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## Development of an allometric model to estimate above-ground biomass of forests using MLPNN algorithm, case study: Hyrcanian forests of Iran | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Caspian Journal of Environmental Sciences | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مقاله 4، دوره 14، شماره 2، شهریور 2016، صفحه 125-137 اصل مقاله (830.85 K)
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نوع مقاله: Research Paper | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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A. Sharifi؛ J. Amini؛ F. Pourshakouri | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{}University of Tehran | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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This research develops an allometric model for estimation of biomass based on the height and DBH of trees in the Hyrcanian forests of Iran. An accurate allometric model reduces the uncertainty of allometric equation in biomass estimation using radar images. In this study, 317 trees were selected randomly from the 4 different dominant tree species for the development of an allometric model covering the wide range of DBH and height classes. The selected trees were measured by fieldwork in different parts and then volumes of these parts were calculated separately. Total volume of tree is obtained by the summation of these volumes. Twelve commonly used allometric models, three generalized models and a proposed model were tested and the most suitable model was selected based on some of the commonly measured statistical parameters including coefficient of determination (R^{2}), Root-Mean-Square Error (RMSE) and Mean Error (ME). We showed that the biomass estimation accuracy was improved in a multilayer perceptron neural network (MLPNN) when density of wood, DBH and height were used in combination compared to estimating the biomass by current allometric models. The RMSE value was decreased when the proposed method was used (RMSE =0.163 Mg and R^{2}=0.986) in comparison with Chave model, as the best current method (RMSE =0.404 Mg and R^{2}=0.957) in this paper. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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Allometric model؛ Biomass؛ DBH؛ Height؛ Hyrcanian forests؛ MLPNN | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

اصل مقاله | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

. Pourshakouri^{2}
(Received: Feb. 02. 2016 Accepted: May. 10. 2016)
This research develops an allometric model for estimation of biomass based on the height and DBH of trees in the Hyrcanian forests of Iran. An accurate allometric model reduces the uncertainty of allometric equation in biomass estimation using radar images. In this study, 317 trees were selected randomly from the 4 different dominant tree species for the development of an allometric model covering the wide range of DBH and height classes. The selected trees were measured by fieldwork in different parts and then volumes of these parts were calculated separately. Total volume of tree is obtained by the summation of these volumes. Twelve commonly used allometric models, three generalized models and a proposed model were tested and the most suitable model was selected based on some of the commonly measured statistical parameters including coefficient of determination (R
Measurement of Above-Ground Biomass (AGB) is necessary for quantifying carbon and biomass storages and also for comparing the result of remotely sensed methods in biomass estimation (Chave 2005). The methods of biomass estimation can be divided into two groups i.e. direct and indirect (Overman Many studies have already developed allometric equations for different purposes, different regions, and different species, for example species-specific allometric models (Saint-Andre Some studies (Brown This paper has two objectives: 1) Development of an allometric model based on the dominant tree species in Hyrcanian forest of Iran in order to reduction of the uncertainties from generalized allometric equations, and 2) Improve the accuracy of estimated biomass using a multilayer perceptron neural network (MLPNN).
