Investigation of phenological components changes of Iranian vegetation in response to climate change using NDVI products of AVHRR sensor from 1982 to 2018
Subject Areas : Applications in earth’s climate changeHadi Zare Khormizi 1 , Hamid Reza Ghafarian Malamiri 2
1 - PhD. Student of Range Management, Faculty of Natural Resources, University of Tehran, Tehran, Iran
2 - Assistant Professor, Department of Geography, Faculty of Humanities and Social Sciences, University of Yazd, Yazd, Iran
Keywords: Normalized difference vegetation index (NDVI), Phenology, Growth season, remote sensing, Climate Change,
Abstract :
Background and ObjectiveClimate change has had a negative impact on agricultural products and environmental systems in different countries. Plant phenology describes the periodical plant life events in relation to living and non-living factors. Phenology is one of the most sensitive biological indicators for studying the effect of global warming on terrestrial ecosystems, as it represents the exchange of energy, carbon, and water vapor between low levels of the atmosphere and the biosphere. Plants phenological changes can have a wide range of effects on environmental processes, agriculture, forestry, food supply, human health and the global economy. There are two common approaches to monitoring vegetation phenology. The first approach used in many previous phenology studies is based on field studies and recording annual changes in phenological events in response to environmental variables. So far, the phenological components changes of Iran's vegetation coverages in response to climate change and global warming have not been studied. The purpose of this study is to determine the changes of each component of Iranian vegetation phenology This approach is suitable for small scales with a limited number of sampling sites and is not only inefficient and inaccurate for large-scale studies but also costly and impossible in some areas. The second approach, developed in recent years, is the use of satellite imagery and remote sensing technology. using NDVI time series of AVHRR sensor. The results of this study can be used in determining the date of cultivation season, environment, rangelands and water resources management, and finally useful and practical recommendations to farmers. Materials and Methods In this study, daily NDVI product of AVHRR sensor, called AVH13C1, was used with a spatial resolution of 0.05 by 0.05 degrees. To investigate the changes in phenological components of Iranian vegetation, four one-year time series related to 1982 to 1985 years (namely as past time) and 2015- 2018 years (namely as present time) were used. Extraction of phenological components from the time series of vegetation indices initially requires continuous gap-free data. The HANTS algorithm was used to reconstruct the gaps and outliers from the time series. Then, in order to extract different phenological components, Timsat software was used. The beginning of the season, end of the season, length of the season, base value, time of mid of the season, maximum value, the seasonal amplitude, value for the start of the season, rate of increase at the beginning of the season and rate of decrease at the end of the season were extracted using Timsat software in each one-year time series, were extracted using Timsat software in each one-year time series, and then the four-year average of the values of these parameters in the past time series was compared to the present time series. Results and Discussion Comparison of the four-year average of phenological components of the time for the start of the season, the time for the end of the season, the Length of the season and the time for the mid of the season in Iran showed that these indicators decreased by 12, 19, 7 and 13 days, respectively. The rate of changes of these components in lowland areas with an altitude of less than 1500 meters are completely different from highland areas which include Alborz and Zagros chains. So that, from an altitude of 1500 meters and above, the time for the start of the season, the length of the season and the time for the mid of the season in the Alborz and Zagros chains have decreased to an average of 38, 46 and 19 days, respectively. In the lowlands area near to the Persian Gulf and the Caspian Sea, the phenological components of the time for the end of the season and the length of the season have increased by approximately 40 and 44 days, respectively. The prolongation of the growing season has been attributed to various climatic factors, especially global warming due to increased greenhouse gases or water availability. In Iran, in most areas, the beginning of the growing season, especially in the Alborz and Zagros highlands, where the temperature is a limiting factor, has decreased. But unlike some studies conducted outside of Iran, the time for the end of the season, the length of the season and the time for the mid of the season have also decreased. This indicates that in arid and semi-arid regions such as Iran, in the middle and final stages of plant growth, moisture and rainfall are limiting factors for growth. In areas such as the Persian Gulf and the Caspian Sea, where low humidity has not been a limiting factor, the end of the growing season and the length of the growing season have also increased. Based on the results, the phenological components such as seasonal amplitude, maximum value, base value, value for the start of the season, rate of increase at the beginning of the season and rate of decrease at the end of the season have increased in Alborz and Zagros heights. This component is generally reduced to areas with altitudes below 1500. It seems that in arid and semi-arid regions, the high temperature can also increase the evapotranspiration of the plant, which causes a lack of moisture in the soil. Therefore, at the area with high altitudes that temperature is a controlling factor at the beginning of the growing season, the increasing temperature in present time series has led to increased plant growth and ecosystem production capacity, and phenological parameters such as growing season range, maximum growth rate, base value and the value at the starting point of growth have increased. However, in lowland areas, as well as at the end of the plant growth period in high altitudes, the increasing temperature has led to increased evapotranspiration and reduced the seasonal amplitude, maximum value, basal value and value for the start of the season. Conclusion Changes in phenological parameters such as the beginning of the season, the time for the end of the season and the length of the season can have a negative impact on the agricultural products and environmental systems. The recent earlier beginning of the growing season compared to the last 35 years can be a significant threat to the agricultural and horticultural products, because cold and frost are the most important climatic parameters in the field of agricultural climate. As a result, it reduces the possibility of producing many agricultural and horticultural products in vulnerable areas. In general, the results of the present study show a series of interconnected events caused by climate change and increase in temperature in various components of phenology in the Alborz and Zagros highlands, as well as in lowland and plain areas, especially in the Persian Gulf and the Caspian Sea.
