Climate-smart forestry: an AI-enabled sustainable forest management solution for climate change adaptation and mitigation

G. Geoff Wang , Deliang Lu , Tian Gao , Jinxin Zhang , Yirong Sun , Dexiong Teng , Fengyuan Yu , Jiaojun Zhu

Journal of Forestry Research ›› 2025, Vol. 36 ›› Issue (1) : 7

PDF
Journal of Forestry Research ›› 2025, Vol. 36 ›› Issue (1) : 7 DOI: 10.1007/s11676-024-01802-x
Review Article

Climate-smart forestry: an AI-enabled sustainable forest management solution for climate change adaptation and mitigation

Author information +
History +
PDF

Abstract

Climate change is the most severe ecological challenge faced by the world today. Forests, the dominant component of terrestrial ecosystems, play a critical role in mitigating climate change due to their powerful carbon sequestration capabilities. Meanwhile, climate change has also become a major factor affecting the sustainable management of forest ecosystems. Climate-Smart Forestry (CSF) is an emerging concept in sustainable forest management. By utilizing advanced technologies, such as information technology and artificial intelligence, CSF aims to develop innovative and proactive forest management methods and decision-making systems to address the challenges of climate change. CSF aims to enhance forest ecosystem resilience (i.e., maintain a condition where, even when the state of the ecosystem changes, the ecosystem functions do not deteriorate) through climate change adaptation, improve the mitigation capabilities of forest ecosystems to climate change, maintain high, stable, and sustainable forest productivity and ecosystem services, and ultimately achieve harmonious development between humans and nature. This concept paper: (1) discusses the emergence and development of CSF, which integrates Ecological Forestry, Carbon Forestry, and Smart Forestry, and proposes the concept of CSF; (2) analyzes the goals of CSF in improving forest ecosystem stability, enhancing forest ecosystem carbon sequestration capacity, and advocating the application and development of new technologies in CSF, including artificial intelligence, robotics, Light Detection and Ranging, and forest digital twin; (3) presents the latest practices of CSF based on prior research on forest structure and function using new generation information technologies at Qingyuan Forest, China. From these practices and reflections, we suggested the development direction of CSF, including the key research topics and technological advancement.

Keywords

Forest management / Climate change / Carbon sink / Carbon forestry / Smart forestry

Cite this article

Download citation ▾
G. Geoff Wang, Deliang Lu, Tian Gao, Jinxin Zhang, Yirong Sun, Dexiong Teng, Fengyuan Yu, Jiaojun Zhu. Climate-smart forestry: an AI-enabled sustainable forest management solution for climate change adaptation and mitigation. Journal of Forestry Research, 2025, 36(1): 7 DOI:10.1007/s11676-024-01802-x

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Birdsey R, Pregitzer K, Lucier A. Forest carbon management in the United states: 1600–2100. J Environ Qual, 2006, 35(4): 1461-1469

[2]

Bowditch E, Santopuoli G, Binder F, del Río M, La Porta N, Kluvankova T, Lesinski J, Motta R, Pach M, Panzacchi P, Pretzsch H, Temperli C, Tonon G, Smith M, Velikova V, Weatherall A, Tognetti R. What is Climate-smart forestry? A definition from a multinational collaborative process focused on mountain regions of Europe. Ecosyst Serv, 2020, 43

[3]

Buonocore L, Yates J, Valentini R. A proposal for a forest digital twin framework and its perspectives. Forests, 2022, 13(4): 498

[4]

Cao L, Zhou K, Shen X, Yang XM, Cao FL, Wang GB. The status and prospects of smart forestry. J Nanjing for Univ Nat Sci Ed, 2022, 46(6): 83-95

[5]

Chen QD, Gao T, Zhu JJ, Wu FY, Li XF, Lu DL, Yu FY. Individual tree segmentation and tree height estimation using leaf-off and leaf-on UAV-LiDAR data in dense deciduous forests. Remote Sens, 2022, 14(12): 2787

[6]

Chen SY, Lu N, Fu BJ, Wang S, Deng L, Wang LX. Current and future carbon stocks of natural forests in China. For Ecol Manag, 2022, 511

[7]

Chinese Academy of Sciences (2023) Blue book on carbon emissions from forest fires (2023), Beijing, December 7, 2023 (in Chinese)

[8]

Cooper L, MacFarlane D. Climate-smart forestry: promise and risks for forests, society, and climate. PLoS Clim, 2023, 2(6):

[9]

Ehrlich-Sommer F, Hoenigsberger F, Gollob C, Nothdurft A, Stampfer K, Holzinger A. Sensors for digital transformation in smart forestry. Sensors, 2024, 24(3): 798

