Information on growing stock is important for understanding health assessment, environmental analysis, carbon storage estimation, and economic analysis of urban forest. The stand volume estimation enables the calculation of ecosystemic services value and growth stock of urban forests. However, most of volume models fitted for multiple species in tropical forests may not be suitable for urban trees. This study was conducted to develop generic volume models for urban trees in Abomey-Calavi at the southern Benin. A total of 1608 trees belonging to 80 plant species were measured for their diameter at breast height (DBH), stem height (h) and stem volume using non-destructive sampling methods. Using a nonlinear procedure, six volume models were constructed. Cross validation and Fit statistics like standard error of estimate (SEE), relative absolute error (RAE), root mean square error (RMSE), fit index (FI), Akaike information criterion (AIC) and Willmott’s agreement index (dw) were used to evaluate the efficiency and stability of different models. The six generic volume models developed in this study included both diameter and height. These models exhibited an absence of multicollinearity, with normal and homoscedastic residuals. Furthermore, they show high efficiency (IF > 0.997) and reduce of prediction errors (RMSE: 0.05388–0.06629 m3; RAE: 0.05186–0.06952), which ensuring stability in the estimates. However, the Model II was the best for predicting the stem volume of urban tree according to evaluation statistics and rank analysis. The models developed can provide stem volumes prediction with accurate estimations. Though, stem heights should be systematically measured. These models can contribute to assess the productivity of urban forests in order to pursue their sustainable management and planning.
| Published in | American Journal of Biological and Environmental Statistics (Volume 7, Issue 4) |
| DOI | 10.11648/j.ajbes.20210704.16 |
| Page(s) | 111-120 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Stem Volume Equation, Urban Forest, Forest Productivity, Sustainable Management, Benin
| [1] | Pataki, D. E. (2013). Urban greening needs better data. Nature, 502 (7473), 624–624. doi: 10.1038/502624d. |
| [2] | Breuste, J., Qureshi, S. & Li, J. (2013). Applied urban ecology for sustainable urban environment. Urban Ecosystems, 16 (4), 675–680. doi: 10.1007/s11252-013-0337-9 |
| [3] | Wu, Z., Chen, R., Meadows, E. M., Sengupta, D. & Xu, D. (2019). Changing urban green spaces in Shanghai: trends, drivers and policy implications. Land Use Policy, 87, 104080. doi: 10.1016/j.landusepol.2019.104080. |
| [4] | Kim, H-J. & Lee, S-H. (2016). Developing the volume models for 5 major species of street trees in Gwangju metropolitan city of Korea. Urban Forestry & Urban Greening, 18, 53–58. doi: 10.1016/j.ufug.2016.05.004. |
| [5] | Park, J. H., Baek, S. G., Kwon, M. Y., Je, S. M. & Woo, S. Y. (2018) Volumetric equation development and carbon storage estimation of urban forest in Daejeon, Korea. Forest Science and Technology, 14 (2), 97–104. doi: 10.1080/21580103.2018.1452799. |
| [6] | Burkhart, H. E. & Tomé, M. (2012). Modeling forest trees and stands. Springer, Dordrecht, Netherlands, p. 458. doi: 10.1007/978-90-481-3170-9. |
