Understanding long-term hunting statistics: the case of Spain (1972-2007)

Hunting is assuming a growing role in the current European forestry and agroforestry landscape. However, consistent statistical sources that provide quantitative information for policy-making, planning and management of game resources are often lacking. In addition, in many instances statistical information can be used without sufficient evaluation or criticism. Recently, the European Commission has declared the importance of high quality hunting statistics and the need to set up a common scheme in Europe for their collection, interpretation and proper use. This work aims to contribute to this current debate on hunting statistics in Europe by exploring data from the last 35 years of Spanish hunting statistics. The analysis focuses on the three major pillars underpinning hunting activity: hunters, hunting grounds and game animals. First, the study aims to provide a better understanding of official hunting statistics for use by researchers, game managers and other potential users. Second, the study highlights the major strengths and weaknesses of the statistical information that was collected. The results of the analysis indicate that official hunting statistics can be incomplete, dispersed and not always homogeneous over a long period of time. This is an issue of which one should be aware when using official hunting data for scientific or technical work. To improve statistical deficiencies associated with hunting data in Spain, our main suggestion is the adoption of a common protocol on data collection to which different regions agree. This protocol should be in accordance with future European hunting statistics and based on robust and well-informed data collection methods. Also it should expand the range of biological, ecological and economic concepts currently included to take account of the profound transformations experienced by the hunting sector in recent years. As much as possible, any future changes in the selection of hunting statistics should allow for comparisons between new variables with the previous ones.

Robust thermal face recognition using region classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.