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Resistance of vegetation sensitivity to climate and human activities under short-term drought in subtropical humid region: a case study of Guangdong, China


Abstract

Understanding how the changes of vegetation sensitivity to climate and human activities under drought is essential for evaluating ecosystem resistance in subtropical humid regions. This paper focused on Guangdong Province, China, and used SPEI-3 (Standardized Precipitation Evapotranspiration Index) to identify short-term drought events (< 6 months). LMG (Lindeman-Merenda-Gold method) and XGBoost (Extreme Gradient Boosting) were used to quantitatively analyze the contributions of temperature, precipitation and nighttime light (NTL) to the NDVI of evergreen forest, grassland and urban region. LMM (Linear Mixed Model) with month and year as random effects were applied. The conclusions revealed that: (1) Short-term drought significantly reduced the sensitivity of vegetation to temperature in the south of the Tropic of Cancer, while vegetation in the north of the Tropic of Cancer maintained a stable relationship with temperature. (2) Under drought, the temperature contribution to evergreen forest decreased the most (10–13%), followed by urban region (6–15%) and grassland (7–12%). The precipitation contribution to evergreen forest increased by 7–10% and 2–7% for grassland. (3) Drought weakened the positive temperature-NDVI correlation in the west located in the south of the Tropic of Cancer, while the positive temperature-NDVI correlation persisted in the north located in the north of the Tropic of Cancer. (4) Under drought, vegetation maintained a stable positive response to climate. The temperature-NDVI relationship in more than half of the cities shifted from positive to negative.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. IPCC. Climate Change 2022-Impacts, Adaptation and Vulnerability: Working Group II Contribution To the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2023).

  2. Mohammed, Y. & Yimam, A. Analysis of meteorological droughts in the Lake’s Region of Ethiopian Rift Valley using reconnaissance drought index (RDI). Geoenvironmental Disasters. 8, 13 (2021).

    Google Scholar 

  3. Almouctar, M. A. S., Wu, Y., Zhao, F. & Qin, C. Drought analysis using normalized difference vegetation index and land surface temperature over Niamey region, the southwestern of the Niger between 2013 and 2019. J. Hydrol. Reg. Stud. 52, 101689 (2024).

    Google Scholar 

  4. Ma, Z., Dong, C., Tang, Z. & Wang, N. Altitude-dependent responses of dryland mountain ecosystems to drought under a warming climate in the Qilian Mountains, NW China. J. Hydrol. 630, 130763 (2024).

    Google Scholar 

  5. Adhikari, S. et al. Analysis of flash drought and its impact on forest normalized difference vegetation index (NDVI) in Northeast China from 2000 to 2020. Atmosphere 15, 818 (2024).

    Google Scholar 

  6. Warter, M. M. et al. Drought onset and propagation into soil moisture and grassland vegetation responses during the 2012–2019 major drought in Southern California. Hydrol. Earth Syst. Sci. 25, 3713–3729 (2021).

    Google Scholar 

  7. Perlikowski, D. & Kosmala, A. Mechanisms of drought resistance in introgression forms of Lolium multiflorum/Festuca arundinacea. Biol. Plant. 64, 497–503 (2020).

    Google Scholar 

  8. O’Connor, J. C. et al. Forests buffer against variations in precipitation. Glob Change Biol. 27, 4686–4696 (2021).

    Google Scholar 

  9. Řehoř, J. et al. Global hotspots in soil moisture-based drought trends. Environ. Res. Lett. 19, 014021 (2024).

    Google Scholar 

  10. Ullah, I. et al. Anthropogenic and atmospheric variability intensifies flash drought episodes in South Asia. Commun. Earth Environ. 5, 1–11 (2024).

    Google Scholar 

  11. Jiao, T., Williams, C. A., De Kauwe, M. G., Schwalm, C. R. & Medlyn, B. E. Patterns of post-drought recovery are strongly influenced by drought duration, frequency, post-drought wetness, and bioclimatic setting. Glob Change Biol. 27, 4630–4643 (2021).

