Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey’s Mediterranean coast

Neşe Gülci

Journal of Forestry Research ›› 2020, Vol. 32 ›› Issue (5) : 1899 -1909.

PDF
Journal of Forestry Research ›› 2020, Vol. 32 ›› Issue (5) : 1899 -1909. DOI: 10.1007/s11676-020-01255-y
Original Paper

Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey’s Mediterranean coast

Author information +
History +
PDF

Abstract

Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change. Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic. Geographical information systems (GIS) and remote sensing (RS) methods offer a number of benefits for researchers and operators in the field of forest fire research. The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes. The effect of timber obtained from compartment units on producers’ pricing policy was modelled. Sapadere forest fire area (2500 ha) located in Antalya in Turkey was selected as the main study area. Topography parameters (aspect, slope and slope position), stand types (diameter class and crown closure), and burn severity were analyzed together using GIS and R software packages. A multi-linear regression model (R2 = 0.752) demonstrated that factors that had the most impact on pricing were slope position, aspect, stand age, crown closure and burn severity. This model can be used to estimate salvage logging prices in Calabrian pine (Pinus brutia Ten.) stands with similar parameters. Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid. This will help reduce operational planning times of harvesting procedures in burned stands.

Keywords

Timber extraction / Forest fires / Stumpage sale / Cost estimation / Sustainability / Crown fire

Cite this article

Download citation ▾
Neşe Gülci. Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey’s Mediterranean coast. Journal of Forestry Research, 2020, 32(5): 1899-1909 DOI:10.1007/s11676-020-01255-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Ackerman P, Belbo H, Eliasson L, de Jong A, Lazdins A, Lyons J. The COST model for calculation of forest operations costs. Int J For Eng, 2014, 25: 75-81.

[2]

Agee JK, Skinner CN. Basic principles of forest fuel reduction treatments. For Ecol Manag, 2005, 211: 83-96.

[3]

Aini A, Curt T, Bekdouche F. Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria). Environ Monit Assess, 2019, 191: 747.

[4]

Akay AE, Sessions J, Bettinger P, Toupin R, Eklund A. Evaluating the salvage value of fire-killed timber by helicopter—effects of yarding distance and time since fire. West J Appl For, 2006, 21: 102-107.

[5]

Akay AE, Erdas O, Kanat M, Tutus A. Post-fire salvage logging for fire-killed Brutian pine (Pinus brutia) trees. J Appl Sci, 2007, 7: 402-406.

[6]

Alexander JD, Seavy NE, Ralph CJ, Hogoboom B. Vegetation and topographical correlates of fire severity from two fires in the Klamath-Siskiyou region of Oregon and California. Int J Wildland Fire, 2006, 15: 237-245.

[7]

Allen I, Chhin S, Zhang J. Fire and forest management in Montane forests of the Northwestern States and California, USA. Fire, 2019 2 2 17

[8]

Bilici E, Eker M, Hasdemir M, Akay AE. Assessment of post-fire salvage logging operations in Mediterranean Region of Turkey. Sumar List, 2017, 141: 363-373.

[9]

Buma B. Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest. Environ Monit Assess, 2012, 184: 3849-3860.

[10]

Chuvieco E. Earth observation of wildland fires in Mediterranean ecosystems, 2009 129 148

[11]

Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Hanson PJ, Irland LC, Lugo AE, Peterson CJ, Simberloff D, Swanson FJ, Stocks BJ, Wotton BM. Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. Bioscience, 2001, 51: 723-734.

[12]

Dilts TE (2015) Topography tools for ArcGIS 10.3. Univ Nevada Reno.

[13]

Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens Environ, 2012, 120: 25-36.

[14]

Eker M (2004) Development of annual operational planning model for timber harvesting. PhD thesis, Graduate School, Blacksea Technical University [In Turkish]

[15]

ESRI (2011) ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. Redlands

[16]

GDF (2015) (General Directorate of Forestry of Turkey) Principles of stumpage sale, Circular letter no: 6877/A. P. 46, Ankara, Turkey. [In Turkish]

[17]

GDF (2017) (General Directorate of Forestry of Turkey) Forest management map of Mahmutlar and Demirtaş State Forest Enterprise, Forest map. Ankara, Turkey [in Turkish]

[18]

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ, 2017, 202: 18-27.

[19]

Jenness J (2006) Topographic position index extension for ArcView 3. x, v. 1.2., Jenness Enterprises.

[20]

Kavgaci A, Örtel E, Torres I, Safford H. Early postfire vegetation recovery of Pinus brutia forests: effects of fire severity, prefire stand age, and aspect. Turk J Agric For, 2016, 40(5): 723-736.

[21]

Keeley JE. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildland Fire, 2009, 18: 116-126.

[22]

Keeley JE, Bond WJ, Bradstock RA, Pausas JG, Rundel PW (2011) Fire in Mediterranean ecosystems: ecology, evolution and management, vol 9780521824. Cambridge University Press. https://doi.org/10.1017/CBO9781139033091

[23]

Lee C, Schlemme C, Murray J, Unsworth R. The cost of climate change: ecosystem services and wildland fires. Ecol Econ, 2015, 116: 261-269.

[24]

Miller JD, Thode AE. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens Environ, 2007, 109: 66-80.

