Compromise Programming in Forest Management

Poff, Boris; Tecle, Aregai; Neary, Daniel G.; Geils, Brian W.

doi:10.2181/036.042.0107

Cited by 19 publications

(16 citation statements)

References 32 publications

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“…In regard to the four management objectives, a collinearity diagnosis detected a high correlation among the independent variables of basal area, crown coverage, and volume (variance inflation factor > 7.6). Given that the basal area has demonstrated adequate applicability in similar studies [38,45], it was decided to use only this variable.…”

Section: Resultsmentioning

confidence: 99%

“…Some authors have suggested that the value of p = 2 represents an average state in the deviations between the ideal point and the efficient frontier [29,37]. As a result, some recommend the use of this value in the solution approach [34,38,45]. Results indicate that the solution shows a similar tree basal area for p values of 1 and 2 (21 m 2 /ha), so the solution is not highly-sensitive to changes in the p value ( Figure 5).…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Evaluating the Multi-Functionality of Forest Ecosystems in Northern Mexico

Pérez-Verdín

Monarrez-Gonzalez

Tecle

et al. 2018

Forests

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Abstract:Managing multiple ecosystem services is a complex task that involves special interactions among different resources, services, and stakeholders. Mexican forests have been traditionally managed for the single purpose of obtaining wood, benefiting a small sector of society. In this study, we evaluated the interactions among various ecosystem services, namely carbon content, tree diversity, surface water runoff, and the net present value of timber production. We also attempted to determine the most suitable basal area level that best satisfies the management of these services combined. Bivariate correlations, non-linear regression models, and a multiobjective decision-making technique are used to analyze the data in the study. Results indicate that trade-offs exist between surface water runoff and tree basal area. A synergistic relationship, between net present value and carbon content with basal area, was also observed. Tree diversity has a synergistic relationship with basal area in open forests, but a trade-off relationship in denser forests. The most preferred forest management level that satisfies the desired ecosystem services is between 17 and 21 m 2 /ha of residual basal area. We hope that adopting this multiobjective study can cement collaborative strategies among Mexican resource managers, landowners, environmental groups, and others interested in forest management.

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Section: Resultsmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Evaluating the Multi-Functionality of Forest Ecosystems in Northern Mexico

Pérez-Verdín

Monarrez-Gonzalez

Tecle

et al. 2018

Forests

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“…The complexity of multiscale GIS-MCDA models tends to increase with a longer timeframe and a greater number of time steps (Agarwal et al 2002). The GIS-MCDA models involving multiple temporal scales are often referred to as spatiotemporal models (e.g., Ratsiatou and Stefanakis 2001; Poff et al 2010;Young et al 2010). In this type of approach to the decision/evaluation problem, the spatial scales do not change over time; that is, the geographic extent and resolution are the same for each time step.…”

Section: Temporal Multiscalesmentioning

confidence: 99%

“…The temporal multiscale GIS-MCDA studies typically involve a time series; a sequence of datasets (e.g., the values of evaluation criteria) measured at successive points in time. Two approaches for incorporating temporal scales into GIS-MCDA procedures are identified: (i) temporal aggregation methods, in which the temporal datasets are aggregated to obtain an overall evaluation of each decision alternative (e.g., Pfeffer 2002;Young et al 2010), and (ii) time series methods, which use GIS-MCDA procedures for multicriteria combination of evaluation criteria and decision making agents preferences in subsequent time steps (e.g., Poff et al 2010;Sabri et al 2012). …”

Section: Temporal Multiscalesmentioning

confidence: 99%

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Scale Issues and GIS-MCDA

Malczewski

Rinner

2015

Advances in Geographic Information Science

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Decision making is a process. In many decision situations, the process operates across geographic, temporal, and organizational scales. Every change in scale brings about a new decision/evaluation problem, and consequently, the results of GIS-MCDA are scale dependent. The scale consists of extent (observational or geographic scale) and resolution (measurement scale). Each of these scales has spatial and temporal dimensions. Multiscale GIS-MCDA approaches tend to focus on issues of spatial scale and, in particular, the influence of resolution on the results of GIS-MCDA. This can be attributed to two interrelated issues of scale in spatial analysis. First, geographic (areal) datasets collected for analyzing decision situations (e.g., spatial patterns) may have little or no connection to the underlying decision making processes. The GIS-MCDA procedure executed on such data is not independent of how these areal units are configured. This situation is referred to as the modifiable areal unit problem (MAUP). MAUP has two components: (i) the scale effect; that is, the sensitivity of GIS-MCDA results due to the changing number (size) of spatial units of analysis, and (ii) the zoning effect; that is, the results of GIS-MCDA are influenced by the changing shape of the spatial units.Second, a major source of scale effects is spatial heterogeneity. Accordingly, the methods for analyzing the effects are based on the premise that the variability of geographic data is scale dependent and the processes underlying a spatial pattern are best represented at the scale characterized by maximum variability. In the context of decision/evaluation problems, the measure of variability should be related to the main components of multicriteria analysis: the value function (the criterion standardization procedure) and the criterion weights. We suggest that the criterion range value can provide a measure for identifying the most appropriate scale of GIS-MCDA. This Chapter will discuss the two issues of scale in the context of GIS-MCDA. It will also provide an overview of approaches for tackling multiscale decision situations using GIS-MCDA.

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