Conveying multiple, complex themes and classes for natural resource assessments

Elijah Ramsey III; Marshall L. Strong

doi:10.1080/10106049908542150

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Conveying multiple, complex themes and classes for natural resource assessments

Elijah Ramsey III·Marshall L. Strong

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Abstract

Two methods were used to convey the spatial association between a classified forested landscape, the distribution of a hurricane impact, and the subsequent recovery of the habitat from the storm. The first method used a constant hue matrix with varying intensity to visually represent combinations of impact and recovery magnitudes. The second combined two colors of constant hue to represent the impact and recovery succession. Maps produced using either method generally simulated known impact and recovery spatial distributions. In the constant hue representation with varying intensity, the combination of impact and recovery could not be uniquely represented in all cases and the relationship was confused between the map and legend color. In the two color representation, however, a definite link existed between the map and legend color and the impact and recovery magnitudes. A drawback to this approach over the constant hue approach was that a separate map had to be used to represent each forest type and its associated impact and recovery covariation. The two color combination, however, provided improved contrast and uniquely defined the link between map and legend colors, and ultimately, a unique impact and recovery combination. In addition, the two color combination produced the highest conveyance of spatial information related to the covariation of forest type, impact, and recovery.

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Geocarto International Article

Source-reported bounding extent: 29.31514119318728° to 31.527043924837933° latitude; -92.1588134765625° to -90.8184814453125° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Elijah Ramsey III, Marshall L. Strong. 2008-01-02. Conveying multiple, complex themes and classes for natural resource assessments. https://doi.org/10.1080/10106049908542150

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