Spatiotemporal analysis of drought in Iran’s Hyrcanian Forests using remote sensing-based vegetation and thermal indices

Document Type : Scientific article

Authors

1 Assistant Prof., Forestry Department, Faculty of Natural Resources, Urmia University, Urmia, I. R. Iran

2 B.Sc. in Natural Resources Engineering-Forestry, Faculty of Natural Resources, Urmia University, Urmia, I. R. Iran

Abstract

Background and Objective: Drought is one of the most significant global climatic hazards, exerting wide-ranging impacts on the water cycle, agriculture, and ecosystems. The Hyrcanian forests of northern Iran, characterized by high biodiversity and critical roles in carbon storage and water regulation, are particularly vulnerable to drought and climate change. Due to the complexity and high costs of field monitoring, the use of remote sensing data and vegetation and thermal indices provides an effective means to assess drought. This study employs MODIS and Landsat data, integrating multiple vegetation and thermal indices, to analyze the spatiotemporal trends of drought in the Hyrcanian forests during 2000 and 2024, aiming to identify drought-sensitive areas and changes in ecosystem stability.
Material and Methods: Satellite data from Landsat and MODIS, including NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDWI (Normalized Difference Water Index), TVDI (Temperature Vegetation Dryness Index), and Land Surface Temperature (LST), were utilized to assess vegetation health, surface moisture, and the severity of water stress. Data were preprocessed in Google Earth Engine, and 1,678 randomly sampled points with a minimum distance of one kilometer were selected for index extraction. Relationships between LST and vegetation indices were analyzed using Pearson correlation and collinearity analyses to accurately evaluate the impact of environmental stressors on vegetation.
Results: Analysis of vegetation and drought indices in the Hyrcanian forests between 2000 and 2024 indicated a marked decline in vegetation density and health. Annual mean NDVI decreased from 0.642 to 0.498, VCI from 68.42 to 54.37, SAVI from 0.527 to 0.412, and NDWI from 0.214 to -0.067. Concurrently, TVDI increased from 0.412 to 0.536, and maximum LST rose from 47.60°C to 53.08°C, indicating intensified drought and heat stress. Due to strong collinearity among NDVI, SAVI, and NDWI, NDVI and SAVI were removed from the model while NDWI was retained due to its relevance for moisture assessment. Correlation analysis showed that LST had a very strong positive correlation with TVDI in 2000 (r = 0.948), which decreased to a weak correlation in 2024 (r = 0.248). Additionally, LST exhibited significant negative correlations with NDWI (r = -0.076 and r = -0.127) and EVI (r = -0.002 and r = -0.244), reflecting the substantial impact of drought and heat stress on vegetation health and moisture in the Hyrcanian forests.
Conclusion: This study highlights the importance of vegetation and thermal indices for assessing environmental stress and drought in the Hyrcanian forests. Rising LST and intensifying drought stress have significantly affected vegetation health and functionality, emphasizing the need for continuous and comprehensive monitoring. The use of a multi-index approach, incorporating NDWI and TVDI as moisture- and drought-sensitive indicators, allows for more precise drought detection and management in humid forest ecosystems. Special attention to more vulnerable areas, such as the eastern (particularly Golestan Province) and southern sections of the Hyrcanian forests, is essential for conservation and management planning. These approaches can play a key role in developing effective policies to address climate challenges and sustainably protect sensitive forests.

