Skip to main content
SpringerLink
Log in

Improving tree biomass models through crown ratio patterns and incomplete data sources

  • Original Paper
  • Published:
European Journal of Forest Research Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Aboveground biomass quantification is essential for determining carbon stocks in forests. Multiple tree biomass models are available, but estimations can be biased outside the fitting range. This is due to the lack of data for larger trees, mainly because of the cost and time required. This study proposed a methodology based on tree crown biomass ratio (crown biomass: total aboveground biomass) modelling. The original data used in the existing biomass models in Spain have been notably extended by the inclusion of stem data from First Spanish National Forest Inventory and other databases, covering better tree size variability. The analysis of the crown biomass ratio against tree size (d2h), allowed us to distinguish three different patterns: an increasing pattern, a constant one, and a decreasing pattern. A new system of biomass models was fitted simultaneously by species, including a model for crown biomass ratio according to the identified pattern, a stem biomass model, and a total aboveground biomass model. Using this methodology, models were fitted for the 29 most important species in Spain. The fitted models result in more accurate and unbiased predictions for stem biomass, and realistic estimations for the crown biomass. This methodology means more robust and flexible biomass estimations with the possibility of using different data sources. The absence of crown information is not an obstacle because this component is a percentage of total aboveground biomass. Moreover, determining the crown biomass ratio pattern allows improving the accuracy of tree biomass estimation beyond the range of tree sizes (2–70 cm) for which these models were fitted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Álvarez-Álvarez P, Pizarro C, Barrio-Anta M et al (2018) Evaluation of tree species for biomass energy production in Northwest Spain. Forests 9:1–15. https://doi.org/10.3390/f9040160

    Article  Google Scholar 

  • Baker TR, Phillips OL, Malhi Y, Almeidas S, Arroyo L, Di Fiore A, Erwin T, Killeen TJ, Laurance SG, Laurance WF, Lewis SL, Lloyd J, Monteagudos A, Neill DA, Patiño S, Pitman NCA, Silva JNM, Vásquez-Martínez R (2004) Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob Chang Biol 10:545–562. https://doi.org/10.1111/j.1529-8817.2003.00751.x

    Article  Google Scholar 

  • Balboa-Murias MA, Rojo A, Álvarez JG, Merino A (2006) Carbon and nutrient stocks in mature Quercus robur L. stands in NW Spain. Ann For Sci 63:557–565. https://doi.org/10.1051/forest:2006038

    Article  CAS  Google Scholar 

  • Basuki TM, van Laake PE, Skidmore AK, Hussin YA (2009) Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For Ecol Manag 257:1684–1694. https://doi.org/10.1016/j.foreco.2009.01.027

    Article  Google Scholar 

  • Belsey DA (1991) Conditioning diagnostics, collinearity and weak data in regression. Wiley, New York

    Google Scholar 

  • Borders BE (1989) System of equations in forest stand modelling. For Sci 35:548–556

    Google Scholar 

  • Bragg DC (2001) A local basal area adjustment for crown width prediction. North J Appl For 18:22–28. https://doi.org/10.1093/njaf/18.1.22

    Article  Google Scholar 

  • Bravo F, Alvarez-Gonzalez JG, Del Rio M, Barrio M, Bonet JA, Bravo-Oviedo A, Calama R, Castedo-Dorado F, Crecente-Campo F, Condés S, Diéguez-Aranda U, González-Martínez SC, Lizarralde I, Nanos N, Madrigal A, Martínez-Millán FJ, Montero G, Ordoñez C, Palahí M, Piqué M, Rodríguez F, Rodríguez-Soalleiro R, Rojo A, Ruiz-Peinado R, Sánchez-González M, Trasobares A, Vázquez-Piqué J (2011) Growth and yield models in Spain: Historical overview, contemporary examples and perspectives. For Syst 20:315. https://doi.org/10.5424/fs/2011202-11512

    Article  Google Scholar 

  • Chave J, Condit R, Aguilar S, Hernández A, Lao S, Pérez R (2004) Error propagation and sealing for tropical forest biomass estimates. Philos Trans R Soc B Biol Sci 359:409–420. https://doi.org/10.1098/rstb.2003.1425

    Article  Google Scholar 

  • Clough BJ, Domke G, MacFarlane DW, Radtke PH, Russell MB, Weiskittel AR (2018) Testing a new component ratio method for predicting total tree aboveground and component biomass for widespread pine and hardwood species of eastern US. Forestry 91:575–588. https://doi.org/10.1093/forestry/cpy016

