a b s t r a c t Offshore wind energy Thermal video Classification of birds and bats that use areas targeted for offshore wind farm development is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea is transect-surveys conducted by trained individuals in boats or planes, or analysis of imagery collected from aerial surveys. These methods can be costly and pose safety concerns so that observation times are limited to daylight hours and fair weather. We propose an alternative method based on analysis of thermal video that could be recorded autonomously. We present a framework for building models to classify birds and bats and their associated behaviors from their flight tracks. As an example, we developed a discriminant model for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-minute video clips. The agreement between model-and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and the number of different path types was reduced. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models could be improved if the distance between the camera and the target was known.
PrefaceDuring FY2012, internal funding from the Pacific Northwest National Laboratory's Lab Directed Research and Development (LDRD) Program provided support to begin exploring the development of software that could identify objects of interest (birds and bats) sensed with thermal imaging video equipment. Previous research indicated that the use of infrared video cameras was an effective method to survey the sky for birds and bats. Unlike traditional methods that require a human observer recording events as they are observed, video recording provides a real-time archive of what was observed and could be conducted at remote locations. However, the identification of observed phenomena still requires a trained observer viewing the video, which becomes both time consuming and expensive, and, like traditional methods, is still prone to observer bias. The research team began by acquiring existing thermal video files gathered by Sid Gauthreax for a previous research project at the Clemson University Radar Ornithology Laboratory. A new algorithm for automatically detecting birds and bats in infrared video was designed and implemented within MATLAB. The algorithm design is a unique combination of video peak store (VPS) processing, region growing, and perceptual grouping techniques. Birds, bats, and other warm targets moving through the camera's field of view (FOV) produce bright spots in the video that change position from frame to frame. VPS is the process of storing the peak intensity of each pixel in the video over the course of a fixed time window into a single image. The resulting image then contains the history of a target's motion, or its track, through the camera's FOV. VPS is usually done with a dedicated hardware device, but we wrote our own code within MATLAB to do the VPS processing.The algorithm then detects tracks in the set of VPS images produced from a video recording. Conceptually, a track is composed of a series of objects. An object is a spatially connected group of pixels that had peak values in the same frame. Individual pixels are first grouped into objects using a form of region growing that was tailored to this application. Objects then are combined into tracks using perceptual grouping, a general method of image processing inspired by human visual perception. In our algorithm, similar objects that lie in a line or along a curve are grouped together as a track. Much of the algorithm development involved defining the terms similar and lies in a line or along a curve in an appropriate mathematical form. Each track is identified by the time it starts in the recorded video for independent verification with observer annotations. The text file also contains a number of measures of each track, such as the mean size and intensity of the objects in the track, and the sinuosity of the track. The sinuosity is a measure of the change in direction between successive objects in a track.Although efforts within this project showed that processing of video files to extract information could be automated, further w...
SummaryProposed development of domestic energy resources, including wind energy, is expected to impact the sagebrush steppe ecosystem in the western United States. The greater sage-grouse relies on habitats within this ecosystem for survival, yet very little is known about how wind energy development may affect sage-grouse. The purpose of this report is to inform organizations of the impacts wind energy development could have on greater sage-grouse populations and identify information needed to fill gaps in knowledge.Sage-grouse are highly dependent on sagebrush-dominated landscapes for all phases of their life history. Much of their current range overlaps with wind power resources characterized as superb to good across 11 western states. Sage grouse may utilize different habitats during different seasons and usually require a large home range. However, they are habitual, using specific locales during all seasons, and are sensitive to habitat disturbance. Sage-grouse populations have generally been in decline since the mid1960s; the species is currently under review for listing as threatened or endangered by the U.S. Fish and Wildlife Service.Very little is known about wind energy and sage-grouse, but oil-and gas-field developments within the range of the sage-grouse often have caused measureable effects to their populations. Activities and disturbance related to both energy development scenarios are believed to pose some similar threats to the grouse. Sage-grouse populations typically decline following oil and gas development, and birds have been displaced from habitat near infrastructure and locations with human. Notably, it has been shown that female grouse nesting in developed areas had lower annual survival rates. Chick mortality rates also were higher within sight of oil wells.It is not known to what extent the development of wind energy resources will affect sage-grouse populations. Information on local and landscape-level impacts is needed. Before-after control-impact studies are needed to determine impacts to grouse, and information gained could be used within an adaptive management strategy. Research protocols and efforts should be developed collaboratively between industry, resource management, and the research community.v
In July 2005, a field survey of the EVOC burrow complex was conducted to determine use and demography at each site. Burrow locations were mapped, and signs of activity (feces, owl tracks, castings, feathers) were recorded. Of the 20 burrows, 12 were found to be active. Of the eight inactive burrows, three appeared to have been active earlier in the 2005 breeding season. A total of 19 owls were counted, but demography could not be determined. It appears that the EVOC mitigation exceeded burrow use goals during 2005. Continued site monitoring and maintenance, according to mitigation plan guidelines, should be conducted as prescribed.iii
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