IENE 2016

Environmental monitoring of transportation infrastructure is a critical activity for transportation organizations (TOs). Understanding wildlife connectivity (the ability for wildlife to move freely) improves highway planning where wildlife occurs. Many TOs use camera traps to study wildlife use of purpose-built wildlife crossing structures, or opportunistic use of other structures and associated right-of-way fencing. By recording wildlife use of mitigation structures under or over roads, TOs can measure: 1) effectiveness of structures and structure types in maintaining movement of protected animals, 2) connection of wildlife populations, 3) animal activity levels, and 4) natural behaviors. Camera trap measurements are a cost-effective way to capture information about the occurrence of multiple species. Despite advances in wireless instruments and computing capabilities, environmental monitoring technology for highways (e.g., for wildlife occurrence) lags behind those used for traffic. We describe the creation of an online system (hereafter Cam-WON; http://wildlifeobserver.net) for ingesting and organizing camera trap data (image files with their associated  annotations) as individual or bulk file uploads. This system is open to anyone in the world and is free of charge. We used a GNU/Linux operating system, a web server, a relational database management system, and “middleware” tools utilizing several programming languages, including Javascript, Java, Python, and PHP.  The web-system described here uses Ubuntu Linux Server running Apache2, PostgreSQL with the PostGIS extension, Geoserver with Apache Tomcat 7, GeoWebCache, Drupal 7, and various other community contributed libraries and tools.  We describe automated methods for web-system processing of photographic monitoring data from cell/WiFi-communicating camera systems at purpose-built and opportunistic wildlife crossing structures. The work flow culminates in a common strategy for taking image files from the camera supply chain and automatically creating records in a web-database. The primary human involvement is in animal species identification, animal activity/behavior description, and noting demographics. In order to integrate the physical camera system into transportation decision-making, environmental monitoring, and stewardship activities, the project was conducted with state TO technical staff. The combined web-camera systems were tested in US states: California, Utah, Wyoming, South Dakota, Maine, Colorado, and Washington. In each case, manually-maintained and cell-communicating cameras were used in tandem. The combined cell/wireless camera and web-systems represent an advance over traditional remote cameras with desktop databases in that their output is viewable almost instantly on the web via a wireless data transportation service. Although streaming wildlife web-cameras are not new, “wildlife traffic cameras” have not been previously used before in this way. We developed a model system that any TO could adopt for use in areas with wired, wireless, or cell system connectivity, including using existing infrastructure to add wildlife monitoring to the data input stream. We are also developing automated image analysis approaches to further simplify the workflow. Systematic collection of wildlife movement information at the roadway not only benefits long-term and project related stewardship needs, but it also provides an opportunity to share wildlife data and imagery directly with the public. This combination of science and public relations is increasingly important as public knowledge and awareness contributes towards safer wildlife interactions with highways and traffic.