SpringerLink
Log in

Vegetation Dynamics Assessment: Remote Sensing and Statistical Approaches to Determine the Contributions of Driving Factors

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

To properly manage the terrestrial ecosystem, it is essential to understand the vegetation sensitivity to climate variations and human actions. The main target of this survey was to evaluate the spatiotemporal variation in vegetation cover, and its relationship with climate variations and to calculate the contributions of driving factors in Namak Lake basin, Iran, during 2001–2019. To this end, Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI) in 3, 6, 9, and 12-month time scales were used to assess vegetation dynamics and its reactions to climate variations based on coefficient of determination (R2) and Linear Regression (LR). The results presented that vegetation cover had an improving trend in 87.78% and a decreasing trend in 12.19% of the basin, while it was stable in 0.03% of areas. The correlation between VHI and different time scales of SPEI indicated that coverage was mainly affected by 3-month SPEI in more than half of the basin (53.74%). High correlations between VHI and SPEI were found in upland areas in the northeast and some areas in the east of the basin. These areas also had the highest slope of VHI changes in relation to climate factors. Climate variability affected about four-fifths (79.22%) of coverage, while 16.36% was influenced by human actions, and 4.42% by both factors. Moreover, more than 99% of the significant improvements and degradations in coverage were related to climate variations and mankind’s actions, respectively. The outcomes can serve as a foundation for initiating vegetation growth and protection in the Namak Lake basin.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  • Ahmadi, S., Khosravi, H., & Dehghan, P. (2016). Evolution of land use changes using remote sensing (Case study: Hiv basin, Taleghan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2), 49–55. https://www.ijfse.com/uploadedfiles/IJFSEArchive/IJFSE2016/6(2)/03.pdf

  • Ahmadi, S., Azarnivand, H., Khosravi, H., Dehghan, P., & Manesh, B., M (2019). Assessment the effect of drought and land use change on vegetation using Landsat data. Desert, 24(1), 23–31. https://doi.org/10.22059/JDESERT.2019.72432

    Google Scholar 

  • Ali, S., Haixing, Z., Qi, M., Liang, S., Ning, J., Jia, Q., & Hou, F. (2021). Monitoring drought events and vegetation dynamics in relation to climate change over mainland China from 1983 to 2016. Environmental Science and Pollution Research, 28, 21910–21925. https://doi.org/10.1007/s11356-020-12146-4

  • Ali, S., Henchiri, M., Yao, F., & Zhang, J. (2019). Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011. Environmental Science and Pollution Research, 26, 11470–11481. https://doi.org/10.1007/s11356-019-04512-8

    Article  Google Scholar 

  • Almeida-Ñauñay, A. F., Villeta, M., Quemada, M., & Tarquis, A. M. (2022). Assessment of drought indexes on different time scales: A case in semiarid mediterranean grasslands. Remote Sensing, 14(3), 565. https://doi.org/10.3390/rs14030565

    Article  Google Scholar 

  • Ambadkar, A., Kathe, P., Pande, C. B., & Diwate, P. (2024). Assessment of spatial and temporal changes in strength of vegetation using normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI): A case study from Akola District, Central India. Geospatial technology to support communities and policy: Pathways to resiliency (pp. 289–304). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52561-2_16

  • Azarnivand, A. R., Sadrinasab, M., & Nasseri, M. (2023). Climate change impacts on the seasonal variation of hydro-physical characteristics in the Persian Gulf. Estuaries and Coasts, 46(2), 302–322. https://doi.org/10.1007/s12237-022-01151-0

    Article  Google Scholar 

  • Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289–302. https://doi.org/10.1016/j.jag.2006.03.002

    Article  Google Scholar 

  • Chen, J., Yan, F., & Lu, Q. (2020). Spatiotemporal variation of vegetation on the Qinghai–Tibet Plateau and the influence of climatic factors and human activities on vegetation trend (2000–2019). Remote Sensing, 12(19), 3150. https://doi.org/10.3390/rs12193150

    Article  Google Scholar 

  • Dai, A., Trenberth, K. E., & Qian, T. (2004). A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology, 5(6), 1117–1130. https://doi.org/10.1175/JHM-386.1

    Article  Google Scholar 

  • Dehghan, P., Azarnivand, H., Khosravi, H., Zehtabian, G., & Moghaddam Nia, A. (2018). Design of agricultural ecological and rangeland capability model using integrated approach of FUZZY-AHP (a case study: Eshtehard city). Journal of Range and Watershed Managment, 71(1), 11–24.