    Google Scholar 

  12. Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    Google Scholar 

  13. Anderegg, W. R. L., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Change. 10, 1091–1095 (2020).

    Google Scholar 

  14. Jung, H., Won, J., Lee, J. H. & Kim, S. Quantitative assessment of vegetation drought vulnerability based on multi-weighted averaging of multiple meteorological drought indices and vegetation indices. Nat. Hazards. 120, 13161–13180 (2024).

    Google Scholar 

  15. Tei, S. & Sugimoto, A. Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests. Glob Change Biol. 24, 4225–4237 (2018).

    Google Scholar 

  16. Zhang, Y. et al. Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2. Nat. Commun. 13, 4875 (2022).

    Google Scholar 

  17. Zhou, Z. et al. Comprehensive evaluation of vegetation responses to meteorological drought from both linear and nonlinear perspectives. Front. Earth Sci. 10, (2022).

  18. Hwang, Y., Ryu, Y. & Qu, S. Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul. Landsc. Urban Plan. 227, 104518 (2022).

    Google Scholar 

  19. Li, L., Kross, A., Eicker, U. & Ziter, C. D. Tree presence and level of aggregation in urban parks are associated with opposite daytime and nighttime urban cooling. Urban Urban Green. 114, 129159 (2025).

    Google Scholar 

  20. Jiang, L., Guli, J., Bao, A., Guo, H. & Ndayisaba, F. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total Environ. 599–600, 967–980 (2017).

    Google Scholar 

  21. Wang, H. et al. Impacts of drought and human activity on vegetation growth in the grain for green program region, China. Chin. Geogr. Sci. 28, 470–481 (2018).

    Google Scholar 

  22. Wang, L. et al. Response of vegetation to different climate extremes on a monthly scale in Guangdong, China. Remote Sens. 14, (2022).

  23. Wu, Y., Qiu, X., Liang, D., Zeng, X. & Liu, Q. How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001–2022. Environ. Monit. Assess. 196, 1020 (2024).

    Google Scholar 

  24. Chen, Y., Chen, W., Gong, J. & Yuan, H. Uncommonly known change characteristics of land use pattern in Guangdong Province-Hong Kong-Macao, China: space time pattern, terrain gradient effects and policy implication. Land. Use Policy. 125, 106461 (2023).

    Google Scholar 

  25. Bai, Y., Yang, Y. & Jiang, H. Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI products over the Mongolian plateau. Remote Sens. 11, 2030 (2019).

    Google Scholar 

  26. Cheng, Y. et al. Spatiotemporal variation and influence factors of vegetation cover in the Yellow River Basin (1982–2021) based on GIMMS NDVI and MOD13A1. Water 14, 3274 (2022).

    Google Scholar 

  27. Andrade, M. D. et al. Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic forest. Environ. Monit. Assess. 193, 45 (2021).

    Google Scholar 

  28. Jawad, M. et al. Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: comparison with ECOSTRESS, MODIS, and openet. J. Hydrol. 660, 133460 (2025).

    Google Scholar 

  29. Ding, Y. & Peng, S. Spatiotemporal trends and attribution of drought across China from 1901–2100. Sustainability 12, 477 (2020).

    Google Scholar 

  30. Peng, S., Ding, Y., Liu, W. & Li, Z. 1km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 11, 1931–1946 (2019).

  31. Hu, Y., Zhou, X., Yamazaki, D. & Chen, J. A self-adjusting method to generate daily consistent nighttime light data for the detection of short-term rapid human activities. Remote Sens. Environ. 304, 114077 (2024).

    Google Scholar 

  32. Xie, Q. et al. Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China. Ecol. Indic. 160, 111945 (2024).

    Google Scholar 

  33. Zhao, J., Dong, Y., Zhang, M. & Huang, L. Comparison of identifying land cover tempo-spatial changes using globcover and MCD12Q1 global land cover products. Arab. J. Geosci. 13, 792 (2020).

    Google Scholar 

  34. Pei, W., Hao, L., Fu, Q., Ren, Y. & Li, T. The standardized precipitation evapotranspiration index based on cumulative effect attenuation. J. Hydrol. 635, 131148 (2024).

