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Predicting crown fuel biomass of Turkish red pine (Pinus brutia Ten.) for the Mediterranean regions of Turkey
Procjena goriva iz biomase krošanja bora (Pinus brutia Ten.) u mediteranskim područjima Turske
Cumhur Güngöroglu, Ç. Okan Güney, Abdullah Sari, Ayhan Serttaş
Accurate fuel load estimation is an important prerequisite for effective forest fire management. The aim of this study was to develop empirical allometric equations for the estimation of crown fuel loading of Calabrian pine (Pinus brutia Ten.) trees of Southwestern Mediterranean region of Turkey using dendrometric variables. For this study, 84 trees were sampled destructively. Branch samples of crown fuel biomass were classified as foliage and as branches within the following diameter ranges: very fine (≤0,3 cm), fine (0,31–0,6 cm), medium (0,61–1.0 cm), thick (1,01–2,5 cm) and active fuels. To estimate the crown biomass, the diameter at breast height, tree height, crown length, and crown width were used as the independent variables. Stepwise function and logarithmic linear regression models were used to analyze the relationships between the fuel biomass and properties of the sampled trees. Among all of the obtained allometric equations, the variation in fine branches was explained the most by crown width and crown length which together explained R2adj of 90.2 of the variation in fine branches. The variation in very fine branches was explained the least by tree height, which only explained R2adj of 60.4% the variation in very fine branches. The total crown fuel loading of Calabrian pine in present study compared with studies distributed in Greece and Turkey indicate, the fuel biomass of Calabrian pines can differ between regions.
Key words: crown biomass, fuel load, Calabrian pine, Mediterranean regions
Although fires are an important and natural part of Mediterranean ecosystems, there has been a notable increase over the past decades in the number and total area of forest fires in the European Mediterranean Basin, which has resulted in significant damage (Martínez et al,. 2008; Tampakis et al., 2005). Since the 1950s, the relationships between socio-economic and geophysical factors played an important role in increasing the risk of forest fires in the region (Viedma et al., 2017).
Fire plans need to be effective in preventing potential fires and minimizing possible damages, while also offering a useful method for fighting fires (Vasconcelos et al., 2001). An important prerequisite for successful fire management is the accurate estimation of the fuel load (Bond-Lamberty et

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al., 2002). Fuel load models are an important basis for determining fire behavior, fire hazard risks, fire management plans, and decision-supporting system for fire management (Alexander, 2007; Sandberg et al., 2001). An important prerequisite for successful fire management is the accurate estimation of the fuel load (Bond-Lamberty et al., 2002). Equations for estimating fuel load are an important basis for determining fire behavior, fire hazard risks, fire management plans, and decision-supporting system for fire management (Alexander, 2007; Sandberg et al. 2001). The amount of data required for the development of fire propagation models in fire estimation systems is gradually increasing (Sandberg et al., 2001). Estimations of typical forest fire features such as fire propagation ratio, fuel consumption, fire intensity, and flame size utilize fuel loading values (Dimitrakopulos, 2002), as well as spatial heterogeneity, which affects these factors (Fernandes, 2009; Hiers et al., 2009). Predicting tree biomass based on fuel inventories provide important modeling data for fire managers and researchers to determine aboveground primary production and so to estimate the fuel load characteristics (Bond-Lamberty et al., 2002; Küçük et al., 2008). Allometric relationships related to structural components of tree species are generally used for predicting intensity and rate of spread of crown fires (Cruz et al., 2003; Gray and Reinhardt, 2003). As independent variables, most allometric equations for measuring tree biomass use either the diameter at breast height (DBH, 1,3 m) (Zianis and Mencuccini, 2004), or the DBH value along with the tree height (H) (Poudel and Temesgen, 2015). DBH is a commonly used and preferred parameter, owing to the fact that it has high correlation with tree biomass (Jiménez et al., 2013), and can be easily obtained from forest inventories (Mitsopoulos and Dimitrakopoulos, 2007a). RCD (root collar diameter) is usually used in allometric equations for estimating biomass of young trees below 1.3 m in DBH (Annighöfer et al., 2016). It is also used to estimate the fuel biomass of young forest areas Küçük et al., 2008). Aside from DBH, H and RCD, crown length (CL) and crown width (CW) have also been used as independent factors to estimate crown biomass in homogeneous stands of many coniferous tree species (Cruz et al., 2003; Mitsopoulos and Dimitrakopoulos, 2007b).