| [7] | Gschwantner, T., Alberdi, I., Balázs, A., Bauwens, S., Bender, S., Borota, D., Bosela, M., Bouriaud, O., Cañellas, I., Donis, J., Freudenschub, A., Hervé, J. C., Hladnik, D., Jansons, J., Kolozs, L., Korhonen, K. T., Kucera, M., Kulbokas, G., Kuliesis, A., Lanz, A., Lejeune, P., Lind, T., Marin, G., Morneau, F., Nagy, D., Norden-Larsen, T., Nunes, L., Pantic, D., Paulo, J. A., Pikula, T., Redmond, J., Rego, F. C., Riedel, T., Saint-André, L., Seben, V., Sims, A., Skudnik, M., Solti, G., Tomter, S. M., Twomey, M. & Westerlund, B. (2019). Harmonisation of stem volume estimates in European National Forest Inventories. Annals of Forest Science, 76 (1), pp. 24. doi: 10.1007/s13595-019-0800-8. |
| [8] | Avery, T. E. & Burkhart, H. E. (2001). Forest Measurements. 5th Ed., McGraw-Hill Higher Education, New York, USA, p. 456. |
| [9] | Masiero, M., Pettenella, D., Boscolo, M., Barua, S. K., Animon, I. & Matta, R. (2019). Valuing forest ecosystem services: A training manual for planners and project developers. Food and Agriculture Organization (FAO) of the United Nations, Rome, Italia. Forest working paper 11, 1–220. |
| [10] | Likingurainey, K. L., Kayombo, J. C. & Kashindye, A. (2020). Comparative Study on Volume Estimation Using a Model with one and Model with two Independent Variables in Meru/USA Forest Plantation, Northern Tanzania. East African Journal of Forestry and Agroforestry, 2 (2), 54–63. doi: 10.37284/eajfa.2.2.236. |
| [11] | Shuaibu, B. R. & Alao, S. J. (2016). Multiple Linear Regression Tree Stem Volume Equations for the Estimation of Merchantable Volume of Azadirachta Indica (Neem Tree) in North-West Region of Nigeria. International Journal of Forestry and Horticulture (IJFH), Vol. 2, Issue 1, pp. 1–10. ISSN 2454-9487. |
| [12] | Akindele, O. S. & LeMay, M. V. (2016). Development of tree volume equations for common timber species in the tropical rain forest area of Nigeria. Forest Ecology and Management, 226, 41–48. doi: 10.1016/j.foreco.2006.01.022. |
| [13] | Husch, B., Beers, T. W. & Kershaw, Jr. J. A. (2002). Forest Mensuration. 4th ed., John Wiley and Sons, Inc., Hoboken, New Jersey, USA, p. 456. |
| [14] | Izekor, N. D. & Amiandamhen, O. S. (2011). Comparative Analysis on the trends in the volume of logs supplied to sawmill in Edo State, Nigeria. Nigerian Society for Experimental Biology Journal, Vol. 11, No. 3, 257–263. |
| [15] | Zhao, D., Kane, M., Teskey, R. & Markewitz, D. (2016). Modeling Aboveground Biomass Components and Volume-to-Weight Conversion Ratios for Loblolly Pine Trees. Forest Science, 62 (5), 463–473. doi: 10.5849/forsci.15-129. |
| [16] | Gereslassie, T., Workineh, A., Takele, G., Adem, M. & Berhe, L. (2019). Total Volume and Aboveground Biomass Models for Juniperus procera Plantation in Wondo Genet, Southern Ethiopia. Open Journal of Forestry, 9, 89–108. doi: 10.4236/ojf.2019.92004. |
| [17] | Vanclay, J. K. (1994). Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests. CAB International, Wallingford, UK, p. 312. |
| [18] | Adekunle, V. A. J. (2007). Non-linear regression models for timber volume estimation in natural forest ecosystem, Southwest Nigeria. Research Journal of Forestry, 1 (2), 40–54. |
| [19] | Nowak, D. J. (1994). Atmospheric Carbon dioxide reduction by Chicago’s urban forest, pp. 83–94. In: McPherson E. G., Nowak D. J., Rowntree R. A. editors. Chicago’s urban forest ecosystem: results of the Chicago Urban Forest Climate Project. General Technical Report NE-186. U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, Radnor, Pennsylvania, USA. |
| [20] | McHale, M. R., Burke, I. C., Lefsky, M. A., Peper, P. J. & McPherson, E. G. (2009). Urban forest biomass estimates: is it important to use allometric relationships developed specifically for urban trees?. Urban Ecosystems, 12 (1), 95–113. doi: 10.1007/s11252-009-0081-3. |
