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Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events

Nature Plants volume 11, pages 731–742 (2025)Cite this article

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Abstract

Climate-driven forest mortality events have been extensively observed in recent decades, prompting the question of how quickly these affected forests can recover their functionality following such events. Here we assessed forest recovery in vegetation greenness (normalized difference vegetation index) and canopy water content (normalized difference infrared index) for 1,699 well-documented forest mortality events across 1,600 sites worldwide. By analysing 158,427 Landsat surface reflectance images sampled from these sites, we provided a global assessment on the time required for impacted forests to return to their pre-mortality state (recovery time). Our findings reveal a consistent decline in global forest recovery rate over the past decades indicated by both greenness and canopy water content. This decline is particularly noticeable since the 1990s. Further analysis on underlying mechanisms suggests that this reduction in global forest recovery rates is primarily associated with rising temperatures and increased water scarcity, while the escalation in the severity of forest mortality contributes only partially to this reduction. Moreover, our global-scale analysis reveals that the recovery of forest canopy water content lags significantly behind that of vegetation greenness, implying that vegetation indices based solely on greenness can overestimate post-mortality recovery rates globally. Our findings underscore the increasing vulnerability of forest ecosystems to future warming and water insufficiency, accentuating the need to prioritize forest conservation and restoration as an integral component of efforts to mitigate climate change impacts.

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Fig. 1: Recovery of NDVI and NDII after tree mortality events.
Fig. 2: Comparison of RT after tree mortality events among different decades.
Fig. 3: Potential drivers affecting RT.

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Data availability

The tree mortality sites are available at https://www.tree-mortality.net/globaltreemortalitydatabase/ (ref. 5), https://doi.org/10.3897/oneeco.4.e37753 (ref. 57) and https://doi.org/10.1016/j.scitotenv.2021.151604 (ref. 58). These are summarized and available via figshare at https://doi.org/10.6084/m9.figshare.28375442 (ref. 104). The original NDVI and NDII data are available via figshare at https://doi.org/10.6084/m9.figshare.28375529 (ref. 105). The climate, vegetation and soil data are available via figshare at https://doi.org/10.6084/m9.figshare.28375550 (ref. 106). The world continental boundaries were sourced from the Environmental Systems Research Institute World Continents dataset at https://hub.arcgis.com/datasets/esri::world-continents/about. The temperature and precipitation data can be retrieved from https://data.ceda.ac.uk/badc/cru/data/cru_ts. The SPEI data are available at https://digital.csic.es/handle/10261/202305. The available water storage capacity, CEC, soil bulk density, soil clay and soil pH data were downloaded from https://daac.ornl.gov/SOILS/guides/HWSD.html. The STN can be obtained from https://data.isric.org/. The canopy height can be retrieved from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1. The global maximum rooting depth was derived from the Global Earth Observation for Integrated Water Resource Assessment (Earth2Observe) dataset available via figshare at https://doi.org/10.6084/m9.figshare.12047241.v6 (ref. 107). The tree density was derived from https://elischolar.library.yale.edu/yale_fes_data/1/. The forest age data were downloaded from https://doi.org/10.17871/ForestAgeBGI.2021. The SLA data were downloaded from https://www.try-db.org/. The forest biomes were classified as DBF, ENF and shrubland based on the European Space Agency/Climate Change Initiative Land Cover (http://maps.elie.ucl.ac.be/CCI/viewer/). The LAI was obtained from MOD15A2H.061 (https://lpdaac.usgs.gov/products/mod15a2hv061/).

Code availability

Java, MATLAB and Python Codes for the analysis of these data are available via GitHub at https://github.com/YCY-github-YCY/Forest (ref. 108). The Landsat-based NDVI and NDII, and digital elevation model (including elevation, slope and aspect) are calculated on GEE, which is available at https://code.earthengine.google.com/.

References

  1. Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).

    Google Scholar 

  2. Reid, W. V. et al. Ecosystems and Human Well-being: Synthesis: A Report of the Millennium Ecosystem Assessment (Island, 2005).