In DBH-based allometric equations, errors in biomass stock measurements generally stem from their usage on larger diameter trees, rather than on the faster growing, smaller diameter trees for which they are more appropriate (Singh et al., 2011). In fact, an increasing number of papers are being published on crown fuel characteristics of trees with DBH values above 8 cm in diameter (Jiménez et al., 2013; Molina et al., 2014; Zianis et al., 2011). On the other hand, problems in estimating the fuel biomass properties of saplings and seedling in fire-prone forests render the estimation of fire behavior even more difficult (Küçük et al., 2008). In Turkey’s Mediterranean biogeography, the proper and sufficient assessment of fuel loads for trees with varying diameters is of strategic importance in fighting fires in this region, which is characterized by a variable topography and stand type. The aim of this study was to develop empirical allometric equations for the estimation of crown fuel loading of Turkish red pine (Pinus brutia Ten.) trees of Southwestern Mediterranean region of Turkey using dendrometric variables. Estimated crown fuel loads could be used in fire behaviour models. Calabrian pine forests cover a total area of 5,85 million ha in Turkey, and constitute 27% of the country’s forests (OGM, 2015). These forests are distributed on the Mediterranean coasts of Turkey, between 1,200 to 1,400 m (supra-Mediterranean stratum) from sea level (Boydak et al., 2006). Calabrian pine forests of Turkey’s Mediterranean, Aegean, and Marmara regions are designated as first-degree fire sensitive (i.e. fire prone) areas by Turkey’s competent forestry authority (OGM, 2013).
Study Site – Područje istraživanja
This study was conducted in Turkish red pine forests distributed in the Antalya province, which is located on Turkey’s Southwestern Mediterranean coast (Figure 1). The Antalya province of Turkey is in the country’s Southwestern Mediterranean region; its climate is described by Iyigun et al. (2013) as a dry summer subtropical humid coastal Mediterranean climate. This type of Mediterranean climate is characterized by high levels of winter precipitation, as well as hot and dry long-summer season mainly associated with

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In this study, the tree properties exhibited high variation. RCD of saplings varied from 1 cm to 5 cm, while H of RCD saplings varied from 1,25 m to 1,65 m . DBH varied from 1,5 cm to 42 cm, while H varied from 1,9 m to 26,7 m. Other descriptive statistics are provided in Table 2. The results of the correlation analysis are shown in Table 3. In general, the crown fuel components were found to be significantly correlated to H, RCD, DBH, CW, and CL. All of the crown fuel components had a strong relationship with CL. According to Table 3, all the crown fuel components had a strong relationship (p=0,01) with all the tree properties.
To develop allometric equations that allow estimating the biomass of crown fuel components, the measured tree properties were used as independent variables in different allometric equations. All allometric relationships identified between the dependent and independent variables are shown in Table 4. Among the different allometric equations, the variation in fine branches was explained the most by CW and CL, which together explained 90% of the variation in this parameter (P<0.001). On the other hand, the variation in very fine branches was explained the least by H, which could only explain 60.4% of the variation (P<0.001). The variation in total fuels was explained the most by CW and CL, which together explained 89.8% of the variation in this parameter (P<0.001). On the other hand, the variation in total fuels was explained the least by H, which could only explain 78.4 % of the variation (P<0.001). The variation in active fuels was explained the most by H, CW, and CL, which together explained 86.8% of the variation (P<0.05). This trio of properties also explained 81.6% of the variation in foliage (P<0.001).
The mean fuel load distribution of the crown fuel components show that thick branches were the most common, representing 31.43% of the components, followed by foliage mass, representing 29.98% of the components. Active fuels constituted 56.6% of the total crown biomass (Figure 2). The relationship between by DBH estimated and actual

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the large-scale weather systems, both of which are influenced by seasonal and inter-annual variations.
