This project explored various methods of how to rapidly collect relevant geo-referenced field data and integrate it with geospatial techniques to address a wide variety of shoreline management issues commonly facing lake managers. Data collection focused on filling “white holes” often identified in geospatial data sets, including lack of information on shore protection structures, docks, condition of riparian vegetation, ecological health or condition, and scenic resources. Shoreline features were mapped at representative shorelines using a combination of: 1) GPS mapping using a Trimble Juno SB unit; 2) offset mapping using the Trimble Juno SB paired with the LTI Laser Rangerfinder (TruPulse 360 B) (both have Bluetooth capabilities); and 3) videomapping using Red Hen Mapping Hardware (VMS 300) and Red Hen Mapping Software (GeoVideo) along with a Sony handheld camcorder. The videomap images were then used to input shoreline features into an ArcMap database. The accuracy and location of shoreline features mapped by each method were analyzed related to aerial photography and the results of each method.
Videos were converted from mpg format to flash video (flv) format to allow for smaller size and easier manipulation in a web browser. Flowplayer was used to display the flv videos in the web browser and OpenLayers along with Geoserver where used to display the map with the route of the video. In order to display a location on the map to correspond to the time/location in the video, a temporary layer is used that changes location each second as the video plays. This is done by creating a query to query the PostGreSQL database to find the point in the path layer on the map that holds the same time variable as the current time in the video.
A slider evaluation bar was also inputed to allow users to rate the scene they are viewing by either a Likert based scale of attactiveness (for aesthetic evaluations) or rapid natural assessment criteria gleaned from the literature (e.g. Pritchard, 1999; Stevensen and Mills, 1999; Hu et al., 2003). The shoreline images are also broken into reach segments that can be tied to digital field data forms to input professional assessments of ecological conditions.
The results of the surveys are automatically georeferenced, and can be used to rank-order views on the basis of scenic preference. In addition, the results of the scenic preference survey can be analyzed in conjunction with physical, biological, and cultural characteristics for each shoreline segment obtained from existing GIS databases compiled by RGIS-PN as part of a decision support system for lake assessment developed with the Washington Department of Ecology (Donoghue et al., 2006).
All methods and steps are explained in an online web-based guidance document, with case study examples and links to relevant geospatial datasets. The results will also be presented at the annual conference of the AAG next spring.