    Google Scholar 

  35. Sabzevari, Y., Eslamian, S., Pamula, A. S. P. & Bazrkar, M. H. Drought trend analysis using standardized precipitation evapotranspiration index in cold-climate regions. Atmosphere 16, 482 (2025).

    Google Scholar 

  36. Andujar, E., Krakauer, N. Y., Yi, C. & Kogan, F. Ecosystem drought response timescales from thermal emission versus shortwave remote sensing. Adv. Meteorol. 2017, 8434020 (2017).

  37. Zhan, C. et al. Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China. Ecol. Indic. 143, 109409 (2022).

    Google Scholar 

  38. Ha, T. V., Uereyen, S. & Kuenzer, C. Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series. GIScience Remote Sens. 61, 2387385 (2024).

    Google Scholar 

  39. Liu, E., Zhou, G. & Zhou, H. The nonlinear drought response and its critical threshold of Stipa krylovii roshev. typical steppe phenology. Int. J. Biometeorol. 69, 2209–2224 (2025).

    Google Scholar 

  40. Laimighofer, J. & Laaha, G. How standard are standardized drought indices? Uncertainty components for the SPI & SPEI case. J. Hydrol. 613, 128385 (2022).

    Google Scholar 

  41. Awange, J. L., Mpelasoka, F. & Goncalves, R. M. When every drop counts: analysis of droughts in Brazil for the 1901–2013 period. Sci. Total Environ. 566–567, 1472–1488 (2016).

    Google Scholar 

  42. Sheffield, J. & Wood, E. F. Characteristics of global and regional drought, 1950–2000: analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J. Geophys. Res. Atmos. 112, (2007).

  43. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. 23, 1696–1718 (2010).

  44. Bi, J. A. Review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking. J. Sens. Stud. 27, 87–101 (2012).

    Google Scholar 

  45. Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147 (2007).

    Google Scholar 

  46. Dong, J., Chen, Y., Yao, B., Zhang, X. & Zeng, N. A neural network boosting regression model based on XGBoost. Appl. Soft Comput. 125, 109067 (2022).

    Google Scholar 

  47. Mamudur, K. & Kattamuri, M. R. Application of boosting-based ensemble learning method for the prediction of compression index. J. Inst. Eng. India Ser. A. 101, 409–419 (2020).

    Google Scholar 

  48. Sokhansefat, S., Kanani-Sadat, Y. & Nasseri, M. Modeling vegetation dynamics in complex topography under impacts of climate change: integration of Spatial clustering and optimized XGBoost. J. Environ. Manage. 387, 125902 (2025).

    Google Scholar 

  49. Xia, N., Li, M. & Cheng, L. Mapping impacts of human activities from nighttime light on vegetation cover changes in southeast Asia. Land 10, 185 (2021).

    Google Scholar 

  50. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 785–794 https://doi.org/10.1145/2939672.2939785 (2016).

  51. Rajagopalan, M. & Broemeling, L. Bayesian inference for the variance components in general mixed linear models. Commun. Stat. – Theory Methods. 12, 701–723 (1983).

    Google Scholar 

  52. Dorji, T., Odeh, I. O. A. & Field, D. J. Elucidating the complex interrelationships of soil organic carbon fractions with land use/land cover types and landform attributes in a montane ecosystem. J. Soils Sediments. 15, 1039–1054 (2015).

    Google Scholar 

  53. Lessels, J. S. & Bishop, T. F. A. Estimating water quality using linear mixed models with stream discharge and turbidity. J. Hydrol. 498, 13–22 (2013).

    Google Scholar 

  54. Gumedze, F. N. & Dunne, T. T. Parameter estimation and inference in the linear mixed model. Linear Algebra its Appl. 435, 1920–1944 (2011).

    Google Scholar 

  55. Wu, Y. & Wu, Z. N. P. P. Variability associated with natural and anthropogenic factors in the tropic of cancer transect, China. Remote Sens. 15, 1091 (2023).

