Energy & Fuels 2009, 23, 1797–1800
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Estimating Above-Ground Fuel Biomass in Young Calabrian Pine (Pinus brutia Ten.)† Ertugrul Bilgili and Omer Kucuk* Faculty of Forestry, Karadeniz Technical UniVersity, Trabzon 61080, Turkey, and Faculty of Forestry, Kastamonu UniVersity, Kastamonu 37100, Turkey ReceiVed May 13, 2008. ReVised Manuscript ReceiVed July 22, 2008
Crown fuel biomass is of great importance in the field of forest-fire science. In this study, regression equations were developed for predicting needle, branch, and total biomass of young calabrian pine (Pinus brutia Ten.) trees and saplings. Equations were based on the data from 71 destructively sampled trees and saplings. The relationships between needle and branch biomass and tree properties were determined by linear regression, considering tree properties as the independent variable and needle, branch, and total biomass as the dependent variables. Tree and sapling properties included tree height (H), crown length (CL), crown width (CW), diameter at breast height (DBH), and root collar diameter (RCD). Results indicated that needle, branch, and total biomass could be accurately predicted using the regression equations obtained. The resulting equations were able to account for 60-94% of the observed variation in the total biomass.
Introduction Quantitative estimates of above-ground biomass are required for determining carbon stocks, assessing fuel inventories, building nutrient budgets,1 and predicting fire behavior.2 Several crown fuel characteristics have been shown to directly or indirectly affect the incidence and behavior of crown fires.3-6 Of these, crown fuel loadings are of interest to managers because of their contribution to crown fire intensity.3 Thus, there is a pressing need for repeatable and meaningful estimates of canopy fuels to better predict crown fire occurrence and behavior.7 Knowledge of fuel materials of forests is useful because it provides a more complete inventory in a stand and quantifies combustible materials to help predict fire intensity and fire behavior in specific forest cover types8 and relates to the potential fire hazard reflected in different magnitudes over the stages of stand development. At the same time, accumulation of fuel biomass in forest stands is an important determination † From the Conference on Fuels and Combustion in Engines. * To whom correspondence should be addressed. Telephone: +90-366215-0900. Fax: +90-366-215-2316. E-mail:
[email protected]. (1) Bond-Lamberty, B.; Wang, C.; Gower, S. T. Can. J. For. Res. 2002, 32, 1441–1450. (2) Sah, J. P.; Ross, M. S.; Koptur, S.; Snyder, J. R. For. Ecol. Manage. 2004, 203, 319–329. (3) Van Wagner, C. E. Can. J. For. Res. 1977, 7, 23–34. (4) Rothermel, R. C. Predicting behavior and size of crown fires in the Northern Rocky Mountains. Intermountain Research Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1991; Research Paper INT-438. (5) Scott, J. H.; Reinhardt, E. D. Assessing crown fire potential by linking models of surface and crown fire behavior. United States Department of Agriculture (USDA) Forest Service, Fort Collins, CO, 2001; Research Paper RMRS-RP-29. (6) Reinhardt, E.; Scott, J.; Gray, K.; Keane, R. Can. J. For. Res. 2006, 36 (11), 2803–2814. (7) Kucuk, O.; Saglam, B.; Bilgili, E. Biotechnol. Biotechnol. Equip. 2007, 21 (2), 235–240. ¨ .; Bilgili, E.; Sag˘lam, B. Int. J. Wildland Fire 2008, 17 (8) Ku¨c¸u¨k, O (1), 147–154. (9) Rothermel, R. C. A mathematical model for predicting fire spread in wildland fuels. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1972; Research Paper INT-115, p 40.