The total area of the study was 598.051,31 ha, while the total forest area was 255.748,82 ha. Turkish red pine forests constitute 192.501,79 ha of this area, which corresponds to 75.26% of the forested areas within the studied region. An evaluation of forest fires in Antalya between 1979 and 2012 revealed that although crown fires occurred less often, 89.07% of the burnt areas in Turkish red pine forests were the crown fires (Table 1).
Data Collection and Sampling – Prikupljanje i uzorkovanje podataka
In this study, 7 saplings and 77 trees were sampled destructively. Trees were randomly sampled only in fire prone areas of Antalya province. H, CW, CL, age, RCD and DBH were measured as independent variables. Vertex IV was used for the H and CL measurements. Following these measurements, the trees were cut in order to measure their crown fuel load. After all the trees were cut, the fuel biomass in their crowns were destructively sampled. Different sampling methods were used to sample the fuel load of the crowns of large and young trees. The first method was used for large trees with a DBH greater than 8 cm and a crown length of at least 3 m. In this method, the live crown (CL) of the cut trees were measured and divided equally into 21 sections. All living and dead fuel materials originating in sections 1, 6, 11, 16, and 21 were removed and separated by fuel size classes (foliages and fuel branches), and then weighed. Weight values obtained through this method were then multiplied with a factor of 4,2 to sample the crown fuel load (Küçük et al. 2008; Robichaud and Methven 1992). In the second method, all fuel crown biomass of young trees with a DBH less than 8 cm and a crown length below 3 m were removed, and then separated by fuel size classes. The removed and classified material was then weighed. Dead fuel branches were not included in the analyses.
Branch samples of crown fuel biomass were separated into standard fuel size classes (Scott and Reinhardt, 2002). Crown fuel biomass classes are foliage, <0,3 cm very fine branches, 0,3–0,6 cm fine branches, 0,6–1,0 cm medium branches and 1,0–2,5 cm thick branches. The main reason to use this classification that the finest fuels are the first to be consumed before the crown fuel biomass are entirely consumed in the flaming front of a crown fire. In fact, only the finest fuels burn in the short duration of a crown fire (Mitsopoulos and Dimitrakopoulos, 2007b; Stocks et al., 2004). In this way, a separate class designated as “active fuels” (Küçük et al., 2008) and foliage and branches thinner than 0,6 cm were added to the crown fuel biomass classification, to indicate materials that are more predisposed to be consumed. Total fuel biomass was determined by weighing the foliage and different fuel branches classes of each tree. Foliage and branches by diameter classes were taken as samples and weighed once again. In this study, we have used oven-drying of fuel samples to determine fuel moisture content (Matthews, 2010). These samples were dried in 103 ± 2°C ovens for 24 ±1 hours to obtain oven-dried samples. The oven-dried samples were then weighed. Based on the ratio between the live weight and oven-dried weight of the samples, the oven-dried weight of the foliage and fuel branches on every tree was determined. In the study, oven-dried weights were used as fuel load values in tables and figures.
Statistical Data Analysis – Statistička analiza podataka
Correlation and regression analyses were used to examine the relationships between tree properties and crown fuel biomass. Stepwise function and logarithmic linear regression models were used to analyze the relationships between fuel biomass and the measured tree properties. Logarithmic regression is commonly used to estimate the relationships between crown fuel load and the different properties of trees and crown (Mitsopoulos and Dimitrakopoulos, 2007a; Molina et al., 2014). The equation used was [ln(Y) = a + bln(x) + ε], where, Y is the dependent variable (needle, branch or total biomass), ln is the natural logarithm, x is the independent variables, a is the constant, b is the regression coefficients, and ε is the error term. The residual variance of dependent and independent variables is both used in the analysis of allometric relationships. For this reason, logarithmic transformation is necessary to remove residual heteroscedasticity (Socha and Wezyk, 2007).
All selected equations were significant at least at P=0,05 significance level. Regression and correlation analyses were performed using IBM SPSS 20.0 for Windows. The measured tree properties H, CL, CW, RCD and DBH were used as independent variables, while the foliage and branch biomasses were used as the dependent variables. Before the variables were analyzed, there were tested for normality.