| [21] | Ngo, M. K. & Lum, S. (2018). Aboveground biomass estimation of tropical street trees. Journal of Urban Ecology, Vol. 4, No. 1, 1–6. doi: 10.1093/jue/jux020. |
| [22] | Kramer, P. J. & Kozlowski, T. T. (1979). Physiology of Woody Plants. 2nd ed., Academic Press, New York, USA, p. 811. |
| [23] | Vibrans, A. C., Moser, P., Oliveira, L. Z. & Maçaneiro, J. P. (2015). Generic and specific stem volume models for three subtropical forest types in southern Brazil. Annals of Forest Science, 72, 865-874. doi: 10.1007/s13595-015-0481-x. |
| [24] | Kora, H. S. A, Guendehou, S. G. H., Goussanou, C. A., Assogbadjo, A. E. & Sinsin B. (2019). Allometric equations from a non-destructive approach for biomass prediction in natural forest and plantation in West Africa. Southern Forests: a Journal of Forest Science, 81 (2), 111–l22. doi: 10.2989/20702620.2018.1512795. |
| [25] | Guendehou, G. H. S., Lehtonen, A., Moudachirou, M., Mäkipää, R. & Sinsin B. (2012). Stem biomass and volume models of selected tropical tree species in West Africa. Southern Forests: a Journal of Forest Science, 74 (2), 77–88. doi: 10.2989/20702620.2012.701432. |
| [26] | Eclou, I. S. B. (2014). Quantification of Woody Speices’ Carbon stock in the Natural Forest of Niaouli, Benin (West Africa). Master of Science, Faculty of Agronomic Sciences (FSA), University of Abomey-Calavi (UAC), Abomey-Calavi, Benin, p. 58. |
| [27] | Goussanou, C. A., Guendehou, S., Assogbadjo, A. E. & Sinsin B. (2018). Application of site-specific biomass models to quantify spatial distribution of stocks and historical emissions from deforestation in a tropical forest ecosystem. Journal of Forestry Research, 29 (1), 205–213. doi: 10.1007/s11676-017-0411-x. |
| [28] | Shater, Z., de-Miguel, S., Kraid, B., Pukkala, T. & Palahí, M. (2011). A growth and yield model for even-aged Pinus brutia Ten. stands in Syria. Annals of Forest Science, 68, 149–157. doi: 10.1007/s13595-011-0016-z. |
| [29] | National Institute of Statistic and Economic Analysis (2015). RGPH4: What to retain from the population in 2013? Service of Demographic studies, Cotonou, Benin. |
| [30] | Nowak, D. J., Crane, E. D., Stevens, J. C., Hoehn, R. E., Walton, J. T. & Bond, J. (2008). A Ground-Based Method of Assessing Urban Forest Structure and Ecosystem Services. Arboriculture & Urban Forestry, 34 (6), 347–358. |
| [31] | Sogbossi, S. E., Zakari, S. & Djego, G. J. (2020). Phytodiversity and Spatial Development of Urban Flora in Lokossa, Benin. International Journal of Natural Resource Ecology and Management, Vol. 5, No. 4, pp. 145-159. doi: 10.11648/j.ijnrem.20200504.12. |
| [32] | Cysneiros, V. C., Gaui, T. D., Silveira Filho, T. B., Pelissari, A. L., Machado, S. dA., De Carvalho, D. C., Moura, T. A. & Amorim, H. B. (2020). Tree volume modeling for forest types in the Atlantic Forest: generic and specific models. iForest, 13, 417–425. doi: 10.3832/ifor3495013. |
| [33] | Laman, T. G. (1995). Safety recommendations for climbing rain forest trees with “single rope technique”. Biotropica, 27 (3), 406–409. doi: 10.2307/2388928. |
| [34] | Gimenez, B. O., Santos, L. T., Gebara, J., Celes, C. H. S., Durgante, F. M., Lima, A. J. N., Santos, J. & Higuchi, N. (2017). Tree climbing techniques and volume equations for Eschweilera (Matá-Matá), a hyper dominant genus in the Amazon Forest. Forests, 8 (154), 1–11. doi: 10.3390/f8050154. |
| [35] | Food and Agriculture Organization of the United Nations (FAO), (2004). National forest inventory field manual: template. FAO Working Paper 94/E, Rome, Italia. |
