Influence of timber harvesting costs on the layout of cuttings and economic return in forest planning based on dynamic treatment units

Adrián Pascual, Timo Pukkala, Sergio de-Miguel, Annukka Pesonen, Petteri Packalen

Abstract


Aim of study: To analyze the influence of harvesting costs on the distribution and type of cuttings when forest management planning is based on the dynamic treatment units (DTUs) approach.

Area of study: A Mediterranean pine forest in Central Spain.

Materials and methods: Airborne laser scanning data were used in area-based approach to predict stand attributes and delineate segments that were used as calculation units. Predicted stand attributes and existing models for diameter distribution and individual-tree growth were used to simulate alternative management schedules for each segment for a 60-year planning horizon divided into three 20-year periods. Three alternative forest planning problems were formulated. They aimed to maximize or minimize net income, or maximize timber production with a constant flow of harvested timber. Spatial goals were used in all cases to enhance the clustering of treatments.

Main results: Maxizing timber production without considering harvesting costs can be costly, even close to the plan that minimized net incomes. Maximizing net incomes led to frequent use of final felling instead of thinnings, placing cuttings near forest roads and creating more compact DTUs than obtained in the plan that maximized timber production.

Research highlights: Compared to previous studies on DTUs, this study integrated felling and forwarding costs, which depended on distance to road and stand attributes, in the process of creating DTUs by means of spatial optimization.


Keywords


spatial optimization; decision-making; forest economics; forest planning

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References


Augustynczik ALD, Arce JE, Yousefpour R., da Silva ACL, 2016. Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing. For Policy Econ 73: 120-129.

Axelsson P, 2000. DEM generation from laser scanner data using adaptive TIN models. ISPRS 33: 111-118.

Baskent EZ, Keles S, 2005. Spatial forest planning: A review. Ecol Model 188: 145-173. https://doi.org/10.1016/j.ecolmodel.2005.01.059

Bettinger P, Johnson DL, Johnson KN, 2003. Spatial forest plan development with ecological and economic goals. Ecol Model 169: 215-236. https://doi.org/10.1016/S0304-3800(03)00271-0

Borges P, Eid T, Bergseng E, 2014. Applying simulated annealing using different methods for the neighborhood search in forest planning problems. Eur J For Res 233: 700-710. https://doi.org/10.1016/j.ejor.2013.08.039

Carvajal R, Constantino M, Goycoolea M, Vielma JP, Weintraub A, 2013. Imposing connectivity constraints in forest planning models. Oper Res 61: 824-836. https://doi.org/10.1287/opre.2013.1183

Diaz-Balteiro L, Romero C, 2008. Making forestry decisions with multiple criteria: A review and an assessment. For Ecol Manage 255: 3222-3241.

Gobakken T, Næsset E, 2008. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res 38: 1095-1109. https://doi.org/10.1139/X07-219

González-Olabarria JR, Pukkala T, 2011. Integrating fire risk considerations in landscape-level forest planning. For Ecol Manage 261: 278-287.

Heinonen T, Pukkala T, 2007. The use of cellular automaton approach in forest planning. Can J For Res 37: 2188-2200. https://doi.org/10.1139/X07-073

Heinonen T, Kurttila M, Pukkala T, 2007. Possibilities to aggregate raster cells through spatial optimization in forest planning. Silva Fenn 41: 89-103. https://doi.org/10.14214/sf.474

Holmgren P, Thuresson T, 1997. Applying objectively estimated and spatially continuous forest parameters in tactical planning to obtain dynamic treatment units. Forest Sci 43: 317-325.

Jin X, Pukkala T, Li F. 2016. Fine-tuning heuristic methods for combinatorial optimization in forest planning. Eur J For Res 135: 765-779. https://doi.org/10.1007/s10342-016-0971-x

Kangas J, Kangas A, 2002. Multiple criteria decision support methods in forest management. In: Multi-objective forest planning, pp: 37-70. Kluwer Acad Publ, Dordrecht. https://doi.org/10.1007/978-94-015-9906-1_3

Maltamo M, Naesset E, Vauhkonen J (eds.), 2014. Forestry applications of airborne laser scanning: concepts and case studies. In: Manag For Ecosyst 27, pp: 215-268. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8663-8

Mathey AH, Krcmar E, Tait D, Vertinsky I, Innes J, 2007. Forest planning using co-evolutionary cellular automata. For Ecol Manage 239: 45-56.

McDill E, 2014. An overview of forest management planning and information management. In: The management of industrial forest plantations: theoretical foundations and applications; Borges JG et al. (eds). Manag For Ecosyst 33, pp: 27-59. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8899-1_2

McRoberts RE, 2006. A model-based approach to estimating forest area. Remote Sens Environ 128: 268-275. https://doi.org/10.1016/j.rse.2012.10.007

Mustonen J, Packalen P, Kangas A, 2008. Automatic segmentation of forest stands using a canopy height model and aerial photography. Scand J For Res 23: 534-545. https://doi.org/10.1080/02827580802552446

Öhman K, 2001. Forest planning with consideration to spatial relationships. Doctoral thesis. Swedish Univ. of Agric. Sci., Umeå (Sweden).

Öhman K, Eriksson LO, 2002. Allowing for spatial consideration in long-term forest planning by linking linear programming with simulated annealing. For Ecol Manage 161: 221-230.

Öhman K, Lämås T, 2003. Clustering of harvest activities in multi-objective long-term forest planning. For Ecol Manage 176: 161-171.

Öhman K, Edenius L, Mikusin G, 2011. Optimizing spatial habitat suitability and timber revenue in long-term forest planning. Can J For Res 41: 543-551. https://doi.org/10.1139/X10-232

Palahi M, Pukkala T, Perez E, Trasobares A, 2004. Herramientas de soporte a la decisión en la planificación y gestión forestal. Montes 78: 40-48.

Pascual A, Pukkala T, Rodríguez F, de-Miguel S, 2016. Using spatial optimization to create dynamic harvest blocks from LiDAR-based small interpretation units. Forests 7: 220. https://doi.org/10.3390/f7100220

Pascual A, Pukkala T, de-Miguel S, Pesonen A, Packalen P, 2017. Assessing the role of forest inventory units to compose dynamic treatment units in forest management planning. 17th Symposium for Systems Analysis in Forest Resources (SSAFR). Suquamish (Washington, US).

Pukkala T, 2002. Multi-objective forest planning. Manag For Ecosyst 4. Kluwer Acad Publ, Netherlands. https://doi.org/10.1007/978-94-015-9906-1

Pukkala T, Heinonen T, Kurttila M, 2009. An application of a reduced cost approach to spatial forest planning. For Sci 55: 13-22.

Pukkala T, Packalén P, Heinonen T, 2014. Dynamic treatment units in forest management planning. In: The management of industrial forest plantations: theoretical foundations and applications; Borges JG et al. (eds). Manag For Ecosyst 33. Springer Sci Bus Media, Dordrecht. https://doi.org/10.1007/978-94-017-8899-1_12

Solano JM, Fernández J, Palahí M, Pukkala T, Prokofieva I, 2007. ¿Es rentable la gestión forestal en Cataluña? Economistas 25: 116-124.

Tóth SF, McDill ME, 2008. Promoting large, compact mature forest patches in harvest scheduling models. Environ Model Assess 13: 1-15. https://doi.org/10.1007/s10666-006-9080-4




DOI: 10.5424/fs/2018271-11897

Webpage: www.inia.es/Forestsystems