Keywords

Main Subjects

References

Alahacoon, N.; Edirisinghe, M., A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. Geomatics, Natural Hazards and Risk 2022, 13 (1), 762–799.
Alazba, A. A.; Mossad, A.; Geli, H. M.; El-Shafei, A.; Elkatoury, A.; Ezzeldin, M.; …; Radwan, F., Mapping Land Surface Drought in Water-Scarce Arid Environments Using Satellite-Based TVDI Analysis. Land 2025, 14 (6), 1302.
Anderson, M. C.; Hain, C.; Otkin, J.; Zhan, X.; Mo, K.; Svoboda, M.; …; Pimstein, A., An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US Drought Monitor classifications. Journal of Hydrometeorology 2013, 14 (4), 1035–1056.
Avazpour, N.; Faramarzi, M.; Omidipour, R.; Mehdizadeh, H., Monitoring the drought effects on vegetation changes using satellite imagery (Case study: Ilam catchment). Geography and Environmental Sustainability 2021, 11 (4), 125–143. (In Persian)
Benesty, J.; Chen, J.; Huang, Y.; Cohen, I., Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin, Heidelberg, 2009; pp 1–4.
Beygi Heidarlou, H.; Oprea-Sorescu, O.; Marcu, M. V.; Borz, S. A., Mapping small-scale willow crops and their health status using Sentinel-2 images in complex agricultural areas. Remote Sensing 2024, 16 (3), 595.
Chanchí Golondrino, G. E.; Ospina Alarcón, M. A.; Saba, M., Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics. Atmosphere 2023, 14 (7), 1148.
Chander, G.; Markham, B. L.; Helder, D. L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 2009, 113 (5), 893–903.
Das, A.C.; Shahriar, S.A.; Chowdhury, M.A.; Hossain, M.L.; Mahmud, S.; Tusar, M.K.; Ahmed, R.; Salam, M.A., Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh. Heliyon 2023, 9 (2): e14758.
Eskandari Dameneh, H.; Gholami, H.; Telfer, M. W.; Comino, J. R.; Collins, A. L.; Jansen, J. D., Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices. Scientific Reports 2021, 11 (1), 20548.
Farrokhzadeh, B.; Mansouri, S.; Sepehri, A., Determining the correlation between NDVI and EVI vegetation indices and SPI drought index (Case Study: Golestan rangelands). Journal of Agricultural Meteorology 2018, 5 (2), 56–65. (In Persian)
Gao, B. C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996, 58 (3), 257–266.
Gelata, F. T.; Jiqin, H.; Chaka Gemeda, S.; Wubishet Asefa, B., Application of GIS using NDVI and LST estimation to measure climate variability-induced drought risk assessment in Ethiopia. Journal of Water and Climate Change 2023, 14 (7), 2479–2489.
Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 2017, 202, 18–27.
Hadinezhad, P.; Asadi, H.; Hojati, S. M.; Tafazoli, M.; Yousefpour, R., Factors affecting tree drought stress in Hyrcanian forests. Forest Research and Development 2025, 10 (4), 431–451. (In Persian)
Haile, G. G.; Tang, Q.; Li, W.; Liu, X.; Zhang, X., Drought: Progress in broadening its understanding. Wiley Interdisciplinary Reviews: Water 2020, 7 (2), e1407.
Hamidi, S. K.; de Luis, M.; Bourque, C. P. A.; Bayat, M.; Serrano-Notivoli, R., Projected biodiversity in the Hyrcanian Mountain Forest of Iran: An investigation based on two climate scenarios. Biodiversity and Conservation 2023, 32 (12), 3791–3808.
Hoover, D. L.; Pfennigwerth, A. A.; Duniway, M. C., Drought resistance and resilience: The role of soil moisture–plant interactions and legacies in a dryland ecosystem. Journal of Ecology 2021, 109 (9), 3280–3294.
Hoque, M. A. A.; Pradhan, B.; Ahmed, N., Assessing drought vulnerability using geospatial techniques in northwestern part of Bangladesh. Science of the Total Environment 2020, 705, 135957.
Hu, Z.; Chen, X.; Chen, D.; Li, J.; Wang, S.; Zhou, Q.; …; Guo, M., “Dry gets drier, wet gets wetter”: A case study over the arid regions of central Asia. International Journal of Climatology 2019, 39 (2), 1072–1091.