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values of total crown biomass, and the relationship between their estimated values and logarithmically converted values are shown in Figure 3. The use of fitting linear models on logtransformed data can cause the results that are biased in geometric rather than arithmetic space (Mascaro et al., 2011). Therefore, it was necessary to perform a logarithmic transformation for variables to account the heteroscedasticity. Systematic biases in estimates for total crown fuel are increasing in this study towards the large diameter trees of the small diameters, especially, trees with > 10 cm diameter are strongly heteroscedastic (Figure 3a). This is indicated for ideal allometry data to show increasing variation in tree biomass with increasing diameter (Chave et al., 2005; Mascaro et al., 2011).
The mean diameter of the trees included in this study was 13.68 cm (s.d. 9.58, mean S.E.E. 1.05). Sixty-seven of the trees had a DBH below 20 cm. The distribution of active fuels in DBH proportions displayed that the biomass of trees with DBH values greater than 10 cm tended to increase with notably greater than trees with smaller DBH values (Figure 4). Foliage mass was the largest component of active fuels, constituting 52.97%, as a mean. We considered it necessary to identify the relationship between the crown ratio (CR=CL/H×100) of the sampled tree and their DBH. As the DBH of the trees increased, we noted that their CRs also began to decrease disproportionately. On the other hand, as the DBH increased, the foliage fuels of the trees began to

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increase disproportionately (Figure 5). These changes can be easily understood from the s.d. and S.E.E. of the foliage fuel distributions in the DBH proportions (Table 5).
Regression models were developed to assess the foliage, branch, and active and total fuel biomass of 84 Turkish red pine. The regression models aimed to describe the relationship between fuel biomass components and tree properties (H, DBH, CW, CL). When the adjusted coefficient of determination (R2adj) values were evaluated, it was observed that fine branches had the highest explanation percentage with 90%, while total fuels had second highest with 89,8%, and thick branches had third highest with 89%. The lowest R2adj value was observed with fine branches, at 60.4%, followed by foliage, at 64.8%. Although the R2adj value was generally high in equations involving both CW and CL, and generally low in equations involving H, it was still understood that all tree properties could be used for determining crown fuel loads in Calabrian pines. Comparisons with other studies on crown fuel load in Calabrian pines indicate that H, DBH, CW, and CL are all important predictive variables for this tree (Küçük et al., 2008; Zianis et al., 2011).
In a study performed by Küçük et al. (2008) on Turkish red pine forests across northwestern Turkey, the mean oven-dried weight of total crown fuel biomass for trees and saplings together, and its properties was 2.47 kg/tree (n= 324, mean DBH= 15.49 cm., mean H= 2.27 m., mean CW=1.1 m., mean CL= 1.6, s.d.= 5557.2, S.E.E.= 319.781, R2adj= 0.944). The mean value of total crown fuel biomass for trees and saplings in our study was 14,098 kg/tree (Table 2). Another study performed by Küçük and Bilgili (2008) found results similar to our study. In the said study, the mean oven-dried weights of fuel biomass was 16.54 kg/tree (n= 35, mean DBH= 15.91 cm, mean H= 10.25 m., mean CW=3.74 m, mean CL= 5.75, R2adj= 0.799). In a study performed by Zianis et al. (2011) in the Turkish red pine forests of Greece’s island of Crete and Lesvos. The total fuel load results on island Create were closer to the findings of Küçük et al. (2008). In this study performed in Crete, the mean oven-dried weight of the total crown fuel biomass (T1+T2) was determined as 3.83 kg/tree (n= 12, mean DBH= 18 cm, mean H= 8 m). T1 is dry biomass of needles and twigs up to 0.63 cm in diameter and T2 is dry biomass of branch wood 0.64-2.5 cm in diameter. As these comparisons indicate, the fuel biomass of Calabrian pines can differ between regions. In this study, an increase in DBH did not inevitably result in an increase in foliage fuel biomass, while CR was generally found to scatter disproportionately. However, this does not mean that trees with high DBH values consistently have lower foliage fuel biomass than trees with low DBH values. It must be considered that such a generalization might lead to erroneous and inaccurate assessments. A study performed by Affleck et al. (2012) to characterize the crown profile and crown mass of conifer forests showed that the total crown fuel biomass distributed disproportionately from the relationship between CR and DBH. The same study concluded that in crown biomass studies, large conifer trees are generally present in smaller number, while their effect on overall biomass per unit area is disproportionate. In fact, the species included in the study of Affleck et al. (2012), which was conducted in the Interior Northwest of USA, are quite different from Calabrian pine. The similar results compared with our study regarding to same tree species appeared in the study of Zianis et al. (2011). The distribution of average total crown fuel biomass to the DBH sizes (7,3 – 30 cm) in their study were disproportionately and differentiated in each site. The variability between biomass equations are generally due to the increasing size between the independent variables (Zianis and Mencuccini, 2004).
