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Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters

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Abstract

Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height (dbh) and merchantable stem volume (V) using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height (H) and crown area (CA). Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predictions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.

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References

  • Andersen H-E, Reutebuch SE, McGaughey RJ. 2006. A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Canadian Journal of Remote Sensing, 32: 355–366.

    Article  Google Scholar 

  • Baldwin VC, Saucier JR. 1983. Aboveground weight and volume of unthinned, planted longleaf pine on West Gulf forest sites. USDA Forest Service, Southern Forest Experiment Station, New Orleans, LA. Research Paper SO-191, p.25.

    Google Scholar 

  • Bi H, Fox JC, Li Y, Lei Y, Pang Y. 2012. Evaluation of nonlinear equations for predicting diameter from tree height. Canadian Journal of Forest Research, 42:1–18.

    Article  Google Scholar 

  • Chen X, Hutley LB, Eamus D. 2003. Carbon balance of a tropical savanna of northern Australia. Oecologia, 137: 405–416.

    Article  PubMed  Google Scholar 

  • Dalponte M, Bruzzone L, Gianelle D. 2011. System for the estimation of single-tree stem diameter and volume using multireturn LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 49: 2481–2490.

    Article  Google Scholar 

  • Dean TJ, Cao QV, Roberts SD, Evans DL. 2009. Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand. Forest Ecology and Management, 257: 126–133.

    Article  Google Scholar 

  • Drake JB, Weishampel JF. 2001. Simulating vertical and horizontal multifractal patterns of a longleaf pine savanna. Ecological Modelling, 145: 129–142.

    Article  Google Scholar 

  • Fahey TJ, Woodbury PB, Battles JJ, Goodale CL, Hamburg SP, Ollinger SV, Woodall CW. 2010. Forest carbon storage: ecology, management, and policy. Frontiers in Ecology and the Environment, 8:245–252.

    Article  Google Scholar 

  • Fox DG. 1981. Judging air quality model performance. Bulletin of the American Meteorological Society, 62: 599–609.

    Article  Google Scholar 

  • Goelz JC, Leduc DJ. 2001. Long-term studies on development of longleaf pine plantations. In: J. S. Kush (ed), Proceedings of the Third Longleaf Alliance Regional Conference, Forests for our Future, October 16–18, 2000, Alexandria, LA. The Longleaf Alliance and Auburn University, Auburn, AL, pp.116–118.

    Google Scholar 

  • Goldstein JH, Caldarone G, Duarte TK, Ennaanay D, Hannahs N, Mendoza G, Polasky S, Wolny S, Daily GC. 2012. Integrating ecosystem-service tradeoffs into land-use decisions. Proceedings of the National Academy of Sciences of the United States of America, 109: 7565–7570.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Gonzalez-Benecke CA, Martin TA, Cropper Jr. WP, Bracho R. 2010. Forest management effects on in situ and ex situ slash pine forest carbon balance. Forest Ecology and Management, 260: 795–805.

    Article  Google Scholar 

  • Gonzalez-Benecke CA, Martin TA, Jokela EJ, de la Torre R. 2011. A flexible hybrid model of life cycle carbon balance for loblolly pine (Pinus taeda L.) management systems. Forests, 2: 749–776.

    Article  Google Scholar 

  • Gonzalez-Benecke CA, Gezan SA, Leduc DJ, Martin TA, Cropper Jr. WP, Samuelson LJ. 2012. Modeling survival, yield, volume partitioning and their response to thinning for longleaf pine plantations. Forests, 3(4): 1104–1132.

    Article  Google Scholar 

  • Gonzalez-Benecke CA, Gezan SA, Martin TA, Cropper Jr. WP, Samuelson LJ, Leduc DJ. 2013. Individual tree diameter, height and volume functions for longleaf pine. Forest Science, dx.doi.org/10.5849/forsci.12-144.

