The temporal and spatial investigation of the long-term changes of Hyrkanian forests and its relationship with climatic parameters

Document Type : Original Article

Authors

1 College of Agriculture & Natural Resources of University of Tehran

2 Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran

10.22034/rsgi.2025.64404.1114

Abstract

Investigating the long-term trend of changes in this ecosystem and their response to climate change is necessary for sustainable managemen. Therefore, the aim of the current research is to investigate the long-term trend of Hyrkanian forests in time and space and their relationship with changes in climatic parameters. For this purpose, the MOD13Q1 and MYD13Q1 products of the MODIS sensor were combined and a time series with an interval of 8 days and a spatial separation of 250 meters was prepared from the EVI vegetation cover index for the period from 2003 to 2020. Also, data from synoptic stations prepared and a time series of T_min, T_max and P every 8 days, it was built in the mentioned time period. Then the Mann-Kendall test and its significance were used to investigate the long-term change trend of forests and the Pearson coefficient was used to investigate the correlation of climatic parameters with the average EVI in the forest areas that are 5 and 10 km away from the meteorological station. The results of the long-term trend showed that the phenomena of greening and browning occurred in 77 and 23 percent of the region, respectively, of which 47.97 and 7.79 percent are significant at the 10% level. Also, the Pearson correlation between EVI and climatic parameters showed that EVI has a positive and strong correlation with temperature and a weak correlation with precipitation.the present study confirms Greening for forests over an 18-year time span, and identifies temperature as its main climatic factor.

Keywords

Main Subjects

طرح مسئله: اکوسیستم‌های جنگلی به‌عنوان یکی از اجزای کلیدی بیوسفر زمین، واکنش‌های منحصربه‌فردی به تغییرات آب‌وهوایی نشان می‌دهند. درک تغییرات بلندمدت این اکوسیستم‌ها و ارتباط آن‌ها با تغییرات اقلیمی برای مدیریت پایدار و فهم بهتر تأثیرات آب‌وهوایی و چرخه جهانی کربن ضروری است. جنگل‌های هیرکانی، به‌عنوان یکی از زیست‌بوم‌های منحصربه‌فرد جهان که توسط یونسکو به رسمیت شناخته شده‌اند، به دلیل تغییرات سریع اقلیمی در ارتفاعات و تنوع زیست‌اقلیمی، اهمیت ویژه‌ای دارند. هدف این پژوهش، تحلیل زمانی و مکانی روند بلندمدت جنگل‌های هیرکانی و بررسی ارتباط آن‌ها با تغییرات پارامترهای اقلیمی است.

هدف: هدف پژوهش حاضر بررسی زمانی و مکانی روند بلندمدت جنگل‌های هیرکانی و ارتباط آن‌ها با تغییرات پارامتر‌های اقلیمی است.

روش تحقیق: پزوهش حاظر در کل جنگل‌های هیرکانی در محدوده جغرافیایی ʹʹ5 ʹ35 45 تا ʹʹ15 ʹ38 26 شمالی و ʹʹ45 ʹ38 33 تا ʹʹ15ʹ56 11 شرقی که تراکم تاج پوشش بالای 10 درصد دارد انجام شده. به این منظور محصولات MOD13Q1 و MYD13Q1 سنجنده MODIS با هم ترکیب و سری زمانی با فاصله 8 روزه و تفکیک مکانی 250 متر از شاخص پوشش‌گیاهی EVI برای دوره زمانی 2003 تا 2020 تهیه شد. همچنین داده‌‌های ایستگاهای سینوپتیک که در فاصله‌ای کمتر از 10 کیلومتری از جنگل قرار داشتند تهیه و سری زمانی از میانگین دمای حداقل، میانگین دمای حداکثر و مجموع بارش در هر 8 روز (متناسب با تاریخ داد‌های EVI) برای هر یک ایستگاه‌ها در بازه زمانی یاد شده ساخته شد. سپس از آزمون کندال و معنی‌داری آن برای بررسی روند تغییرات بلندمدت جنگل‌ها هیرکانی و از ضریب پیرسون برای بررسی همبستگی پارامتر‌های اقلیمی با میانگین EVI در محدوده‌های جنگلی که در فاصله 5 و 10 کیلومتری ایستگاه هواشناسی، استفاده شد.

