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Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

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Abstract

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R 2adj ), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH ≥ 5.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.

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References

  • Abreu J C, Silva J A A, Ferreira R L C, et al. 2016. Setting linear and nonlinear mathematical models for biomass estimate and nutrients in Anadenanthera colubrina var. cebil in the semiarid region of Pernambuco. Scientia Forestalls, 44(111): 739–750. (in Brazilian)

    Google Scholar 

  • Albuquerque U P, Araújo E L, El-Deir AC A, et al. 2012. Caatinga revisited: ecology and conservation of an important seasonal dry forest. Scientific World Journal, 2012: e205182, doi: https://doi.org/10.1100/2012/205182.

    Article  Google Scholar 

  • Alvares C A, Stape J L, Sentelhas P C, et al. 2013. Köppen climate classification map for Brazil. Meteorologische Zeitschrift, 22(6): 711–728. (in Brazilian)

    Article  Google Scholar 

  • Araüjo L V C, Silva J A. 2010. Belo Horizonte farm experimental unit-Mossoró/RN. In: Gariglio M A, Sampaio E V S B, Cestaro L A, et al. Sustainable Use and Conservation of Caatinga Forest Resources. Brazilian Forest Service, 205–214. (in Brazilian)

  • Basuki T M, van Laake P E, Skidmore A K, et al. 2009. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. Forest Ecology and Management, 257(8): 1684–1694.

    Article  Google Scholar 

  • Berger A, Gschwantner T, Mcroberts R E, et al. 2014. Effects of measurement errors on individual tree stem volume estimates for the Austrian national forest inventory. Forest Science, 60(1): 14–24.

    Article  Google Scholar 

  • Brando P. 2018. Tree height matters. Nature Geoscience, 11(1): 390–391.

    Article  Google Scholar 

  • Brasil. 2006. Normative Instruction, No. 5 of December 11. Provides for technical procedures for the preparation, execution and technical evaluation of sustainable forest management plans. [2020-04-24]. https://www.mma.gov.br/estruturas/pnf/_arquivos/in%20mma%2004-06.pdf. (in Brazilian)

  • Brazilian Institute of Geography and Statistics. 2012. Technical Manual of the Brazilian vegetation (2nd). Rio de Janeiro, Brazil, 271. (in Brazilian)

  • Burkhart H E, Tomé M. 2012. Modeling Forest Trees and Stands. Dordrecht: Springer Netherlands, 1–460.

    Book  Google Scholar 

  • Cabacinha C D, Scolforo J R S, Thiersch, C R, et al. 2013. A new approach for the geometric method using the parabola index method. Ciência Florestal, 23(1): 261–271. (in Brazilian)

    Article  Google Scholar 

  • Chave J, Andalo C, Brown S, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(6): 87–99.

    Article  Google Scholar 

  • Chave J, Réjou-Méchain M, Búrquez A, et al. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10): 3177–3190.

    Article  Google Scholar 

  • Costa T L, Sampaio E V S B, Sales M F, et al. 2014. Root and shoot biomasses in the tropical dry forest of semi-arid Northeast Brazil. Plant and Soil, 378: 113–123.

    Article  Google Scholar 

  • Dalmagro H J, Lobo F D A, Vourlitis G L, et al. 2014. The physiological light response of two tree species across a hydrologic gradient in Brazilian savanna (Cerrado). Photosynthetica, 52(1): 22–35.

    Article  Google Scholar 

  • Duncanson L, Rourke O, Dubayah R. 2015. Small sample sizes yield biased allometric equations in temperate forests. Scientific Reports, 5: 17153, doi: https://doi.org/10.1038/srep17153.

    Article  Google Scholar 

  • Duncanson L, Huang W, Johnson K, et al. 2017. Implications of allometric model selection for county-level biomass mapping. Carbon Balance and Management, 12(18): 1–11.