  3. Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).

    Google Scholar 

  4. Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die‐off from hotter drought in the Anthropocene. Ecosphere https://doi.org/10.1890/ES15-00203.1 (2015).

  5. Hammond, W. M. et al. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nat. Commun. 13, 1761 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Hartmann, H. et al. Research frontiers for improving our understanding of drought‐induced tree and forest mortality. New Phytol. 218, 15–28 (2018).

    PubMed  Google Scholar 

  7. Liu, Q. et al. Drought‐induced increase in tree mortality and corresponding decrease in the carbon sink capacity of Canada’s boreal forests from 1970 to 2020. Glob. Change Biol. 29, 2274–2285 (2023).

    CAS  Google Scholar 

  8. McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).

    CAS  PubMed  Google Scholar 

  9. Park Williams, A. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).

    Google Scholar 

  10. Ratajczak, Z. et al. Abrupt change in ecological systems: inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).

    PubMed  Google Scholar 

  11. Reyer, C. P. et al. Forest resilience and tipping points at different spatio‐temporal scales: approaches and challenges. J. Ecol. 103, 5–15 (2015).

    Google Scholar 

  12. Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. Nat. Clim. Chang. 12, 477–484 (2022).

    Google Scholar 

  13. Wu, D. et al. Reduced ecosystem resilience quantifies fine‐scale heterogeneity in tropical forest mortality responses to drought. Glob. Change Biol. 28, 2081–2094 (2022).

    CAS  Google Scholar 

  14. Portmann, R. et al. Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation. Nat. Commun. 13, 5569 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Unger, N. Human land-use-driven reduction of forest volatiles cools global climate. Nat. Clim. Chang. 4, 907–910 (2014).

    CAS  Google Scholar 

  16. Quinn Thomas, R., Canham, C. D., Weathers, K. C. & Goodale, C. L. Increased tree carbon storage in response to nitrogen deposition in the US. Nat. Geosci. 3, 13–17 (2010).

    CAS  Google Scholar 

  17. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Chang. 6, 791–795 (2016).

    CAS  Google Scholar 

  18. Adams, M. A., Buckley, T. N., Binkley, D., Neumann, M. & Turnbull, T. L. CO2, nitrogen deposition and a discontinuous climate response drive water use efficiency in global forests. Nat. Commun. 12, 5194 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Tepley, A. J., Thompson, J. R., Epstein, H. E. & Anderson Teixeira, K. J. Vulnerability to forest loss through altered postfire recovery dynamics in a warming climate in the Klamath Mountains. Glob. Change Biol. 23, 4117–4132 (2017).

    Google Scholar 

  23. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    PubMed  Google Scholar 

  24. Joiner, J. et al. Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. Remote Sens. Environ. 219, 339–352 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. Liu, F. et al. Old‐growth forests show low canopy resilience to droughts at the southern edge of the taiga. Glob. Change Biol. 27, 2392–2402 (2021).

    CAS  Google Scholar 

  26. Yan, Y. et al. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening. Nat. Ecol. Evol. 8, 912–923 (2024).

    PubMed  Google Scholar 

  27. Chen, X. et al. Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. Int. J. Remote Sens. 32, 7905–7927 (2011).

    Google Scholar 

  28. Hislop, S. et al. Using Landsat spectral indices in time-series to assess wildfire disturbance and recovery. Remote Sens. 10, 460 (2018).

    Google Scholar 

  29. Pickell, P. D., Hermosilla, T., Frazier, R. J., Coops, N. C. & Wulder, M. A. Forest recovery trends derived from Landsat time series for North American boreal forests. Int. J. Remote Sens. 37, 138–149 (2016).

    Google Scholar 

  30. Khoury, S. & Coomes, D. A. Resilience of Spanish forests to recent droughts and climate change. Glob. Change Biol. 26, 7079–7098 (2020).

