Potential of Geospatial Technologies in Mechanized Timber Harvesting Planning

Year 2024, , 1 - 14, 27.06.2024

Gilberth Temba , Ernest Mauya

https://doi.org/10.33904/ejfe.1364534

Abstract

Mechanized timber harvesting involves various activities including road planning, and selection of harvesting systems and machineries. The emergence of geospatial technology (GSPT) i.e., geographical information system (GIS) and remote sensing in the recent decades, has been considered as the best tools to facilitate timber harvesting planning in plantation forests. GSPT provide accurate stand information enabling better decision-making and optimizing forest operations. This study was conducted at Sao hill Forest Plantation (SHFP) in Tanzania, with the objective of determining relative efficiency (RE) between geospatial approach (GSPA) and conventional approach (CA) on planning mechanized timber harvesting. 120 grapple skidders (GS) in 30 sample plots within different elevation terrain ranges were studied with time study observations in both approaches. Productivity and costs under the two approaches were estimated and modelled using generalized linear model (GLM) approach. To obtain large scale estimates of productivity and costs, Inverse Distance Weighted (IDW) interpolation approach was used. The results showed that, GSPA demonstrated higher productivity and lower unit skidding costs (i.e., 71.1 m3/hr and 2.121 USD/m3) compared to CA (i.e., 67.5 m3/hr and 2.914 USD/m3) respectively. Skidding distance and slope (p-value < 0.05) were significant predictors of the GS performance in both approaches. The pseudo R2 ranging from 58.1% to 64.3% under CA, and from 62.9% to 60.8% under GSPA. Likewise, relative root mean square error (RMSEr) for the models under CA ranged from 49.3% to 50.4% and 33.4% to 35.2% under GSPA. Generally, the results showed that, models under GSPA have better fits and accuracy, compared to CA. Furthermore, the GSPA provided a raster representation of productivity and costs over the entire study area. Moreover, computed RE values (i.e., 1.18 and 6.17) indicated that parameter estimates for the GS productivity and costs were more precise in geospatial models (GSPM) compared to conventional models (CM). These findings highlight the potential of GSPT for an efficient large scale timber harvesting planning, by considering terrain constraints.

Keywords

Convectional, Geospatial, Grapple skidder, Productivity, Costs, Relative efficiency