    Google Scholar 

  • Duncanson L, Dubayah R. 2018. Monitoring individual tree-based change with airborne lidar. Ecology and Evolution, 8(10): 5079–5089.

    Article  Google Scholar 

  • Fraga M P, Barreto P A B, de Paula A. 2014. Volume estimation of Pterogyne nitens in pure plantation in the southwest of Bahia. Embrapa Forestry, 34(79): 207–215. (in Brazilian)

    Google Scholar 

  • Jenkins J C, Chojnacky D C, Heath L S, et al. 2003. National-scale biomass estimators for United States tree species. Forest Science, 49(1): 12–35.

    Google Scholar 

  • Kachamba D J, Eid T. 2016. Total tree, merchantable stem and branch volume models for miombo woodlands of Malawi. Southern Forests, 78(1): 41–51.

    Article  Google Scholar 

  • Leite F S L, Resende A V. 2010. Estimate of wood volume departing from the diameter at stump height in an exploited area in “terra firme” amazon forest. Ciência Florestal, 20(1): 69–79. (in Brazilian)

    Article  Google Scholar 

  • Levick S R, Hessenmöller D, Schulze E. 2016. Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest. Carbon Balance and Management, 11(7): 1–14.

    Google Scholar 

  • Lima P C F, Oliveira E B, Machado S A. 1996 Equations for biomass estimation of Prosopis species in the brazilian semi-arid region. Boletim de Pesquisa Florestal, 32(33): 67–79. (in Brazilian)

    Google Scholar 

  • Lima R B, Aparício P S, Ferreira R L C, et al. 2014. Volumetry and classification of production capacity for Mora paraensis (Ducke) in Amapa estuary. Scientia Forestalls, 42(101): 141–154. (in Brazilian)

    Google Scholar 

  • Lima R B, Alves Júnior F T, Oliveira C P D, et al. 2017. Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations. Anais da Academia Brasileira de Ciências, 89(3): 1815–1828.

    Article  Google Scholar 

  • Litton C M, Boone K J. 2008. Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica, 40(3): 313–320.

    Article  Google Scholar 

  • Mcroberts R E, Westfall J A. 2014. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science, 60(1): 34–42.

    Article  Google Scholar 

  • Miles L, Newton A C, Defries R S, et al. 2006. A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3): 491–505.

    Article  Google Scholar 

  • Ministry of the Environment. 2006. National Forest Management Plan Contendas do Sincorá. Volume I: General Information about the National Forest. Brasilia: Brazilian Institute of Environment and Renewable Natural Resources, 132. (in Brazilian)

    Google Scholar 

  • Moro M F, Lughadha E N, Araújo F S, et al. 2016. A Phytogeographical metaanalysis of the semiarid Caatinga domain in Brazil. The Botanical Review, 82(2): 91–148.

    Article  Google Scholar 

  • Murphy P G, Lugo A E. 1986. Ecology of tropical dry forest. Annual Review of Ecology and Systematics, 17(1): 67–88.

    Article  Google Scholar 

  • Murphy P G, Lugo A E. 1995. Dry forests of Central America and the Caribbean. In: Bullock S, Mooney H, Medina E. Seasonally Dry Tropical Forests. California: University Press, 9–34.

    Chapter  Google Scholar 

  • Oliveira L Z, Klitzke A R, Fantini A, et al. 2018. Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest. Anais da Academia Brasileira de Ciências, 90(4): 3729–3744. (in Brazilian)

    Article  Google Scholar 

  • Pennington R T, Lewis G P, Ratter J A. 2006. An overview of the plant diversity, biogeography and conservation of Neotropical Savannas and seasonally dry forests. In: Pennington R T, Lewis G P, Ratter J A. Neotropical Savannas and Seasonally Dry Forests: Plant Diversity, Biogeography, and Conservation. Boca Raton: CRC Press, 1–29.

    Chapter  Google Scholar 

  • Queiroz L P. 2009. Leguminous of Caatinga. Feira de Santana: UEFS, 443. (in Brazilian)

    Google Scholar 

  • R Development Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. [2020-04-24]. http://www.R-project.org/.