    Google Scholar 

  • Gregoire TJ, Schabenberger O, Barrett JP. 1995. Linear modelling of irregularly spaced, unbalanced, longitudinal data from permanent-plot measurements. Canadian Journal of Forest Research, 25:137–156.

    Article  Google Scholar 

  • Jose S, Jokela E, Miller D. 2006. The longleaf pine ecosystem: An Overview. In: S. Jose, E. Jokela and D. Miller (eds), The longleaf pine ecosystem. Ecology, silviculture, and restoration. New York: Springer, pp 3–8.

    Chapter  Google Scholar 

  • Kaboyashi K, Salam MU. 2000. Comparing simulated and measured values using mean squared deviation and its components. Agronomy Journal, 92: 345–352.

    Google Scholar 

  • Kalliovirta J, Tokola T. 2005. Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information. Silvae Fenica, 39: 227–248.

    Google Scholar 

  • Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E, Pitkänen J, Popescu S, Solberg S, Wolf BM, Wu JC. 2012. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing, 4: 950–974.

    Article  Google Scholar 

  • Leduc D, Goelz J. 2009. A height-diameter curve for longleaf pine plantations in the Gulf Coastal Plain. Southern Journal of Applied Forestry, 33: 164–170.

    Google Scholar 

  • Lee H, Slatton KC, Roth BE, Cropper Jr. WP. 2009. Prediction of forest canopy light interception using three-dimensional airborne LiDAR data. International Journal of Remote Sensing, 30: 189–207.

    Article  Google Scholar 

  • Lee H, Slatton KC, Roth BE, Cropper Jr. WP. 2010. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests. International Journal of Remote Sensing, 31: 117–139.

    Article  Google Scholar 

  • Lefsky MA, Cohen WB, Parker GG, Harding DJ. 2002. Lidar remote sensing for ecosystem studies. Bioscience, 52:19–30.

    Article  Google Scholar 

  • Li W, Guo Q, Jakubowski MK, Kelly M. 2012. A new method for segmenting individual trees from the Lidar point cloud. Photogrametric Engineering and Remote Sensing, 78: 75–84.

    Article  Google Scholar 

  • Loague K, Green RE. 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. Journal of Contaminant Hydrology, 7: 51–73.

    Article  CAS  Google Scholar 

  • Loudermilk EL, Cropper Jr. WP, Mitchell RJ, Lee H. 2011. Longleaf pine and hardwood dynamics in a fire-maintained ecosystem: A simulation approach. Ecological Modelling, 222: 2733–2750.

    Article  Google Scholar 

  • Luyssaert S, Schulze ED, Boerner A, Knohl A, Hessenmoeller D, Law BE, Ciais P, Grace J. 2008. Old-growth forests as global carbon sinks. Nature, 455: 213–215.

    Article  CAS  PubMed  Google Scholar 

  • McKinley DC, Ryan MG, Birdsey RA, Giardina CP, Harmon ME, Heath LS, Houghton RA, Jackson RB, Morrison JF, Murray BC, Pataki DE, Skog KE. 2011. A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 21: 1902–1924.

    Article  PubMed  Google Scholar 

  • Menaut JC, Cesar J. 1979. Structure and primary productivity of Lamto savannas, Ivory Coast. Ecology, 60: 1197–1210.

    Article  Google Scholar 

  • Nakai Y, Hosoi F, Omasa K. 2010. Estimation of coniferous standing tree volume using airborne LiDAR and passive optical remote sensing. Journal of Agricultural Meteorology, 66: 111–116.

    Article  Google Scholar 

  • Nelson R, Swill R, Krabill W. 1988. Using airborne lasers to estimate forest canopy and stand characteristics. Journal of Forestry, 86: 31–38.

    Google Scholar 

  • Neter J, Kutner MH, Wasserman W, Nachtsheim CJ. 1996. Applied linear statistical models. McGraw-Hill/Irwin, 4th edition. Irwin Series in Statistics, 770 pp.