[10]

Forzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A. Emerging signals of declining forest resilience under climate change. Nature, 2022, 608(7923): 534-539

[11]

Gao T, Yu LZ, Yu FY, Wang XC, Yang K, Lu DL, Li XF, Yan QL, Sun YR, Liu LF, Xu S, Zhen XJ, Ni ZD, Zhang JX, Wang GF, Wei XH, Zhou XH, Zhu JJ. Functions and applications of multi-tower platform of Qingyuan forest ecosystem research station of Chinese academy of sciences. Chin J Appl Ecol, 2020, 31(3): 695-705

[12]

Gao T, Zhu JJ, Zhang JX, Sun YR, Yu FY, Teng DX, Lu DL, Yu LZ, Wang ZG. Estimation of carbon flux of a temperate forest ecosystem based on next-generation information technologies. Front Data Comput, 2023, 5(2): 60-72 (in Chinese)

[13]

Griscom B, Adams J, Ellis PW, Houghton R, Lomax G, Miteva D, Schlesinger W, Shoch D, Siikamäki J, Smith P, Woodbury P, Zganjar C, Blackman A, Campari JS, Conant R, Delgado C, Elias P, Gopalakrishna T, Hamsik MR, Herrero M, Kiesecker J, Landis E, Laestadius L, Leavitt SM, Minnemeyer S, Polasky S, Potapov P, Putz F, Sanderman J, Silvius M, Wollenberg E, Fargione J. Natural climate solutions. Proc Natl Acad Sci U S A, 2017, 114: 11645-11650

[14]

Gupta R, Rao D. Potential of wastelands for sequestering carbon by reforestation. Curr Sci, 1994, 66: 378-380

[15]

Holzinger A, Keiblinger K, Holub P, Zatloukal K, Müller H. AI for life: trends in artificial intelligence for biotechnology. N Biotechnol, 2023, 74: 16-24

[16]

IPCC (2018) Summary for policymakers: Global warming of 1.5°C. Cambridge University Press

[17]

Keenan T, Williams C. The terrestrial carbon sink. Annu Rev Environ Resour, 2018, 43: 219-243

[18]

Keith H, MacKey B, Berry S, Lindenmayer D, Gibbons P. Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error. Glob Change Biol, 2010, 16(11): 2971-2989

[19]

Köhl M, Martes LM. Forests: a passive CO2 sink or an active CO2 pump?. For Policy Econ, 2023, 155

[20]

Li ST, Yan QL, Liu ZH, Wang XC, Yu FY, Teng DX, Sun YR, Lu DL, Zhang JX, Gao T, Zhu JJ. Seasonality of albedo and fraction of absorbed photosynthetically active radiation in the temperate secondary forest ecosystem: a comprehensive observation using Qingyuan Ker towers. Agric for Meteor, 2023, 333

[21]

Lippke B, Oneil E, Harrison R, Skog K, Gustavsson L, Sathre R. Life cycle impacts of forest management and wood utilization on carbon mitigation: knowns and unknowns. Carbon Manag, 2011, 2(3): 303-333

[22]

Lisella C, Antonucci S, Santopuoli G, Marchetti M, Tognetti R (2022) Assessing resilience components in maritime pine provenances grown in common gardens. Forests 13:1986. https://doi.org/10.3390/f13121986

[23]

Liu YC, Yu GR, Wang QF, Zhang YJ, Xu ZH. Carbon carry capacity and carbon sequestration potential in China based on an integrated analysis of mature forest biomass. Sci China Life Sci, 2014, 57(12): 1218-1229

[24]

Lu DL, Zhu JJ, Wu DN, Chen QD, Yu Y, Wang J, Zhu CY, Liu HQ, Gao T, Wang GG. Detecting dynamics and variations of crown asymmetry induced by natural gaps in a temperate secondary forest using terrestrial laser scanning. For Ecol Manag, 2020, 473

[25]

Luo F, Liu L, Wang GG, Kumar V, Ashton MS, Abernethy J, Afghah F, Browning MHEM, Coyle D, Dames P, O’Halloran T, Hays J, Heisl P, Jiang C, Khanal P, Krovi VN, Kuebbing S, Li NY, Liang JJ, Liu NH, McNulty S, Oswalt CM, Pederson N, Terzopoulos D, Woodall CW, Wu YK, Yang J, Yang Y, Zhao L (2023) Artificial intelligence for climate smart forestry: a forward looking vision. In: 2023 IEEE 5th international conference on cognitive machine intelligence (CogMI). Atlanta, GA, USA. IEEE, pp 1–10

[26]