Although fuel characterization and classification is a mathematical modelling (Alexander 2007), the differences in these models is generally due to the distinguishing features of individuals in nature, and the complex compositions that stem from structural and spatial distributions (Affleck et al., 2012; Fernandes, 2009). In addition, it is believed that the hazard, risk, and severity of forest fires are also associated with the ecological context, which includes components such as historical natural fire regimes, time, space, and process (Hardy 2005). For this reason, there is a need to simply and constantly renew and develop fuel classification approaches (Sandberg et al., 2001). Although fuel characterizations and classifications have great importance in fire behavior modelling, using them on their own is not sufficient for fire decision support systems. Especially in large administrative areas, fuel loads will not be unique due to the reasons mentioned above, and there is consequently a need for different fuel load standards rather than a single fuel load standard. In decision-making processes for fire

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management, it is important to take into account standards that will include factors other than fuel appraisal (Alexander, 2007). In addition to its variable topography and forest structures, the Mediterranean region of Turkey is also seeing a considerable demand for tourism, agriculture, and settlement. For this reason, fuel-loading studies should be enriched by also taking human intervention, canopy structures, and site conditions into account. Doing so will allow forest fire behavior analyses to be more effective, thus enabling decision-makers dealing with forest fires in the region to obtain far more accurate data and results.
This study has been supported by the General Directorate of Forestry connected to The Turkish Ministry of Forestry and Water Affairs as a research-development project with number: 19.9200/2012-2014-2015.
Affleck, D.L.R., C.R. Keyes, J.M. Goodburn, 2012. Conifer crown fuel modeling: current limits and potential for improvement. Western Journal of Applied Forestry 27: 165-169.
Alexander, M. 2007. Simple question; difficult answer: how much fuel is acceptable? Fire Management Today 67: 6–11.
Annighöfer P., A. Ameztegui, C. Ammer, P. Balandier, N. Bartsch, A. Bolte, L. Coll, C. Collet, J. Ewald, N. Frischbier, T. Gebereyesus, J. Haase, T. Hamm, B. Hirschfelder, F. Huth, G. Kändler, A. Kahl, H. Kawaletz, C. Kuehne, A. Lacointe, N. Lin, M. Löf, P. Malagoli, A. Marquier, S. Müller, S. Promberger, D. Provendier, H. Röhle, J. Sathornkich, P. Schall, M. Scherer-Lorenzen, J. Schröder, C. Seele, J. Weidig, C. Wirth, H. Wolf, J. Wollmerstädt, M. Mund, 2016. Species-specific and generic biomass equations for seedlings and saplings of European tree species. European Journal of Forest Research 135: 313-329.
Bond-Lamberty, B., C. Wang, S. Gower, 2002. Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba. Canadian Journal of Forest Research 32: 1441-1450.
Boydak, M., H. Dirik, M. Çalıkoğlu, 2006. Biology and silviculture of Turkish red pine (Pinus brutia Ten.). OGEM-VAK, Ankara, 253 p.
Chave, J., C. Andalo, S. Brown, M.A. Cairns, J.Q. Chambers, D. Eamus, H. Folster, F. Fromard, N. Higuchi, T. Kira, J.P. Lescure, B.W. Nelson, H. Ogawa, H. Puig, B. Riera, T. Yamakura, 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87–99.
