GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society ISSN 2308-1708 Online Edition ISBN 978-3-87907-558-4 Print Edition ISBN 978-3-7001-7826-2 Online Edition
doi:10.1553/giscience2015
GI_Forum, 2015Volume 3 2015, 645 pages Print edition is available at Wichmann-Verlag, Berlin
Ulrich Strötz
S. 94 - 102 doi:10.1553/giscience2015s94 Verlag der Österreichischen Akademie der Wissenschaften
Abstract: Optimization models for ecological forestry approaches require consideration of a variety of spatial features, including Harvest Costs, in order to maximize triple bottom line returns. Since the composition and the structure of the forest systems are usually not available for an entire landscape, a model is required that calculates Harvest Costs solely based on Spatial Predictors, which are Slope and Skidding Distance. Currently, no existing study investigates the significance of Spatial Predictors on Timber Harvest Costs. Therefore it is also not known if the significance of Spatial Predictors on Harvest Costs is high enough to calculate Timber Harvest Costs solely based on Spatial Predictors. A dataset containing 160,000 test units based on existing harvest data of the Colorado State Forest is created. The dataset contains the Spatial and Non-Spatial Predictors of Timber Harvest Costs for each unit. Each unit is run through a created Harvest Cost Model, which is based on existing literature and equations. The Harvest Cost Model returns a Cost per ton for each unit. The created data are used to develop a spatially explicit regression model that calculates Harvest Costs solely based on Spatial Predictors. The created spatially explicit regression model has an R-squared value of 0.42. Therefore Spatial Predictors predict 42% of Timber Harvest Costs. Calculating Timber Harvest Costs with an accuracy of 42% is not enough to calculate absolute Harvest Costs solely based on Spatial Predictors. But for optimization models, relative Harvest Costs are sufficient, since relative Harvest Cost allows the comparison of Costs of different stands and scenarios. An accuracy of 42% is then enough to estimate relative Harvest Costs. Published Online: 2015/06/26 08:06:20 Object Identifier: 0xc1aa5576 0x003249e2 Rights:https://creativecommons.org/licenses/by-nd/4.0/
The Journal for Geographic Information Science issue 1-2015 presents peer-reviewed papers
presented at the Geoinformatics
Forum (www.gi-forum.org), held in Salzburg from July 7-10,
2015. The annual GI_Forum symposium provides a platform for dialogue among geospatial minds
in an ongoing effort to support the creation of an informed GISociety.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |