Forest Policy Scenario Analysis: Sensitivity of Songbird Community to Changes in Forest Cover Amount and Configuration

Changes in mature forest cover amount, composition, and configuration can be of significant consequence to wildlife populations. The response of wildlife to forest patterns is of concern to forest managers because it lies at the heart of such competing approaches to forest planning as aggregated vs. dispersed harvest block layouts. In this study, we developed a species assessment framework to evaluate the outcomes of forest management scenarios on biodiversity conservation objectives. Scenarios were assessed in the context of a broad range of forest structures and patterns that would be expected to occur under natural disturbance and succession processes. Spatial habitat models were used to predict the effects of varying degrees of mature forest cover amount, composition, and configuration on habitat occupancy for a set of 13 focal songbird species. We used a spatially explicit harvest scheduling program to model forest management options and simulate future forest conditions resulting from alternative forest management scenarios, and used a process-based fire-simulation model to simulate future forest conditions resulting from natural wildfire disturbance. Spatial pattern signatures were derived for both habitat occupancy and forest conditions, and these were placed in the context of the simulated range of natural variation. Strategic policy analyses were set in the context of current Ontario forest management policies. This included use of sequential time-restricted harvest blocks (created for Woodland caribou (Rangifer tarandus) conservation) and delayed harvest areas (created for American marten (Martes americana atrata) conservation). This approach increased the realism of the analysis, but reduced the generality of interpretations. We found that forest management options that create linear strips of old forest deviate the most from simulated natural patterns, and had the greatest negative effects on habitat occupancy, whereas policy options that specify deferment and timing of harvest for large blocks helped ensure the stable presence of an intact mature forest matrix over time. The management scenario that focused on maintaining compositional targets best supported biodiversity objectives by providing the composition patterns required by the 13 focal species, but this scenario may be improved by adding some broad-scale spatial objectives to better maintain large blocks of interior forest habitat through time.

Feeding–Danger Trade-Offs Underlie Stopover Site Selection by Migrants

To migrate successfully, birds need to store adequate fat reserves to fuel each leg of the journey. Migrants acquire their fuel reserves at stopover sites; this often entails exposure to predators. Therefore, the safety attributes of sites may be as important as the feeding opportunities. Furthermore, site choice might depend on fuel load, with lean birds more willing to accept danger to obtain good feeding. Here, we evaluate the factors underlying stopover-site usage by migrant Western Sandpipers (Calidris mauri) on a landscape scale. We measured the food and danger attributes of 17 potential stopover sites in the Strait of Georgia and Puget Sound region. We used logistic regression models to test whether food, safety, or both were best able to predict usage of these sites by Western Sandpipers. Eight of the 17 sites were used by sandpipers on migration. Generally, sites that were high in food and safety were used, whereas sites that were low in food and safety were not. However, dangerous sites were used if there was ample food abundance, and sites with low food abundance were used if they were safe. The model including both food and safety best-predicted site usage by sandpipers. Furthermore, lean sandpipers used the most dangerous sites, whereas heavier birds (which do not need to risk feeding in dangerous locations) used safer sites. This study demonstrates that both food and danger attributes are considered by migrant birds when selecting stopover sites, thus both these attributes should be considered to prioritize and manage stopover sites for conservation.

Nest Boxes Facilitate Local-Scale Conservation of Common Goldeneye (Bucephala clangula) and Bufflehead (Bucephala albeola) in Alberta, Canada

We tested the general predictions of increased use of nest boxes and positive trends in local populations of Common Goldeneye (Bucephala clangula) and Bufflehead (Bucephala albeola) following the large-scale provision of nest boxes in a study area of central Alberta over a 16-year period. Nest boxes were rapidly occupied, primarily by Common Goldeneye and Bufflehead, but also by European Starling (Sturnus vulgaris). After 5 years of deployment, occupancy of large boxes by Common Goldeneye was 82% to 90% and occupancy of small boxes by Bufflehead was 37% to 58%. Based on a single-stage cluster design, experimental closure of nest boxes resulted in significant reductions in numbers of broods and brood sizes produced by Common Goldeneye and Bufflehead. Occurrence and densities of Common Goldeneye and Bufflehead increased significantly across years following nest box deployment at the local scale, but not at the larger regional scale. Provision of nest boxes may represent a viable strategy for increasing breeding populations of these two waterfowl species on landscapes where large trees and natural cavities are uncommon but wetland density is high.

Filter for Car Tracking Based on Acceleration and Steering Angle

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking.