    Google Scholar 

  • Nilsson M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment, 56: 1–7.

    Article  Google Scholar 

  • Popescu SC, Wynne RH, Nelson RF. 2003. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29: 564–577.

    Article  Google Scholar 

  • Popescu SC, Wynne RH. 2004. Seeing the trees in the forest: using LIDAR and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering and Remote Sensing, 70: 589–604.

    Article  Google Scholar 

  • Popescu SC. 2007. Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 31: 646–655

    Article  Google Scholar 

  • Putz FE, Zuidema PA, Pinard MA, Boot RGA, Sayer JA, Sheil D, Sist P, Elias, Vanclay JK. 2008. Improved tropical forest management for carbon retention. PLOS Biology, 6: 1368–1369

    Article  CAS  Google Scholar 

  • Roberts SD, Dean TJ, Evans DL, McCombs JW, Harrington RL, Glass PA. 2005. Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. Forest Ecology and Management, 213: 54–70.

    Article  Google Scholar 

  • Sawadogo L, Savadogo P, Tiveau D, Dayamba SD, Zida D, Nouvellet Y, Oden PC, Guinko S. 2010. Allometric prediction of above-ground biomass of eleven woody tree species in Sudanian savanna-woodland of West Africa. Journal of Forestry Research, 21: 475–481.

    Article  Google Scholar 

  • Satoo T, Madwick HAI. 1982. Forest Biomass. The Hague / Boston / London: Martinus Ni1hoff / Dr W. Junk Publishers, p.152.

    Google Scholar 

  • Snowdon P. 1991. A ratio estimator for bias correction in logarithmic regressions. Canadian Journal of Forest Research, 21: 720–724.

    Article  Google Scholar 

  • Tallis H, Polasky S. 2009. Mapping and valuing ecosystem services as an approach for conservation and natural-resource management. Annals of the New York Academy of Sciences, 1162: 265–283.

    Article  PubMed  Google Scholar 

  • Thompson ID, Okabe K, Tylianakis JM, Kumar P, Brockerhoff EG, Schellhorn NA, Parrotta JA, Nasi R. 2011. Forest biodiversity and the delivery of ecosystem goods and services: translating science into policy. Bioscience, 61: 972–981.

    Article  Google Scholar 

  • Ulander LMH, Patrik BG, Hagberg JO. 1995. Measuring tree height using ERS-1 SAR Interferometry. In: Proceeding of Geoscience and Remote Sensing Symposium IGARSS’95, Florence, Italy, pp. 2189–2191.

    Google Scholar 

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Authors and Affiliations

  1. School of Forest Resources and Conservation, University of Florida, P.O. Box 110410, Gainesville, FL, 32611, USA

    C. A. Gonzalez-Benecke, Salvador A. Gezan, Wendell P. Cropper Jr. & Timothy A. Martin

  2. School of Forestry and Wildlife Sciences, Auburn University, 3301SFWS Building, Auburn, AL, 36849, USA

    Lisa J. Samuelson

  3. USDA Forest Service, Southern Research Station, Alexandria Forestry Center, 2500 Shreveport Hwy, Pineville, LA, 71360, USA

    Daniel J. Leduc

Authors
  1. C. A. Gonzalez-Benecke
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  2. Salvador A. Gezan
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  3. Lisa J. Samuelson
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  4. Wendell P. Cropper Jr.
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  5. Daniel J. Leduc
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  6. Timothy A. Martin
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Corresponding author

Correspondence to C. A. Gonzalez-Benecke.

Additional information

Project funding: This research was supported by the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP).

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Gonzalez-Benecke, C.A., Gezan, S.A., Samuelson, L.J. et al. Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters. Journal of Forestry Research 25, 43–52 (2014). https://doi.org/10.1007/s11676-014-0427-4

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  • DOI: https://doi.org/10.1007/s11676-014-0427-4

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