    Google Scholar 

  • Dehghan Rahimabadi, P., & Azarnivand, H. (2023). Assessment of the effect of climate fluctuations and human activities on vegetation dynamics and its vulnerability. Theoretical and Applied Climatology, 1–16. https://doi.org/10.22059/jrwm.2017.238015.1147

  • Dehghan Rahimabadi, P., Azarnivand, H., Khosravi, H., Zehtabian, G., & Moghaddamnia, A. (2021). An ecological agricultural model using fuzzy AHP and PROMETHEE II approach. Desert, 26(1), 71–83. https://doi.org/10.22059/jdesert.2020.303314.1006778

    Google Scholar 

  • Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U., & Shang, S. (2023). Drought monitoring using landsat derived indices and Google Earth engine platform: A case study from Al-Lith Watershed, Kingdom of Saudi Arabia. Remote Sensing, 15(4), 984. https://doi.org/10.3390/rs15040984

    Article  Google Scholar 

  • Foster, P. (2001). The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Science Reviews, 55(1–2), 73–106. https://doi.org/10.1016/S0012-8252(01)00056-3

    Article  Google Scholar 

  • Heydari Alamdarloo, E., Manesh, M. B., & Khosravi, H. (2018). Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environmental Monitoring and Assessment, 190, 1–11. https://doi.org/10.1007/s10661-018-7089-1

    Google Scholar 

  • Hou, Q., Ji, Z., Yang, H., & Yu, X. (2022). Impacts of climate change and human activities on different degraded grassland based on NDVI. Scientific Reports, 12(1), 15918. https://doi.org/10.1038/s41598-022-19943-6

    Article  CAS  Google Scholar 

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2

    Article  Google Scholar 

  • Jiang, W., Niu, Z., Wang, L., Yao, R., Gui, X., **ang, F., & Ji, Y. (2022). Impacts of drought and climatic factors on vegetation dynamics in the Yellow River Basin and Yangtze River Basin, China. Remote Sensing, 14(4), 930. https://doi.org/10.3390/rs14040930

    Article  Google Scholar 

  • **, X., Qiang, H., Zhao, L., Jiang, S., Cui, N., Cao, Y., & Feng, Y. (2020). SPEI-based analysis of spatio-temporal variation characteristics for annual and seasonal drought in the Zoige Wetland, Southwest China from 1961 to 2016. Theoretical and Applied Climatology, 139, 711–725. https://doi.org/10.1007/s00704-019-02981-y

    Article  Google Scholar 

  • Kendall, M. G. (1975). Rank correlation methods (Vol. 202, p. 15). Griffin.

  • Khosravi, H., Haydari, E., Shekoohizadegan, S., & Zareie, S. (2017). Assessment the effect of drought on vegetation in desert area using landsat data. The Egyptian Journal of Remote Sensing and Space Science, 20, S3–S12. https://doi.org/10.1016/j.ejrs.2016.11.007

    Article  Google Scholar 

  • Kirana, A. P., Ririd, A. R. T. H., Ariyanto, R., Amalia, E. L., & Bhawiyuga, A. (2020, July). Spatiotemporal pattern distribution of drought area using MODIS vegetation health index. Case study: paddy field in East Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 528(1), 012056. https://doi.org/10.1088/1755-1315/528/1/012056

  • Kloos, S., Yuan, Y., Castelli, M., & Menzel, A. (2021). Agricultural drought detection with MODIS based vegetation health indices in Southeast Germany. Remote Sensing, 13(19), 3907. https://doi.org/10.3390/rs13193907