[25]

Moritz MA, Batllori E, Bradstock RA, Gill AM, Handmer J, Hessburg PF, Leonard J, McCaffrey S, Odion DC, Schoennagel T, Syphard AD. Learning to coexist with wildfire. Nature, 2014, 515: 58-66.

[26]

Nemani R, Votava P, Michaelis A, Melton F, Milesi C (2011) Collaborative supercomputing for global change science. EOS (Washington DC). https://doi.org/10.1029/2011EO130001

[27]

Oliveras I, Gracia M, Moŕ G, Retana J. Factors influencing the pattern of fire severities in a large wildfire under extreme meteorological conditions in the Mediterranean basin. Int J Wildland Fire, 2009, 18: 755-764.

[28]

Pacheco AP, Claro J. Operational flexibility in forest fire prevention and suppression: a spatially explicit intra-annual optimization analysis, considering prevention, (pre) suppression, and escape costs. Eur J For Res, 2018, 137(6): 895-916.

[29]

Petropoulos GP, Griffiths HM, Kalivas DP. Quantifying spatial and temporal vegetation recovery dynamics following a wildfire event in a Mediterranean landscape using EO data and GIS. Appl Geogr, 2014, 50: 120-131.

[30]

Prestemon JP, Holmes TP (2008) Timber salvage economics. In: Holmes TP, Prestemon JP (eds) The economics of forest disturbances. Springer, pp 167–190. https://doi.org/10.1007/978-1-4020-4370-3_9

[31]

R Core Team (2018) R: a language and environment for statistical computing (R version 3.5.2.). Vienna: R Foundation for Statistical Computing. http://www.R-project.org/. Accessed 07 Jan 2019.

[32]

Revelle W, Revelle MW (2015) Package ‘psych’. The comprehensive R archive. Network. http://cran.r-project.org/web/packages/psych/. Accessed 18 Feb 2019.

[33]

Rodríguez y Silva F, Ramón Molina J, González-Cabán A, Machuca MÁH. Economic vulnerability of timber resources to forest fires. J Environ Manag, 2012, 100: 16-21.

[34]

Rodríguez y Silva F, Ramón Molina Martínez J, Castillo Soto M (2013) Methodological approach for assessing the economic impact of forest fires using MODIS remote sensing images. In: González-Cabán, Armando, tech. coord. Proceedings of the fourth international symposium on fire economics, planning, and policy: climate change and wildfires. Gen. Tech. Rep. PSW-GTR-245 (English). Albany, CA: USDA, Forest Serv, pp 281–295. https://www.fs.fed.us/psw/publications/documents/psw_gtr245/psw_gtr245_281.pdf. Accessed 10 Jun 2019.

[35]

Rodríguez E, Morris CS, Belz JE. A global assessment of the SRTM performance. Photogramm Eng Remote Sens, 2006, 72: 249-260.

[36]

Sessions J, Bettinger P, Buckman R, Newton M, Hamann J. Hastening the return of complex forests following fire: the consequences of delay. J For, 2004, 102(3): 38-45.

[37]

She J, Chung W, Han H. Economic and environmental optimization of the forest supply chain for timber and bioenergy production from beetle-killed forests in Northern Colorado. Forests, 2019 10 8 689

[38]

Stephens SL, Ruth LW. Federal forest-fire policy in the United States. Ecol Appl, 2005, 15: 532-542.

[39]

Thomas D, Butry D, Gilbert S, Webb D, Fung J. The costs and losses of wildfires: a literature review. NIST Spec Publ, 2017, 1215: 72.

[40]

Thompson MP, Anderson NM. Modelling fuel treatment impacts on fire suppression cost savings: a review. Calif Agric, 2015, 69: 164-170.

[41]

Turner MG, Romme WH, Gardner RH. Prefire heterogeneity, fire severity, and early postfire plant reestablishment in subalpine forests of Yellowstone National Park, Wyoming. Int J Wildland Fire, 1999, 9: 21-36.

[42]

UNOOSA (2018) (The United Nations Office for Outer Space Affairs) Step by Step: burn severity mapping in Google Earth Engine by Johannes Heisig on Tue, 04/12/2018—13:54. http://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/burn-severity-earth-engine. Accessed 10 Apr 2019.

[43]

USDA (2006) (United States Department of Agriculture) FIREMON: fire effects monitoring and inventory system. USDA forest service gen. Tech. Rep. RMRS-GTR-164-CD. FIREMON BR Cheat Sheet V4, June 2004. https://www.fs.fed.us/rm/pubs/rmrs_gtr164.pdf. Accessed 12 May 2019.

[44]

Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017) Package ‘corrplot.’ Statistician 56:316–324. https://rdrr.io/cran/corrplot/man/corrplot-package.html. Accessed 10 Apr 2019.

[45]

Weiss AD (2001) Topographic position and landforms analysis. Poster Present, ESRI User Conference, San Diego, CA 64:227–245. http://www.jennessent.com/downloads/TPI-poster-TNC_18x22.pdf. Accessed 10 Apr 2018.

[46]

Wu Z, He HS, Liang Y, Cai L, Lewis BJ. Determining relative contributions of vegetation and topography to burn severity from LANDSAT imagery. Environ Manag, 2013, 52: 821-836.

AI Summary AI Mindmap
PDF

212

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/