Advances in Model-Based Traffic Vision

Model based vision allows use of prior knowledge of the shape and appearance of specific objects to be used in the interpretation of a visual scene; it provides a powerful and natural way to enforce the view consistency constraint. A model based vision system has been developed within ESPRIT VIEWS: P2152 which is able to classify and track moving objects (cars and other vehicles) in complex, cluttered traffic scenes. The fundamental basis of the method has been previously reported. This paper presents recent developments which have extended the scope of the system to include (i) multiple cameras, (ii) variable camera geometry, and (iii) articulated objects. All three enhancements have easily been accommodated within the original model-based approach

Pose Refinement of Active Models using Forces in 3D

A new algorithm is described for refining the pose of a model of a rigid object, to conform more accurately to the image structure. Elemental 3D forces are considered to act on the model. These are derived from directional derivatives of the image local to the projected model features. The convergence properties of the algorithm is investigated and compared to a previous technique. Its use in a video sequence of a cluttered outdoor traffic scene is also illustrated and assessed.

A simple, intuitive camera calibration tool for natural images

The paper reports an interactive tool for calibrating a camera, suitable for use in outdoor scenes. The motivation for the tool was the need to obtain an approximate calibration for images taken with no explicit calibration data. Such images are frequently presented to research laboratories, especially in surveillance applications, with a request to demonstrate algorithms. The method decomposes the calibration parameters into intuitively simple components, and relies on the operator interactively adjusting the parameter settings to achieve a visually acceptable agreement between a rectilinear calibration model and his own perception of the scene. Using the tool, we have been able to calibrate images of unknown scenes, taken with unknown cameras, in a matter of minutes. The standard of calibration has proved to be sufficient for model-based pose recovery and tracking of vehicles.

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

A Generic Deformable Model for Vehicle Recognition

This paper reports the development of a highly parameterised 3-D model able to adopt the shapes of a wide variety of different classes of vehicles (cars, vans, buses, etc), and its subsequent specialisation to a generic car class which accounts for most commonly encountered types of car (includng saloon, hatchback and estate cars). An interactive tool has been developed to obtain sample data for vehicles from video images. A PCA description of the manually sampled data provides a deformable model in which a single instance is described as a 6 parameter vector. Both the pose and the structure of a car can be recovered by fitting the PCA model to an image. The recovered description is sufficiently accurate to discriminate between vehicle sub-classes.

Pose and Structure Recovery using Active Models

A new formulation of a pose refinement technique using ``active'' models is described. An error term derived from the detection of image derivatives close to an initial object hypothesis is linearised and solved by least squares. The method is particularly well suited to problems involving external geometrical constraints (such as the ground-plane constraint). We show that the method is able to recover both the pose of a rigid model, and the structure of a deformable model. We report an initial assessment of the performance and cost of pose and structure recovery using the active model in comparison with our previously reported ``passive'' model-based techniques in the context of traffic surveillance. The new method is more stable, and requires fewer iterations, especially when the number of free parameters increases, but shows somewhat poorer convergence.

Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour

A driver controls a car by turning the steering wheel or by pressing on the accelerator or the brake. These actions are modelled by Gaussian processes, leading to a stochastic model for the motion of the car. The stochastic model is the basis of a new filter for tracking and predicting the motion of the car, using measurements obtained by fitting a rigid 3D model to a monocular sequence of video images. Experiments show that the filter easily outperforms traditional filters.

Facebook & Blackboard: comparison of learner perspectives

Different systems, different purposes – but how do they compare as learning environments? We undertook a survey of students at the University, asking whether they learned from their use of the systems, whether they made contact with other students through them, and how often they used them. Although it was a small scale survey, the results are quite enlightening and quite surprising. Blackboard is populated with learning material, has all the students on a module signed up to it, a safe environment (in terms of Acceptable Use and some degree of staff monitoring) and provides privacy within the learning group (plus lecturer and relevant support staff). Facebook, on the other hand, has no learning material, only some of the students using the system, and on the face of it, it has the opportunity for slips in privacy and potential bullying because the Acceptable Use policy is more lax than an institutional one, and breaches must be dealt with on an exception basis, when reported. So why do more students find people on their courses through Facebook than Blackboard? And why are up to 50% of students reporting that they have learned from using Facebook? Interviews indicate that students in subjects which use seminars are using Facebook to facilitate working groups – they can set up private groups which give them privacy to discuss ideas in an environment which perceived as safer than Blackboard can provide. No staff interference, unless they choose to invite them in, and the opportunity to select who in the class can engage. The other striking finding is the difference in use between the genders. Males are using blackboard more frequently than females, whilst the reverse is true for Facebook. Interviews suggest that this may have something to do with needing to access lecture notes… Overall, though, it appears that there is little relationship between the time spent engaging with Blackboard and reports that students have learned from it. Because Blackboard is our central repository for notes, any contact is likely to result in some learning. Facebook, however, shows a clear relationship between frequency of use and perception of learning – and our students post frequently to Facebook. Whilst much of this is probably trivia and social chit chat, the educational elements of it are, de facto, contructivist in nature. Further questions need to be answered - Is the reason the students learn from Facebook because they are creating content which others will see and comment on? Is it because they can engage in a dialogue, without the risk of interruption by others?