| [36] | Castillo-López, A., Quiñonez-Barraza, G., Diéguez-Aranda, U. & Corral-Rivas, J. J. (2021). Compatible Taper and Volume Systems Based on Volume Ratio Models for Four Pine Species in Oaxaca Mexico. Forests, 12 (145), 1–15. doi: 10.3390/f12020145. |
| [37] | Loetsch, F., Zöhrer, F. & Haller, K. E. (1973). Forest inventory. Volume 2, BLV Verlagsgesellschaft, München Basel Wien, p. 469. |
| [38] | Avery, T. E. & Burkhart, H. E. (2015). Forest measurements. 5th ed., Waveland Press Inc., Long Grove, IL, USA, p. 456. |
| [39] | Seo, Y. O., Lumbres, R. I. C., Won, H. K., Jung, S. C. & Lee, Y. J. (2015). Evaluation and validation of stem volume models for Quercus glauca in the subtropical forest of Jeju Island, Korea. Journal of Ecology and Environment, 38 (4), 485–491. doi: 10.5141/ecoenv.2015.051. |
| [40] | Clutter, J. L., Fortson, J. C., Pienaar, L. V., Brister, G. H. & Bailey, R. L. (1983). Timber Management: A Quantitative Approach. John Wiley and Sons, New York, USA, p. 333. |
| [41] | Avery, T. E. & Burkhart H. E. (1994). Forest Measurements. 4th ed., McGraw-Hill College, New York, USA, p. 408. |
| [42] | Van Laar, A. & Akca, A. (2007). Forest Mensuration. Springer, Dordrecht, The Netherlands, p. 383. ISBN-13 978-1-4020-5990-2. |
| [43] | Hjelm, B. & Johansson, T. (2012). Volume equations for poplars growing on farmland in Sweden. Scandinavian. Journal of Forest Research, 27 (6), 561–566. doi: 10.1080/02827581.2012.679678. |
| [44] | Mate, R., Johansson, T. & Sitoe, A. (2015). Stem Volume Equations for Valuable Timber Species in Mozambique. Journal of Sustainable Forestry, 34, 787–806, doi: 10.1080/10549811.2015.1039043. |
| [45] | Tsega, M., Guadie, A., Teffera, L. Z., Belayneh, Y. & Niu, D. (2019). Development and validation of a stem volume equation for Cupressus lusitanica in Gergeda Forest, Ethiopia. Southern Forests: a Journal of Forest Science, 81 (1), 79–84. doi: 10.1080/02571862.2018.1512786. |
| [46] | Shahzad, K. M., Hussain, A. & Jiang L. (2019). A model form for stem taper and volume estimates of white birch (Betula platyphylla Sukaczev): a major commercial tree species of Northeast China. Canadian Journal of forest Research, 50 (3), 274–286. doi: 10.1139/cjfr-2019-0088. |
| [47] | Myers, R. H. (1990). Classical and modern regression with applications. 2nd ed., Duxbury Press, PWS-KENT, Boston, USA, p. 488. |
| [48] | R Core Team (2020). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. |
| [49] | Ritz, C. & Streibig, C. J. (2008). Nonlinear Regression with R. Springer Science+Business Media: LLC 233 Spring Street, New York, NY10013, USA, p. 144. doi: 10.1007/978-0-387-09616-2. |
| [50] | Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D. & Andreassian, V. (2013). Characterising performance of environmental models. Environmental Modelling and Software, 40, 1–20. doi: 10.1016/j.envsoft.2012.09.011. |
| [51] | Ojedokun, J. O., Ayoola, F. J. & Iyaniwura, J. O. (2016). On the Estimation of Parameters of Nonlinear Model in the Presence of Variance Homogeneity and Variance Heterogeneity. Pacific Journal of Science and Technology, Vol. 17, No. 1, pp. 58–67. |
| [52] | Özçelik, R. & Cao, Q. V. (2017). Evaluation of Fitting and Adjustment Methods for Taper and Volume Prediction of Black Pine in Turkey. Forest Science, 63 (4), 349–355. doi: 10.5849/FS.2016-067. |
| [53] | Poudel, K. P. & Cao, Q. V. (2013). Evaluation of methods to predict Weibull parameters for characterizing diameter distributions. Forest Science, 59 (2), 243–252. doi: 10.5849/forsci.12-001. |