    Article  Google Scholar 

  • Correia AC, Faias SP, Ruiz-Peinado R, Chianucci F, Cutini A, Fontes L, Manetti MC, Montero G, Soares P, Tomé M (2018) Generalized biomass equations for Stone pine (Pinus pinea L.) across the Mediterranean basin. For Ecol Manag 429:425–436. https://doi.org/10.1016/j.foreco.2018.07.037

    Article  Google Scholar 

  • Cunia T (1986) Construction of tree biomass tables by linear regression techniques. In: Estimating tree biomass regressions and their error. Proceedings of the workshop on tree biomass regresion functions and their contribution to the error of forest inventory estimates. May, Siracuse, New York, pp 27–37

  • Dahlhausen J, Uhl E, Heym M, Biber P, Ventura M, Panzacchi P, Tonon G, Horváth T, Pretzsch H (2017) Stand density sensitive biomass functions for young oak trees at four different European sites. Trees Struct Funct 31:1811–1826. https://doi.org/10.1007/s00468-017-1586-7

    Article  CAS  Google Scholar 

  • De-Miguel S, Pukkala T, Assaf N, Shater Z (2014) Intra-specific differences in allometric equations for aboveground biomass of eastern Mediterranean Pinus brutia. Ann For Sci 71:101–112. https://doi.org/10.1007/s13595-013-0334-4

    Article  Google Scholar 

  • Disney MI, Boni Vicari M, Burt A, Calders K, Lewis SL, Raumonen P, Wilkes P (2018) Weighing trees with lasers: advances, challenges and opportunities. Interface Focus 8:1–14. https://doi.org/10.1098/rsfs.2017.0048

    Article  Google Scholar 

  • Domke GM, Woodall CW, Smith JE, Westfall JA, McRoberts RE (2012) Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates. For Ecol Manag 270:108–116. https://doi.org/10.1016/j.foreco.2012.01.022

    Article  Google Scholar 

  • Eker M, Poudel KP, Özçelik R (2017) Aboveground biomass equations for small trees of brutian pine in Turkey to facilitate harvesting and management. Forests 8(12):477–492. https://doi.org/10.3390/f8120477

    Article  Google Scholar 

  • Falster DS, Duursma RA, Ishihara MI, Barneche DR, FitzJohn RG, Vårhammar A, Aiba M, Ando M, Anten N, Aspinwall MJ, Baltzer JL, Baraloto C, Battaglia M, Battles JJ, Lamberty BB, Van Breugel M, Camac J, Claveau Y, Coll L, Dannoura M, Delagrange S, Domec JC, Fatemi F, Feng W, Gargaglione V, Goto Y, Hagihara A, Hall JS, Hamilton S, Harja D, Hiura T, Holdaway R, Hutley LB, Ichie T, Jokela EJ, Kantola A, Kelly JWG, Kenzo T, King D, Kloeppel BD, Kohyama T, Komiyama A, Laclau JP, Lusk CH, Maguire DA, Le Maire G, Mäkelä A, Markesteijn L, Marshall J, McCulloh K, Miyata I, Mokany K, Mori S, Myster RW, Nagano M, Naidu SL, Nouvellon Y, O’Grady AP, O’Hara KL, Ohtsuka T, Osada N, Osunkoya OO, Peri PL, Petritan AM, Poorter L, Portsmuth A, Potvin C, Ransijn J, Reid D, Ribeiro SC, Roberts SD, Rodríguez R, Acosta AS, Santa-Regina I, Sasa K, Selaya NG, Sillett SC, Sterck F, Takagi K, Tange T, Tanouchi H, Tissue D, Umehara T, Utsugi H, Vadeboncoeur MA, Valladares F, Vanninen P, Wang JR, Wenk E, Williams R, De Aquino XF, Yamaba A, Yamada T, Yamakura T, Yanai RD, York RA (2015) BAAD: a biomass and allometry database for woody plants. Ecology 96:1445. https://doi.org/10.1890/14-1889.1

    Article  Google Scholar 

  • Forrester DI, Tachauer IHH, Annighoefer P, Barbeito I, Pretzsch H, Ruiz-Peinado R, Stark H, Vacchiano G, Zlatanov T, Chakraborty T, Saha S, Sileshi GW (2017) Generalized biomass and leaf area allometric equations for European tree species incorporating stand structure, tree age and climate. For Ecol Manag 396:160–175. https://doi.org/10.1016/j.foreco.2017.04.011

    Article  Google Scholar 

  • Goodman RC, Phillips OL, Baker TR (2014) The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl 24:680–698

    Article  Google Scholar 

  • Gutiérrez-Oliva A, Fernández-Golfín JI (1997) Cálculo de la densidad y de las variaciones dimensionales de la madera. Equivalencias numéricas entre valores