    Google Scholar 

  56. Geng, S. et al. Climatic and anthropogenic contributions to vegetation changes in Guangdong Province of South China. Remote Sens. 15, 5377 (2023).

    Google Scholar 

  57. Wu, Y. et al. The variation of vegetation greenness and underlying mechanisms in Guangdong Province of China during 2001–2013 based on MODIS data. Sci. Total Environ. 653, 536–546 (2019).

    Google Scholar 

  58. Zhou, R., Wang, H., Duan, K. & Liu, B. Diverse responses of vegetation to hydroclimate across temporal scales in a humid subtropical region. J. Hydrol. Reg. Stud. 33, 100775 (2021).

    Google Scholar 

  59. Luo, H. et al. NDVI-based analysis of the influence of climate changes and human activities on vegetation variation on Hainan Island. J. Indian Soc. Remote Sens. 49, 1755–1767 (2021).

    Google Scholar 

  60. Mohammad, L. et al. Urban growth and environmental impact assessment in malda: A comprehensive study using Shannon’s entropy and remote sensing. Phys. Chem. Earth Parts ABC. 140, 104021 (2025).

    Google Scholar 

  61. Verma, P., Tiwari, P., Singh, R. & Raghubanshi, A. S. Effect of rainfall variability on tree phenology in moist tropical deciduous forests. Environ. Monit. Assess. 194, 537 (2022).

    Google Scholar 

  62. Azevedo, S., Cardim, G., Puga, F., Singh, R. & Da Silva, E. A. Analysis of the 2012–2016 drought in the northeast Brazil and its impacts on the Sobradinho water reservoir. Remote Sens. Lett. 9, 438–446 (2018).

    Google Scholar 

  63. Kang, J. et al. Limitation of summer extreme high temperatures on radial growth relieve with increasing latitude in subtropics. Sci. Total Environ. 956, 177400 (2024).

    Google Scholar 

  64. Li, X. et al. Warming-induced phenological mismatch between trees and shrubs explains high-elevation forest expansion. Natl. Sci. Rev. 10, nwad182 (2023).

    Google Scholar 

  65. Sakschewski, B. et al. Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests. Biogeosciences 18, 4091–4116 (2021).

    Google Scholar 

  66. Zhang, Y. et al. Extreme drought along the tropic of cancer (Yunnan section) and its impact on vegetation. Sci. Rep. 14, 7508 (2024).

    Google Scholar 

  67. Lin, J. et al. Evolution of vegetation cover and impacts of climate change and human activities in arid regions of Northwest China: a Mu Us Sandy Lan case. Environ. Dev. Sustain. 27, 18977–18996 (2025).

    Google Scholar 

  68. Liu, J. et al. Vegetation greening and driving factors in the Eurasian drylands under sustained drought conditions over recent two decades. J. Environ. Manage. 392, 126604 (2025).

    Google Scholar 

  69. Dai, T. et al. The impact of climate change and human activities on the change in the net primary productivity of vegetation—taking Sichuan Province as an example. Environ. Sci. Pollut Res. 31, 7514–7532 (2024).

    Google Scholar 

  70. He, Y., Lin, C., Wu, C., Pu, N. & Zhang, X. The urban hierarchy and agglomeration effects influence the response of NPP to climate change and human activities. Glob Ecol. Conserv. 51, e02904 (2024).

    Google Scholar 

  71. Bastos, A. et al. Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019. Earth Syst. Dyn. 12, 1015–1035 (2021).

    Google Scholar 

  72. Mahim, M. M. A., Rasel, M. I. A., Hasan, M. M. & Reza, A. H. M. S. Assessment of heatwave, drought, vegetation and moisture content using remote sensing and GIS: a comprehensive study in north-western part of Bangladesh. Nat. Hazards. 121, 14563–14590 (2025).

    Google Scholar 

  73. Dang, H. et al. Key strategies underlying the adaptation of Mongolian Scots Pine (Pinussylvestris var. mongolica) in sandy land under climate change. Rev. Forests. 13, 846 (2022).