    Google Scholar 

  31. Fan, L. et al. Siberian carbon sink reduced by forest disturbances. Nat. Geosci. 16, 56–62 (2023).

    CAS  Google Scholar 

  32. Larjavaara, M., Berninger, F., Palviainen, M., Prokushkin, A. & Wallenius, T. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci. Rep. 7, 12776 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Berner, L. T. et al. Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia. Biogeosciences 9, 3943–3959 (2012).

    Google Scholar 

  34. Zhang, X., Yu, P., Wang, D. & Xu, Z. Density-and age-dependent influences of droughts and intrinsic water use efficiency on growth in temperate plantations. Agric. For. Meteorol. 325, 109134 (2022).

    Google Scholar 

  35. Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Chang. 12, 271–278 (2022).

    Google Scholar 

  36. Verbesselt, J. et al. Remotely sensed resilience of tropical forests. Nat. Clim. Chang. 6, 1028–1031 (2016).

    Google Scholar 

  37. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).

  38. Lundberg, S. & Lee, S. A unified approach to interpreting model predictions. In Proc. 31st International Conference on Neural Information Processing Systems (NIPS’17) (eds von Luxburg, U. et al.) 4768–4777 (Curran Associates, 2017).

  39. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    PubMed  PubMed Central  Google Scholar 

  40. Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).

    PubMed  Google Scholar 

  41. Corlett, R. T. The impacts of droughts in tropical forests. Trends Plant Sci. 21, 584–593 (2016).

    CAS  PubMed  Google Scholar 

  42. Nabuurs, G. et al. First signs of carbon sink saturation in European forest biomass. Nat. Clim. Chang. 3, 792–796 (2013).

    CAS  Google Scholar 

  43. Penuelas, J. Decreasing efficiency and slowdown of the increase in terrestrial carbon-sink activity. One Earth 6, 591–594 (2023).

    Google Scholar 

  44. Raupach, M. R. et al. The declining uptake rate of atmospheric CO2 by land and ocean sinks. Biogeosciences 11, 3453–3475 (2014).

    Google Scholar 

  45. Denissen, J. M. et al. Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Chang. 12, 677–684 (2022).

    Google Scholar 

  46. Mason, R. E. et al. Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems. Science 376, eabh3767 (2022).

    CAS  PubMed  Google Scholar 

  47. Penuelas, J. et al. Increasing atmospheric CO2 concentrations correlate with declining nutritional status of European forests. Commun. Biol. 3, 125 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Peñuelas, J. et al. Human-induced nitrogen–phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 2934 (2013).

    PubMed  Google Scholar 

  49. Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).

    CAS  Google Scholar 

  50. Turner, M. G. & Seidl, R. Novel disturbance regimes and ecological responses. Annu. Rev. Ecol. Evol. Syst 54, 63–83 (2023).

    Google Scholar 

  51. Zhang, F. et al. Attributing carbon changes in conterminous US forests to disturbance and non-disturbance factors from 1901 to 2010. J. Geophys. Res. Biogeo. 117, G02021 (2012).

    Google Scholar 

  52. Seidl, R. & Turner, M. G. Post-disturbance reorganization of forest ecosystems in a changing world. Proc. Natl Acad. Sci. USA 119, e2092777177 (2022).

    Google Scholar 

  53. Forzieri, G., Dakos, V., McDowell, N. G., Ramdane, A. & Cescatti, A. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Chang. 9, 880–885 (2019).

    Google Scholar 

  55. Schuur, E. A. et al. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556–559 (2009).

    CAS  PubMed  Google Scholar 

  56. Vogelmann, J. E., Gallant, A. L., Shi, H. & Zhu, Z. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sens. Environ. 185, 258–270 (2016).

    Google Scholar 

  57. Caudullo, G. & Barredo, J. I. A georeferenced dataset of drought and heat-induced tree mortality in Europe. One Ecosyst. 4, e37753 (2019).

    Google Scholar 

  58. Gazol, A. & Camarero, J. J. Compound climate events increase tree drought mortality across European forests. Sci. Total Environ. 816, 151604 (2022).

