Spatially comprehensive data for forestry scenario analysis : consequences of errors and methods to enhance usability. Barth, A. Ph.D. Thesis, 2007. ![Spatially comprehensive data for forestry scenario analysis : consequences of errors and methods to enhance usability [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This thesis focuses on the use of forest data for national level policy making. Three major issues were considered: (i) to determine typical requirements of data in forestry scenario analysis, (ii) to evaluate and further develop methods to determine data requirements, and (iii) to develop methods that improve data usability in forestry scenario analysis. Increasingly, the trend is to use spatially comprehensive data as a basis for forestry scenario analysis. Compared to traditional approaches, often limited to sample data, this allows for a broader scope. This is needed since sustainable forestry today must encompass economical and ecological, as well as social perspectives. Different approaches to linking data acquisition strategies with decisions that typically are based on forestry scenario analyses were used in the determination of data requirements. In Paper I, a qualitative framework was developed and applied. The conclusions were that none of the currently used Swedish data acquisition strategies were able to provide data for adequate multi-resource forestry scenario analysis at national level. In Papers II and III, two quantitative approaches were used for the evaluation of sample-plot imputations; using a decision support system the quantitative consequences of errors and cost-plus-loss with simulations were considered. From Paper II it was clear that traditional approaches to acquiring spatially comprehensive data may lead to severe errors in scenario analyses. Both papers concluded that improvements are required in the methodology of assessing the data. In Paper IV, an analytical cost-plus-loss approach was used to address the issue of decision-making at the national level linked to national forest inventories. The conclusion was that the current level of Swedish national forest inventory is motivated fully by the role of the inventory to provide information for national level timber harvesting planning, whereas the inventory serves many other purposes as well. In Papers V and VI, methods were developed and tested regarding how the usability of spatially comprehensive data for national level forestry scenario analysis can be enhanced. In Paper V an algorithm for spatially consistent imputation within forest stands was developed and found to deliver good results in a case study. In Paper VI, a framework for landscape level imputation aiming at preserving overall composition while enhancing spatial configuration was outlined and tested. A core component of the framework was a restricted imputation algorithm that ensured that the classical imputation problem of data “tending towards the mean” was avoided. Case studies showed promising results, but it is clear that the methodological tool-kit must be further developed before it can be applied in practice.
@phdthesis{RN395,
author = {Barth, Andreas},
title = {Spatially comprehensive data for forestry scenario analysis : consequences of errors and methods to enhance usability},
university = {Swedish University of Agricultural Sciences, SLU},
abstract = {This thesis focuses on the use of forest data for national level policy making. Three major issues were considered: (i) to determine typical requirements of data in forestry scenario analysis, (ii) to evaluate and further develop methods to determine data requirements, and (iii) to develop methods that improve data usability in forestry scenario analysis. Increasingly, the trend is to use spatially comprehensive data as a basis for forestry scenario analysis. Compared to traditional approaches, often limited to sample data, this allows for a broader scope. This is needed since sustainable forestry today must encompass economical and ecological, as well as social perspectives. Different approaches to linking data acquisition strategies with decisions that typically are based on forestry scenario analyses were used in the determination of data requirements. In Paper I, a qualitative framework was developed and applied. The conclusions were that none of the currently used Swedish data acquisition strategies were able to provide data for adequate multi-resource forestry scenario analysis at national level. In Papers II and III, two quantitative approaches were used for the evaluation of sample-plot imputations; using a decision support system the quantitative consequences of errors and cost-plus-loss with simulations were considered. From Paper II it was clear that traditional approaches to acquiring spatially comprehensive data may lead to severe errors in scenario analyses. Both papers concluded that improvements are required in the methodology of assessing the data. In Paper IV, an analytical cost-plus-loss approach was used to address the issue of decision-making at the national level linked to national forest inventories. The conclusion was that the current level of Swedish national forest inventory is motivated fully by the role of the inventory to provide information for national level timber harvesting planning, whereas the inventory serves many other purposes as well. In Papers V and VI, methods were developed and tested regarding how the usability of spatially comprehensive data for national level forestry scenario analysis can be enhanced. In Paper V an algorithm for spatially consistent imputation within forest stands was developed and found to deliver good results in a case study. In Paper VI, a framework for landscape level imputation aiming at preserving overall composition while enhancing spatial configuration was outlined and tested. A core component of the framework was a restricted imputation algorithm that ensured that the classical imputation problem of data “tending towards the mean” was avoided. Case studies showed promising results, but it is clear that the methodological tool-kit must be further developed before it can be applied in practice.},
keywords = {forest inventory, data acquisition, forest management, decision support systems, forestry scenario analysis, data requirements, national level forest planning, decision-making, policy-making, cost-plus-loss analysis, medium-resolution satellite data, la
scanner data, national forest inventory, data usability, optimisation, imputation},
url = {http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-1863},
year = {2007},
type = {Thesis}
}
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Compared to traditional approaches, often limited to sample data, this allows for a broader scope. This is needed since sustainable forestry today must encompass economical and ecological, as well as social perspectives. Different approaches to linking data acquisition strategies with decisions that typically are based on forestry scenario analyses were used in the determination of data requirements. In Paper I, a qualitative framework was developed and applied. The conclusions were that none of the currently used Swedish data acquisition strategies were able to provide data for adequate multi-resource forestry scenario analysis at national level. In Papers II and III, two quantitative approaches were used for the evaluation of sample-plot imputations; using a decision support system the quantitative consequences of errors and cost-plus-loss with simulations were considered. From Paper II it was clear that traditional approaches to acquiring spatially comprehensive data may lead to severe errors in scenario analyses. Both papers concluded that improvements are required in the methodology of assessing the data. In Paper IV, an analytical cost-plus-loss approach was used to address the issue of decision-making at the national level linked to national forest inventories. The conclusion was that the current level of Swedish national forest inventory is motivated fully by the role of the inventory to provide information for national level timber harvesting planning, whereas the inventory serves many other purposes as well. In Papers V and VI, methods were developed and tested regarding how the usability of spatially comprehensive data for national level forestry scenario analysis can be enhanced. In Paper V an algorithm for spatially consistent imputation within forest stands was developed and found to deliver good results in a case study. In Paper VI, a framework for landscape level imputation aiming at preserving overall composition while enhancing spatial configuration was outlined and tested. A core component of the framework was a restricted imputation algorithm that ensured that the classical imputation problem of data “tending towards the mean” was avoided. 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