Volume equations for tree species in the tropical dry forest of East Nusa Tenggara, Indonesia
DOI:
https://doi.org/10.32522/ujht.v9i1.14649Abstract
The aims of this study is to develop and validate tree volume prediction models of tree species in East Nusa Tenggara tropical dry forest, Indonesia. Data collection in this study was conducted bymeasuring diameter andheight of trees. Tree species volume equation is estimated using models of Berkhout, Kopezky-Gehrhardt and Hohenadl-Krenn. Selectionof the best models was based on the values of the coefficient of standar deviation (SD), R2 adj, aggregate deviation (AD) and average deviation (AveD), root mean square error (RMSE) and bias. Hohenadl-Krenn and Kopezky-Gehrhardt models were accepted as the best model for tree species in all research sites. The selected volume model for these tree species has Hohenadl-Krenn (V = 0.572 - 0.033Dbh + 0.00106Dbh2 for Binafun and V = 0.683 - 0.0366Dbh + 0.001278Dbh2 for Bonmuti) and Kopezky-Gehrhardt models (V = 0.0502 + 0.000537 Dbh2 for Letkole and V = -0.1776 + 0.000677Dbh2 for Oelbanu).References
Almulqu, A. A. (2020). Forest diversity and modeling diameter distribution of the tropical dry forest in East Nusa Tenggara, Indonesia. Asia Pacific Journal of Sustainable Agriculture, Food and Energy, 9(1), 1–28. https://doi.org/10.36782/apjsafe.v9i1.75
Almulqu, A. A., Arpornpong, N., & Boonyanuphap, J. (2018). Tree species composition and structure of dry forest in Mutis Timau Protected Forest Management Unit of East Nusa Tenggara, Indonesia. Biodiversitas Journal of Biological Diversity, 19(2), 496–503. https://doi.org/10.13057/biodiv/d190217
Cysneiros, V. C., Gaui, T. D., Filho, T. B. S., Pelissari, A. L., Machado, S. A., 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 - Biogeosciences and Forestry, 13(1), 417–425. https://doi.org/10.3832/ifor3495-013
DeCauwer, V., Beeckman, H., Kleinn, C., Moses, M., Nott, A., Seifert, T., & Muys, B. (2020). Improving the knowledge base for tropical dry forest management in Southern Africa: Regional volume models for Pterocarpus angolensis. Forest Ecology and Management, 477, 118485. https://doi.org/10.1016/j.foreco.2020.118485
Giri, K., Chandra, G., Jayaraj, R. S. C., Borah, R. K., Kardong, P., Borah, S., & Goswami, A. K. (2021). Regression models for estimating stem volume of Aquilaria malaccensis (Lam.) in North East India. Environmental Challenges, 5, 100279. https://doi.org/10.1016/j.envc.2021.100279
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., Sim, 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(24), 1–23. https://doi.org/10.1007/s13595-019-0800-8
Hutapea, F. J., & Kuswandi, R. (2019). Timber volume estimation model for commercial tree species in the logging area concession of PT. Tunas Timber Lestari in Boven Digul, Papua. Jurnal Wasian, 6(1), 27–36. https://doi.org/10.20886/jwas.v6i1.4714
Hu, T., Sun, Y., Jia, W., Li, D., Zou, M., & Zhang, M. (2021). Study on the estimation of forest volume based on multi-source data. Sensors, 21(7796), 1–25. https://doi.org/10.3390/s21237796
Kangas, A., Pitkänen, T. P., Mehtätalo, L., & Heikkinen, J. (2023). Mixed linear and non-linear tree volume models with regional parameters to main tree species in Finland. Forestry: An International Journal of Forest Research, 96(2), 188–206. https://doi.org/10.1093/forestry/cpac038
Karatepe, Y., Diamantopoulou, M. J., Özçelik, R., & Sürücü, Z. (2022). Total tree height predictions via parametric and artificial neural network modeling approaches. iForest - Biogeosciences and Forestry, 15(2), 95–105. https://doi.org/10.3832/ifor3990-015
Myllyviita, T., Soimakallio, S., Judl, J., & Seppälä, J. (2021). Wood substitution potential in greenhouse gas emission reduction: Review on current state and application of displacement factors. Forest Ecosystems, 8(42), 1–18. https://doi.org/10.1186/s40663-021-00326-8
Pitkänen, T. P., Raumonen, P., Liang, X., Lehtomäki, M., & Kangas, A. S. (2021). Improving TLS-based stem volume estimates by field measurements. Computers and Electronics in Agriculture, 180, 105882. https://doi.org/10.1016/j.compag.2020.105882
Salih, T. K. (2023). The use of mathematical and statistical analysis in selecting the best regression equation for Quercus infectoria G. Olivier. IOP Conference Series: Earth and Environmental Science, 1213(1), 012115. https://doi.org/10.1088/1755-1315/1213/1/012115
Shrestha, H. L., Kafle, M. R., Khanal, K., Mandal, R. A., & Khanal, K. (2018). Developing local volume tables for three important tree species in Nawalparasi and Kapilvastu districts. Banko Janakari, 27(3), 84–91. https://doi.org/10.3126/banko.v27i3.20552
Štícha, V., Sharma, R. P., Vacek, Z., Vacek, S., & Nuhlíček, O. (2019). Timber and branch volume prediction: Effects of stand and site characteristics on dendromass and timber-to-branch volume ratio of Norway spruce in managed forests. Forests, 10(2), 144. https://doi.org/10.3390/f10020144
