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Evaluation of sampling strategies to estimate crown biomass
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Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.
Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE), relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12) are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.
Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.
Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy. However, this is attained by having to sample larger amount of biomass. Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.
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Evaluation of sampling strategies to estimate crown biomass
),
Abstract
Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.
Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE), relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12) are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.
Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.
Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy. However, this is attained by having to sample larger amount of biomass. Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.
References(42)
Barney RJ, Vancleve K, Schlenter R (1978) Biomass distribution and crown characteristics in two Alaskan Picea mariana ecosystems. Can J For Res 8:36-41
Beauchamp JJ, Olson JS (1973) Corrections for bias in regression estimates after logarithmic transformation. Ecology 54(6):1403-1407
Catchpole WR, Wheeler CJ (1992) Estimating plant biomass: a review of techniques. Aust J Ecol 17:121-131
Chiric G, Puletti N, Salvati R, Arbi F, Zolli C, Corona P (2014) Is randomized branch sampling suitable to assess wood volume of temperate broadleaved old-growth forests? For Ecol Manag 312:225-230
de-Miguel S, Mehtatlo L, Durkaya A (2014a) Developing generalized, calibratable, mixed-effects meta models for large-scale biomass prediction. Can J For Res 44:648-656, a or b
de-Miguel S, Pukkala T, Assaf N, Shater Z (2014b) Intra-specific difference in allometric equations for aboveground biomass of eastern Mediterranean Pinus brutia. Ann For Sci 71:101-112, a or b
Flewelling JW, Pienaar LV (1981) Multiplicative regression with lognormal errors. For Sci 27(2):281-289
Good NM, Paterson M, Brack C, Mengersen K (2001) Estimating tree component biomass using variable probability sampling methods. J Agric Biol Environ Stat 6(2):258-267
Gregoire TG, Valentine HT, Furnival GM (1995) Sampling methods to estimate foliage and other characteristics of individual trees. Ecology 76(4):1181-1194
Harrison RB, Terry TA, Licata CW, Flaming BL, Meade R, Guerrini IA, Strahm BD, Xue D, Lolley MR, Sidell AR, Wagoner GL, Briggs D, Turnblom EC (2009) Biomass and stand characteristics of a highly productive mixed Douglas-Fir and Western Hemlock plantation in Coastal Washington. West J Appl For 24(4):180-186
He Q, Chen E, An R, Li Y (2013) Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forests. Forests 4:984-1002
Henry M, Picard N, Trotta C, Manlay RJ, Valentini R, Bernoux M, Saint-André L (2011) Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fenn 45(3B):477-569
Hepp TE, Brister GH (1982) Estimating crown biomass in loblolly pine plantations in the Carolina Flatwoods. For Sci 28(1):115-127
Horvitz DG, Thompson DJ (1952) A generalization of sampling without replacement from a finite universe. J Am Stat Assoc 47:663-685
Ishii H, McDowell N (2001) Age-related development of crown structure in coastal Douglas-fir trees. For Ecol Manag 169:257-270
Ishii H, Wilson ME (2001) Crown structure of old-growth Douglas-fir in the western Cascade Range, Washington. Can J For Res 31:1250-1261
Jenkins CJ, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49(1):12-35
Jessen RJ (1955) Determining the fruit count on a tree by randomized branch sampling. Biometrics 11(1):99-109
Kershaw JA, Maguire DA (1995) Crown structure in Western hemlock, Douglas-fir, and grand fir in western Washington: trends in branch-level mass and leaf area. Can J For Res 25:1897-1912
Kuyaha S, Dietz J, Muthuri C, Noordwijk MV, Neufeldt H (2013) Allometry and partitioning of above- and below-ground biomass in farmed eucalyptus species dominant in Western Kenyan agricultural landscapes. Biomass Bioenergy 55:276-284
Lu D (2005) Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Remote Sens 26(12):509-2525
Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 7:1297-1328
Naidu SL, DeLucia EH, Thomas RB (1998) Contrasting patterns of biomass allocation in dominant and suppressed loblolly pine. Can J For Res 28:1116-1124
Paladinic E, Vuletic D, Martinic I, Marjanovic H, Indir K, Benko M, Novotny V (2009) Forest biomass and sequestered carbon estimation according to main tree components on the forest stand scale. Period Biol 111(4):459-466
Pooreter 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
Ritchie MW, Zhang J, Hamilton TA (2013) Aboveground tree biomass for Pinus ponderosa in northeastern California. Forests 4:179-196
Saatchi S, Halligan K, Despain DG, Crabtree RL (2007) Estimation of forest fuel load from radar remote sensing. IEEE Trans Geosci Remote Sens 45:1726-1740
Sedjo R, Sohngen B (2012) Carbon sequestration in forests and soils. Annu Rev Resour Econ 4:127-144
Snowdon P (1986) Sampling strategies and methods of estimating the biomass of crown components in individual trees of Pinus radiata D Don. Aust For Res 16(1):63-72
Snowdon P (1991) A ratio estimator for bias correction in logarithmic regressions. Can J For Res 21:720-724
Swank WT, Schreuder HT (1974) Comparison of three methods of estimating surface area and biomass for a forest of young eastern white pine. For Sci 20:91-100
Temesgen H (2003) Evaluation of sampling alternatives to quantify tree leaf area. Can J For Res 33:82-95
Temesgen H, Monleon V, Weiskittel A, Wilson D (2011) Sampling strategies for efficient estimation of tree foliage biomass. For Sci 57(2):153-163
Tumwebaze SB, Bevilacqua E, Briggs R, Volk T (2013) Allometric biomass equations for tree species used in agroforestry systems in Uganda. Agroforest Syst 87:781-795
Valentine HT, Hilton SJ (1977) Sampling oak foliage by the randomized-branch method. Can J For Res 7:295-298
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Acknowledgements
We thank Professors Lisa Madsen and Glen Murphy (both at Oregon State University) for their insights and comments on an earlier draft, and the Forest Inventory Analysis Unit for funding the data collection and analysis phases of this project.
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
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