  • Gutiérrez-Oliva A, Plaza-Pulgar F (1967) Características físico-mecánicas de las maderas españolas. Madrid

  • Harvey AC (1976) Estimating regression models with multiplicative heteroscedasticity. Econometrica 44:461–465

    Article  Google Scholar 

  • Heath LS, Hansen MH, Smith JE, Smith WB, Miles PD (2009) Investigation into calculating tree biomass and carbon in the FIADB using a biomass expansion factor approach. In: USDA forest service proceedings—RMRS-P-56

  • Henry HAL, Aarssen LW (1999) The interpretation of stem diameter-height allometry in trees: biomechanical constraints, neighbour effects, or biased regressions? Ecol Lett 2:89–97. https://doi.org/10.1046/j.1461-0248.1999.22054.x

    Article  Google Scholar 

  • Houghton RA, Lawrence KT, Hackler JL, Brown S (2001) The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Glob Chang Biol 7:731–746. https://doi.org/10.1046/j.1365-2486.2001.00426.x

    Article  Google Scholar 

  • Jansons A, Rieksts-Riekstins J, Senhofa S, Katrevics J, Lazdina D, Sisenis L (2017) Above-ground biomass equations of populus hybrids in Latvia. Balt For 23:507–514

    Google Scholar 

  • Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49:12–35. https://doi.org/10.1093/forestscience/49.1.12

    Article  Google Scholar 

  • Kuznetsova T, Tilk M, Pärn H, Lukjanova A, Mandre M (2011) Growth, aboveground biomass, and nutrient concentration of young Scots pine and lodgepole pine in oil shale post-mining landscapes in Estonia. Environ Monit Assess 183:341–350. https://doi.org/10.1007/s10661-011-1925-x

    Article  CAS  PubMed  Google Scholar 

  • Lines ER, Zavala MA, Purves DW, Coomes DA (2012) Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Glob Ecol Biogeogr 21:1017–1028. https://doi.org/10.1111/j.1466-8238.2011.00746.x

    Article  Google Scholar 

  • Lu D (2005) Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Remote Sens 26:2509–2525. https://doi.org/10.1080/01431160500142145

    Article  Google Scholar 

  • McCarthy MC, Enquist BJ, Kerkhoff AJ (2007) Organ partitioning and distribution across the seed plants: assessing the relative importance of phylogeny and function. Int J Plant Sci 168:751–761. https://doi.org/10.1086/513491

    Article  Google Scholar 

  • Menéndez-Miguélez M, Canga E, Barrio-Anta M, Majada J, Álvarez-Álvarez P (2013) A three level system for estimating the biomass of Castanea sativa Mill. coppice stands in north-west Spain. For Ecol Manag 291:417–426. https://doi.org/10.1016/j.foreco.2012.11.040

    Article  Google Scholar 

  • Metz JÔ, Seidel D, Schall P, Scheffer D, Schulze ED, Ammer C (2013) 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. https://doi.org/10.1016/j.foreco.2013.08.014

    Article  Google Scholar 

  • Mikšys V, Varnagiryte-Kabasinskiene I, Stupak I, Armolaitis K, Kukkola M, Wójcik J (2007) Above-ground biomass functions for Scots pine in Lithuania. Biomass Bioenerg 31:685–692. https://doi.org/10.1016/j.biombioe.2007.06.013

    Article  Google Scholar 

  • Molto Q, Rossi V, Blanc L (2013) Error propagation in biomass estimation in tropical forests. Methods Ecol Evol 4:175–183. https://doi.org/10.1111/j.2041-210x.2012.00266.x

    Article  Google Scholar 

  • Montero G, Ortega Muela C, Rey C, de Viñas I, Bachiller Bachiller A (1999) Productividad aérea y dinámica de nutrientes en una repoblación de Pinus pinaster Ait. sometida a distintos regímenes de claras. Investig Agrar Sist y Recur For 8:175–206. https://doi.org/10.5424/637

    Article  Google Scholar 

  • Montero G, Ruiz-Peinado R, Muñoz M (2005) Producción de biomasa y fijación de CO2 por los bosques españoles. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid

  • Muth CC, Bazzaz FA (2003) Tree canopy displacement and neighbourhood interactions. Can J For Res 33:1323–1330. https://doi.org/10.1139/x03-045

    Article  Google Scholar 

  • Pajtík J, Konôpka B, Lukac M (2008) Biomass functions and expansion factors in young Norway spruce (Picea abies [L.] Karst) trees. For Ecol Manag 256:1096–1103. https://doi.org/10.1016/j.foreco.2008.06.013

