Evaluation of three remote camera systems for detecting mammals and birds

Automated camera systems have widespread application in wildlife studies and their use is increasing (Kucera & Barrett 1993; Cutler & Swann 1999; Swann et al. 2004; Parker et al. 2008). Among other applications, they have been used to produce species inventories, estimate population sizes, study behaviour and examine the impact and activity of predators (Cutler & Swann 1999; Swann et al. 2004). Modern camera systems can operate for extended durations, are relatively non-invasive, easy to operate, portable, durable and can take good-quality images by day and night (Kucera & Barrett 1993; Peterson & Thomas 1998; Allison & Destefano 2006; Parker et al. 2008). Beyond their scientific applications, the generation of high-quality images can be useful for educational and conservation purposes (Cutler & Swann 1999). The two most common types of systems currently used in ecological research are passive and active infrared (IR) systems (Cutler & Swann 1999; Parker et al. 2008). An older form of remote photography is video which captures a continuous record of activity at a focal site (Stewart et al.1997; King et al. 2001). Camera systems have certain limitations and biases (Swann et al. 2004), yet these have not been well studied. Refinement of the use of camera systems is required to fully realize their value (Towerton et al. 2008). Here, we describe a comparison of detection rates of mammals and birds by passive and active IR camera systems, using a video system to benchmark detection rates.

Distributed Robotic Vision as a service

The vision sense of standalone robots is limited by line of sight and onboard camera capabilities, but processing video from remote cameras puts a high computational burden on robots. This paper describes the Distributed Robotic Vision Service, DRVS, which implements an on-demand distributed visual object detection service. Robots specify visual information requirements in terms of regions of interest and object detection algorithms. DRVS dynamically distributes the object detection computation to remote vision systems with processing capabilities, and the robots receive high-level object detection information. DRVS relieves robots of managing sensor discovery and reduces data transmission compared to image sharing models of distributed vision. Navigating a sensorless robot from remote vision systems is demonstrated in simulation as a proof of concept.

Impacts on nontarget avian species from aerial meat baiting for feral pigs

Bait containing sodium fluoroacetate (1080) is widely used for the routine control of feral pigs in Australia. In Queensland, meat baits are popular in western and northern pastoral areas where they are readily accepted by feral pigs and can be distributed aerially. Field studies have indicated some levels of interference and consumption of baits by nontarget species and, based on toxicity data and the 1080 content of baits, many nontarget species (particularly birds and varanids) are potentially at risk through primary poisoning. While occasional deaths of species have been recorded, it remains unclear whether the level of mortality is sufficient to threaten the viability or ecological function of species. A series of field trials at Culgoa National Park in south-western Queensland was conducted to determine the effect of broadscale aerial baiting (1.7 baits per km2) on the density of nontarget avian species that may consume baits. Counts of susceptible bird species were conducted prior to and following aerial baiting, and on three nearby unbaited properties, in May and November 2011, and May 2012. A sample of baits was monitored with remote cameras in the November 2011 and May 2012 trials. Over the three baiting campaigns, there was no evidence of a population-level decline among the seven avian nontarget species that were monitored. Thirty per cent and 15% of baits monitored by remote cameras in the November 2011 and May 2012 trials were sampled by birds, varanids or other reptiles. These results support the continued use of 1080 meat baits for feral pig management in western Queensland and similar environs.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

Why is this Grizzly Bear in My Backyard?

This presentation provides an overview of the type of work that involves trying to resolve human/grizzly bear conflicts. Much of this work involves public education, cap-turing, handling, and monitoring grizzly bears, the use of Karelian Bear Dogs, and using new technology. Some of the new technology includes the use of remote cameras, an au-tomated bear trap, microchips, DNA, GPS radio collars, and an Electro-Optic/Infrared imagery system to locate grizzly bears.

Habitat Use of the Key Largo Woodrat (Neotoma floridana smalli)

Key Largo woodrats are an endangered subspecies with an extremely limited habitat. This study sought to understand woodrat habitat preferences in order to guide management. Woodrats build stick nests from natural and artificial materials, so nest distribution and nest occupancy were used as indicators of preference. Distribution was determined by nest surveys, and remote cameras were used to assess occupancy. Forest structure, human disturbance, nest, and animal presence metrics were also collected. More nests were found along abandoned roads than along forest transects and more artificial nests were occupied than natural nests. These findings indicate that woodrats prefer areas with human disturbance, rather than forest age and structure as previously believed. This may have consequences on woodrat management, as it suggests that man-made materials are currently needed by woodrats even in a protected natural area.

Response of feral cats to a track-based baiting programme using Eradicat baits

Summary The feral Cat (Felis catus) is a significant threat to Australian fauna, and reducing their impacts is considered an essential action for threatened species conservation. Poison baiting is increasingly being used for the broad scale control of feral cats. In this study, we measured the population response of feral cats to a track-based baiting programme using Eradicat baits in the semi-arid northern wheatbelt region of Western Australia. Over two years, 1500 baits were laid once annually and the response of feral cats was measured using remote cameras in a before–after, control–impact design. There was a significant reduction in feral cat activity in the second year, but not the first. During bait uptake trials, corvids removed the most number of baits, followed by cats and varanids. The lack of a response to baiting in the first year may be due to existing low cat numbers in the baited area and/or the timing of the baiting. We provide a list of key recommendations to help inform future cat baiting programmes and research.

