 2259. 112 pp.; 33 figs.; 15 tab.; 34 ref.
Dambreville, A., Lauri, P.E., Normand, F., & Guedon, Y. (2014). Analysing growth and development of plants jointly using developmental growth stages. Annals of Botany, 115(1), 93–105. doi: 10.1093/aob/mcu227.
Didan, K., Munoz, A.B., Solano, R., & Huete, A. (2015).  MODIS Vegetation Index User’s Guide. (MOD13 Series). Version 3. The University of Arizona, Tucson.
Eklundha, L., & Jönsson, P. (2017). TIMESAT 3.3 with seasonal trend decomposition and parallel processing. Software Manual, Lund University and Malmö University, Sweden.
Fu, Y.H., Zhao, H., Piao, S., Peaucelle, M., Peng, S., Zhou, G., Ciais, P., Huang, M., Menzel, A., Peñuelas, J., Song, Y., Vitasse, Y., Zeng, Z., & Janssens, I.A. (2015). Declining global warming effects on the phenology of spring leaf unfolding. Nature, 526(7571), 104–107. doi: 10.1038/nature15402.
Gallinat A.S., Primack R.B., & Wagner D.L. (2015). Autumn, the neglected season in climate change research. Trends in Ecology & Evolution, 30(3), 169–176. https://doi.org/10.1016/j.tree.2015.01.004.
Gatsuk, L.E., Smirnova, O.V., Vorontzova, L.I., Zaugolnova, L.B., & Zhukova, L.A. (1980). Age states of plants of various growth forms: a review. Journal of Ecology, 68(68), 675–696. DOI:10.2307/2259429.
Gerst, K.L., Rossington, N.L., & Mazer, S.J. (2017). Phenological responsiveness to climate differs among four species of Quercus in North America. Journal of Ecology, 105(6): 1610–1622· https://doi.org/10.1111/1365-2745.12774.
Gordo, O., & Sanz, J.J. (2010). Impact of climate change on plant phenology in Mediterranean ecosystems. Global Change Biology, 16(3): 1082–1106. https://doi.org/10.1111/j.1365-2486.2009.02084.x.
Helman, D. (2018). Land surface phenology: What do we really ‘see’ from space?. Science of the Total Environment, 618, 665-673. https://doi.org/10.1016/j.scitotenv.2017.07.237.
Huete, A.R. (2012). Vegetation indices, remote sensing and forest monitoring. Geography Compass,  6(9): 513–532. https://doi.org/10.1111/j.1749-8198.2012.00507.x.
Hufkens, K., Friedl, M., Sonnentag, O., Braswell, B.H., Milliman, T., & Richardson, A.D. (2012). Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sensing of Environment, 117(D11), 307–321. https://doi.org/10.1016/j.rse.2011.10.006.
Jönsson, P., & Eklundh, L. (2004). TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences, 30(8), 833-845. https://doi.org/10.1016/j.cageo.2004.05.006.
Katal, N., Rzanny, M., Mäder, P., & Wäldchen, J. (2022). Deep Learning in Plant Phenological Research: A Systematic Literature Review. Front Plant Science. 13, 805738. doi: 10.3389/fpls.2022.805738.
Keenan, T.F., Gray, J., Friedl, M., Toomey, M., Bohrer, G., Hollinger, D.Y., Munger, J.W., O’Keefe, J., Schmid, H.P., Wing, I.S., Yang, B., & Richardson, A.D. (2014). Net carbon uptake has increased through warming induced changes in temperate forest phenology. Nature Climate Change, 4, 598–604. DOI:10.1038/NCLIMATE2253.
Khalili, A., Bazrafshan, J., & Cheraghalizadeh, M. (2022). A Comparative study on climate maps of Iran in extended de Martonne classification and application of the method for world climate zoning. Journal of Agricultural Meteorology, 10(1), 3-16. 10.22125/agmj.2022.156309. (in Persian)
Li, K.; Wang, C., Sun, Q., Rong, G., Tong, Z., Liu, X., & Zhang, J. (2021). Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis. Remote Sensing, 13(10), 1972. https://doi.org/10.3390/ rs13101972.
Li, X., Guo, W., Chen, J., Ni, X., & Wei, X. (2019). Responses of vegetation green-up date to temperature variation in alpine grassland on the Tibetan Plateau. Ecological Indicators, 104(5972)و 390-397. DOI:10.1016/j.ecolind.2019.05.003.
Liang, L. (2019). Phenology. Elsevier. University of Kentucky, Lexington, KY, United States.
Liang, L., Mark, D., Schwartz, M.D., & Fei, S. (2011). Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115(1), 143–157. https://doi.org/10.1016/j.rse.2010.08.013.
Lieth, H. (1974). Phenology and seasonal modeling. vol. 8. Springer-Verlag. New York.
Liu, Q., Fu, Y.H., Zeng, Z., Huang, M., Li, X., & Piao, S., (2016a). Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Global Change Biology, 22(2), 644–655. https://doi.org/10.1111/gcb.13081.
Liu, Q., Fu, Y.H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I.A., & Piao, S. (2016b). Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Global Change Biology, 22(11), 3702–3711. https://doi.org/10.1111/gcb.13311.
Liu, Y., Shen, X., Zhang, J., Wang, Y., Wu, L., Ma, R., Lu, X., & Jiang, M. (2023). Variation in Vegetation Phenology and Its Response to Climate Change in Marshes of Inner Mongolian. Plants, 12(11): 2072. https:// doi.org/10.3390/plants12112072.