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Cheng M, Jin J, Zhang J, Jiang H, Wang R. 2018. Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau. International Journal of Remote Sensing, 39(2): 470-487. doi:https://doi.org/10.1080/01431161.2017.1387308.
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Forkel M, Migliavacca M, Thonicke K, Reichstein M, Schaphoff S, Weber U, Carvalhais N. 2015. Codominant water control on global interannual variability and trends in land surface phenology and greenness. Global Change Biology, 21(9): 3414-3435. doi:https://doi.org/10.1111/gcb.12950.
Fu YH, Piao S, Op de Beeck M, Cong N, Zhao H, Zhang Y, Menzel A, Janssens IA. 2014. Recent spring phenology shifts in western C entral E urope based on multiscale observations. Global Ecology and Biogeography, 23(11): 1255-1263. doi:https://doi.org/10.1111/geb.12210.
Ghafarian Malamiri H, Zare Khormizi H. 2017. Reconstruction of cloud-free time series satellite observations of land surface temperature (LST) using harmonic analysis of time series algorithm (HANTS). Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 8(3): 37-55. (In Persian).
Guay KC, Beck PS, Berner LT, Goetz SJ, Baccini A, Buermann W. 2014. Vegetation productivity patterns at high northern latitudes: A multi‐sensor satellite data assessment. Global Change Biology, 20(10): 3147-3158. doi:https://doi.org/10.1111/gcb.12647.
Guo L, Dai J, Wang M, Xu J, Luedeling E. 2015. Responses of spring phenology in temperate zone trees to climate warming: A case study of apricot flowering in China. Agricultural and Forest Meteorology, 201: 1-7. doi:https://doi.org/10.1016/j.agrformet.2014.10.016.
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Jönsson P, Eklundh L. 2004. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8): 833-845. doi:https://doi.org/10.1016/j.cageo.2004.05.006.
Julien Y, Sobrino JA. 2019. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation, 76: 93-111. doi:https://doi.org/10.1016/j.jag.2018.11.008.
Liu L, Liang L, Schwartz MD, Donnelly A, Wang Z, Schaaf CB, Liu L. 2015. Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest. Remote Sensing of Environment, 160: 156-165. doi:https://doi.org/10.1016/j.rse.2015.01.011.
Liu Q, Fu YH, Zhu Z, Liu Y, Liu Z, Huang M, Janssens IA, Piao S. 2016. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Global Change Biology, 22(11): 3702-3711. doi:https://doi.org/10.1111/gcb.13311.
Malayeri F, Ashourloo D, Shakiba A, Matkan AA, 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).
Pellerin M, Delestrade A, Mathieu G, Rigault O, Yoccoz NG. 2012. Spring tree phenology in the Alps: effects of air temperature, altitude and local topography. European Journal of Forest Research, 131(6): 1957-1965. doi:10.1007/s10342-012-0646-1.
Piao S, Cui M, Chen A, Wang X, Ciais P, Liu J, Tang Y. 2011. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology, 151(12): 1599-1608. doi:https://doi.org/10.1016/j.agrformet.2011.06.016.
Rayegani B, Arzani H, Heydari Alamdarloo E, Moghadami MM. 2019. Application of remote sensing to assess climate change effects on plant productivity and phenology (Case study area: Tehran Province). Journal of Rangland, 3(13): 450-460. (In Persian).
Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169: 156-173. doi:https://doi.org/10.1016/j.agrformet.2012.09.012.
Tang H, Li Z, Zhu Z, Chen B, Zhang B, Xin X. 2015. Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China. Remote sensing, 7(9): 11914-11932. doi:https://doi.org/10.3390/rs70911914.