Millar CI, Stephenson NL. Temperate forest health in an era of emerging megadisturbance. Science, 2015, 349(6250): 823-826

[27]

Mitchell CD, Harper RJ, Keenan RJ. Current status and future prospects for carbon forestry in Australia. Aust for, 2012, 75(3): 200-212

[28]

Nabuurs GJ, Delacote P, Ellison D, Hanewinkel M, Hetemaki L, Lindner M (2017) By 2050 the mitigation effects of EU forests could nearly double through climate smart forestry. Forests 8:484. https://doi.org/10.3390/f8120484

[29]

Nabuurs GJ, Verkerk PJ, Schelhaas MJ, González Olabarria JR, Trasobares A, Cienciala E. Climate-smart forestry: mitigation impacts in three European regions. Eur Forest Inst, 2018

[30]

Roebroek CTJ, Duveiller G, Seneviratne SI, Davin EL, Cescatti A. Releasing global forests from human management: how much more carbon could be stored?. Science, 2023, 380(6646): 749-753

[31]

Shephard N, Narine L, Peng YC, Maggard A. Climate smart forestry in the southern United States. Forests, 2022, 13(9): 1460

[32]

Sterck F, Vos M, Hannula SE, de Goede S, de Vries W, den Ouden J, Nabuurs GJ, van der Putten W, Veen C (2021) Optimizing stand density for climate-smart forestry: A way forward towards resilient forests with enhanced carbon storage under extreme climate events. Soil Biol Biochem 162:108396. https://doi.org/10.1016/j.soilbio.2021.108396

[33]

Tognetti R, Smith M, Panzacchi P. Climate-smart forestry in mountain regions. Springer Int Publ, 2022

[34]

Verkerk PJ, Costanza R, Hetemäki L, Kubiszewski I, Leskinen P, Nabuurs GJ, Potočnik J, Palahí M. Climate-smart forestry: the missing link. For Policy Econ, 2020, 115

[35]

Wang GG (2024) Climate-Smart Forestry and its implication to southern silviculture. In: Bragg DC, Oswald BP, Koerth NE, eds. Proceedings of the 22nd biennial southern silvicultural research conference. Gen. Tech. Rep. SRS-274. Asheville, NC: U.S. department of agriculture, forest service, southern research station, pp 186–195. https://doi.org/10.2737/SRS-GTR-274-Pap30

[36]

Weatherall A, Nabuurs GJ, Velikova V, Santopuoli G, Neroj B, Bowditch E, Temperli C, Binder F, Ditmarová L, Jamnická G, Lesinski J, Porta NL, Pach M, Panzacchi P, Sarginci M, Serengil Y, Tognetti R (2021) Defining climate-smart forestry. In: Managing forest ecosystems. Springer international publishing, pp 35–58. https://doi.org/10.1007/978-3-030-80767-2_2

[37]

Yu Y, Gao T, Zhu JJ, Wei XH, Guo QH, Su YJ, Li YM, Deng SQ, Li MC. Terrestrial laser scanning-derived canopy interception index for predicting rainfall interception. Ecohydrology, 2020, 13(5):

[38]

Zhao G, Shao GF, Reynolds KM, Wimberly MC, Warner T, Moser JW, Rennolls K, Magnussen S, Köhl M, Anderson HE, Mendoza GA, Dai LM, Huth A, Zhang LJ, Brey J, Sun YJ, Ye RH, Martin BA, Li FR. Digital forestry: a white paper. J Forest, 2005, 103(1): 47-50

[39]

Zhu JJ, Gao T, Yu LZ, Yu FY, Yang K, Lu DL, Yan QL, Sun YR, Liu LF, Xu S, Zhang JX, Zheng X, Song LN, Zhou XH. Functions and applications of multi-tower platform of Qingyuan forest ecosystem research station of Chinese academy of sciences (Qingyuan Ker towers). Bull Chin Acad Sci, 2021, 36(3): 351-361

[40]

Zhu JJ, Gao T, Zhang JX, Sun YR, Sun T, Liu ZH, Yu LZ, Lu DL, Yu FY, Teng DX, Yan QL, Yang K, Song LN, Zheng X, Wang XG, Wang QW, Liang Y, Li HD, Liu LF, Xu S, Liu HQ, Hu YY, Li XF, Wang ZG, Zhou XH. Methods of “Three-dimensional and Holographic” observation for forest ecosystems centered on “Multi-Tower”. Chin J Ecol, 2023, 42(12): 3050-3054

[41]

Zhu JJ, Wang GF, Zhang HQ, Gao T. On the research of climate-smart forestry. Sci Silvae Sin, 2024, 60(7): 1-7

AI Summary AI Mindmap
PDF

365

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/