نتایج و بحث: نتایج روند بلند‌مدت نشان داد، پدیده‌های Greening و Browning به ترتیب در 77 و 23 درصد منطقه رخ داده‌اند که از این مقدار 54/34 درصد از منطقه شامل 56/30 درصد از روندهای Greening و 98/3 درصد از روند‌های Browning در سطوح 1% معنی‌دار هستند. همچنین روند‌های Greening معنی‌دار در سطوح 1 تا 5% و 5 تا 10% به ترتیب در 19/11 و 22/6 درصد از منطقه و روند‌های Browning معنی‌دار در سطوح 1 تا 5% و 5 تا 10% به ترتیب در 20/2 و 61/1 درصد از منطقه رخ‌داده است. همبستگی پیرسون بین EVI و پارامترهای اقلیمی نشان داد که EVI همبستگی مثبت و قوی با دما (بیش از 6/0) و همبستگی ضعیف با بارش (بین 11/0 تا 181/0) دارد.

نتیجه گیری: در کل، مطالعه حاضر Greening را برای جنگل‌های هیرکانی در بازه زمانی 18 ساله، تایید و دما را به عنوان عامل اصلی اقلیمی آن معرفی می‌کند.

Alavinia, S. H., & Zarei, M. (2021). Analysis of spatial changes of extreme precipitation and temperature in Iran over a 50-year period. International Journal of Climatology, 41(S1), E2269-E2289. doi: https://doi.org/10.1002/joc.6691.
Alcaraz‐Segura, D. O. M. I. N. G. O., Chuvieco, E., Epstein, H. E., Kasischke, E. S., & Trishchenko, A. (2010). Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets. Global Change Biology, 16(2), 760-770. doi: https://doi.org/10.1111/j.1365-2486.2009.01956.x.
Berlanga-Robles, C. A., & Ruiz-Luna, A. (2020). Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management, 28(2), 229-249. doi: https://doi.org/10.1007/s11273-020-09698-4.
Bigsby, H. (2009). Carbon banking: Creating flexibility for forest owners. Forest Ecology and Management, 257(1), 378-383. doi: https://doi.org/10.1016/j.foreco.2008.09.053.
Bórnez, K., Descals, A., Verger, A. and Peñuelas, J., (2020). Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests. International Journal of Applied Earth Observation and Geoinformation, 84, p.101974. doi: https://doi.org/10.1016/j.jag.2019.101974.
Burrell, A. L., Evans, J. P., & Liu, Y. (2017). Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sensing of Environment, 197, 43-57. doi: https://doi.org/10.1016/j.rse.2017.05.018.
Chaudhuri, S., & Dutta, D. (2014). Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environmental Monitoring and Assessment, 186(8), 4719–4742. doi: https://doi.org/10.1007/s10661-014-3716-8.
Chen, F., Liu, Z., Zhong, H., & Wang, S. (2021). Exploring the Applicability and Scaling Effects of Satellite-Observed Spring and Autumn Phenology in Complex Terrain Regions Using Four Different Spatial Resolution Products. Remote Sensing, 13(22), 4582. doi: https://doi.org/10.3390/rs13224582.
Chen, T., Guo, R., Yan, Q., Chen, X., Zhou, S., Liang, C., Wei, X., & Dolman, H. (2022). Land management contributes significantly to observed vegetation browning in Syria during 2001–2018. Biogeosciences, 19(5), 1515-1525. doi: https://doi.org/10.5194/bg-19-1515-2022.
Deka, J., Kalita, S., & Khan, M. L. (2019). Vegetation Phenological Characterization of Alluvial Plain Shorea robusta-dominated Tropical Moist Deciduous Forest of Northeast India Using MODIS NDVI Time Series Data. Journal of the Indian Society of Remote Sensing, 47(8), 1287-1293. doi: https://doi.org/10.1007/s12524-019-00973-y.
Eastman, J.R. (2009). IDRISI Taiga - Guide to GIS and Image Processing. Clark Labs Clark University, Worcester.  
Emmett, K. D., Renwick, K. M., & Poulter, B. (2019). Disentangling climate and disturbance effects on regional vegetation greening trends. Ecosystems, 22(4), 873-891. doi: https://doi.org/10.1007/s10021-018-0300-3.
Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., & Hostert, P. (2015). Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312-325. doi: https://doi.org/10.1016/j.rse.2015.03.028.
Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D., Neigh, C. S., & Reichstein, M. (2013). Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing, 5(5), 2113-2144. doi: https://doi.org/10.3390/rs5052113.
Ghanbari Motlagh, M., Abbasnezhad Alchin, A., & Daghestani, M. (2022). Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques. Arabian Journal of Geosciences, 15(9), 835. doi: https://doi.org/10.1007/s12517-022-10192-5