Verhoef W. 1996. Application of Harmonic Analysis of NDVI Time Series (HANTS). In S. Azzali & M. Menenti (Eds.), In: Fourier analysis of temporal NDVI in southern Africa and America continent. The Netherlands, DLO Winand Staring Centre, Report 108: 19-24.
Vermote E, Justice C, Csiszar I, Eidenshink J, Myneni R, Baret F, Masuoka E, Wolfe R, Claverie M. 2014. NOAA Climate Data Record (CDR) of normalized Difference Vegetation Index (NDVI), Version 4. NOAA Natl Clim Data Cent, doi:https://doiorg/107289/V5PZ56R6.
Vrieling A, De Leeuw J, Said MY. 2013. Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote sensing, 5(2): 982-1000. doi:https://doi.org/10.3390/rs5020982.
Workie TG, Debella HJ. 2018. Climate change and its effects on vegetation phenology across ecoregions of Ethiopia. Global Ecology and Conservation, 13: e00366. doi:https://doi.org/10.1016/j.gecco.2017.e00366.
Yu L, Liu T, Bu K, Yan F, Yang J, Chang L, Zhang S. 2017. Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Scientific Reports, 7(1): 14770. doi:https://doi.org/10.1038/s41598-017-14918-4.
Zhang G, Zhang Y, Dong J, Xiao X. 2013. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy of Sciences, 110(11): 4309-4314. doi:https://doi.org/10.1073/pnas.1210423110.
Zhao J, Wang Y, Zhang Z, Zhang H, Guo X, Yu S, Du W, Huang F. 2016. The variations of land surface phenology in Northeast China and its responses to climate change from 1982 to 2013. Remote Sensing, 8(5): 400. doi:https://doi.org/10.3390/rs8050400.
Zhao J, Zhang H, Zhang Z, Guo X, Li X, Chen C. 2015. Spatial and temporal changes in vegetation phenology at middle and high latitudes of the Northern Hemisphere over the past three decades. Remote Sensing, 7(8): 10973-10995. doi:https://doi.org/10.3390/rs70810973.
Zheng Z, Zhu W, Chen G, Jiang N, Fan D, Zhang D. 2016. Continuous but diverse advancement of spring-summer phenology in response to climate warming across the Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 223: 194-202. doi:https://doi.org/10.1016/j.agrformet.2016.04.012.
_||_Atkinson PM, Jeganathan C, Dash J, Atzberger C. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123: 400-417. doi:https://doi.org/10.1016/j.rse.2012.04.001.
Chen A, He B, Wang H, Huang L, Zhu Y, Lv A. 2015. Notable shifting in the responses of vegetation activity to climate change in China. Physics and Chemistry of the Earth, Parts A/B/C, 87-88: 60-66. doi:https://doi.org/10.1016/j.pce.2015.08.008.
Cheng M, Jin J, Zhang J, Jiang H, Wang R. 2018. Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau. International Journal of Remote Sensing, 39(2): 470-487. doi:https://doi.org/10.1080/01431161.2017.1387308.
Eastman JR, Sangermano F, Machado EA, Rogan J, Anyamba A. 2013. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sensing, 5(10): 4799-4818. doi:https://doi.org/10.3390/rs5104799.
Forkel M, Migliavacca M, Thonicke K, Reichstein M, Schaphoff S, Weber U, Carvalhais N. 2015. Codominant water control on global interannual variability and trends in land surface phenology and greenness. Global Change Biology, 21(9): 3414-3435. doi:https://doi.org/10.1111/gcb.12950.
Fu YH, Piao S, Op de Beeck M, Cong N, Zhao H, Zhang Y, Menzel A, Janssens IA. 2014. Recent spring phenology shifts in western C entral E urope based on multiscale observations. Global Ecology and Biogeography, 23(11): 1255-1263. doi:https://doi.org/10.1111/geb.12210.
Ghafarian Malamiri H, Zare Khormizi H. 2017. Reconstruction of cloud-free time series satellite observations of land surface temperature (LST) using harmonic analysis of time series algorithm (HANTS). Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 8(3): 37-55. (In Persian).
Guay KC, Beck PS, Berner LT, Goetz SJ, Baccini A, Buermann W. 2014. Vegetation productivity patterns at high northern latitudes: A multi‐sensor satellite data assessment. Global Change Biology, 20(10): 3147-3158. doi:https://doi.org/10.1111/gcb.12647.
Guo L, Dai J, Wang M, Xu J, Luedeling E. 2015. Responses of spring phenology in temperate zone trees to climate warming: A case study of apricot flowering in China. Agricultural and Forest Meteorology, 201: 1-7. doi:https://doi.org/10.1016/j.agrformet.2014.10.016.