    Article  Google Scholar 

  • Parresol B (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45(4):573–593

    Google Scholar 

  • Parresol B (2001) Additivity of nonlinear biomass equations. Can J For Res 31:865–878. https://doi.org/10.1139/x00-202

    Article  Google Scholar 

  • Picard N, Rutishauser E, Ploton P, Ngomanda A, Henry M (2015) Should tree biomass allometry be restricted to power models? For Ecol Manag 353:156–163. https://doi.org/10.1016/j.foreco.2015.05.035

    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. Food and Agricultural Organization of the United Nations, Rome, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement

  • Poorter H, Jagodzinski AM, Ruiz-Peinado R, Kuyah S, Luo Y, Oleksyn J, Usoltsev VA, Buckley TN, ReichPB SL (2015) How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol 208:736–749. https://doi.org/10.1111/nph.13571

    Article  PubMed  PubMed Central  Google Scholar 

  • Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012) Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol 193:30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x

    Article  CAS  PubMed  Google Scholar 

  • Poudel KP, Temesgen H (2016a) Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees. Can J For Res 46:77–87. https://doi.org/10.1139/cjfr-2015-0256

    Article  Google Scholar 

  • Poudel KP, Temesgen H (2016b) Developing biomass equations for Western hemlock and red alder trees in Western Oregon forests. Forests 7:88. https://doi.org/10.3390/f7040088

    Article  Google Scholar 

  • Poudel KP, Temesgen H, Gray AN (2015) Evaluation of sampling strategies to estimate crown biomass. For Ecosyst 2:1–11. https://doi.org/10.1186/s40663-014-0025-0

    Article  Google Scholar 

  • Pretzsch H (2014) Canopy space filling and tree crown morphology in mixed-species stands compared with monocultures. For Ecol Manag 327:251–264. https://doi.org/10.1016/j.foreco.2014.04.027

    Article  Google Scholar 

  • Reich PB, Luo Y, Bradford JB, Poorter H, Perry CH, Oleksyn J (2014) Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc Natl Acad Sci USA 111:13721–13726. https://doi.org/10.1073/pnas.1216053111

    Article  CAS  PubMed  Google Scholar 

  • Rodríguez-García C, Montes F, Ruiz F, Cañellas I, Pita P (2014) Stem mapping and estimating standing volume from stereoscopic hemispherical images. Eur J For Res 133:895–904. https://doi.org/10.1007/s10342-014-0806-6

    Article  Google Scholar 

  • Ruiz-Peinado R, del Rio M, Montero G (2011) New models for estimating the carbon sink capacity of Spanish softwood species. For Syst 20:176–188

    Google Scholar 

  • Ruiz-Peinado R, Montero G, del Río M (2012) Biomass models to estimate carbon stocks for hardwood tree species. For Syst 21:42–52. https://doi.org/10.5424/fs/2112211-02193

    Article  Google Scholar 

  • Salazar S, Sanchez LE, Galindo P, Santa-Regina I (2010) Above-ground tree biomass equations and nutrient pools for a paraclimax chestnut stand and for a climax oak stand in the sierra de Francia mountains, Salamanca, Spain. Sci Res Essays 5:1294–1301

    Google Scholar 

  • Santa-Regina I, Tarazona T (1999) Organic matter dynamics in beech and pine stands of mountainous Mediterranean climate area. Ann For Sci 56:667–677. https://doi.org/10.1051/forest:19990804

    Article  Google Scholar 

  • Santa-Regina I, Tarazona T, Calvo R (1997) Aboveground biomass in a beech forest and a scots pine plantation in the Sierra de la Demanda area of northern Spain. Ann Sci For 54:261–269. https://doi.org/10.1051/forest:19970304

    Article  Google Scholar 

  • SAS Institute Inc. (2004) SAS/STAT®. 9.1. User's Guide. SAS Institute Inc., Cary, NC

  • Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329:237–254. https://doi.org/10.1016/j.foreco.2014.06.026

    Article  Google Scholar 

  • Stovall AEL, Anderson-Teixeira KJ, Shugart HH (2018) Assessing terrestrial laser scanning for developing non-destructive biomass allometry. For Ecol Manag 427:217–229. https://doi.org/10.1016/j.foreco.2018.06.004

    Article  Google Scholar 

  • Tumwebaze SB, Bevilacqua E, Briggs R, Volk T (2013) Allometric biomass equations for tree species used in agroforestry systems in Uganda. Agrofor Syst 87:781–795. https://doi.org/10.1007/s10457-013-9596-y