| [54] | De Lima, R. B., Ferreira, R. L. C., da Silva, J. A. A., Alves, J. F. T. & de Oliveira, C. P. (2020a). Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison between Regression and Artificial Neural Networks. Journal of Sustainable Forestry, 1–19. doi: 10.1080/10549811.2020.1754241. |
| [55] | Adekunle, V. A. J., Nair, K. N., Srivastava, A. K. & Singh, N. K. (2013). Models and form factors for stand volume estimation in natural forest ecosystems: a case study of Katarniaghat Wildlife Sanctuary (KGWS), Bahraich District, India. Journal of Forestry Research, 24 (2), 217–226. doi: 10.1007/s11676-013-0347-8. |
| [56] | Hussain, A., Shahzad, M. K., He, P. & Jiang, L. (2020). Stem taper equations for three major conifer species of Northeast China. Scandinavian Journal of Forest Research, 35 (8), 562–576. doi: 10.1080/02827581.2020.1843703. |
| [57] | Willmott, C. J. (1981). On the validation of models. Physical Geography, 2 (2), 184–194. doi: 10.1080/02723646.1981.10642213. |
| [58] | McRoberts, R. E. & Westfall, J. A. (2014). Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science, 60 (1), 34–42. doi: 10.5849/forsci.12-141. |
| [59] | Oliveira, L. Z., Klitzke, A. R., Fantini, A. C., Uller, H. F., Correia, J. & Vibrans, A. C. (2018). Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest. Anais da Academia Brasileira de Ciências, 90 (4), 3729–3744. doi: 10.1590/0001-3765201820180111. |
| [60] | Mauya, E. W., Mugasha, W. A., Zahabu, E., Bollandsås, O. M. & Eid, T. (2014). Models for estimation of tree volume in the Miombo woodlands of Tanzania. Southern Forests: a Journal of Forest Science, 76 (4), 209–219. doi: 10.2989/20702620.2014.957594. |
| [61] | De Lima, R. B., Rutishauser, E., da Silva, J. A. A., Guedes, M. C., Herault, B., de Oliveira, C. P., da Silva, P. A., Sotta, E. D., Silva, D. A. dS. & Ferreira, R. L. C. (2020b). Accurate Estimation of Commercial Volume in Tropical Forests. Forest Science, 67 (1), 14–21. doi: 10.1093/forsci/fxaa032. |
| [62] | Hunter, O. M., Keller, M., Victoria, D. & Morton, C. D. (2013). Tree height and tropical forest biomass estimation. Biogeosciences Discussion, 10, 10491–10529. doi: 10.5194/bgd-10-10491-2013. |
| [63] | Feldpausch, T. R., Lloyd, J., Lewis, S. L., Brienen, R. W. J., Gloor, E., Monteagudo Mendoza, A., Lopez-Gonzalez, G., Banin, L., Abu Salim, K., Affum-Baffoe, K., Alexiades, M., Almeida, S., Amaral, I., Andrade, A., Aragão, L. E. O. C., Araujo Murakami, A., Arets, E. J. M. M., Arroyo, L., Aymard, C. G. A., Baker, T. R., Bànki, O. S., Berry, N. J., Cardozo, N., Chave, J., Comiskey, J. A., Alvarez, E., de Oliveira, A., Di Fiore, A., Djagbletey, G., Domingues, T. F., Erwin, T. L., Fearnside, P. M., França, M. B., Freitas, M. A., Higuchi, N., Honorio, C. E., Iida, Y., Jimènez, E., Kassim, A. R., Killeen, T. J., Laurance, W. F., Lovett, J. C., Malhi, Y., Marimon, B. S., Marimon-Junior, B. H., Lenza, E., Marshall, A. R., Mendoza, C., Metcalfe, D. J., Mitchard, E. T. A., Neill, D. A., Nelson, B. W., Nilus, R., Nogueira, E. M., Parada, A., Peh, K. S.-H., Pena Cruz, A., Peñuela, M. C., Pitman, N. C. A., Prieto, A., Quesada, C. A., Ramìrez, F., Ramìrez-Angulo, H., Reitsma, J. M., Rudas, A., Saiz, G., Salomão, R. P., Schwarz, M., Silva, N., Silva-Espejo, J. E., Silveira, M., Sonké, B., Stropp, J., Taedoumg, H. E., Tan, S., ter Steege, H., Terborgh, J., Torello-Raventos, M., van der Heijden, G. M. F., Vàsquez, R., Vilanova, E., Vos, V. A., White, L., Willcock, S., Woell, H. & Phillips, O. L. (2012). Tree height integrated into pan-tropical forest biomass estimates. Biogeosciences, 9, 3381–3403. doi: 10.5194/bg-9-3381-2012. |
