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A non-destructive approach to develop tree-level allometric equations for estimating aboveground biomass in the forests of Tripura, Northeast India

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Abstract

Forest tree biomass estimation is a significant issue for the forest management system and to mitigate climate change. Aboveground biomass (AGB) is an indicator of the potential productivity of the ecosystem. However, an accurate estimation method of carbon stock is important for perfect carbon accounting. So, selecting a more appropriate method for biomass assessment is crucial to reaching the goal. Though allometric equations have been used to estimate AGB for both individual species as well as multiple species for regional and pan-tropical scales, species-specific models for a smaller area produce better accuracy. The current initiative has been taken to develop individual tree-level species-specific and generalized local allometric models in a non-destructive manner for the Tripura state of Northeast India, which was the main limitation of carbon stock estimation in the region. Five different forms of log-transformed linearized power equations were tested in the regression models, with AGB as the response variable. The best fit models were chosen based on adjusted R2, F-statistic, Akaike information criterion, Breusch-Pagan test, Variance Inflation Factor, and three assumptions of linearity. A paired t test was conducted between the predicted and observed AGB values. The best fit generalized equation developed in this study was found to be AGB = 1.03 × exp (− 3.95 + 1.09 ln D2 + 0.97 ln H − 0.20 N). Compared with some existing pan-tropical and regional models for their predictive accuracy, this equation expressed the highest R2 value of 0.9089 in an independent testing dataset. When measuring the tree height is difficult due to a closed canopy, the equation AGB = 1.07 × exp (− 2.77 + 2.55 ln D) can be applied to obtain a quick estimation using just the DBH. The equations presented here may be the instrument for estimating carbon stock at the local level.

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References

  • Alam K, Nizami SM (2014) Assessing biomass expansion factor of birch tree Betula utilis D. Don. Open J For 4:181–190

    Google Scholar 

  • Baishya R, Barik SK, Upadhaya K (2009) Distribution pattern of aboveground biomass in natural and plantation forests of humid tropics in Northeast India. Trop Ecol 50(2):295

    Google Scholar 

  • Banik B, Deb D, Deb S, Datta BK (2018) Assessment of biomass and carbon stock in sal (Shorea robusta Gaertn.) forests under two management regimes in Tripura, Northeast India. J For Env Sci 34(3):209–223

    Google Scholar 

  • Basuki TM, Van Laake PE, Skidmore AK, Hussin YA (2009) Allometric equations for estimating the aboveground biomass in tropical lowland Dipterocarp forests. For Ecol Manag 257(8):1684–1694

    Article  Google Scholar 

  • Berenguer E, Ferreira J, Gardner TA, Aragão LEOC, De Camargo PB, Cerri CE, Durigan M, Oliveira RCD, Vieira ICG, Barlow J (2014) A large-scale field assessment of carbon stocks in human‐modified tropical forests. Glob Change Biol 20(12):3713–3726

    Article  Google Scholar 

  • Bora N, Nath AJ, Das AK (2013) Aboveground biomass and carbon stocks of tree species in tropical forests of Cachar District, Assam, Northeast India. Int J Ecol Environ Sci 39(2):97–106

    Google Scholar 

  • Brown S. (1997) Estimating biomass and biomass change of tropical forests: a primer. For the food and agriculture organization of the United Nations. Rome, 1997. FAO Forestry Paper – 134. ISBN 92-5-103955-0

  • Brown S, Gillespie AJ, Lugo AE (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35(4):881–902

    Google Scholar 

  • Chambers JQ, Santos DJ, Ribeiro RJ, Higuchi N (2001) Tree damage, allometric relationships, and aboveground net primary production in central Amazon forest. For Ecol Manag 152(1–3):73–84

    Article  Google Scholar 

  • Champion HG, Seth SK (1968) A revised survey of the forest types of India. Government of India, Delhi

    Google Scholar 

  • Chave J (2006) Measuring wood density for tropical forest trees. A field manual for the CTFS sites 1–7

  • Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1):87–99

    Article  CAS  PubMed  Google Scholar 

  • Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20(10):3177–3190

    Article  Google Scholar 

  • Choudhary BK, Majumdar K, Datta BK (2017) Tree diversity and ecosystem carbon stock patterns along selected land use, land cover systems in Tripura, Northeastern India. Int J Ecol Environ Sci 42(5):91–106

    Google Scholar 

  • Curtis RO (1967) Height-diameter and height-diameter-age equations for second-growth Douglas-fir. For Sci 13(4):365–375

    Google Scholar 

  • Deb DB (1981–1983) The Flora of Tripura State. Vols. 1–2, Today and Tomorrow’s Printers and Publishers, New Delhi

  • Djomo AN, Knohl A, Gravenhorst G (2011) Estimations of total ecosystem carbon pools distribution and carbon biomass current annual increment of a moist tropical forest. For Ecol Manag 261(8):1448–1459

    Article  Google Scholar 

  • FAO (2015) Evaluation des ressources forestières mondiales 2015, 2nd edn. FAO, Rome

    Google Scholar 

  • FSI (2011) India State of Forest Report 2011, Forest Survey of India. Ministry of Environment and Forests, Dehradun

    Google Scholar 

  • FSI (2019) India State of Forest Report. Forest Survey of India. Ministry of Environment and Forests, Dehradun

    Google Scholar 

  • Hamner B, Frasco M, LeDell E (2020) Evaluation metrics for machine learning. R package version 0.4-0

