Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature509, 600–603 (2014).
Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science348, 895–899 (2015).
Legates, D. R. & Willmott, C. J. Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Clim.10, 111–127 (1990).
Rotenberg, E. & Yakir, D. Contribution of semi-arid forests to the climate system. Science327, 451–454 (2010).
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature429, 651–654 (2004).
Lal, R. Carbon sequestration in dryland ecosystems. Environ. Manag.33, 528–544 (2004).
Arzai, A. H. & Aliyu, B. S. Fruit tree and vine sprayer calibration based on canopy size and length of row: unit canopy row method. Bayero J. Pure Appl. Sci.3, 260–263 (2010).
Hartmann, H. Will a 385 million year-struggle for light become a struggle for water and for carbon?–How trees may cope with more frequent climate change-type drought events. Glob. Change Biol.17, 642–655 (2011).
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Bio.9, 161–185 (2003).
Zaehle, S., Sitch, S., Smith, B. & Hatterman, F. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochem. Cy. 19, GB3020 (2005).
Collins, W. D. et al. The Community Climate System Model Version 3 (CCSM3). J. Clim.19, 2122–2143 (2006).
Levis, S., Bonan, G., Vertenstein, M. & Oleson, K. The Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM): technical description and user’s guide. NCAR Tech. Note459, 1–50 (2004).
Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model, I: model description, site-scale evaluation and sensitivity to parameter estimates. Global Biogeochem. Cy.24, GB1005 (2010).
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy.19, GB1015 (2005).
Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model. Dev.11, 2995–3026 (2018).
Friend, A. D., Stevens, A. K., Knox, R. G. & Cannell, M. G. R. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecol. Model.95, 0–287 (1997).
Levy, P. E., Cannell, M. G. R. & Friend, A. D. Modelling the impact of future changes in climate, CO2 concentration and land use on natural ecosystems and the terrestrial carbon sink. Glob. Environ. Change14, 0–30 (2004).
Zeng, X., Li, F. & Song, X. Development of the IAP dynamic global vegetation model. Adv. Atmos. Sci.31, 505–514 (2014).
Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences11, 2027–2054 (2014).
Sato, H., Itoh, A. & Kohyama, T. SEIB-DGVM: a new dynamic global vegetation model using a spatially explicit individual-based approach. Ecol. Model.200, 279–307 (2007).
Zhou, R. et al. Estimation of DBH at forest stand level based on multi-parameters and generalized regression neural network. Forests10, 778–796 (2019).
Franceschini, T. & Schneider, R. Influence of shade tolerance and development stage on the allometry of ten temperate tree species. Oecologia176, 739–749 (2014).
Tao, S., Guo, Q., Li, C., Wang, Z. & Fang, J. Global patterns and determinants of forest canopy height. Ecology12, 3265–3270 (2016).
Zimmermann, M. H. Hydraulic architecture of some diffuse-porous trees. Can. J. Bot.-Rev. Canadienne De. Botanique56, 2286–2295 (1978).
Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. letters to nature. Nature428, 851–854 (2004).
Sterck, F. J., Bongers, F. & Newbery, D. M. Tree architecture in a Bornean lowland rain forest: intraspecific and interspecific patterns. Plant Ecol.153, 279–292 (2001).
Stark, S. C. et al. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol. Lett.15, 1406–1414 (2012).
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol.178, 719–739 (2008).
Risto, S., Christophe, G., Theodore, M. D. & Eero, N. Functional–structural plant models: a growing paradigm for plant studies. Ann. Bot.-Lond.114, 599–603 (2014).
Kevin, L., Paul, T., Michael, W., Benoît, S. & Martin, F. LiDAR remote sensing of forest structure. Prog. Phys. Geog.27, 88–106 (2003).
Watt, P. J. & Donoghue, D. N. M. Measuring forest structure with terrestrial laser scanning. Int. J. Remote Sens.26, 1437–1446 (2005).
Fernández-Sarríaa, A., Velázquez-Martíb, B., Sajdakb, M., Martíneza, L. & Estornella, J. Residual biomass calculation from individual tree architecture using terrestrial laser scanner and ground-level measurements. Comput. Electron. Agr.93, 90–97 (2013).