    Article  Google Scholar 

  • Kocaaslan, S., Musaoğlu, N., & Karamzadeh, S. (2021). Evaluating drought events by time-frequency analysis: A case study in Aegean region of Turkey. IEEE Access: Practical Innovations, Open Solutions, 9, 125032–125041. https://doi.org/10.1109/ACCESS.2021.3110816

    Article  Google Scholar 

  • Kogan, F., Salazar, L., & Roytman, L. (2012). Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. International Journal of Remote Sensing, 33(9), 2798–2814. https://doi.org/10.1080/01431161.2011.621464

    Article  Google Scholar 

  • Kumari, N., Saco, P. M., Rodriguez, J. F., Johnstone, S. A., Srivastava, A., Chun, K. P., & Yetemen, O. (2020). The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems. Geophysical Research Letters, 47(15), e2020GL088918. https://doi.org/10.1029/2020GL088918

    Article  Google Scholar 

  • 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. https://doi.org/10.3390/cli9070109

    Article  Google Scholar 

  • Kundu, A., Dwivedi, S., & Dutta, D. (2016). Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arabian Journal of Geosciences, 9, 1–15. https://doi.org/10.1007/s12517-015-2185-9

    Article  CAS  Google Scholar 

  • Lamchin, M., Lee, W. K., Jeon, S. W., Wang, S. W., Lim, C. H., Song, C., & Sung, M. (2018). Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Science of the Total Environment, 618, 1089–1095. https://doi.org/10.1016/j.scitotenv.2017.09.145

    Article  CAS  Google Scholar 

  • Li, J., **, M., Wang, L., Li, N., Wang, H., & Qin, F. (2022). Vegetation responses to climate change and anthropogenic activity in China, 1982 to 2018. International Journal of Environmental Research and Public Health, 19(12), 7391. https://doi.org/10.3390/ijerph19127391

    Article  Google Scholar 

  • Lima, S. C. D., Moraes, J. M. D., Lima, J. P., Lima, F. C. D., & Saboya, L. M. (2023). Response of semi-arid vegetation to agricultural drought determined by indices derived from MODIS satellite. Revista Brasileira De Engenharia Agrícola E Ambiental, 27(8), 632–642. https://doi.org/10.1590/1807-1929/agriambi.v27n8p632-642

    Article  Google Scholar 

  • Luo, H., Zhou, T., Wu, H., Zhao, X., Wang, Q., Gao, S., & Li, Z. (2016). Contrasting responses of planted and natural forests to drought intensity in Yunnan, China. Remote Sensing, 8(8), 635. https://doi.org/10.3390/rs8080635

    Article  Google Scholar 

  • Luo, H., Dai, S., **e, Z., & Fang, J. (2018, February). NDVI-Based analysis on the influence of human activities on vegetation variation on Hainan Island. IOP Conference Series: Earth and Environmental Science, 121(3), 032045. https://doi.org/10.1088/1755-1315/121/3/032045

  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245–259. https://doi.org/10.2307/1907187

  • Marengo, J. A., Cunha, A. P. M., Nobre, C. A., Neto, R., Magalhaes, G. G., Torres, A. R., Sampaio, R. R., Alexandre, G., Alves, F., Cuartas, L. M., L.A., & Álvala, R. C. (2020). Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4C. Natural Hazards, 103, 2589–2611. https://doi.org/10.1007/s11069-020-04097-3

    Article  Google Scholar 

  • Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors (Basel, Switzerland), 7(11), 2636–2651. https://doi.org/10.3390/s7112636

    Article  Google Scholar 

  • Meng, N., Wang, N. A., Cheng, H., Liu, X., & Niu, Z. (2023). Impacts of climate change and anthropogenic activities on the normalized difference vegetation index of desertified areas in northern China. Journal of Geographical Sciences, 33(3), 483–507. https://doi.org/10.1007/s11442-023-2093-y

    Article  Google Scholar 

  • Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012

    Article  Google Scholar 

  • Moutia, S., Sinan, M., & Lekhlif, B. (2021). Assessment of agricultural drought in Morocco based on a composite of the vegetation health index (VHI) and standardized precipitation evapotranspiration index (SPEI). In E3S Web of Conferences (Vol. 314, p. 04003). EDP Sciences. https://doi.org/10.1051/e3sconf/202131404003