The study area is located at Hyrcanian forests of Iran around the Asalem forest (Fig. 1). Hyrcanian forests of Iran are high forest and are managed using selection system method. The natural forest vegetation is temperate deciduous broadleaved forest that the main dominant trees of this forest are
Samples were randomly selected because the exact volume of the trees should be calculated. In some diameter classes specially the lower one, there were not sufficient cut trees in order to be used for sampling from the diameter classes. Hence completely random method was used instead of diagonal and height classes for sampling. Regarding total cost of inventory, 317 trees were selected. Trees were retrieved in the nature and desired characteristics were measured (Height and DBH) for this study. Minimum error and inventory costs are two determinant factors in samples components numbers (West 2009). Field data collection was based on a stratiﬁed sampling methodology. Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata. It is important to note that the strata must be non-overlapping. Having overlapping subgroups will grant some individuals higher chances of being selected as subject. This completely negates the concept of stratified sampling as a type of probability sampling. Trees were measured for each tree species in order to get a desired precision level (in this case, an error level of 10% expressed as the 95% conﬁdence interval of the mean). For determination of total volume calculation of trunk over 20 cm diameters, firewood and stump volumes are necessary. Total volume of tree is obtained from the summation of these volumes. Volume of Trunks and branches were calculated using Smalian formula in 2 m pieces (Eq. 1):
where ^{3}), l is piece length (m), and d and _{L}d are diagonals diameter of trunk (m_{U}^{3}) at the beginning and the end of 2m piece, respectively.Firewood volume of branches was divided into 1m pieces and their diameter was measured in the middle, then the volume of each branch was calculated by Huber formula (Eq.2):
where l is piece length, and d is diagonal middle diameter of branch._{M}Stump volume was calculated from another form of Smalian formula (Eq. 3) as follows:
where H is piece length, and d is diagonal diameter of tree in cut location. Finally total volume is the sum of trunk, firewood and stump volumes (West 2009)._{L}
In this paper forest volume data for calculation of biomass is used. Required data for this method is the volume for sample trees that was determined in section 2.3. AGB in megagram (Mg) per hectare (ha) is estimated by Eq. 4.
where AG If we couldn’t calculate volume, another way was estimation of volume by multiplying trunk volume (V Wood density is defined as the mass of dry wood per green wood volume unit. Its unit is Mg per m
where Wood densities in 12% moisture for
The relationship between the physical parameters (DBH or/and height) and the AGB of all sampled trees needed to be established in order to estimate the AGB of non-harvested trees. Although there are several empirical methods available, this study established this relationship using allometric equations because an allometric model is a useful tool which can approximate the AGB of single trees according to easily measured variables, such as diameter at breast height (DBH) or height (H) (Brown 1997(. The most common allometric model in biomass studies is the power function (Brown, 1997) as follows:
where
This transformation is appropriate when the standard deviation of Different types of regression models and combinations of parameters have been used including ordinary least squares on log-transformed data (Overman However, apparently there is no single optimal regression model that can give a good calibration function for the estimation of AGB because the values of coefficients are varied based on many factors (Ketterings Three distinct types of layers are present in the MLPNN. The input layer is not itself a processing layer but is simply a set of neurons acting as source nodes which supply input feature vector components to the second layer. Typically, the number of neurons in the input layer is equal to the dimensionality of the input feature vector. Then, there is one or more hidden layers, each of these layers comprising a given number of neurons called hidden neurons. Finally, the output layer provides the response of neural network to the pattern vector submitted in the input layer. The number of neurons in this layer corresponds to the number of classes that the neural network should differentiate (Haykin 1999). The neural network that is used in this paper is arranged in layers as follows. The number of neurons in the output layer is taken to be equal to the estimated biomass. The input layer contains three neurons corresponding to the number of attributes in the input vectors. The input vector to the network for pixel ν, _{i1}ν, _{i2}ν}, where _{i3}v belongs to the height, _{i1}vbelongs to DBH, and _{i2 }v belongs to wood density._{i3}After the determination of the input layer, the number of hidden layers required, as well as the number of neurons in these layers, still needs to be decided upon. An important result, established by the Russian mathematician Kolmogorov in the 1950s, states that any discriminate function can be derived by a three-layer feed forward neural network (Haykin 1999). Increasing the number of hidden layers can then improve the accuracy of the fitting model, picking up some special requirements of the recognition procedure during the training, or enabling a practical implementation of the network. However, a network with more than one hidden layer is more prone to be poorly trained than one with only one hidden layer. Thus, a three-layer neural network with the structure 3-2-1 (three input neurons, two hidden neurons and one output neurons) is used to fit a model to the data sets.Training the neural network involves tuning all the synaptic weights so that the network learns to recognize the given patterns or classes of samples sharing similar properties. The learning stage is critical for effective modeling, and the success of an approach by neural networks depends mainly on this phase.
† Diameter at breast height ‡ Above-ground biomass
Testing the goodness of fit of each model is very important in order to find the most suitable model for AGB estimation. The statistics of accuracy assessment included the Root-Mean-Square Error (RMSE), and the relative errors to the mean value of AGB. The value of the RMSE is affected by large errors which give disproportionately large weights because of the squaring process. The ME is a signed measure of error which indicates whether the predicted AGB is biased. The predicted AGB is underestimated (UE) with a negative ME and overestimated (OE) with a positive ME. Additionally, the coefficient of determination (R2) was calculated as the square of Pearson’s correlation coefficient.