| [64] | Mensah, S., Veldtman, R. & Seifert, T. (2017). Allometric models for height and aboveground biomass of dominant tree species in South African Mistbelt forests. Southern Forests: a Journal of Forest Science, 79 (1), 19–30. doi: 10.2989/20702620.2016.1225187. |
| [65] | Clark, M. L., Clark, D. B. & Roberts, D. A. (2004). Small footprint Lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment, 91 (1), 68–89. doi: 10.1016/j.rse.2004.02.008. |
| [66] | Tesfamichael G. S., van Aardt N. A. J., Ahmed F. (2010). Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data. Progress in Physical Geography, 34 (4), 515–540. doi: 10.1177/0309133310365596. |
| [67] | Brown, S., Gillespie, A. J. R. & Lugo, A. E. (1989). Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science, 35 (4), 881–902. doi: 10.1093/forestscience/35.4.881. |
| [68] | Chave, J., Mechain, M. R., Burquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., Henry, M., Martinez-Yrizar, A., Mugasha, W. A., Muller-Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz-Malavassi, E., Pelissier, R., Ploton, P., Ryan, C. M., Saldarriaga, J. G. & Vieilledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20, 3177–3190. doi: 10.1111/gcb.12629. |
APA Style
Erick Senademi Sogbossi, Julien Gaudence Djego. (2021). Development of Stem Volume Equation for Urban Trees of Abomey-Calavi in Southern Benin (West Africa). American Journal of Biological and Environmental Statistics, 7(4), 111-120. https://doi.org/10.11648/j.ajbes.20210704.16
ACS Style
Erick Senademi Sogbossi; Julien Gaudence Djego. Development of Stem Volume Equation for Urban Trees of Abomey-Calavi in Southern Benin (West Africa). Am. J. Biol. Environ. Stat. 2021, 7(4), 111-120. doi: 10.11648/j.ajbes.20210704.16
AMA Style
Erick Senademi Sogbossi, Julien Gaudence Djego. Development of Stem Volume Equation for Urban Trees of Abomey-Calavi in Southern Benin (West Africa). Am J Biol Environ Stat. 2021;7(4):111-120. doi: 10.11648/j.ajbes.20210704.16
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Download@article{10.11648/j.ajbes.20210704.16,
author = {Erick Senademi Sogbossi and Julien Gaudence Djego},
title = {Development of Stem Volume Equation for Urban Trees of Abomey-Calavi in Southern Benin (West Africa)},
journal = {American Journal of Biological and Environmental Statistics},
volume = {7},
number = {4},
pages = {111-120},
doi = {10.11648/j.ajbes.20210704.16},
url = {https://doi.org/10.11648/j.ajbes.20210704.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20210704.16},
abstract = {Information on growing stock is important for understanding health assessment, environmental analysis, carbon storage estimation, and economic analysis of urban forest. The stand volume estimation enables the calculation of ecosystemic services value and growth stock of urban forests. However, most of volume models fitted for multiple species in tropical forests may not be suitable for urban trees. This study was conducted to develop generic volume models for urban trees in Abomey-Calavi at the southern Benin. A total of 1608 trees belonging to 80 plant species were measured for their diameter at breast height (DBH), stem height (h) and stem volume using non-destructive sampling methods. Using a nonlinear procedure, six volume models were constructed. Cross validation and Fit statistics like