of fire frequency and severity2 and ultimately their ecological effects.9,10 For this reason, many researchers have developed allometric equations to predict biomass. Practically, most allometric equations employ diameter at breast height (DBH), tree height (H), crown length (CL), crown width (CW), and component biomass.11-14 Although many biomass studies involving tree species have been conducted in many countries,1,15 most growth and yield studies provide biomass values for individuals having diameters (DBH) greater than 8 cm. While this can be considered sufficient for typical growth and yield purposes, it has little value in areas where detailed fuel properties are of great concern. This is especially problematic for fire-prone forests, where detailed knowledge about fuel properties is needed for fire behavior prediction. To the knowledge of the authors, only a few studies exist in Turkey that could help provide this more detailed information.7,8 The objective of this study was to develop regression models (equations) to determine fuel biomass of calabrian pine for fire behavior prediction. However, the results of this study should not only contribute to the prediction of fire behavior but surely be of invaluable use in other forestry disciplines. Materials and Methods Study Area. Calabrian pine (Pinus brutia Ten.) composes one of the major conifer forest types of importance to fire managers and is the first most widely distributed conifer species in Turkey, (10) Kauffman, J. B.; Cummings, D. L.; Ward, D. E. J. Ecol. 1994, 82 (3), 519–531. (11) Sando, R. W.; Wick, C. H. A method of evaluating crown fuels in forest stands. United States Department of Agriculture (USDA) Forest Service, St Paul, MN, 1972; Research paper NC-84. (12) Brown, J. K. Weight and density of crowns of Rocky Mountain conifers. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1978; Research Paper INT-197, p 56. (13) Johnson, A. F.; Woodard, P. M.; Titus, S. J. For. Chron. 1990, 66, 596–599. (14) Scott, J. H.; Reinhardt, E. D. Fire Manag. Today 2002, 62 (4), 45–50. (15) Mikaelian, M. T.; Korzukhin, M. D. For. Ecol. Manage. 1997, 97, 1–24.
10.1021/ef800346s CCC: $40.75 2009 American Chemical Society Published on Web 08/27/2008
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Bilgili and Kucuk Table 1. Descriptive Statistics for Calabrian Pine Trees and Saplingsa
Figure 1. Location of the study area.
covering a land area of 5.42 million ha.16 Pure stands of calabrian pine is mostly found in fire-prone areas and usually originated from high-intensity, stand-replacing fires.17 The samples used in the study were obtained from pure calabrian pine stands located in the Korudag˘ Forest District in Kesan State Forest Enterprise, C¸anakkale, northwestern Turkey (Figure 1). The stands from which the samples were taken are similar in structure and form to those found in many other regions in the country. The area is characterized by a typical Marmara climate, with long hot summers and mild short winters. Mean annual rainfall on the site is 650 mm, with precipitation being mainly from December to May. The main vegetation type in the area is calabrian pine. Measurements and Data Collection. A total of 71 calabrian pine (35 trees plus 36 saplings) individuals were selected for the study. Individuals with diameters (DBH) 8 cm and above were considered trees, and those with less than 8 cm were considered saplings. The age of the trees and saplings ranged from 25 to 38 years and from 6 to 15 years, respectively. Selection was made such that samples represented a range of crown dimensions. Some measurements were taken before the trees were cut. These measurements included height (H), crown width (CW), crown length (CL), diameter at breast height (DBH), root collar diameter (RCD) of young trees, and age. After the felling, trees were destructively sampled. The study involved a wide range of crown sizes. Thus, different sampling procedures were employed in the measurement of biomass. With young or small trees, it was possible to remove and transport the entire crown. With large trees, however, it was necessary to subsample the crown to estimate total biomass. Therefore, partial sampling was accomplished by dividing the crown into 21 sections and removing all branches originating in sections 1, 6, 11, 16, and 21. The resulting sample weight was then multiplied by a factor of 4.2 to represent the full crown.8,18 Samples were then separated on the basis of predetermined size classes. Size classes conform with those currently adopted in fire danger rating systems19-21 and fuel characterization and fire behavior prediction studies.22,23 The biomass categories recognized (16) Orman Genel Mu¨du¨rlu¨g˘u¨ (OGM). Orman Atlası; OGM: Ankara, Turkey, 2007; p 88 (in Turkish). (17) Turna, I.; Bilgili, E. Int. J. Wildland Fire 2006, 15, 283–286. (18) Robichaud, E.; Methven, I. R. Can. J. For. Res. 1992, 22, 1118– 1123. (19) Deeming, J. D.; Burgan, R. E.; Cohen, J. D. The National FireDanger Rating System-1978. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1977; General Technical Report INT-39. (20) Bradshaw, L. S.; Deeming, J. E.; Burgan, R. E.; Cohen, J. D. The 1978 National Fire-Danger Rating System: Technical documentation. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1983; General Technical Report INT-169. (21) Hardy, C. C.; Hardy, C. E. Int. J. Wildland Fire 2007, 16, 217– 231. (22) Burgan, R. E.; Rothermel, R. C. BEHAVE: Fire behavior prediction and fuel modeling system-FUEL subsystem. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1984; General Technical Report INT-167, p 130.