Intergovernmental Panel on Climate Change (IPCC). 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability; Fifth Assessment Report on the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, NY, USA, 688 p.
Jeong SJ, HO CH, GIM HJ, Brown ME. 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Global change biology, 17(7): 2385-2399. doi:https://doi.org/10.1111/j.1365-2486.2011.02397.x.
Jönsson P, Eklundh L. 2004. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8): 833-845. doi:https://doi.org/10.1016/j.cageo.2004.05.006.
Julien Y, Sobrino JA. 2019. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation, 76: 93-111. doi:https://doi.org/10.1016/j.jag.2018.11.008.
Liu L, Liang L, Schwartz MD, Donnelly A, Wang Z, Schaaf CB, Liu L. 2015. Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest. Remote Sensing of Environment, 160: 156-165. doi:https://doi.org/10.1016/j.rse.2015.01.011.
Liu Q, Fu YH, Zhu Z, Liu Y, Liu Z, Huang M, Janssens IA, Piao S. 2016. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Global Change Biology, 22(11): 3702-3711. doi:https://doi.org/10.1111/gcb.13311.
Malayeri F, Ashourloo D, Shakiba A, Matkan AA, 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).
Pellerin M, Delestrade A, Mathieu G, Rigault O, Yoccoz NG. 2012. Spring tree phenology in the Alps: effects of air temperature, altitude and local topography. European Journal of Forest Research, 131(6): 1957-1965. doi:10.1007/s10342-012-0646-1.
Piao S, Cui M, Chen A, Wang X, Ciais P, Liu J, Tang Y. 2011. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology, 151(12): 1599-1608. doi:https://doi.org/10.1016/j.agrformet.2011.06.016.
Rayegani B, Arzani H, Heydari Alamdarloo E, Moghadami MM. 2019. Application of remote sensing to assess climate change effects on plant productivity and phenology (Case study area: Tehran Province). Journal of Rangland, 3(13): 450-460. (In Persian).
Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169: 156-173. doi:https://doi.org/10.1016/j.agrformet.2012.09.012.
Tang H, Li Z, Zhu Z, Chen B, Zhang B, Xin X. 2015. Variability and climate change trend in vegetation phenology of recent decades in the Greater Khingan Mountain area, Northeastern China. Remote sensing, 7(9): 11914-11932. doi:https://doi.org/10.3390/rs70911914.
Verhoef W. 1996. Application of Harmonic Analysis of NDVI Time Series (HANTS). In S. Azzali & M. Menenti (Eds.), In: Fourier analysis of temporal NDVI in southern Africa and America continent. The Netherlands, DLO Winand Staring Centre, Report 108: 19-24.
Vermote E, Justice C, Csiszar I, Eidenshink J, Myneni R, Baret F, Masuoka E, Wolfe R, Claverie M. 2014. NOAA Climate Data Record (CDR) of normalized Difference Vegetation Index (NDVI), Version 4. NOAA Natl Clim Data Cent, doi:https://doiorg/107289/V5PZ56R6.
Vrieling A, De Leeuw J, Said MY. 2013. Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote sensing, 5(2): 982-1000. doi:https://doi.org/10.3390/rs5020982.
Workie TG, Debella HJ. 2018. Climate change and its effects on vegetation phenology across ecoregions of Ethiopia. Global Ecology and Conservation, 13: e00366. doi:https://doi.org/10.1016/j.gecco.2017.e00366.
Yu L, Liu T, Bu K, Yan F, Yang J, Chang L, Zhang S. 2017. Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Scientific Reports, 7(1): 14770. doi:https://doi.org/10.1038/s41598-017-14918-4.
Zhang G, Zhang Y, Dong J, Xiao X. 2013. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy of Sciences, 110(11): 4309-4314. doi:https://doi.org/10.1073/pnas.1210423110.
Zhao J, Wang Y, Zhang Z, Zhang H, Guo X, Yu S, Du W, Huang F. 2016. The variations of land surface phenology in Northeast China and its responses to climate change from 1982 to 2013. Remote Sensing, 8(5): 400. doi:https://doi.org/10.3390/rs8050400.
Zhao J, Zhang H, Zhang Z, Guo X, Li X, Chen C. 2015. Spatial and temporal changes in vegetation phenology at middle and high latitudes of the Northern Hemisphere over the past three decades. Remote Sensing, 7(8): 10973-10995. doi:https://doi.org/10.3390/rs70810973.
Zheng Z, Zhu W, Chen G, Jiang N, Fan D, Zhang D. 2016. Continuous but diverse advancement of spring-summer phenology in response to climate warming across the Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 223: 194-202. doi:https://doi.org/10.1016/j.agrformet.2016.04.012.