    CAS  PubMed  Google Scholar 

  59. Klemas, V. & Smart, R. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm. Eng. Remote Sens. 49, 77–83 (1983).

  60. Liu, F. et al. Remotely sensed birch forest resilience against climate change in the northern China forest-steppe ecotone. Ecol. Indic. 125, 107526 (2021).

    Google Scholar 

  61. Barta, K. A., Hais, M. & Heurich, M. Characterizing forest disturbance and recovery with thermal trajectories derived from Landsat time series data. Remote Sens. Environ. 282, 113274 (2022).

    Google Scholar 

  62. Viana-Soto, A. et al. Quantifying post-fire shifts in woody-vegetation cover composition in Mediterranean pine forests using Landsat time series and regression-based unmixing. Remote Sens. Environ. 281, 113239 (2022).

    Google Scholar 

  63. White, J. C., Hermosilla, T., Wulder, M. A. & Coops, N. C. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sens. Environ. 271, 112904 (2022).

    Google Scholar 

  64. White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C. & Hobart, G. W. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ. 194, 303–321 (2017).

    Google Scholar 

  65. Masek, J. G. et al. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3, 68–72 (2006).

  66. Vermote, E. & Saleous, N. LEDAPS Surface Reflectance Product Description (University of Maryland, 2007).

  67. Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).

    Google Scholar 

  68. Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).

    Google Scholar 

  69. Peng, X. et al. Northern Hemisphere greening in association with warming permafrost. J. Geophys. Res. Biogeo. 125, e2019JG005086 (2020).

    Google Scholar 

  70. Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).

    CAS  PubMed  Google Scholar 

  71. Wu, X. et al. Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere. Geophys. Res. Lett. 44, 6173–6181 (2017).

    Google Scholar 

  72. Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).

    Google Scholar 

  73. Amani, M. et al. Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 5326–5350 (2020).

    Google Scholar 

  74. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Google Scholar 

  75. Tamiminia, H. et al. Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170 (2020).

    Google Scholar 

  76. Yilmaz, M. T., Hunt, E. R. Jr & Jackson, T. J. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sens. Environ. 112, 2514–2522 (2008).

    Google Scholar 

  77. Baret, F., Jacquemoud, S., Guyot, G. & Leprieur, C. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens. Environ. 41, 133–142 (1992).

    Google Scholar 

  78. Jacquemoud, S. et al. PROSPECT+ SAIL models: a review of use for vegetation characterization. Remote Sens. Environ. 113, S56–S66 (2009).

  79. Zeng, Y. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3, 477–493 (2022).

    Google Scholar 

  80. Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).

    CAS  PubMed  Google Scholar 

  81. Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Chang. 13, 182–188 (2023).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  83. Wang, L. Spring phenology alters vegetation drought recovery. Nat. Clim. Chang. 13, 123–124 (2023).

    Google Scholar 

  84. Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M. & El Kenawy, A. A global 0.5 gridded dataset (1901–2006) of a multiscalar drought index considering the joint effects of precipitation and temperature. J. Hydrometeorol. 11, 1033–1043 (2010).

    Google Scholar 

  85. Tong, X. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 1, 44–50 (2018).

    Google Scholar 

  86. Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

  87. Gessler, A., Schaub, M. & McDowell, N. G. The role of nutrients in drought‐induced tree mortality and recovery. New Phytol. 214, 513–520 (2017).

    CAS  PubMed  Google Scholar 

  88. Xu, T. et al. Soil property plays a vital role in vegetation drought recovery in karst region of Southwest China. J. Geophys. Res. Biogeo. 126, e2021JG006544 (2021).

    Google Scholar 

  89. Yao, Y., Liu, Y., Zhou, S., Song, J. & Fu, B. Soil moisture determines the recovery time of ecosystems from drought. Glob. Change Biol. 29, 3562–3574 (2023).

    CAS  Google Scholar 

  90. Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2. (ORNL DAAC, 2014).

  91. Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).

    CAS  Google Scholar 

  92. Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).