Project Number

NONE

References

• Agüera-Vega, F., Agüera-Puntas, M., Martínez-Carricondo, P., Mancini, F., Carvajal, F. 2020. Effects of point cloud density, interpolation method and grid size on derived Digital Terrain Model accuracy at micro topography level Effects of point cloud density, interpolation method and grid topography level. International Journal of Remote Sensing, 41(21):8281-8299.https://doi.org/10.1080/01431161. 2020.1771788
• Banaś, J., Utnik-Banaś, K., Zięba, S., Janeczko, K. 2021. Assessing the technical efficiency of timber production during the transition from a production-oriented management model to a multifunctional one: A case from Poland 1990–2019. Forests, 12(9):1287. https://doi.org/10.3390/f12091287
• Bettinger, P., Sessions, J., Boston, K. 2009. A review of the status and use of validation procedures for heuristics used in forest planning. International Journal of Mathematical and Computational Forestry and Natural-ResourceSciences, 1(1):13.
• Borz, S. A., Ignea, G., Popa, B., Iordache, E., Spârchez, G. 2015. Estimating time consumption and productivity of roundwood skidding in group shelterwood system – a case study in a broadleaved mixed stand located in reduced accessibility conditions. Croatian Journal of Forest Engineering, 36(1):137-146.
• Bredström, D., Jönsson, P., Rönnqvist, M. 2010. Annual planning of harvesting resources in the forest industry. International Transactions in Operational Research, 17(2):155–177.
• Çalişkan, E., Karahalil, U. 2017. Evaluation of Forest Road network and determining timber extraction system using GIS: A case study ˇ planning unit in Anbardag Planning Unit. Šumarski List, 383(001):163–171.
• Conrad, J.L., Bolding, M. C., Aust, W.M., Smith, R.L., Horcher, A. 2013. Harvesting productivity and costs when utilizing energywood from pine plantations of the southern Coastal Plain USA. Biomass and Bioenergy, 52:85–95. https://doi.org/10.1016/j. biombioe.2013.02.038
• Conway, S. 1986. Logging Practice, Principles of Timber Harvesting System. Miller Freeman, USA. 416 pp.
• Đuka, A., Porsinsky, T., Vusic, D. 2015. DTM models to enhance planning of timber harvesting. Glasnik Sumarskog Fakulteta, suppl., 35-44. https://doi.org/ 10.2298/gsf15s1035d
• James, G., Witten, D., Tibshirani, R., Hastie, T. 2013. An Introduction to Statistical Learning with Applications in R (Springer. (ed.). Springer.
• Jimmy, G., Seiler, R., Maeder, U. 2013. Development and Validation of Energy Expenditure Prediction Models Based on GT3X Accelerometer Data in 5- to 9-Year-Old Children. Journal of Physical Activity and Healt, 10:1057–1067.
• Kachamba, D. J., Ørka, H. O., Næsset, E., Eid, T., Gobakken, T. 2017. Influence of plot size on efficiency of biomass estimates in inventories of dry tropical forests assisted by photogrammetric data from an unmanned aircraft system. Remote Sensing, 9(6):1-15. https://doi.org/10.3390/rs9060610
• Kienzle, S. 2004. The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives. Transactions in GIS, 8(1):83–111.
• Korkmaz, S., Goksuluk, D., Zararsiz, G. 2014. MVN: An R package for assessing multivariate normality. R Journal, 6(2):151–162. https://doi.org/10.32614/rj-2014-031
• Kühmaier, M., Stampfer, K. 2010. Development of a Multi-Attribute Spatial Decision Support System in Selecting Timber Harvesting Systems. Croatian Journal of Forest Engineering, 32(2):75–88.
• Lindsey, J.K. 1998. Applying Generalized Linear Models. In Technometrics 40:2. https://doi.org/10. 2307/1270654
• Liu, X., Zhang, Z. 2008. LiDAR Data Reduction for Efficient and high-quality DEM generation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(3b):6.
• Long, C.R. 2003. Production and cost analysis of two harvesting systems in central Appalachia [West Virginia University]. https://researchrepository. wvu.edu/etd/1327/
• Lubello, D. 2008. A Rule-Based SDSS for Integrated forest harvesting planning. University of Padua.
• Malimbwi, R.E., Mugasha, W.A., Mauya, E. 2016. Pinus Patula Yield Tables for Sao Hill Forest Plantations, Tanzania (Issue September).
• Mauya, E.W. 2022. Production Rates of Mechanized Tree Felling Operations at Sao-Hill Forest Plantation, Tanzania. Tanzania Journal of Forestry and Nature Conservation, 91(1):45-57.
• Mauya, E.W., Hansen, E.H., Gobakken, T., Bollandsås, O.M., Malimbwi, R.E., Næsset, E. 2015. Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance and Management, 10(1):14. https://doi.org/10.1186/s13021-015-0021-x
• McKendry, J., Eastman, J. 1991. Applications of GIS in Forestry: A Review. Natural Resource Analysis Center; West Virginia University, 18.
• Miyajima, R.H., Fenner, P.T., Batistela, G.C., Simões, D. 2021. Effect of feller-buncher model, slope class and cutting area on the productivity and costs of whole tree harvesting in Brazilian eucalyptus stands. Forests, 12(8). https://doi.org/10.3390/f12081092
• MNRT (Ministry of Natural Resource and Tourism). 2018. Sao Hill Division 1 (Irundi) Forest Plantation management plan (2018/19-2022/23) (Vol. 1). MNRT.
• Murphy, G.E. 2005. Determining sample size for harvesting cost estimation. New Zealand Journal of Forestry Science, 35(2/3):166–169.
• Okey, F. O., Visser, R. 2020. Estimating the influence of extraction method and processing location on forest harvesting efficiency - A categorical DEA approach. European Journal of Forest Engineering, 6(2):60–67. https://doi.org/10.33904/ejfe.722822
• Ole-Meiludie, R. E., Skaar, R. 1990. Secondary transportation systems, Transport costs. Analysis and control of timber harvesting operations. Compendium in Forest Engineering.
• Orlovský, L., Messingerová, V., Danihelová, Z. 2020. Analysis of the time efficiency of skidding technology based on the skidders. Central European Forestry Journal, 66(3):177–187. https://doi.org/ 10.2478/forj-2020-0016
• Pecora, G., Todaro, L., Moretti, N. 2014. Optimization of timber harvesting using GIS-based system. Proceedings of the Second International Congress of Silviculture Florence, November 26th - 29th 2014, January 2016, 5. https://doi.org/10.4129/2cis-gp-opt
• Perpiñá, C., Alfonso, D., Pérez-Navarro, A., Peñalvo, E., Vargas, C., Cárdenas, R. 2009. Methodology based on Geographic Information Systems for biomass logistics and transport optimisation. Renewable Energy, 34(3):555–565. https://doi.org/10.1016/ j.renene.2008.05.047
• Phelps, K., Hiesl, P., Hagan, D., Hagan, A.H. 2021. The harvest operability index (HOI): A decision support tool for mechanized timber harvesting in mountainous terrain. Forests, 12(10):17. https://doi. org/10.3390/f12101307
• Picchio, R., Latterini, F., Mederski, P. S., Tocci, D., Venanzi, R., Stefanoni, W., Pari, L. 2020. Applications of GIS-Based Software to Improve the Sustainability of a Forwarding Operation in Central Italy. Sustainability (Switzerland), 5716(12):15.
• RP (Ritchie Specs). 2007. Carterpillar 525 Grapple skidder Specifications and Dimensions. https://www.ritchiespecs.com/model/caterpillar-525-skidder (Accessed: 20 September 2023).
• Shemwetta, D.T.K., R.E.L., O.-M., G.A., M., A.W.S., Silayo, D.A. 2007. Optimizing productivity on multistage timber harvesting systems. A case of Shume/Mkumbara system, Tanzania. Tanzania Journal of Forestry and Nature Conservation, 75(2):1–9. https://doi.org/10.4314/dai.v19i1-2.15775
• Shemwetta, D.T.K. 1997. Comprehensive Timber Harvest Planning for Plantation Forests on Difficult Terrain: Sokoine University of Agriculture Training Forest, Tanzania.: Vol. c (Issue 1). Oregon State University.
• Suvinen, A. 2006. A GIS-based simulation model for terrain tractability. Journal of Terramechanics, 43(4):427–449. https://doi.org/10.1016/j.jterra.2005. 05.002
• Wang, J., LeDoux, C.B., Li, Y. 2005. Simulating Cut-to-Length Harvesting Operations in Appalachian Hardwoods. International Journal of Forest Engineering, 16(2): 11–27. https://doi.org/10.1080/ 14942119.2005.10702510
• Wang, J., Long, C., McNeel, J., Baumgras, J. 2004. Productivity and cost of manual felling and cable skidding in central Appalachian hardwood forests. Forest Products Journal, 54(12):45–51.