  • Haripriya GS (2000) Estimates of biomass in Indian forests. Biomass Bioenergy 19(4):245–258

    Article  Google Scholar 

  • Huang S, Yang Y, Wang Y (2003) A critical look at procedures for validating growth and yield models. Modelling forest systems, Sesimbra, Portugal

    Book  Google Scholar 

  • Ketterings QM, Coe R, Noordwijk VM, Palm CA (2001) Reducing uncertainty in the use of allometric biomass equations for predicting aboveground tree biomass in mixed secondary forests. For Ecol Manag 146(1–3):199–209

    Article  Google Scholar 

  • King DA, Davies SJ, Tan S, Noor NSM (2006) The role of wood density and stem support costs in the growth and mortality of tropical trees. J Ecol 94(3):670–680

    Article  Google Scholar 

  • Mahmood H, Siddique MR, Costello L, Birigazzi L, Abdullah SR, Henry M et al (2019) Allometric models for estimating biomass, carbon and nutrient stock in the Sal zone of Bangladesh. IForest-Biogeosciences and Forestry 12(1):69

    Article  Google Scholar 

  • Majumdar K, Choudhary BK, Datta BK (2016) Aboveground Woody Biomass, Carbon Stocks Potential in Selected Tropical Forest Patches of Tripura, Northeast India. Open J Ecol 06(10):598–612. https://doi.org/10.4236/oje.2016.610057

    Article  Google Scholar 

  • Mensah S, Veldtman R, Toit DB, Kakaï GR, Seifert T (2016) Aboveground biomass and carbon in a South African mistbelt forest and the relationships with tree species diversity and forest structures. Forests 7(4):79

    Article  Google Scholar 

  • Nath AJ, Das AK (2011) Carbon storage and sequestration in bamboo-based smallholder homegardens of Barak Valley, Assam. Curr Sci 100(2):229–233

    CAS  Google Scholar 

  • Nath AJ, Tiwari BK, Sileshi GW, Sahoo UK, Brahma B, Deb S, Devi NB, Das AK, Reang D, Chaturvedi SS, Tripathi OP, Das DJ, Gupta A (2019) Allometric models for estimation of forest biomass in North East India. Forests 10(2):103

    Article  Google Scholar 

  • Nossent J, Bauwens W. (2012) Application of a normalized Nash-Sutcliffe efficiency to improve the accuracy of the Sobol’sensitivity analysis of a hydrological model. In EGU General Assembly Conference Abstracts, p.237

  • Ogle DH, Wheeler P, Dinno A (2020) FSA: fisheries stock analysis. R package version 0.8.31

  • Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA et al (2011) A large and persistent carbon sink in the world’s forests. Science 333(6045):988–993

    Article  CAS  PubMed  Google Scholar 

  • Paul KI, Radtke PJ, Roxburgh SH, Larmour J, Waterworth R, Butler D et al (2018) Validation of allometric biomass models: How to have confidence in the application of existing models. For Ecol Manag 412:70–79

    Article  Google Scholar 

  • Picard N, Saint-André L, Henry M (2012) Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Manual for building tree volume and biomass allometric equations: from field measurement to prediction, FAO

  • Pressler M (1865) Das Gesetz der. Stambildung Leipzig, pp153

  • Salunkhe O, Khare PK, Kumari R, Khan ML (2018) A systematic review on the aboveground biomass and carbon stocks of Indian forest ecosystems. Ecol Processes 7:17

    Article  Google Scholar 

  • Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329:237–254

    Article  Google Scholar 

  • Sprugel DG (1983) Correcting for bias in log-transformed allometric equations. Ecol (Durham) 64(1):209–210

    Article  Google Scholar 

  • Yan Y. (2016) Machine learning evaluation metrics. R package version 1.1.1

  • Zambrano-Bigiarini M. (2020) hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.4-0

  • Zanne A, Lopez-Gonzalez G, Coomes D, Ilic J, Jansen S, Lewis S, Miller R, Swenson N, Wiemann M, Chave J. (2009) Global wood density database. dryad digital repository

  • Zeileis A, Hothorn T (2002) Diagnostic checking in regression relationships. R News 2(3):7–10

    Google Scholar 

  • Zianis D, Mencuccini M (2004) On simplifying allometric analyses of forest biomass. For Ecol Manag 187(2–3):311–332

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. John A. Kershaw Jr., Professor of Forest Mensuration, University of New Brunswick, for his insight on the model selection process. The authors also thank Mr. Asim Kumar Kalai, Mr. R.M. Darlong of the Tripura Forest Department, and Mr. Abraham Halam for aiding in data collection.

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Authors and Affiliations

  1. College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh, 791105, India

    Abhilash Dutta Roy

  2. Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, Agartala, 799022, India

    Sumit Kumar Das & Bimal Debnath

Authors
  1. Abhilash Dutta Roy
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  2. Sumit Kumar Das
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  3. Bimal Debnath
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Contributions

ADR: Conceptualization, Data collection, Investigation, and Writing the original draft. SKD: Data curation, Validation, and Writing the original draft. BD: Supervision and final editing.

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Correspondence to Bimal Debnath.

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Dutta Roy, A., Das, S.K. & Debnath, B. A non-destructive approach to develop tree-level allometric equations for estimating aboveground biomass in the forests of Tripura, Northeast India. Trop Ecol 64, 532–542 (2023). https://doi.org/10.1007/s42965-022-00280-8

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