Cruz, M.G., M.E. Alexander, R.H. Wakimoto, 2003. Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire 12: 39–50.
Dimitrakopoulos, A.P. 2002. Mediterranean fuel models and potential fire behaviour in Greece. International Journal of Wildland Fire 11: 127–130.
Fernandes, P.M., 2009. Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Annals of Forest Science 66: 415p9.
Gray K.L, E. Reinhardt., (2003) 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, November 16–20, 2003, Orlando, FL’. Paper P5.8. (American Meteorological Society: Boston, MA)
Hardy, C.C., 2005. Wildland fire hazard and risk: Problems, definitions, and context. Forest Ecology and Management 211: 73–82.
Hiers, J. K., J.J. O’Brien, R.J. Mitchell, J.M. Grego, E.L. Loudermilk, 2009. The wildland fuel cell concept: an approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests. International Journal of Wildland Fire 18: 315–325.
Iyigun C., M. Türkeş, İ. Batmaz, C. Yozgatligil, V. Purutçuoğlu, E. Kartal Koç, MZ Öztürk. 2013. Clustering current climate regions of Turkey by using a multivariate statistical method. Theoretical and Applied Climatology 114: 95–106.
Jiménez, E., J. A. Vega, J. M. Fernandez-Alonso, D. Vega-Nieva, J. G. Alvarez-Gonzalez, and A. D. Ruiz-Gonzalez. 2013. Allometric equations for estimating canopy fuel load and distribution of polesize maritime pine trees in five Iberian provenances. Canadian Journal of Forest Research 43:149-158.
Küçük, Ö., E. Bilgili, 2008. Crown fuel characteristics and fuel load estimates in young calabrian pine (Pinus brutia Ten.) stands in northwestern of Turkey. Fresenius Environmental Bulletin 17, 2226-2231.
Küçük, Ö., E. Bilgili, B. Sağlam, 2008. Estimating crown fuel loading for calabrian pine and Anatolian black pine. International Journal of Wildland Fire 17: 147–154.
Martínez, J,, E. Chuvieco, P. Martín, 2008. Estimation of risk factors of human ignition of fires in Spain by means of logistic regression. González-Cabán, Armando eds. Proceedings of the second international symposium on fire economics, planning, and policy: a global view. Gen. Tech. Rep. PSW-GTR-208. (Albany, CA). Consulted 12 May 2017. Available in
Mascaro, J., C.M. Litton, F.R. Hughes, A. Uowolo, S.A. Schnitzer, 2011. Minimizing bias in biomass allometry: model selection and log-transformation of data. Biotropica 43:649–653.
Matthews, S., 2010. Effect of drying temperature on fuel moisture content Measurements. International Journal of Wildland Fire, 19: 800–802.
Mitsopoulos, I.D., A.P. Dimitrakopoulos, 2007a. Allometric equations for crown fuel biomass of Aleppo pine (Pinus halepensis Mill.) in Greece. International Journal of Wildland Fire 16: 642–647.
Mitsopoulos, I.D., A.P. Dimitrakopoulos, 2007b. Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests. Annals of Forest Science 64: 287–299.
Molina, J.R., F.R. Silva, E. Mérida, M.A. Herrera, 2014. Modelling available crown fuel for Pinus pinaster Ait. stands in the “Cazorla, Segura and Las Villas Natural Park” (Spain). Journal of Environmental Management 144: 26-33.
OGM, 2013. Orman Atlası. Orman Genel Müdürlüğü (​General Directorate of Forestry). Ankara. Consulted 30 June 2016.

ŠUMARSKI LIST 11-12/2018 str. 52     <-- 52 -->        PDF visited on 14.12.2017
OGM, 2015. Türkiye Orman Varlığı. Orman İdaresi ve Planlama Dairesi Başkanlığı Yayın No. 115. Ankara. Consulted 15 May 2017. visited on 15.12.2017
Poudel, K.P., H. Temesgen. 2015. Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees. Canadian Journal of Forest Research 46: 77–87
Robichaud, E, IR Methven, 1992. The applicability of the pipe model theory for the prediction of foliage biomass in trees from natural, untreated black spruce stands. Canadian Journal of Forest Research 22: 1118–1123.