H (m) CL (m) CW (m) age (year) DBH (cm) RCD (cm) needle (kg) fine branch (kg) medium branch (kg) thick branch (kg) coarse branch (kg) total biomass (kg)
N
minimum
maximum
mean
SEE
SD
71 71 71 71 35 36 71 71 71 71 35 71
1.40 1.35 0.95 6.00 13.00 3.40 0.11 0.07 0.02 0.00 1.46 0.23
12.00 7.20 5.30 38.00 19.00 11.00 5.64 4.47 4.98 7.26 8.76 26.75
6.40 3.98 2.64 19.83 15.91 7.11 1.92 1.51 1.60 1.82 4.53 9.09
0.46 0.22 0.14 1.24 0.31 0.38 0.18 0.13 0.16 0.21 0.29 0.97
3.90 1.88 1.22 10.52 1.86 2.33 1.52 1.14 1.41 1.83 1.74 8.20
a H, height; CL, crown length; CW, crown width; DBH, diameter at breast height; RCD, root collar diameter; N, number; SEE, standard error; SD, standard deviation.
were needles, branches 2.5 cm (coarse branch) in diameter.12 Each branch sample was separately labeled and transferred to the laboratory for detailed analyses. All needles were removed from each branch sample, and subsequently, fresh weight of branch and all needles were measured. Then, the needle and branch samples were dried to a constant weight for 24 h at 100 °C and weighed to the nearest 0.01 g. Final needle and branch biomass determinations were made on the basis of oven-dry measurements. Statistical Analysis. Data were initially graphed to provide a visual assessment of the relationships between biomass and independent variables. Correlation and regression analyses were performed to determine the relationship between needle, branch biomass, and tree properties. Regression analyses considered tree properties as the independent variables and needle and branch biomass as the dependent variables.8,12 The independent variables used were H, CL, CW, DBH, and RCD. Before the analyses, the variables were tested for normality,24 and as a result, a logarithmic transformation was deemed necessary for all variables. To analyze the relationships between biomass and tree properties (independent variables), a stepwise function and logarithmic linear regression models were used. The equations were of the form: ln(y) ) a + bi ln(xi), where y is the dependent variable (needle, branch, or total biomass), ln is the natural logarithm, xi are the independent variables, a is the constant, bi are the regression coefficients. All selected equations were significant at the p ) 0.05 significance level. Statistical analyses were performed using SPSS 10.0 for Windows.25
Results Quantitative estimates of crown fuels are needed to predict crown fire occurrence and behavior effectively and assess and mitigate crown fire hazard. The present study provided important results in this regard. The data used in the present study involved calabrian pine trees and saplings of varying ages, sizes, and properties. The summary descriptive statistics of tree and sapling characteristics and biomass are given in Table 1. Correlation and regression analyses were undertaken to investigate the relationships between tree properties and associated tree components biomass. The analyses indicated that needle biomass was closely related to RCD, DBH, H, CL, and CW. Fine, medium, thick, and coarse branches were correlated well with the RCD, DBH, CL, and CW. Similarly, total fuel (23) Andrews, P. L. BEHAVE: Fire behavior prediction and fuel modeling system-Burn subsystem part I. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1986; General Technical Report INT-194, p 130. (24) Sprugel, D. G. Ecology 1983, 64, 209–210. (25) Statistical Package for Social Sciences (SPSS). Statistical Package for Social Sciences (SPSS) 10.0 for Windows; SPSS, Chicago, IL, 1999.
AboVe-Ground Fuel Biomass in Young Calabrian Pine
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Table 2. Correlation Matrix between the Variables Used in the Analyses H CL CW DBH RCD needle fine branch medium branch thick branch coarse branch total biomass a
H
CL
CW
1 0.952a 0.906a 0.291 0.726a 0.829a 0.896a 0.901a 0.889a 0.147 0.910a
1 0.933a 0.674a 0.756a 0.914a 0.927a 0.918a 0.912a 0.842a 0.958a
1 0.671a 0.830a 0.912a 0.938a 0.919a 0.884a 0.633a 0.944a
DBH
RCD
needle
fine branch
medium branch
thick branch
coarse branch
total biomass
1 0.872a 0.860a 0.827a 0.847a b 0.937a
1 0.899a 0.922a 0.857a 0.842a 0.957a
1 0.917a 0.895a 0.641a 0.951a
1 0.905a 0.756a 0.972a
1 0.543a 0.948a
1 0.927a
1
1 b 0.752a 0.692a 0.621a 0.456a 0.795a 0.792a
Correlations significant at 1% significance level. Nonsignificant; p > 0.05. b
Figure 2. Logarithmic relationship between predicted and observed needle biomass.