He, B., Wu, X., Liu, K., Yao, Y., Chen, W., & Zhao, W. (2022). Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020. Remote Sensing, 14(23), 5982. doi: https://doi.org/10.3390/rs14235982.
Hongchao, J., Guang, Y., Xiaomin, L., Bingrui, J., Zhenzhu, X., & Yuhui, W. (2023). Climate extremes drive the phenology of a dominant species in meadow steppe under gradual warming. Science of The Total Environment, 869, 161687. doi: https://doi.org/10.1016/j.scitotenv.2023.161687.
Hosseini, S. M. (2019). Outstanding universal values of Hyrcanian Forest, the newest Iranian property, inscribed in the UNESCO’s World Heritage List. Tourism Res1(3), 1-17.
Huang, X., An, R., Wang, H., Xing, F., Wang, B., Fan, M., ... & Lu, H. (2023). Differential effects of climatic and non-climatic factors on the distribution of vegetation phenology trends on the Tibetan plateau. Heliyon, 9(10). doi: https://doi.org/10.1016/j.heliyon.2023.e14389.
Jiang, L., Bao, A., Guo, H., & Ndayisaba, F. (2017). Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment, 599, 967-980. doi: https://doi.org/10.1016/j.scitotenv.2017.05.012.
Jin, X., Li, Z., Feng, H., Ren, Z., & Li, S. (2020). Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. Agricultural Water Management, 227, 105846. doi: https://doi.org/10.1016/j.agwat.2019.105846.
Julien, Y. and J. A. Sobrino, (2010). “Comparison of cloud-reconstruction methods for time series of composite NDVI data,” Remote Sensing of Environmental, vol. 114, pp. 618–625. doi: https://doi.org/10.1016/j.rse.2009.11.001.
Karkauskaite, P., Tagesson, T., & Fensholt, R. (2017). Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sensing, 9(5), 485. doi: https://doi.org/10.3390/rs9050485.
Kuenzer, C., Dech, S., & Wagner, W. (2015). Remote sensing time series revealing land surface dynamics: Status quo and the pathway ahead. In Remote Sensing Time Series (pp. 1-24). doi: https://doi.org/10.1007/978-3-319-15967-6_1.
Kumari, N., Srivastava, A., & Dumka, U. C. (2021). A long-term spatiotemporal analysis of vegetation greenness over the himalayan region using google earth engine. Climate, 9(7), 109. doi: https://doi.org/10.3390/cli9070109.
Li, D., Lu, D., Wu, M., Shao, X., & Wei, J. (2018). Examining land cover and greenness dynamics in Hangzhou Bay in 1985–2016 using Landsat time-series data. Remote Sensing, 10(1), 32. doi: https://doi.org/10.3390/rs10010032.
Li, Y., Chen, Y., Sun, F., & Li, Z. (2021). Recent vegetation browning and its drivers on Tianshan Mountain, Central Asia. Ecological Indicators, 129, 107912. doi: https://doi.org/10.1016/j.ecolind.2021.107912.
Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., & Li, S. (2015). Local cooling and warming effects of forests based on satellite observations. Nature communications, 6(1), 6603. https://doi.org/10.1038/ncomms7603.
Liu, Y., Wu, C., Peng, D., Xu, S., Gonsamo, A., Jassal, R. S., ... & Chen, J. M. (2016). Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America. Remote Sensing of Environment, 176, 152-162. doi: https://doi.org/10.1016/j.rse.2016.01.001.
Luintel, N., Ma, W., Ma, Y., Wang, B., Xu, J., Dawadi, B., & Mishra, B. (2021). Tracking the dynamics of paddy rice cultivation practice through MODIS time series and PhenoRice algorithm. Agricultural and Forest Meteorology, 307, 108538. doi: https://doi.org/10.1016/j.agrformet.2021.108538.
Marvie Mohadjer, M. R., (2005). Silviculture. Tehran, Iran: Tehran Univ. Publ., p. 387 (in Persian).
Mbatha, N., & Xulu, S. (2018). Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought. Climate, 6(4), 95. doi: https://doi.org/10.3390/cli6040095.
Mishra, N. B., & Mainali, K. P. (2017). Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers. Science of The Total Environment, 587, 326-339. doi: https://doi.org/10.1016/j.scitotenv.2017.02.177.
Nanzad, L., Zhang, J., Tuvdendorj, B., Nabil, M., Zhang, S., & Bai, Y. (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. doi: https://doi.org/10.1016/j.jaridenv.2019.02.003.
Nasiri, V., Heidarlou, H. B., Alchin, A. A., Moradi, F., Rahmanian, S., Afshari, S., ... & Griess, V. C. (2023). How do conservation policies, climate and socioeconomic changes impact Hyrcanian forests of northern Iran?. Ecological Informatics, 78, 102351. doi: https://doi.org/10.1016/j.ecoinf.2023.102351.  
