Evaluation of potential habitat with an integrated analysis of a spatial conservation strategy for David’s deer, Elaphurus davidians

How to assess the potential habitat integrating landscape dynamics and population research, and how to reintroduce animals to potential habitats in environments highly human disturbed are still questions to be answered in conservation biology. According to behavioral research on Elaphurus davidians, we have developed a suitability index and a risk index to evaluate the potential habitats for the deer. With these indices, we conducted two transect assessments to evaluate the gradient change of the target region. Then, taking rivers as border lines, we tabulated the forest areas, high grassland area and total area and then compared the forest and high grassland area in each subregion. Furthermore, we computed the land use transfer matrix for the whole Yancheng coast during 1987–2000. We also computed human modified index (HMI) in six subregions. Lastly with a geographical information system support we obtained the spatial distribution of the indices and evaluation of the whole potential habitats from a neighborhood analysis. The transect assessment showed that the suitability of the coastal area was higher than that of the inland area for the deer, while the southern area was higher than the northern. Landscape metrics and HMI analysis showed that different landscape patterns and different anthropogenic disturbance existed within the region, and the increasing human disturbance was the key factor causing the pattern dynamics. The evaluation of potential habitats showed that there was an estimated carrying capacity of no more than 10,000 for David’s deer reintroduction into the natural area. Also the reintroduction strategy was discussed. This integrated approach linked the population research and the landscape metrics, and the dataset with different scale; thus, it is an approach likely to be useful for the protection of other large animal in a landscape highly disturbed by humans

Multi-frame image restoration for face recognition

Face recognition at a distance is a challenging and important law-enforcement surveillance problem, with low image resolution and blur contributing to the difficulties. We present a method for combining a sequence of video frames of a subject in order to create a restored image of the face with reduced blur. A generic Active Appearance Model of face shape and appearance is used for registration. By warping and averaging registered video frames, noise is reduced, allowing a Wiener filter to deblur the face to a greater degree than can be achieved on a single video frame. This process is theoretically justified and tested with real-world outdoor video using a PTZ camera and a commercial face recognition engine. Improvement is demonstrated for both face recognition and authentication. 1

A Data-Based Conservation Planning Tool for Florida Panthers

Habitat loss and fragmentation are the greatest threats to the endangered Florida panther (Puma concolor coryi). We developed a data-based habitat model and userfriendly interface so that land managers can objectively evaluate Florida panther habitat. We used a geographic information system (GIS) and the Mahalanobis distance statistic (D2) to develop a model based on broad-scale landscape characteristics associated with panther home ranges. Variables in our model were Euclidean distance to natural land cover, road density, distance to major roads, human density, amount of natural land cover, amount of semi-natural land cover, amount of permanent or semipermanent flooded area–open water, and a cost–distance variable. We then developed a Florida Panther Habitat Estimator tool, which automates and replicates the GIS processes used to apply the statistical habitat model. The estimator can be used by persons with moderate GIS skills to quantify effects of land-use changes on panther habitat at local and landscape scales. Example applications of the tool are presented

Subspace-Based Localization and Inverse Scattering of Multiply Scattering Point Targets

The nonlinear inverse scattering problem of estimating the locations and scattering strengths or reflectivities of a number of small, point-like inhomogeneities (targets) to a known background medium from single-snapshot active wave sensor array data is investigated in connection with time-reversal multiple signal classification and an alternative signal subspace method which is based on search in high-dimensional parameter space and which is found to outperform the time-reversal approach in number of localizable targets and in estimation variance. A noniterative formula for the calculation of the target reflectivities is derived which completes the solution of the nonlinear inverse scattering problem for the general case when there is significant multiple scattering between the targets. The paper includes computer simulations illustrating the theory and methods discussed in the paper