biomass was closely related to all sapling and tree properties (p < 0.01). Correlation analysis results are given in Table 2. The different allometric biomass relationships of the different tree parts as well as total biomass were separately compared and selected on the basis of their R2 values. As a result, the allometric relationships between biomass and CL, CW, DBH, RCD, and H were determined. The relationships that best predicted needle, branch, and total biomass were selected. CL alone explained 66% of the observed variation (p < 0.05) in the needle biomass. The addition of the CW as the second independent variable improved the needle biomass prediction significantly (R2 ) 0.755; p < 0.05) (Figure 2). RCD alone explained 82% of the observed variation (p < 0.05) in the needle biomass for saplings. CW alone explained 85% of the observed variation (p < 0.05) in fine branches. The addition of the CL as the second independent variable improved very little the percent variability explained (R2 ) 0.862; p < 0.05) (Figure 3). CL alone explained 86% of the observed variation (p < 0.05) in medium branch biomass. CW and CL together explained 87% of the observed variation (p < 0.05) (Figure 4). CL alone explained 85% of the variation (p < 0.05) in thick branch (Figure 5). Table 3 lists the summary regression equations for predicting needle, branch (fine, medium, thick, and coarse branch), and total biomass in calabrian pine based on the data obtained in this study. Five regression models were developed for estimating total biomass. DBH alone explained 60% of the observed variation (p < 0.05) in total biomass for trees. The addition of the CL as the second independent variable improved the needle biomass prediction significantly (R2 ) 0.815; p < 0.05). Also, CL alone explained 92% of the observed variation (p < 0.05) in total biomass. RCD alone explained 82% of the observed variation
Figure 3. Logarithmic relationship between predicted and observed fine branch biomass.
Figure 4. Logarithmic relationship between predicted and observed medium branch biomass.
(p < 0.05) in total biomass. CW and CL together explained 94% of the observed variation (p < 0.05) in total biomass (Figure 6).
Discussion and Conclusions Regression equations were developed to predict needle and branch biomass for calabrian pine using the data for 71 trees and saplings. Equations were based on the relationships between tree and sapling properties (CL, CW, DBH, RCD, and H) and fuel biomass (needle, branch, and total biomass). Analyses revealed that tree properties were quite significant in predicting crown fuel biomass. The relationships developed were able to explain 82% of the variation in needle biomass, 83% in fine branches, 87% in medium branches, 86% in thick branches,
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Figure 5. Logarithmic relationship between predicted and observed thick branch biomass. Table 3. Regression Equations for Predicting Needle and Branch Biomass in Calabrian Pine dependent variables (kg sapling or tree-1) needle
fine branch
medium branch
thick branch
coarse branch
total biomass
a
constant coefficients model forma ln y ) a + b ln CL ln y ) a + b ln RCD ln y ) a + b ln CL + c ln CW ln y ) a + b ln DBH ln y ) a + b ln RCD ln y ) a + b ln CW ln y ) a + b ln CL + c ln CW ln y ) a + b ln CW ln y ) a + b ln RCD ln y ) a + b ln CL + c ln CW ln y ) a + b ln CL ln y ) a + b ln CL + c ln CW ln y ) a + b ln CL ln y ) a + b ln CL + c ln CW ln y ) a + b ln CL + c ln DBH ln y ) a + b ln DBH ln y ) a + b ln CL ln y ) a + b ln RCD ln y ) a + b ln CL + c ln DBH ln y ) a + b ln CL + c ln CW
a
b
c
R2
SEE
-5.004 2.162 0.666 0.228 -6.002 1.903 0.755 0.198 -1.164 0.396 0.570 0.821 0.303 -3.625 -6.696 -0.534 -2.067
1.636 1.993 1.094 0.836 1.158
0.489 0.813 0.503 0.862
0.200 0.331 0.197 0.403
-5.690 2.624 0.857 0.528 -6.046 2.462 0.765 0.473 -6.080 1.218 1.373 0.873 0.501 -8.447 3.098 0.848 0.645 -8.649 1.755 1.533 0.865 0.614 -5.925 1.820 0.734 0.220 -6.696 1.522 0.663 0.765 0.209 -8.626 1.214 0.389 0.802 0.192 -4.786 -4.564 -3.875 -4.603
2.006 2.487 2.212 1.293 0.952
0.596 0.920 0.929 0.815
0.198 0.386 0.211 0.136
-4.476 1.312 1.308 0.937 0.345
Models using RCD as an independent variable are for saplings only.