    Article  Google Scholar 

  • Usoltsev VA, Shobairi SOR, Tsepordey IS, Chasovskikh VP (2018) Modeling the additive structure of stand biomass equations in climatic gradients of Eurasia. Environ Qual Manag 28:55–61. https://doi.org/10.1002/tqem.21603

    Article  Google Scholar 

  • Vanninen P, Ylitalo H, Sievänen R, Mäkelä A (1996) Effects of age and site quality on the distribution of biomass in Scots pine (Pinus sylvestris L.). Trees Struct Funct 10:231–238. https://doi.org/10.1007/s004680050028

    Article  Google Scholar 

  • Vejpustková M, Zahradník D, Čihák T, Šrámek V (2015) Models for predicting aboveground biomass of European beech (Fagus sylvatica L.) in the Czech Republic. J For Sci 61:45–54. https://doi.org/10.17221/100/2014-JFS

    Article  Google Scholar 

  • Wang X, Zhao D, Liu G, Yang C, Teskey RO (2018) Additive tree biomass equations for Betula platyphylla Suk. plantations in Northeast China. Ann For Sci. https://doi.org/10.1007/s13595-018-0738-2

    Article  Google Scholar 

  • Waring RH, Running SW (1998) Forest ecosystems: analysis at multiple scales, 2nd edn. Academic Press, San Diego

    Google Scholar 

  • Weiskittel AR, MacFarlane DW, Radtke PJ, Affleck DLR, Temesgen H, Woodall CW, WestfallJA CJW (2015) A call to improve methods for estimating tree biomass for regional and national assessments. J For 113:414–424. https://doi.org/10.5849/jof.14-091

    Article  Google Scholar 

  • Wirth C, Schumacher J, Schulze ED (2004) Generic biomass functions for Norway spruce in Central Europe—a meta-analysis approach toward prediction and uncertainty estimation. Tree Physiol 24:121–139. https://doi.org/10.1093/treephys/24.2.121

    Article  PubMed  Google Scholar 

  • Woodall CW, Heath LS, Domke GM, Nichols MC (2011) Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. Forest Inventory, 2010. USDA Forest Service. General Technical Report NRS-88

  • Yang B, Xue W, Yu S, Zhou J, Zhang W (2019) Effects of Stand Age On Biomass Allocation And Allometry of Quercus acutissima in the Central Loess Plateau of China. Forests 10:41. https://doi.org/10.3390/f10010041

    Article  Google Scholar 

  • Zhao D, Kane M, Teskey R, Markewith D (2016) Modeling aboveground biomass components and volume-to-weight conversion ratios for loblolly pine trees. Forest Science 62(5):463–473. https://doi.org/10.5849/forsci.15-129

    Article  Google Scholar 

  • Zhou X, Hemstrom MA (2009) Estimating aboveground tree biomass on forest land in the pacifc northwest: a comparison of approaches. USDA For Serv - Res Pap PNW-RP 1–18

Download references

Acknowledgements

The authors thank the GIS-Forest Research Group of the University of Oviedo for providing the Castanea sativa database obtained in a previous project (CN-08-069), and the BAAD database for the new samples of Fagus sylvatica, Castanea sativa and Pinus sylvestris. We also thank Adam Collins for revising and editing the English grammar. We finally thank the reviewers and the academic editor who provided good comments to improve the paper.

Funding

This research was funded by Ministry of Science, Innovation and Universities, grant number AGL2017-83828-C2-1-R; Ministry of Agriculture, grant number EG17-042-C02-02; INIA grant number IMP-2018-004-C02-02, and Horizon2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 77832 (CARE4C).

Author information

Authors and Affiliations

  1. Department of Forest Management and Dynamics, INIA-CIFOR, Ctra. La Coruña, km 7.5, 28040, Madrid, Spain

    María Menéndez-Miguélez, Ricardo Ruiz-Peinado, Miren Del Río & Rafael Calama

  2. Sustainable Forest Management Research Institute, Universidad de Valladolid & INIA, Palencia, Spain

    Ricardo Ruiz-Peinado, Miren Del Río & Rafael Calama

Authors
  1. María Menéndez-Miguélez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo Ruiz-Peinado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miren Del Río
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafael Calama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María Menéndez-Miguélez.

Additional information

Communicated by Derek Sattler.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2019 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Menéndez-Miguélez, M., Ruiz-Peinado, R., Del Río, M. et al. Improving tree biomass models through crown ratio patterns and incomplete data sources. Eur J Forest Res 140, 675–689 (2021). https://doi.org/10.1007/s10342-021-01354-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10342-021-01354-3

Keywords

Search

Navigation