    Google Scholar 

  • Robinson A P, Hamann J D. 2011. Forest Analytics with R. New York: Springer, 1–311.

    Book  Google Scholar 

  • Sampaio E, Gasson P, Baracat A, et al. 2010. Tree biomass estimation in regenerating areas of tropical dry vegetation in northeast Brazil. Forest Ecology and Management, 259(6): 1135–1140.

    Article  Google Scholar 

  • Segura M, Kanninen M. 2005. Allometric models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica. Biotropica, 37(1): 2–8.

    Article  Google Scholar 

  • Silva J M C, Bates J M. 2002. Biogeographic patterns and conservation in the South American Cerrado: A tropical Savanna hotspot. Bioscience, 52(3): 225–234.

    Article  Google Scholar 

  • Silva-Ribeiro R B, Gama J R V, Melo L O. 2014. Sectional analysis for volume determination and selection of volume equations for the Tapajos Nacional Forest. Cerne, 20(4): 605–612. (in Brazilian)

    Article  Google Scholar 

  • Souza P F, Silva J A, Lucena D S, et al. 2016. Dendrometric and phytosociological studies in a fragment of caatinga, São José de Espinharas — PB. Ciência Florestal, 26(4): 1317–1330. (in Brazilian)

    Article  Google Scholar 

  • Sullivan M J P, Lewis S L, Hubau W, et al. 2018. Field methods for sampling tree height for tropical forest biomass estimation. Methods in Ecology and Evolution, 9(5): 1179–1189.

    Article  Google Scholar 

  • Vanclay J K. 1994. Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests. Oxford: CAB International, 1–330.

    Google Scholar 

  • Vibrans A C, Moser P, Oliveira L Z, et al. 2015. Generic and specific stem volume models for three subtropical forest types in southern Brazil. Annals of Forest Science, 72: 865–874.

    Article  Google Scholar 

  • Watt M S, Adams T, Gonzalez-Aracil S, et al. 2013. The influence of LiDAR pulse density and plot size on the accuracy of New Zealand plantation stand volume equations. New Zealand Journal of Forestry Science, 43(15): 1–10.

    Article  Google Scholar 

  • Werneck F P. 2011. The diversification of eastern South American open vegetation biomes: historical biogeography and perspectives. Quaternary Science Reviews, 30(13–14): 1630–1648.

    Article  Google Scholar 

  • Zeng W S, Tang S Z. 2011. Bias correction in logarithmic regression and comparison with weighted regression for nonlinear models. Nature Precedings, 24(2): 137–143.

    Google Scholar 

Download references

Acknowledgements

Special thanks to the National Council for Scientific and Technological Development-CNPq for granting financial support to the project (484260/2013-8).

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Authors and Affiliations

  1. Department of Forest Engineering, State University of Amapá, Macapá, 68900070, Brazil

    Robson B. de Lima

  2. Department of Forest Science, State University of Southwest Bahia, Vitória da Conquista, 45083900, Brazil

    Patrícia A. B. Barreto-Garcia, Alessandro de Paula & Flávia F. de Carvalho

  3. Universidade Federal de Lavras, Lavras, 37200900, Brazil

    Jhuly E. S. Pereira

  4. Universidade de São Paulo, Piracicaba, 13418900, Brazil

    Silvio H. M. Gomes

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  1. Robson B. de Lima
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  2. Patrícia A. B. Barreto-Garcia
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  3. Alessandro de Paula
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  5. Flávia F. de Carvalho
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Correspondence to Robson B. de Lima.

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de Lima, R.B., Barreto-Garcia, P.A.B., de Paula, A. et al. Improving wood volume predictions in dry tropical forest in the semi-arid Brazil. J. Arid Land 12, 1046–1055 (2020). https://doi.org/10.1007/s40333-020-0082-x

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