Huete, A. R., A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988, 25 (3), 295–309.
Huete, A.; Didan, K.; Miura, T.; Rodriguez, E. P.; Gao, X.; Ferreira, L. G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83 (1–2), 195–213.
Kloos, S.; Yuan, Y.; Castelli, M.; Menzel, A., Agricultural drought detection with MODIS based vegetation health indices in southeast Germany. Remote Sensing 2021, 13 (19), 3907.
Kogan, F., Global drought detection and impact assessment from space. In Droughts; Routledge: 2016; pp 196–209.
Kulkarni, S. S.; Wardlow, B. D.; Bayissa, Y. A.; Tadesse, T.; Svoboda, M. D.; Gedam, S. S., Developing a remote sensing-based combined drought indicator approach for agricultural drought monitoring over Marathwada, India. Remote Sensing 2020, 12 (13), 2091.
Li, Z. L.; Tang, B. H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; …; Sobrino, J. A., Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment 2013, 131, 14–37.
Liu, Q.; Peng, C.; Schneider, R.; Cyr, D.; McDowell, N. G.; Kneeshaw, D., Drought‐induced increase in tree mortality and corresponding decrease in the carbon sink capacity of Canada’s boreal forests from 1970 to 2020. Global Change Biology 2023, 29 (8), 2274–2285.
Liu, Q.; Zhang, F.; Chen, J.; Li, Y., Water stress altered photosynthesis‐vegetation index relationships for winter wheat. Agronomy Journal 2020a, 112 (4), 2944–2955.
Liu, Q.; Zhang, S.; Zhang, H.; Bai, Y.; Zhang, J., Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment 2020b, 711, 134585.
Ma, H.; Cui, T.; Cao, L., Monitoring of drought stress in Chinese forests based on satellite solar-induced chlorophyll fluorescence and multi-source remote sensing indices. Remote Sensing 2023, 15 (4), 879.
Masek, J. G.; Vermote, E. F.; Saleous, N. E.; Wolfe, R.; Hall, F. G.; Huemmrich, K. F.; Gao, F.; Kutler, J.; Lim, T. K. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 2006, 3(1), 68–72.
McFeeters, S. K., The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 1996, 17 (7), 1425–1432.
Mullapudi, A.; Vibhute, A. D.; Mali, S.; Patil, C. H., A review of agricultural drought assessment with remote sensing data: Methods, issues, challenges and opportunities. Applied Geomatics 2023, 15 (1), 1–13.
Nasiri, V.; Beloiu, M.; Darvishsefat, A. A.; Griess, V. C.; Maftei, C.; Waser, L. T., Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning. International Journal of Applied Earth Observation and Geoinformation 2023a, 116, 103154.
Nasiri, V.; Heidarlou, H. B.; Alchin, A. A.; Moradi, F.; Rahmanian, S.; Afshari, S.; …; Griess, V. C., How do conservation policies, climate and socioeconomic changes impact Hyrcanian forests of northern Iran? Ecological Informatics 2023b, 78, 102351.
Nejatiyanpour, E.; Ghorbanzadeh, O.; Strobl, J.; Yousefpour, R.; Kakhki, M. D.; Amirnejad, H.; …; Sabouni, M. S., Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives. Journal of Forestry Research 2025, 36 (1), 35.
Nila, M. U. S.; Beierkuhnlein, C.; Jaeschke, A.; Hoffmann, S.; Hossain, M. L., Predicting the effectiveness of protected areas of Natura 2000 under climate change. Ecological Processes 2019, 8 (1), 13.
Orimoloye, I. R.; Ololade, O. O.; Mazinyo, S. P.; Kalumba, A. M.; Ekundayo, O. Y.; Busayo, E. T.; …; Nel, W., Spatial assessment of drought severity in Cape Town area, South Africa. Heliyon 2019, 5 (7).
Patil, P. P.; Jagtap, M. P.; Khatri, N.; Madan, H.; Vadduri, A. A.; Patodia, T., Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study. Case Studies in Chemical and Environmental Engineering 2024, 9, 100573.
Pumo, D.; Viola, F.; Noto, L. V., Climate changes’ effects on vegetation water stress in Mediterranean areas. Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology 2010, 3 (2), 166–176.