Figure 6. Logarithmic relationship between predicted and observed total biomass.
and 93% in total biomass. The results presented agree well with relevant studies on other species.2,8,26 (26) Xiao, C. W.; Ceulemans, R. For. Ecol. Manage. 2004, 203, 177– 186.
It is a known fact that standard fuel estimate relationships predict crown fuel biomass for a given tree DBH and/or H as predictive variable(s).11,12,27 The use of CL and CW in the predictions is not very common. However, it is a general requirement that accurate and timely information on fuel estimates in easily accessible form be available in evaluating fire hazard and crown fire potential14,28,29 and for effective use of fire behavior and effects models.30,31 DBH, H, and RCD mostly require ground-based measurements that are cumbersome, time-consuming, and costly. In this regard, the use of crown dimensions (CL and CW) in the estimations may prove invaluable because they can easily be predicted with remote sensing, aerial photos, and satellite images,32,33 making the prediction of fuel biomass possible for large areas at low costs. The relationships developed in this study use tree and crown properties that can easily be measured and/or predicted. It is also known that not all crown fuel biomass is consumed in the flaming front of a crown fire; only the finest fuels burn in the short duration of a crown fire.34,35 Needle and fine fuels are the most flammable portions of the canopy fuel biomass. Crown fuel loading (37%) was constituted by needle and fine branch in the present study. Using the results obtained, fire managers can evaluate fire hazards and crown fire potential more effectively, through estimating and characterizing fuel components. It should be noted that a logarithmic transformation of all variables was necessary to satisfy the homoscedasticity (homogeneity of the variance over the range of the sample data) and the linearity assumption. This transformation, however, creates a logarithmic bias during retransforming back to arithmetic units, resulting in a systematic underestimation of weight.36 Thus, a correction factor is required to deal with systematic biases introduced when data are log-transformed.24,37 Given the range of the data on which the relationships were based, the study makes an invaluable contribution to biomass research in general and fire behavior in particular. However, it should be kept in mind that the range of fuel properties on which the relationships were based represents the range conditions under which it is possible to use the relationships generated from this study. EF800346S (27) Gray, K. L.; Reinhardt, E. Analysis of algorithms for predicting canopy fuel. In Proceedings of the Second International Wildland Fire Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest Meteorology, Orlando, FL, American Meteorological Society, Boston, MA, Nov 16-20, 2003; Paper P5.8. (28) Johnson, K. N.; Sessions, J.; Franklin, J.; Gabriel, J. J. For. 1998, 96 (1), 42–49. (29) Fule´, P. Z.; Waltz, A. E. M.; Covington, W. W.; Heinlein, T. A. J. For. 2001, 99 (11), 24–29. (30) Brown, J. K. Fuel and fire behavior predicting in big sagebrush. Intermountain Forest and Range Experiment Station, United States Department of Agriculture (USDA) Forest Service, Ogden, UT, 1982; Research Paper INT-290. (31) Sandberg, D. V.; Ottmar, R. D.; Cushon, G. H. Int. J. Wildland Fire 2001, 10, 381–387. (32) Oswald, B. P.; Fancher, J. T.; Kulhavy, D. L.; Reeves, H. C. Int. J. Wildland Fire 2000, 9 (2), 109–113. (33) Scott, K.; Oswald, B.; Farrish, K.; Unger, D. Int. J. Wildland Fire 2002, 11, 85–90. (34) Call, P. T.; Ablini, F. A. Int. J. Wildland Fire 1997, 7, 259–264. (35) Stocks, B. J.; Alexander, M. E.; Wotton, B. M.; Stefner, C. N.; Flannigan, M. D.; Taylor, S. W.; Lavoie, N.; Mason, J. A.; Hartley, G. R.; Maffey, M. E.; Dalrymple, G. N.; Blake, T. W.; Cruz, M. G.; Lanoville, R. A. Can. J. For. Res. 2004, 34, 1548–1560. (36) Zar, J. H. Bioscience 1968, 18, 1118–1120. (37) Baskerville, G. L. Can. J. For. Res. 1792, 13, 1248–1251.