A novel three-dimensional imaging array recording and computational method by means of coded cameras reconstruction

In this paper, we propose a novel three-dimensional imaging method by which the object is captured by a coded cameras array (CCA) and computationally reconstructed as a series of longitudinal layered surface images of the object. The distribution of cameras in array, named code pattern, is crucial for reconstructed images fidelity when the correlation decoding is used. We use DIRECT global optimization algorithm to design the code patterns that possess proper imaging property. We have conducted primary experiments to verify and test the performance of the proposed method with a simple discontinuous object and a small-scale CCA including nine cameras. After certain procedures such as capturing, photograph integrating, computational reconstructing and filtering, etc., we obtain reconstructed longitudinal layered surface images of the object with higher signal-to-noise ratio. The results of experiments show that the proposed method is feasible. It is a promising method to be used in fields such as remote sensing, machine vision, etc. (c) 2006 Elsevier GmbH. All rights reserved.

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.

Predators of Spotted Flycatcher Muscicapa striata nests in southern England as determined by digital nest cameras

Capsule Avian predators are principally responsible. Aims To document the fate of Spotted Flycatcher nests and to identify the species responsible for nest predation. Methods During 2005-06, purpose-built, remote, digital nest-cameras were deployed at 65 out of 141 Spotted Flycatcher nests monitored in two study areas, one in south Devon and the second on the border of Bedfordshire and Cambridgeshire. Results Of the 141 nests monitored, 90 were successful (non-camera nests, 49 out of 76 successful, camera nests, 41 out of 65). Fate was determined for 63 of the 65 nests monitored by camera, with 20 predation events documented, all of which occurred during daylight hours. Avian predators carried out 17 of the 20 predations, with the principal nest predator identified as Eurasian Jay Garrulus glandarius. The only mammal recorded predating nests was the Domestic Cat Felis catus, the study therefore providing no evidence that Grey Squirrels Sciurus carolinensis are an important predator of Spotted Flycatcher nests. There was no evidence of differences in nest survival rates at nests with and without cameras. Nest remains following predation events gave little clue as to the identity of the predator species responsible. Conclusions Nest-cameras can be useful tools in the identification of nest predators, and may be deployed with no subsequent effect on nest survival. The majority of predation of Spotted Flycatcher nests in this study was by avian predators, principally the Jay. There was little evidence of predation by mammalian predators. Identification of specific nest predators enhances studies of breeding productivity and predation risk.

Modeling image motion in airborne Three-Line-Array (TLA) push-broom cameras

This paper presents an image motion model for airborne three-line-array (TLA) push-broom cameras. Both aircraft velocity and attitude instability are taken into account in modeling image motion. Effects of aircraft pitch, roll, and yaw on image motion are analyzed based on geometric relations in designated coordinate systems. The image motion is mathematically modeled by image motion velocity multiplied by exposure time. Quantitative analysis to image motion velocity is then conducted in simulation experiments. The results have shown that image motion caused by aircraft velocity is space invariant while image motion caused by aircraft attitude instability is more complicated. Pitch,roll and yaw all contribute to image motion to different extents. Pitch dominates the along-track image motion and both roll and yaw greatly contribute to the cross-track image motion. These results provide a valuable base for image motion compensation to ensure high accuracy imagery in aerial photogrammetry.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

The rotation state of 67P/Churyumov-Gerasimenko from approach observations with the OSIRIS cameras on Rosetta

Aims. Approach observations with the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS) experiment onboard Rosetta are used to determine the rotation period, the direction of the spin axis, and the state of rotation of comet 67P’s nucleus. Methods. Photometric time series of 67P have been acquired by OSIRIS since the post wake-up commissioning of the payload in March 2014. Fourier analysis and convex shape inversion methods have been applied to the Rosetta data as well to the available ground-based observations. Results. Evidence is found that the rotation rate of 67P has significantly changed near the time of its 2009 perihelion passage, probably due to sublimation-induced torque. We find that the sidereal rotation periods P1 = 12.76129 ± 0.00005 h and P2 = 12.4043 ± 0.0007 h for the apparitions before and after the 2009 perihelion, respectively, provide the best fit to the observations. No signs of multiple periodicity are found in the light curves down to the noise level, which implies that the comet is presently in a simple rotation state around its axis of largest moment of inertia. We derive a prograde rotation model with spin vector J2000 ecliptic coordinates λ = 65° ± 15°, β = + 59° ± 15°, corresponding to equatorial coordinates RA = 22°, Dec = + 76°. However, we find that the mirror solution, also prograde, at λ = 275° ± 15°, β = + 50° ± 15° (or RA = 274°, Dec = + 27°), is also possible at the same confidence level, due to the intrinsic ambiguity of the photometric problem for observations performed close to the ecliptic plane.