Bayer, D., Seifert, S. & Pretzsch, H. Structural crown properties of Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) in mixed versus pure stands revealed by terrestrial laser scanning. Trees27, 1035–1047 (2013).
Hackenberg, J., Wassenberg, M., Spiecker, H. & Sun, D. Non-destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests6, 1274–1300 (2015).
Metz, J. et al. Crown modeling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. For. Ecol. Manag.310, 275–288 (2013).
Disney, M. Terrestrial LiDAR: a 3D revolution in how we look at trees. N. Phytol.222, 1736–1741 (2018).
Zeide, B. Primary unit of the tree crown. Ecology74, 1598–1602 (1993).
Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Physiol. Plant Mol. Biol.40, 19–38 (2003).
Schepper, V. D., Dusschoten, D., Copini, P., Jahnke, S. & Steppe, K. MRI links stem water content to stem diameter variations in transpiring trees. J. Exp. Bot.63, 2645–2653 (2012).
Pivovaroff, A. L. et al. Multiple strategies for drought survival among woody plant species. Func. Ecol.30, 517–526 (2016).
Walcroft, A. S. et al. Radiative transfer and carbon assimilation in relation to canopy architecture, foliage area distribution and clumping in a mature temperate rainforest canopy in New Zealand. Agr. For. Meteorol.135, 326–339 (2005).
Smith, J. M. B. Scrubland. https://www.britannica.com/science/scrubland (2009).
Archibald, S. & Bond, W. J. Growing tall vs growing wide: tree architecture and allometry of Acacia karroo in forest, savanna, and arid environments. Oikos102, 3–14 (2003).
Erdős, L. et al. The edge of two worlds: a new review and synthesis on Eurasian forest-steppes. Appl. Veg. Sci.21, 345–362 (2018).
Jackson, T. et al. An architectural understanding of natural sway frequencies in trees. J. R. Soc. Interface16, 20190116 (2019).
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys.53, 785–818 (2015).
Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun.10, 885–893 (2019).
Parazoo, N. et al. Optimal estimates of global terrestrial gross primary production from satellite fluorescence and DGVMs. 5th Int. Workshop Remote Sens. Vegetation Fluorescence1, 1–12 (2014).
Xu, X., Wang, Z., Rahbek, C., Sanders, N. J. & Fang, J. Geographical variation in the importance of water and energy for oak diversity. J. Biogeogr.43, 279–288 (2016).
Fick, S. E. & Hijmans, R. J. (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2016).
Fleck, S. et al. Terrestrial lidar measurements for analysing canopy structure in an old-growth forest. In Proc. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007 (2007).
Taubin, G. Estimation of planar curves, surfaces, and nonplanar space-curves defined by implicit equations with applications to edge and range image segmentation. IEEE T. Pattern Anal.13, 1115–1138 (1991).
Ohashi, Y. Machine vision methods and articles of manufacture for determination of convex hull and convex hull angle. U.S. Patent No. 5,801,966. 1 Sep. (1998).
Li, Y. et al. Derivation, validation, and sensitivity analysis of terrestrial laser scanning-based leaf area index. Can. J. Remote Sens.42, 719–729 (2016).
Grossiord, C. et al. Effect of climate change on reference evapotrature, drives functional responses of trees in semi-arid ecosystems. J. Ecol.105, 163–175 (2017).
Feldpausch, T. R. et al. Height-diameter allometry of tropical forest trees. Biogeosciences8, 1081–1106 (2011).
Solargis Database. http://solargis.cn/imaps. Accessed 21 Nov 2018 (2018).
Dai, A. & National Center for Atmospheric Research Staff (eds). Last modified 18 Jul 2017. “The Climate Data Guide: Palmer Drought Severity Index (PDSI).” https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi. Accessed 5 June 2018 (2017).
Zomer, R. J. et al. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agr. Ecosyst. Environ.126, 67–80 (2008).
Geographic Data Sharing Infrastructure. College of Urban and Environmental Science, Peking University. http://geodata.pku.edu.cn. Accessed 25 Feb 2020 (2020).
Fang, J., Wang, Z. & Tang, Z. Atlas of Woody Plants in China. (China Higher Education Press, 2009).
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