  • Mu, S., Zhou, S., Chen, Y., Li, J., Ju, W., & Odeh, I. O. A. (2013). Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in inner Mongolian grassland, China. Global and Planetary Change, 108, 29–41. https://doi.org/10.1016/j.gloplacha.2013.06.007

    Article  Google Scholar 

  • Nejadrekabi, M., Eslamian, S., & Zareian, M. J. (2022). Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology, 19(7), 6573–6594. https://doi.org/10.1007/s13762-021-03852-8

    Article  CAS  Google Scholar 

  • Neyns, R., & Canters, F. (2022). Map** of urban vegetation with high-resolution remote sensing: A review. Remote Sensing, 14(4), 1031. https://doi.org/10.3390/rs14041031

    Article  Google Scholar 

  • Nodefarahani, M., Aradpour, S., Noori, R., Tang, Q., Partani, S., & Klöve, B. (2020). Metal pollution assessment in surface sediments of Namak Lake, Iran. Environmental Science and Pollution Research, 27, 45639–45649. https://doi.org/10.1007/s11356-020-10298-x

    Article  CAS  Google Scholar 

  • Olmos-Trujillo, E., González-Trinidad, J., Júnez-Ferreira, H., Pacheco-Guerrero, A., Bautista-Capetillo, C., Avila-Sandoval, C., & Galván-Tejada, E. (2020). Spatio-temporal response of vegetation indices to rainfall and temperature in a semiarid region. Sustainability, 12(5), 1939. https://doi.org/10.3390/su12051939

  • Pacheco, J., Solera, A., Avilés, A., & Tonón, M. D. (2022). Influence of ENSO on droughts and vegetation in a high mountain equatorial climate basin. Atmosphere, 13(12), 2123. https://doi.org/10.3390/atmos13122123

    Article  Google Scholar 

  • Páscoa, P., Gouveia, C. M., Russo, A. C., Bojariu, R., Vicente-Serrano, S. M., & Trigo, R. M. (2018). Vegetation vulnerability to drought on southeastern Europe. Hydrology and Earth System Sciences Discussions, 1–29. https://doi.org/10.5194/hess-2018-264

  • Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., Wang, K., Xu, L., & **a, G. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology, 248, 215–227. https://doi.org/10.1016/j.agrformet.2017.10.001

    Article  Google Scholar 

  • Pogačar, T., Žnidaršič, Z., Vlahović, Ž., Črepinšek, Z., & Sušnik, A. (2022). Grassland Model based evaluation of Drought indices: A Case Study from the Slovenian Alpine Region. Agronomy, 12(4), 936. https://doi.org/10.3390/agronomy12040936

    Article  Google Scholar 

  • Qu, S., Wang, L., Lin, A., Yu, D., & Yuan, M. (2020). Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecological Indicators, 108, 105724. https://doi.org/10.1016/j.ecolind.2019.105724

    Article  Google Scholar 

  • Ren, Y., Liu, J., Liu, S., Wang, Z., Liu, T., & Shalamzari, M. J. (2022). Effects of climate change on vegetation growth in the Yellow River Basin from 2000 to 2019. Remote Sensing, 14(3), 687. https://doi.org/10.3390/rs14030687

    Article  Google Scholar 

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  Google Scholar 

  • Shahzad, A. L. I., Basit, A., Umair, M., Makanda, T. A., Khan, F. U., Siqi, S. H. I., & Jian, N. I. (2023). 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, 22(10), 3193–3208. https://doi.org/10.1016/j.jia.2023.04.028

    Article  Google Scholar 

  • Sheikh, Z., Nia, A. M., & Ganjali, M. (2024). Climate change and anthropogenic effects on the drying of a saline lake in an arid region (Namak Lake, Iran). Theoretical and Applied Climatology, 155(1), 715–734. https://doi.org/10.1007/s00704-023-04622-x

    Article  Google Scholar 

  • Shi, X., Chen, F., Ding, H., Li, Y., & Shi, M. (2022). Quantifying Vegetation Stability under Drought in the Middle reaches of Yellow River Basin, China. Forests, 13(7), 1138. https://doi.org/10.3390/f13071138