Neeti, N., & Eastman, J. R. (2011). A contextual mann‐kendall approach for the assessment of trend significance in image time series. Transactions in GIS, 15(5), 599-611. doi: https://doi.org/10.1111/j.1467-9671.2011.01280.x.
Padhee, S. K., & Dutta, S. (2019). Spatio-temporal reconstruction of MODIS NDVI by regional land surface phenology and harmonic analysis of time-series. GIScience & Remote Sensing, 56(8), 1261-1288. doi: https://doi.org/10.1080/15481603.2019.1648854.   
Pan, N., Feng, X., Fu, B., Wang, S., Ji, F., & Pan, S. (2018). Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sensing of Environment, 214, 59-72. doi: https://doi.org/10.1016/j.rse.2018.05.034.
Parida, B. R., Pandey, A. C., & Patel, N. R. (2020). Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. Climate, 8(8), 92. doi: https://doi.org/10.3390/cli8080092.
Prăvălie, R., Sîrodoev, I., Nita, I. A., Patriche, C., Dumitraşcu, M., Roşca, B., ... & Birsan, M. V. (2022). NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecological Indicators, 136, 108629. doi: https://doi.org/10.1016/j.ecolind.2022.108629
Qiu, J., Yang, J., Wang, Y., & Su, H. (2018). A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: A case study in Southern China. International Journal of Remote Sensing, 39(8): 2105-2118. doi: https://doi.org/10.1080/01431161.2018.1443229
Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D., & Herold, M. (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204, 147-161. https://doi.org/10.1016/j.rse.2017.10.034.
Sagheb Talebi, K., Sajedi, T., & Pourhashemi, M. (2014). Forests of Iran: A Treasure from the Past, a Hope for the Future (No. 15325). Springer Netherlands. http://dx.doi.org/10.1007/978-94-007-7371-4
Schucknecht, A., Erasmi, S., Niemeyer, I., and Matschullat, J. (2013). Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series. European Journal of Remote Sensing, 46(1), 40-59. doi: https://doi.org/10.5721/EuJRS20134601 
Saboohi, R., Soltani, S., & Khodagholi, M. (2012). Trend analysis of temperature parameters in Iran. Theoretical and Applied Climatology, 109, 529-547. Doi: https://doi.org/10.1007/s00704-012-0617-9
Testa, S., Soudani, K., Boschetti, L., & Mondino, E. B. (2018). MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. International journal of applied earth observation and geoinformation, 64, 132-144. doi: https://doi.org/10.1016/j.jag.2017.10.005
Tian, F., Liu, L. Z., Yang, J. H., & Wu, J. J. (2021). Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities. Ecological Indicators, 125, 107479. https://doi.org/10.1016/j.ecolind.2021.107479.
Xulu, S., Peerbhay, K., Gebreslasie, M., & Ismail, R. (2018). Drought influence on forest plantations in Zululand, South Africa, using MODIS time series and climate data. Forests, 9(9), 528. doi: https://doi.org/10.3390/f9090528
Yang, T., Li, Q., Zou, Q., Hamdi, R., Cui, F., & Li, L. (2022). Impact of snowpack on the land surface phenology in the tianshan mountains, central Asia. Remote Sensing, 14(14), 3462. doi: https://doi.org/10.3390/rs14143462
Yu, L., Yan, Z., & Zhang, S. (2020). Forest Phenology Shifts in Response to Climate Change over China–Mongolia–Russia International Economic Corridor. Forests, 11(7), 757. doi: https://doi.org/10.3390/f11070757
Zhang, J., Zhao, J., Wang, Y., Zhang, H., Zhang, Z., & Guo, X. (2020). Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 1-16. doi: https://doi.org/10.1016/j.isprsjprs.2020.05.005  
Zhang, R., Qi, J., Leng, S., & Wang, Q. (2022). Long-term vegetation phenology changes and responses to pre-season temperature and precipitation in Northern China. Remote Sensing, 14(6), 1396. doi: https://doi.org/10.3390/rs14061396  
Zhang, Y., Song, C., Band, L. E., Sun, G., & Li, J. (2017). Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?. Remote Sensing of Environment, 191, 145-155. doi: https://doi.org/10.1016/j.rse.2017.01.011 
Volume 5, Issue 15
August 2025
Pages 138-117
  • Receive Date: 18 November 2024
  • Revise Date: 03 March 2025
  • Accept Date: 10 May 2025