Rafiei Sardooi, E.; Azareh, A.; Eskandari Damaneh, H.; Skandari Damaneh, H., Drought monitoring using MODIS land surface temperature and normalized difference vegetation index products in semi-arid areas of Iran. Journal of Rangeland Science 2021, 11 (4), 402–418. (In Persian)
Roy, D. P.; Wulder, M. A.; Loveland, T. R.; Cep, W.; Allen, R. G.; Anderson, M. C.; Helder, D.; Johnson, D. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment 2014, 145, 154–172.
Savari, M.; Khaleghi, B.; Sheheytavi, A., Iranian farmers' response to the drought crisis: How can the consequences of drought be reduced?. International Journal of Disaster Risk Reduction 2024, 114, 104910.
Sękiewicz, K.; Salvà-Catarineu, M.; Walas, Ł.; Romo, A.; Gholizadeh, H.; Naqinezhad, A.; …; Boratyński, A., Consequence of habitat specificity: a rising risk of habitat loss for endemic and sub-endemic woody species under climate change in the Hyrcanian ecoregion. Regional Environmental Change 2024, 24 (2), 68.
Selka, I.; Mokhtari, A. M.; Tabet Aoul, K. A.; Bengusmia, D.; Kacemi, M.; Djebbar, K. E. B., Assessing the impact of land use and land cover changes on surface temperature dynamics using Google Earth Engine: a case study of Tlemcen municipality, northwestern Algeria (1989–2019). ISPRS International Journal of Geo-Information 2024, 13 (7), 237.
Shahzad, A. L. I.; Basit, A.; Umair, M.; Makanda, T. A.; Khan, F. U.; Siqi, S. H. I.; Jian, N. I., Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia. Journal of Integrative Agriculture 2023, 22 (10), 3193–3208.
Tian, Z.; Fan, J.; Yu, T.; de Leon, N.; Kaeppler, S. M.; Zhang, Z., Mitigating NDVI saturation in imagery of dense and healthy vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 2025, 227, 234–250.
Velastegui-Montoya, A.; Montalván-Burbano, N.; Carrión-Mero, P.; Rivera-Torres, H.; Sadeck, L.; Adami, M., Google Earth Engine: a global analysis and future trends. Remote Sensing 2023, 15 (14), 3675.
Wan, Z.; Hook, S.; Hulley, G., MYD11A1 MODIS/aqua land surface temperature/emissivity daily L3 global 1 km SIN grid V006. NASA EOSDIS Land Processes Distributed Active Archive Center (DAAC) data set 2015, MYD11A1-006.
Wei, W.; Zhang, J.; Zhou, L.; Xie, B.; Zhou, J.; Li, C., Comparative evaluation of drought indices for monitoring drought based on remote sensing data. Environmental Science and Pollution Research 2021, 28 (16), 20408–20425.
Wei, X.; Huang, S.; Huang, Q.; Liu, D.; Leng, G.; Yang, H.; …; Peng, J., Analysis of vegetation vulnerability dynamics and driving forces to multiple drought stresses in a changing environment. Remote Sensing 2022, 14 (17), 4231.
Xu, Z.; Wu, Z.; Shao, Q.; He, H.; Guo, X., From meteorological to agricultural drought: Propagation time and probabilistic linkages. Journal of Hydrology: Regional Studies 2023, 46, 101329.
Yan, K.; Gao, S.; Yan, G.; Ma, X.; Chen, X.; Zhu, P.; Li, J.; Gao, S.; Gastellu-Etchegorry, J.-P.; Myneni, R. B.; Wang, Q., A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation 2025, 139, 104560.
Yang, D.; Yang, Y.; Xia, J., Hydrological cycle and water resources in a changing world: A review. Geography and Sustainability 2021, 2 (2), 115–122.
Yang, X.; Zhou, H.; Liu, F., Utilizing a vegetation restoration potential model to derive a reference for assessing ecological restoration of the Qinghai-Tibet Plateau. Ecological Engineering 2025, 212, 107514.
Yao, Y.; Liu, Y.; Zhou, S.; Song, J.; Fu, B., Soil moisture determines the recovery time of ecosystems from drought. Global Change Biology 2023, 29 (13), 3562–3574.
Zhu, Z.; Woodcock, C. E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment 2014, 144, 152–171.
Forest Research and Development
Volume 11, Issue 4
January 2026
Pages 557-581

Files

Share

How to cite

Statistics