    Article  Google Scholar 

  • Srivastava, A., Saco, P. M., Rodriguez, J. F., Kumari, N., Chun, K. P., & Yetemen, O. (2021a). The role of landscape morphology on soil moisture variability in semi-arid ecosystems. Hydrological Processes, 35(1), e13990. https://doi.org/10.1002/hyp.13990

  • Srivastava, A., Rodriguez, J. F., Saco, P. M., Kumari, N., & Yetemen, O. (2021b). Global analysis of atmospheric transmissivity using cloud cover, aridity and flux network datasets. Remote Sensing, 13(9), 1716. https://doi.org/10.3390/rs13091716

    Article  Google Scholar 

  • Srivastava, A., Yetemen, O., Kumari, N., & Saco, P. (2019, December). Aspect-controlled spatial and temporal soil moisture patterns across three different latitudes. In Proceedings of the 23rd international congress on modeling and simulation (MODSIM2019), Canberra, Australia (pp. 1–6).

  • Sun, Y., Yang, Y., Zhang, L., & Wang, Z. (2015). The relative roles of climate variations and human activities in vegetation change in North China. Physics and Chemistry of the Earth Parts A/B/C, 87, 67–78. https://doi.org/10.1016/j.pce.2015.09.017

    Article  Google Scholar 

  • Teferi, E., Uhlenbrook, S., & Bewket, W. (2015). Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: Dual-scale time series analysis. Earth System Dynamics, 6(2), 617–636. https://doi.org/10.5194/esd-6-617-2015

    Article  Google Scholar 

  • Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Indagationes Mathematicae, 12(85), 173.

    Google Scholar 

  • Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38(1), 55–94. https://doi.org/10.2307/210739

    Article  Google Scholar 

  • Van Viet, L., & Thuy, T. T. T. (2023). Improving the quality of coffee yield forecasting in Dak Lak Province, Vietnam, through the utilization of remote sensing data. Environmental Research Communications, 5(9), 095011. https://doi.org/10.1088/2515-7620/acf6fd

    Article  Google Scholar 

  • Vicente-Serrano, S. M., McVicar, T. R., Miralles, D. G., Yang, Y., & Tomas‐Burguera, M. (2020). Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(2), e632. https://doi.org/10.1002/wcc.632

    Google Scholar 

  • Wang, Y., Yang, J., Chen, Y., Fang, G., Duan, W., Li, Y., & De Maeyer, P. (2019). Quantifying the effects of climate and vegetation on soil moisture in an arid area, China. Water, 11(4), 767. https://doi.org/10.3390/w11040767

    Article  Google Scholar 

  • Wang, F., Lu, X., Mei, S., Su, Y., Zhen, Z., Zou, Z., Zhang, X., Yin, R., Duić, N., Shafie-khah, M., & Catalão, J. P. (2022a). A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant. Energy, 238, 121946. https://doi.org/10.1016/j.energy.2021.121946

    Article  Google Scholar 

  • Wang, J., Fan, Y., Yang, Y., Zhang, L., Zhang, Y., Li, S., & Wei, Y. (2022b). Spatial-temporal evolution characteristics and driving force analysis of NDVI in the Minjiang River Basin, China, from 2001 to 2020. Water, 14(18), 2923. https://doi.org/10.3390/w14182923

    Article  Google Scholar 

  • Wang, S., **ng, X., Wu, Y., Guo, J., Li, M., & Fu, B. (2024). Seasonal response of the NDVI to the SPEI at different Time scales in Yinshanbeilu, Inner Mongolia, China. Land, 13(4), 523. https://doi.org/10.3390/land13040523

    Article  Google Scholar 

  • Wen, Y., Liu, X., **n, Q., Wu, J., Xu, X., Pei, F., Li, X., Du, G., Cai, Y., Lin, K., & Wang, Y. (2019). Cumulative effects of climatic factors on terrestrial vegetation growth. Journal of Geophysical Research: Biogeosciences, 124(4), 789–806. https://doi.org/10.1029/2018JG004751