    Google Scholar 

  74. Yuan, Y. et al. Assessing vegetation stability to climate variability in Central Asia. J. Environ. Manage. 298, 113330 (2021).

    Google Scholar 

  75. Molle, F. & Berkoff, J. Cities vs. agriculture: A review of intersectoral water re-allocation. Nat. Resour. Forum. 33, 6–18 (2009).

    Google Scholar 

  76. Sun, K. et al. Dynamic risk assessment method of urban drought based on water balance and optimal allocation analysis. IOP Conf. Ser. Mater. Sci. Eng. 780, 072010 (2020).

    Google Scholar 

  77. Román, M. O. et al. NASA’s black marble nighttime lights product suite. Remote Sens. Environ. 210, 113–143 (2018).

    Google Scholar 

  78. Wang, Z., Shrestha, R. M., Román, M. O. & Kalb, V. L. NASA’s black marble multiangle nighttime lights temporal composites. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022).

    Google Scholar 

  79. Chen, Y. et al. Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan. Ecol. Indic. 158, 111567 (2024).

    Google Scholar 

  80. Marumbwa, F. M., Cho, M. A. & Chirwa, P. W. An assessment of remote sensing-based drought index over different land cover types in southern Africa. Int. J. Remote Sens. 41, 7368–7382 (2020).

    Google Scholar 

  81. Xiao, C. et al. Land cover and management effects on ecosystem resistance to drought stress. Earth Syst. Dyn. 14, 1211–1237 (2023).

    Google Scholar 

  82. Docherty, E. M. et al. Long-term drought effects on the thermal sensitivity of Amazon forest trees. Plant. Cell. Environ. 46, 185–198 (2023).

    Google Scholar 

  83. Huang, K. & Xia, J. High ecosystem stability of evergreen broadleaf forests under severe droughts. Glob Change Biol. 25, 3494–3503 (2019).

    Google Scholar 

  84. Yin, H. & Cao, Y. Test on the policy effect of natural forest protection project using double difference model from the perspective of forestry total factor productivity. Math. Probl. Eng. 2022, 9800727 (2022).

  85. Khan, R., Wheeler, P. & Gowing, D. Characterising heatwave responses and climate driver impacts using multicollinearity-controlled generalised linear mixed models in urban and forest trees (2018–2023). Earth Syst. Environ. https://doi.org/10.1007/s41748-025-00648-5 (2025).

    Google Scholar 

  86. Weng, Q. & Lu, D. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int. J. Appl. Earth Obs Geoinf. 10, 68–83 (2008).

    Google Scholar 

  87. Zhang, P., Imhoff, M. L., Wolfe, R. E. & Bounoua, L. Characterizing urban heat Islands of global settlements using MODIS and nighttime lights products. Can. J. Remote Sens. 36, 185–196 (2010).

    Google Scholar 

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Acknowledgements

Thanks to Dr. Xuming Wu for helping us find the literatures. This work was supported by Guangdong Basic and Applied Basic Research Foundation (2025A1515011042), National Natural Science Foundation of China (42404066) and the research foundation of Lingnan Normal University (ZL22034).

Funding

This work was supported by Guangdong Basic and Applied Basic Research Foundation (2025A1515011042), National Natural Science Foundation of China (42404066) and the research foundation of Lingnan Normal University (ZL22034).

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Yuzhen Wu: Data analysis, plotting, writing, editing and reviewing manuscripts; An Fan: Data analysis, writing, editing and reviewing manuscripts; Yuanda Lei: Data analysis, reviewing manuscripts; Weishi Xiao: Collecting data, plotting and data analysis; Rumin Wu: Collecting data, plotting and data analysis. All authors read and approved the final manuscript.

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Yuzhen Wu.

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Wu, Y., Fan, A., Lei, Y. et al. Resistance of vegetation sensitivity to climate and human activities under short-term drought in subtropical humid region: a case study of Guangdong, China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40399-5

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  • DOI: https://doi.org/10.1038/s41598-026-40399-5

Keywords

  • Drought
  • Vegetation greenness
  • Subtropical humid region
  • Vegetation resistance


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