Ma, R., Shen, X., Zhang, J., Xia, C., Liu, Y., Wu, L., Wang, Y., Jiang, M., & Lu, X. (2022). Variation of vegetation autumn phenology and its climatic drivers in temperate grasslands of China. International Journal of Applied Earth Observation and Geoinformation, 114, 103064. https://doi.org/10.1016/j.jag.2022.103064.
Malayeri, F., Ashourloo, D., Shakiba, A., Matkan, A.A. & Aghighi, H. (2018). Investigating the Effects of Climate Change on Vegetation Phenology Using AVHRR Time Series Data. Journal of Agroecology, 8 (2), 98-117. (in Persian)
Masihpoor, M., Darvishsefat, A.A., Rahmani, R., & Fatehi, P. (2021). Phenological parameters trend of the southern Zagros forests based on MODIS-NDVI time series during 2000-2017. Iranian journal of Forest, 12(4), 577-590. DOI:10.22034/ijf.2021.127807 . (in Persian)  
Matthews, E.R., & Mazer, S.J. (2015). Historical changes in flowering phenology are governed by temperature * precipitation interactions in a widespread perennial herb in western North America. New Phytologist, 210(1), 157-167. doi: 10.1111/nph.13751. 
Menzel, A., Sparks, T.H., Estrella, N., & Roy, D.B. (2006). Altered geographic and temporal variability in phenology in response to climate change. Global Ecology and Biogeography, 15(5), 498–504. doi: 10.1111/j.1466-822X.2006.00247.x
Piao, S., Fang, J., Zhou, L., Ciais, P., & Zhu, B. (2006). Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12(4), 672-685. DOI:10.1111/j.1365-2486.2006.01123.x.
Piao, S., Liu, Q., Chen, A., Janssens, I.A., Fu, Y,H., Dai, J., Liu, L., Lian, X., Shen, M., & Zhu, X. (2019). Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 25(6), 1922–1940. https://doi.org/10.1111/gcb.14619.
Piao, S., Tan, J., Chen, A., Fu, Y.H., Ciais, P., Liu, Q., Janssens, I.A., Vicca, S., Zeng, Z., Jeong, S.J., Li, Y., Myneni, R.B., Peng, S., Shen, M., & Penuelas, J. (2015). Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 6(1), 6911. DOI: 10.1038/ncomms7911.
Rayegani, B., Arzani, H., Heydari Alamdarloo, E., & Moghadami, M.M. (2019). Application of remote sensing to assess climate change effects on plant productivity and phenology (Case study area: Tehran Province). Rangelandsrm, 13(3), 450-460. (in Persian)
Richardson, A.D., Anderson, R.S., Arain, M.A., Barr, A.G., Bohrer, G., Chen, G., Chen, J.M., Ciais, P., Davis, K.J., Desai A.R., Dietze, M.C., Dragoni, D., Garrity, S.R., Gough, C.M., Grant, R., Hollinger, D.Y., Margolis, H.A., McCaughey, H., Migliavacca, M., Monson, R.K., Munger, J,W., Poulter, B., Raczka, B.M., Ricciuto, D.M., Sahoo, A.K., Schaefer, K., Tian, H., Vargas, R., Verbeeck, H., Xiao, J., & Xue, Y. (2012). Terrestrial biosphere models need better representation of vegetation phenology: Results from the north American carbon program site synthesis. Global Change Biology, 18(2), 566–584. https://doi.org/10.1111/j.1365-2486.2011.02562.x.
Schwartz, M.D. (2013). Phenology: An integrative environmental science. Springer, The Netherlands, Dordrecht.
Schwartz, M.D., Ahas, R., & Aasa, A. (2006). Onset of spring starting earlier across the northern hemisphere. Global Change Biology, 12(2), 343–351. doi: 10.1111/j.1365-2486.2005.01097.x
White M.A., Running, S.W., & Thornton, P.E. (1999). The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. International Journal of Biometeorology, 42(3), 139-145.
Wu, C., Gonsamo, A., Chen, J.M., Kurz, W.A., Price, D.T., Lafleur, P.M., Jassal, R.S., Dragoni, D., Bohrer, G., Gough, C.M., Verma. S.B., Suyker, A.E., & Munger, J.W. (2012). Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis. Global and Planetary Change, 92–93, 179-190. https://doi.org/10.1016/j.gloplacha.2012.05.021.
Xia, J., Niu, S., Ciais, P., Janssens, I.A., Chen, J., Ammann, C., Arain, A., Blanken, P.D., Cescatti, A., Bonal, D., Buchmann, N., Curtis, P.S., Chen, S., Dong, J., Flanagan, L.B., Frankenberg, C., Georgiadis, T., Gough, C.M., Hui, D., Kiely, G., Li, J., Lund, M., Magliulo, V., Marcolla, B., Merbold, L., Montagnani, L., Moors, E.J., Olesen, J.E., Piao, S., Raschi, A., Roupsard, O., Suyker, A.E., Urbaniak, M., Vaccari, F.P., Varlagin, A., Vesala, T., Wilkinson, M., Weng, E., Wohlfahrt, G., Yan, L., & Luo, Y. (2015). Joint control of terrestrial gross primary productivity by plant phenology and physiology. Ecology, 112(9): 2788–2793. https://doi.org/10.1073/pnas.1413090112.
Iranian Journal of Soil and Water Research
Volume 56, Issue 4
July 2025
Pages 881-897

Files

Share

How to cite

Statistics