The correlations coefficient (r) between DBH, and height with AGB were 0.93 and 0.86, respectively (Fig. 2). Thus using these parameters together for modeling may lead to better result. The results of models are shown in Table 2. The simple regression models (models 1, 2 & 3) were not found to be suitable. The power-function models (models 4, 5 & 6) displayed very good performances. The log-transformed models (models 7, 8 & 9) were found to be effective for AGB measurement because of the fact that log-transform has the potential to correct for the heterogeneous variance of AGB. The methods of Brown Although we achieved better result than these models when we used sample data of north of Iran for calibration of coefficients of these equations (models 11, 13 & 15, respectively). From the 15 models tested, model 15, or the calibrated Chave The neural network is trained by using a back-propagation rule (Paola 1995). The numbers of training data are 222 samples (70% of all samples) with their wood density. The set of training patterns is presented repeatedly to the neural network until it has learned to recognize them. A training pattern is said to have been learned when the absolute difference between the output of each output neuron and its desired value is less than a given threshold. Indeed, it is pointless to train the network to reach the target outputs of zero or one since the sigmoid function never attains its minimum and maximum. The network is trained when all training patterns have been learned. Once the network is trained, the weights of the network are applied on the data sets to fitting model. The result of the neural network is shown in Table 2 in comparison with current models. For accuracy assessment and calibration, 95 samples (30% of samples) were selected as the test samples, randomly. The values 98.6% and 0.163 Mg are achieved for R Fig. 3 indicates that density of points around identity line ( Among models based on the power function, the highest R As shown in Table 2, although this model uses both DBH and height parameter but has the lowest R In addition to R Table 2 shows errors reduction in model 11 compared to model 10. As Fig. 3 exhibits, density of points around identity line is low in model 10. After calibration of coefficients and producing model 11 the density of points is highly increased. Results indicate that with the coefficients calibration of the Brown Therefore, measurement of trees height, DBH and coefficients calibration suited to local species leads to highest accuracy for ground biomass estimation by Brown
Among the models based on logarithmic transformation, the highest R As Fig. 3 illustrates, the density of points along the identity line in model 9 is better than models 7 & 8.
Model 14 demonstrated in Table 2 is the Chave By applying this model the uncertainty of allometric equation in biomass estimation by radar images can be greatly reduced, because the main reference of ground forest biomass estimation for remote sensing investigations is allometric relations. Finally, the considered model of this study was implemented using the MLPNN. Model 16 in Table 2 is developed based on neural networks. This model leads to more accurate result than current methods with highest R
Overall, Hyrcanian forests of Iran are the temperate deciduous broadleaved forests which must be met through scientific research aimed at reducing carbon emissions through a better land use/land cover management. Therefore, an accurate and spatially explicit AGB of the forest cover of these forests is paramount if carbon stocks and respective changes over time are to be quantiﬁed and assessed. It is often difficult to transfer a developed model of a specific study area to another due to many factors, such as tree species, stand age, site quality, climate, and the stocking of stands, which could affect the success of model transferability. This study aimed at modeling a novel allometric model from ﬁeld data. Many different modeling approaches were tested and a proposed model was selected for biomass estimation. We have shown that the biomass estimation accuracy was improved when MLPNN was used in comparison with estimating biomass using the generalized allometric models and no need calibration. The proposed methods were assessed and resulting a RMSE of 0.163 Mg and coefficient of determination between observed and predicted AGB values of 0.986. However, accuracy of model using the wide range of tree species for a regional context would be better in future research. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مراجع | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Amini, J & Sumantyo, JTS 2009, Employing a Method on SAR and Optical Images for Forest Biomass Estimation. IEEE Transactions on image Processing. 47(12): 4020-4026.
Araujo, TM, Hnguchn, N & Junior JAC 1999, Comparison of formulae for biomass content determination in a tropical ram forest site in the state of Para. Brazil Forest Ecology and Management, 117: 43-52.