standard error of estimate (SEE), relative absolute error (RAE), root mean square error (RMSE), fit index (FI), Akaike information criterion (AIC) and Willmott’s agreement index (dw) were used to evaluate the efficiency and stability of different models. The six generic volume models developed in this study included both diameter and height. These models exhibited an absence of multicollinearity, with normal and homoscedastic residuals. Furthermore, they show high efficiency (IF > 0.997) and reduce of prediction errors (RMSE: 0.05388–0.06629 m3; RAE: 0.05186–0.06952), which ensuring stability in the estimates. However, the Model II was the best for predicting the stem volume of urban tree according to evaluation statistics and rank analysis. The models developed can provide stem volumes prediction with accurate estimations. Though, stem heights should be systematically measured. These models can contribute to assess the productivity of urban forests in order to pursue their sustainable management and planning.},
year = {2021}
}
TY - JOUR T1 - Development of Stem Volume Equation for Urban Trees of Abomey-Calavi in Southern Benin (West Africa) AU - Erick Senademi Sogbossi AU - Julien Gaudence Djego Y1 - 2021/12/24 PY - 2021 N1 - https://doi.org/10.11648/j.ajbes.20210704.16 DO - 10.11648/j.ajbes.20210704.16 T2 - American Journal of Biological and Environmental Statistics JF - American Journal of Biological and Environmental Statistics JO - American Journal of Biological and Environmental Statistics SP - 111 EP - 120 PB - Science Publishing Group SN - 2471-979X UR - https://doi.org/10.11648/j.ajbes.20210704.16 AB - Information on growing stock is important for understanding health assessment, environmental analysis, carbon storage estimation, and economic analysis of urban forest. The stand volume estimation enables the calculation of ecosystemic services value and growth stock of urban forests. However, most of volume models fitted for multiple species in tropical forests may not be suitable for urban trees. This study was conducted to develop generic volume models for urban trees in Abomey-Calavi at the southern Benin. A total of 1608 trees belonging to 80 plant species were measured for their diameter at breast height (DBH), stem height (h) and stem volume using non-destructive sampling methods. Using a nonlinear procedure, six volume models were constructed. Cross validation and Fit statistics like standard error of estimate (SEE), relative absolute error (RAE), root mean square error (RMSE), fit index (FI), Akaike information criterion (AIC) and Willmott’s agreement index (dw) were used to evaluate the efficiency and stability of different models. The six generic volume models developed in this study included both diameter and height. These models exhibited an absence of multicollinearity, with normal and homoscedastic residuals. Furthermore, they show high efficiency (IF > 0.997) and reduce of prediction errors (RMSE: 0.05388–0.06629 m3; RAE: 0.05186–0.06952), which ensuring stability in the estimates. However, the Model II was the best for predicting the stem volume of urban tree according to evaluation statistics and rank analysis. The models developed can provide stem volumes prediction with accurate estimations. Though, stem heights should be systematically measured. These models can contribute to assess the productivity of urban forests in order to pursue their sustainable management and planning. VL - 7 IS - 4 ER -
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