    Article  Google Scholar 

  • Xu, H. J., Wang, X. P., & Zhao, C. Y. (2021). Drought sensitivity of vegetation photosynthesis along the aridity gradient in northern China. International Journal of Applied Earth Observation and Geoinformation, 102, 102418. https://doi.org/10.1016/j.jag.2021.102418

    Article  Google Scholar 

  • Xu, B., Li, J., Luo, Z., Wu, J., Liu, Y., Yang, H., & Pei, X. (2022). Analyzing the spatiotemporal vegetation dynamics and their responses to climate change along the ya’an–linzhi section of the Sichuan–Tibet Railway. Remote Sensing, 14(15), 3584. https://doi.org/10.3390/rs14153584

    Article  Google Scholar 

  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017. https://doi.org/10.1155/2017/1353691

  • Yang, M., Mou, Y., Meng, Y., Liu, S., Peng, C., & Zhou, X. (2020). Modeling the effects of precipitation and temperature patterns on agricultural drought in China from 1949 to 2015. Science of the Total Environment, 711, 135139. https://doi.org/10.1016/j.scitotenv.2019.135139

    Article  CAS  Google Scholar 

  • Zeng, J., Zhang, R., Qu, Y., Bento, V. A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., & Wang, Q. (2022). Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather and Climate Extremes, 35, 100412. https://doi.org/10.1016/j.wace.2022.100412

    Article  Google Scholar 

  • Zeng, J., Zhou, T., Qu, Y., Bento, V. A., Qi, J., Xu, Y., Li, Y., & Wang, Q. (2023). An improved global vegetation health index dataset in detecting vegetation drought. Scientific Data, 10(1), 338. https://doi.org/10.1038/s41597-023-02255-3

    Article  Google Scholar 

  • Zhang, R., Chen, Z. Y., Xu, L. J., & Ou, C. Q. (2019). Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China. Science of the Total Environment, 665, 338–346. https://doi.org/10.1016/j.scitotenv.2019.01.431

  • Zhang, Y., Jiang, X., Lei, Y., & Gao, S. (2022). The contributions of natural and anthropogenic factors to NDVI variations on the Loess Plateau in China during 2000–2020. Ecological Indicators, 143, 109342. https://doi.org/10.1016/j.ecolind.2022.109342

    Article  Google Scholar 

  • Zhao, Z., Zhang, Y., Liu, L., & Hu, Z. (2018). The impact of drought on vegetation conditions within the Damqu river basin, Yangtze river source region, China. Plos One, 13(8), e0202966. https://doi.org/10.1371/journal.pone.0202966

  • Zhen, Z., Chen, S., Yin, T., & Gastellu-Etchegorry, J. P. (2023). Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8. ISPRS Journal of Photogrammetry and Remote Sensing, 205, 206–226. https://doi.org/10.1016/j.isprsjprs.2023.09.024

    Article  Google Scholar 

  • Zhou, X., Yamaguchi, Y., & Arjasakusuma, S. (2018). Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau. Science of the Total Environment, 616, 730–743. https://doi.org/10.1016/j.scitotenv.2017.10.253

    Article  Google Scholar 

  • Zhu, X., Liu, Y., Xu, K., & Pan, Y. (2021). Effects of drought on vegetation productivity of farmland ecosystems in the drylands of northern China. Remote Sensing, 13(6), 1179. https://doi.org/10.3390/rs13061179

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tehran University College of Agriculture and Natural Resources, Tehran, Iran, Islamic Republic of

    Pouyan Dehghan Rahimabadi, Mahsa Abdolshahnejad, Esmail Heydari Alamdarloo & Hossein Azarnivand

Authors
  1. Pouyan Dehghan Rahimabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahsa Abdolshahnejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Esmail Heydari Alamdarloo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hossein Azarnivand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Azarnivand.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahimabadi, P.D., Abdolshahnejad, M., Alamdarloo, E.H. et al. Vegetation Dynamics Assessment: Remote Sensing and Statistical Approaches to Determine the Contributions of Driving Factors. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01917-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12524-024-01917-y

Keywords

Search

Navigation