Ares, A & Fownes, JH 2000, Comparisons between generalized and specific tree biomass functions as applied to tropical ash ( Fraxinus uhdei). New Forests, 20: 277-286.
Arevalo, CBM, Volk, TA, Bevilacqua, E & Abrahamson, L 2007, Development and validation of aboveground biomass estimations for four Salix clones in central New York. Biomass and Bioenergy, 31: 1-12.
Bergès, L, Nepveu, G & Franc, A 2008, Eﬀects of ecological factors on radial growth and wood density components of sessile oak (Quercus petraea Liebl.) in Northern France. Forest Ecology and Management , 255: 567–579.
Brown, S 1997, Estimating biomass and Biomass Change of Tropical Forests a Primer, FAO Forestry Paper - 134, FAO - Food and Agriculture Organization of the United Nations, Rome, 1997.
Brown, S, Gillespie, AJR & Lugo, AE 1989, Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data. Forest Science, 35: 881-902.
Brown, S & Lugo, E 1992, Above Ground Biomass Estimation for Tropical moist forests of the Brazilian Amazon. Interciencia, 17: 8-18.
Carreiras, JM, Vasconcelos, MJ & Lucas, RM 2012, Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment, 121: 426–442.
Chave, J, Andalo, C, Brown, S, Cairns, M, Chambers, J & Eamus, D 2005, Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145: 87–99.
Cole, TG & Ewel, JJ 2006, Allometric equations for four valuable tropical tree species. Forest Ecology and Management, 229: 351-360.
Crow, TR 1978, Common Regression to Estimate Tree Biomass in Tropical Stands. Forest Science, 24: 110-114.
Djomo, AN, Ibrahima, A, Saborowski, J & Gravenhorst, G, 2010, Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. Forest Ecology and Management, 260: 1873–1885.
Ebuy, J, Lokombé Dimandja, JP, Ponette, Q, Sonwa, D & Picard, N 2011, Biomass equation for predicting tree aboveground biomass at Yangambi, DRC. J. Trop. For. Sci., 23: 125–132.
Englhart, S, Keuck, V & Siegert, F 2011, aboveground biomass retrieval in tropical forests — the potential of combined X- and L-band SAR data use. Remote Sensing of Environment, 115: 1260-1271.
Englhart, S, Keuck, V & Siegert, F, 2012, Modeling Aboveground Biomass in Tropical Forests Using Multi-Frequency SAR Data—A Comparison of Methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5: 298−306.
Fang, JY, Wang GG, Liu GH & Xu SL 1998, Forest biomass of China an estimate based on the biomass volume relationship. Ecological Applications, 8: 1084-1091.
Fang, I & Wang, ZM 2001, Forest biomass estimation at regional and global levels, with special reference to China's forest biomass. Ecological Research, 16: 587-592.
Fransson, JES & lsraelsson, H, 1999, Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR. International Journal of Remote Sensing, 20: 123-137.
Genet, A, Wernsdörfer, H, Jonard, M, Pretzsch, H, Rauch, M, Ponette, Q, Nys, C, Legout, A, Ranger, J, Vallet, P & Saint-André, L 2011, Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe. Forest Ecology and Management, 261(7): 1188–1202.
Haykin, S 1999, Neural Networks: A Comprehensive Foundation, 2 ^{nd} ed. Upper Saddle River, NJ: Prentice-Hall.
Henry, M, Picard, N, Trotta, C, Manlay, R, Valentini, R, Bernoux, M & Saint-André, L, 2011, Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fennica, 45: 477–569.
Iranmanesh, Y, Jalali, SGA, Sagheb-Talebi, Kh, Hosseini, SM & H, Sohrabi 2012, Allometric equations of biomass and carbon stocks for Quercus brantti acorn and its nutrition elements in Lordegan, ChaharmahalVaBakhtiari. Iranian Journal of Forest and Poplar Research, 20(4): 551-564.
Ketterings, QM, Coe, R, van Noordwijk, M, Ambagau, Y & Palm, CA, 2001, Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass an maxed secondary forests. Forest Ecology and Management. 146: 199-209.
