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Overview: An ecological sensor observatory
With the growth and expansion of human populations has come an increasingly greater need to understand the dynamics of ecosystems and their complex interactions. As urban areas continue to expand, fragile surrounding ecosystems are often affected as they are redesigned to support urban infrastructure. For instance, the fragmentation of forests within the National Parks and Reserves, and its effects on animal habitats, is becoming a significant problem.

Acoustic signals have been used for many years to census vocal organisms. For example, the North American Breeding Bird Survey, one of the largest long-term, national-scale avian monitor programs, has been conducted for more than thirty years using human auditory and visual cues. The North American Amphibian Monitoring Program is based on identifying amphibian species primarily by listening for their calls. Recent advances in sensor networks enable large-scale, automated collection of acoustic signals in natural areas. The systematic and synchronous collection of acoustic samples at multiple locations, combined with measurements of ancillary data such as light, temperature, and humidity, can produce an enormous volume of ecologically relevant data. Transmuting this raw data into useful knowledge requires timely and effective processing and analysis.

The potential for automating environmental monitoring using acoustic and other sensor types in conjunction with advanced computational techniques is significant. A holistic solution (sensor-to-user) is required to enable interpretation of the soundscape. Although sound has been used to conduct species specific census of organisms that communicate using acoustics, there is significant work to be done to enable the scientific community to apply acoustics to the task of environmental monitoring. REAL has identified four research areas that are important for implementing an holistic solution: sensor technologies, soundscape interpretation, ecosystem assessment, and data stewardship.

Current operational paradigm
The current operational paradigm designed and developed by REAL is shown in the figures below. Depicted is the flow of information from in-field sensors to the laboratory where the collected data is stored, cataloged, interpreted and can subsequently be used by analysts for assessing environmental status and health. This sensor-to-user solution to environmental monitoring provides an operational infrastructure that can be populated with new sensor observations and new analytical tools to provide a robust research and outreach framework.
High-level view of the sensor network
Figure 1: High-level view of the sensor network architecture and apparatus.
As shown in Figure 1, each sensor cluster comprises two or more sensor platforms and a sensor server. Each sensor platform comprises a pole-mounted sensor unit and a solar panel coupled with a deep cycle battery for providing power over extended periods. Acoustic clips are collected by the sensor units and transmitted over a wireless network to a sensor server in a protected enclosure for temporary storage and later relay back to the laboratory. In addition to acoustic data, each platform transmits camera images as a record of general environmental conditions.
Depicted in Figure 2 is a high-level view of the communication and data processing infrastructure used for transmitting, storing, and analyzing data collected by the sensor platforms shown in Figure 1. This infrastructure enabled the realization of the Long Term Ecological Research (LTER) Grid Pilot Study, conducted by the National Center for Supercomputing Applications (NCSA), the LTER Network Office at the University of New Mexico, and Michigan State University to design and build a prototype grid for the ecological community. The featured grid application, the Biophony Grid Portal, is based on the acoustic research at the Kellogg Biological Station (KBS) and enables researchers to conduct real time digital signal processing analysis on high-performance systems via a web-based portal.
High-level view of data processing and analysis
Figure 2: High-level view of sensor data storage, processing and analysis.

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