    CAS  PubMed  Google Scholar 

  93. Fang, O. & Zhang, Q. B. Tree resilience to drought increases in the Tibetan Plateau. Glob. Change Biol. 25, 245–253 (2019).

    Google Scholar 

  94. Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).

    PubMed  Google Scholar 

  95. Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeo. 116, G04021 (2011).

    Google Scholar 

  96. Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).

    CAS  PubMed  Google Scholar 

  97. Besnard, S. et al. Mapping global forest age from forest inventories, biomass and climate data. Earth Syst. Sci. Data. 2021, 4881–4896 (2021).

    Google Scholar 

  98. Moreno-Martínez, Á. et al. A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sens. Environ. 218, 69–88 (2018).

    Google Scholar 

  99. Gazol, A., Camarero, J. J., Anderegg, W. & Vicente Serrano, S. M. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).

    Google Scholar 

  100. Schwartz, N. B., Budsock, A. M. & Uriarte, M. Fragmentation, forest structure, and topography modulate impacts of drought in a tropical forest landscape. Ecology 100, e02677 (2019).

    PubMed  Google Scholar 

  101. Sturm, J., Santos, M. J., Schmid, B. & Damm, A. Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought. Glob. Change Biol. 28, 2956–2978 (2022).

    CAS  Google Scholar 

  102. Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).

    Google Scholar 

  103. Gareth, J., Daniela, W., Trevor, H. & Robert, T. An Introduction to Statistical Learning: With Applications in R (Spinger, 2013).

  104. Yan, Y. et al. Tree mortality sites. figshare https://doi.org/10.6084/m9.figshare.28375442 (2025).

  105. Yan, Y. et al. Recovery of NDVI and NDII. figshare https://doi.org/10.6084/m9.figshare.28375529 (2025).

  106. Yan, Y. et al. Influencing factors. figshare https://doi.org/10.6084/m9.figshare.28375550 (2025).

  107. Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. figshare https://doi.org/10.6084/m9.figshare.12047241.v6 (2020).

  108. Yan, Y. et al. YCY-github-YCY/Forest. GitHub https://github.com/YCY-github-YCY/Forest (2025).

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Acknowledgements

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research programme (2024QZKK0301) and the National Natural Science Foundation of China (41988101). A.C. acknowledges support from the US Geological Survey (G22AC00431) and the US Department of Agriculture National Institute of Food and Agriculture (2024-67019-42396). We thank J. Sun, Z. Zeng, Y. Guo and H. Zhuang for their technical assistance in performing PROSAIL model simulations. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

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Author notes

  1. These authors contributed equally: Yuchao Yan, Songbai Hong.

Authors and Affiliations

  1. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Yuchao Yan & Shilong Piao

  2. Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

    Yuchao Yan

  3. School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China

    Songbai Hong

  4. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

  5. CREAF, Cerdanyola del Vallès, Spain

    Josep Peñuelas

  6. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Peñuelas

  7. Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM, USA

    Craig D. Allen

  8. Institute of Food and Agricultural Sciences, Agronomy Department, University of Florida, Gainesville, FL, USA

    William M. Hammond

  9. US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USA

    Seth M. Munson

  10. Department of Earth and Environment, Boston University, Boston, MA, USA

    Ranga B. Myneni

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  1. Yuchao Yan
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S.P. and A.C. designed the research. Y.Y. conducted all data processing, calculated RT and ran the model. S.H. and Y.Y. performed statistical analysis and drafted the figures. S.H. and Y.Y. wrote the first draft of the manuscript. W.M.H. collected the tree-mortality sites. Y.Y., S.H., A.C., J.P., S.M.M., C.D.A., W.M.H. and R.B.M. revised the manuscript. All authors discussed the design, methods and results and contributed to the text.

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Correspondence to Anping Chen or Shilong Piao.

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Nature Plants thanks Daniel Kneeshaw and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Yan, Y., Hong, S., Chen, A. et al. Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events. Nat. Plants 11, 731–742 (2025). https://doi.org/10.1038/s41477-025-01948-4

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