Kiaei, M & Samariha, A 2011, Fiber dimensions, physical and mechanical properties of five important hardwood plants. Indian Journal of Science and Technology, 11: 1460-1463.
KohI, M, Magnussen, S & Marchetti, M, 2006, Sampling Methods, Remote Sensing and GlS Multiresource Forest Inventory, Springer Berlin Heidelberg, Germany.
Montagu, KD, Duttmer, K, Barton, CVM & Cowie, AL 2005, developing general allometric relationships for regional estimates of carbon sequestration - an example using Eucalyptus pilularis from seven contrasting sites. Forest Ecology and Management, 204: 113-127.
Murali, KS, Bhat, DM & Ravindranath, NH, 2005, Biomass estimation equations for tropical deciduous and evergreen forests. International Journal of Agricultural Resources Governance and Ecology. 4: 329-338.
Niklas, KJ, 2006, A phyletic perspective on the algometry of plant biomass partitioning patterns and functionally equivalent organ-categories. New Phytologist. . 171: 27-40.
Overman, JPM, Watt, HJL & Saldarriaga, JG, 1994, Evaluation of Regression Models for Above-Ground Biomass Determination an Amazon Rainforest. Journal of Tropical Ecology, 10: 207-218.
Paola, J & Schowengerdt, RA 1995, A review and analysis of back propagation neural networks for classiﬁcation of remotely-sensed multispectral imagery. International Journal of Remote Sensing, 16: 3033–3058.
Parsapour, MK, Sohrabi, H, Soltani, A, andIranmanesh, Y 2013, Allometric equations for estimating biomass in four poplar species at Charmahal and Bakhtiari province. Iranian Journal of Forest and Poplar Research, 21: 517-528.
Picard, N, Henry, M, Mortier, F, Trotta, C & Saint-André, L 2012, Using Bayesian model averaging to predict tree aboveground biomass. Forest Science, 58: 15–23.
Rauste, Y, Hame, T, Pulliainen, J, Heiska, K & Hallikainen, M, 1994, Radar-based forest biomass estimation. International Journal of Remote Sensing, 15: 2797-2808.
Reyes, G, Brown, S, Chapman, J & Lugo, AE 1992, Wood densities of Tropical Tree Species, USDA Forest Service. General Technical Report SO-88, Southern Forest Experiment Station. New Orleans, Louisiana, USA.
Saatchi, S, Marlier, M, Chazdon, RL, Clark, DB & Russell, AE 2011, Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass. Remote Sensing of Environment, 115: 2836-2849.
Saint-Andre, L, M'Bou, AT, Mabiala, A, Mouvondy, W, Jourdan, C, Roupsard, O, Deleporte, P, Hamel, O & Nouvellon, Y, 2004, Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo. Forest Ecology and Management, 205: 199-214.
Segura, M & Kanninen, M 2005, Allometric Models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica. Biotropica, 37: 2-8.
Sohrabi, H & Shirvani, A 2012, Allometric equations for estimating standing biomass of Atlantic Pistache (Pistacia atlantica var. mutica) in Khojir National Park. Iranian Journal of Forest, 4: 55-64.
Steininger, MK 2000, Satellite estimation of tropical secondary forest aboveground biomass data from Brazil and Bolivia. International Journal of Remote Sensing, 21: 1139-1157.
Vahedi, AA, Metaji, A, Babaei-Kafaei, S, Eshaghi-Rad, J & Hojjati, M 2013, Modeling the bole mass of beech (Fagus Orientalis Lipsky) through allometric equations within Hyrcanian forests. Iranian Journal of Forest, 5: 309-322.
Van Breugel, M, Ransijn, J, Craven, D, Bongers, F & Hall, JS 2011, Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models. Forest Ecology and Management . 262(8): 1648–1657.
West, PW 2009, Tree and Forest Measurement. Springer Publications.
Zheng, D, Radernacher, J, Chen, I, Crow, T, Bresee, M, LE Moine, J & Ryu, S 2004, Estimating aboveground biomass using Landsat-7 ETM+ data across a managed landscape in northern Wisconsin. Remote Sensing of Environment, 93: 402-411.
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