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Biomass model development for carbon stock estimation in the tropical forest of Eastern India: an allometric approach

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Abstract

Allometric regression models are one of the common methods of carbon stock estimation based on growing stock data conversion to estimates of above ground biomass (AGB). Therefore, allometric model selection is important functional aspect that has considerable influence on accuracy of biomass estimation. As destructive sampling is restricted in our study area, the site specific biomass model is developed for the first time based upon the forest inventory data that includes measurements of diameter at breast height (DBH) and tree height (H). To minimize the error in AGB estimation, intensive sampling was done where 78,201 individual tree were enumerated (6034 quadrats laid over 1207 plots). 20 locally abundant tree species were assessed. Tree volume and biomass were calculated and examined for best fit allometric model for the area. Species-specific models were established which best fits with the DBH as predictor variable. For multi-species models, inclusion of wood density (WD) enhanced the model fitness with increased adjusted R2 by 99.9%. Significant variations in predicted and observed values were noticed while considering the regional and pan-tropical models (model prediction error − 614.364 to 288.304%). Therefore, development of local models would provide more accurate AGB estimates. Best fit multi-species allometric model in our study is represented by ln (AGB) = a + b ln (DBH) + c ln (H) + d ln(WD). The equation developed for tropical forest of Eastern India applicable for Sal zone of Bihar is ln(AGB) = − 0.886 + 2ln(DBH) + ln(H) + ln(WD).

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Abbreviations

AGB :

Above ground biomass

AIC:

Akaike Information Criterion

AICw:

Akaike information criterion weight

DBH:

Diameter at breast height

H:

Tree height

ME:

Model efficiency

MPE:

Model prediction error

MSE:

Mean squared error

MSR:

Mean square residual

REDD:

Reducing emissions from deforestation and degradation

RMSE:

Root mean square error

SD:

Standard deviation

SE:

Standard error

VIF:

Variance influencial factor

WD:

Wood densitywood density

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Acknowledgements

S.B. (first author) acknowledges Department of Science and Technology, Government of India for continuing research under Women Scientist Scheme A (File No.SR/WOS-A/LS-178/2017(G)). A.B. acknowledges University Grants Commission, Government of India for continuing research under Dr. D.S. Kothari Post-Doctoral Fellowship (No.F.4-2/2006(BSR)/OT/18-19/0009). The Department of Forest, Bihar and Indian Institute of Bio-social Research and Development, Kolkata is thankfully acknowledged for their assistance. Authors are also thankful to the Principal Chief Conservator of Forest, Chief Conservator of Forest, Conservator of Forest, District Forest Officer, Working Plan Officer of Bihar Forest Department and field staffs for their assistance during the field work.

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Authors and Affiliations

  1. Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Kalyani, West Bengal, 741252, India

    Saroni Biswas & Saon Banerjee

  2. Department of Chemical Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India

    Anirban Biswas

  3. Department of Statistics, Acharya Prafulla Chandra College, New Barrackpore, West Bengal, 700131, India

    Arabinda Das

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  1. Saroni Biswas
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Correspondence to Saon Banerjee.

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The authors declare no conflict of interest about this research in any financial or non-financial form as it was solely a scientific study.

Ethical statement

The authors declare that the study did not require any special ethical permission due to absence of any direct or indirect involvement of living animals including human. The study solely concentrated on the various assessments considering plant biomass where only non-destructive sampling and measurements were sufficient to justify the aims and objectives of current study.

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Biswas, S., Biswas, A., Das, A. et al. Biomass model development for carbon stock estimation in the tropical forest of Eastern India: an allometric approach. Trop Ecol 61, 360–370 (2020). https://doi.org/10.1007/s42965-020-00098-2

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