GEOMORPHOSE – A TARGETED ROSE DIAGRAM PROGRAM FOR LANDSCAPE ANALYSIS

In an introductory geomorphology class, investigating the relationship between a landscape’s texture, structure and affecting geomorphic processes is facilitated using rose diagrams. Preferred orientations of valleys, slopes, hills and other morphometric characteristics can be directly measured from lidar images and topographic maps and visually displayed in a series of rose diagrams that can be compared with structural measurements, glacial flow indicators and other data measured in the field, thereby allowing students to hypothesize and draw conclusions based on field data. However, plotting rose diagrams by hand is time consuming and available freeware is limiting. Software-based plotting with existing freeware proved difficult for most students, and few programs are multiplatform making instruction complicated. The ideal program should be multiplatform, have a shallow learning curve and easily understood data input methods, produce customizable images, and be able to display multiple sets of data in one graph. To that end, Richard Le Mon, under the direction of Lindley Hanson and Beifang Yi wrote Geomorphose, a program that meets these requirements. The Java programming language was chosen for its multiplatform (including web-based) strengths. Data can be loaded in a few different formats or entered directly, and with updated data changes in graph appearance is reflected immediately. Graphs can be saved in multiple formats and sizes for inclusion in papers or posters, and with transparent backgrounds suitable for overlaying on maps and imagery. Finally, two or more plots can be overlain such that relationships (or the lack thereof) can be directly evaluated.

Students using Geomorphose reported that they found the program intuitive and easy to use. They spent less time generating plots and more time collecting and analyzing data. New data were easily incorporated into existing data, and updated plots instantly visualized. Inconclusive, incomplete, or contradictory data were identifiable, giving students motivation to gather more data.