Sandberg, D.V., R.D. Ottmar, G.H. Cushon, 2001. Characterizing fuels in the 21st century. International Journal of Wildland Fire 10: 381-387.
Scott J.H., E.D. Reinhardt, 2002. Estimating canopy fuels in conifer forests. Fire Management Today 62: 45–50.
Singh, V., A. Tewari, S.P.S. Kushwaha, V.K. Dadhwal, 2011. Formulating allometric equations for estimating biomass and carbon stock in small diameter trees. Forest Ecology and Management 261 (11): 1945-1949.
Socha, J., P. Wezyk, 2007. Allometric equations for estimating the foliage biomass of Scots pine. European Journal of Forest Research 126, 263–270.
Stocks, B.J., M.E. Alexander, B.M. Wotton, C.N. Stefner, M.D. Flannigan, T.N. Lavoie, J.A. Mason, G.R. Hartley, M.E. Maffey, G.N. Dalrymple, T.W. Blake, M.G. Cruz, R.A. Lanoville, 2004. Crown fire behavior in a northern jack pine-black spruce forest. Canadian Journal of Forest Research 34: 1548–1560.
Tampakis, S., A. Papageorgiou, P. Karanikola, G. Arabatzis, G. Tsantopoulos, 2005. The forest fires in the Mediterranean from a policy point of view. New Medit, Mediterranean Journal of Economics, Agriculture, and Environment 4: 47-51.
Vasconcelos ,M.J., S. Silva, M. Tome, M. Alvim, J.C. Pereira, 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric engineering and remote sensing 67: 73-81.
Viedma, O., J.M. Moreno, C. Güngöroglu, U. Cosgun, A. Kavgaci, 2017. Recent land-use and land-cover changes and its driving factors in a fire-prone area of southwestern Turkey. Journal of Environmental Management 197: 1-13
Zianis, D., M. Mencuccini, 2004. On simplifying allometric analyses of forest biomass. Forest Ecology and Management 187: 311–332.
Zianis, D., G. Xanthopoulos, K. Kalabokidis, G. Kazakis, D. Ghosn, O. Roussou, 2011. Allometric equations for aboveground biomass estimation by size class for Pinus brutia Ten. trees growing in North and South Aegean Islands, Greece. European Journal of Forest Research 130: 145–160
Točna procjena količine goriva je važan preduvjet za učinkovito upravljanje šumskim požarima. Ovo je istraživanje imalo za cilj razviti empirijske alometrijske jednadžbe za procjenu količine goriva iz krošnji stabala kalabrijskog bora (Pinus brutia Ten.) u jugozapadnom mediteranskom području Turske korištenjem dendrometrijskih varijabli. Destruktivnom metodom uzorkovana su 84 stabla. Uzorci grana u gorivu iz biomase krošnji klasificirani su kao iglice i kao grane prema sljedećem rasponu promjera: vrlo tanke (≤0,3 cm), tanke (0,31–0,6 cm), srednje (0,61–1,0 cm), debele (1,01–2,5 cm) i aktivno gorivo. Za procjenu biomase krošnjanja korištene su prsne visine, visine stabala, visine krošnji i širine krošnji kao neovisne varijable. Za analizu odnosa između goriva iz biomase i značajki uzorkovanih stabala korišteni su stepwise funkcija i modeli logaritamske linearne regresije. Od svih dobivenih alometrijskih jednadžbi, varijacija u tankim granama najbolje je objašnjena širinom krošnji i visinom krošnji, koje zajedno objašnjavaju R2adj od 90.2 varijacije u tankim granama. Varijacija u vrlo tankim granama objašnjena je najslabije visinom stabla, koja samo objašnjava R2adj od 60.4% varijacije u vrlo tankim granama. Ukupna količina goriva iz krošnji kalabrijskog bora u ovom istraživanju u usporedbi s istraživanjima u Grčkoj i Turskoj pokazuje da se gorivo iz biomase kalabrijskog bora može razlikovati od regije do regije.
Ključne riječi; biomasa krošnje, šumsko gorivo, kalabrijski bor, mediteranska područja