Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network's weights after the training procedure (i.e., the percentage of influence-PI, the weight product-WP, and the partial derivatives-PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables' importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

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The use of photogrammetry to determine dimensional and volume data of animals from a remote distance is investigated.The animal tranquilization process requires a precise knowledge of the weight of the animal for adjusting the appropriate dosage of medication. Practical experimentation using graphical and analogical processing methods proves that close -range photogrammetry is the best tool for the determination of the correlation between dimensional and volume data and weight of the animal. A practical example of the correlation determination which involved a study of several horses is presented. Introduction and background

It was 1975 when I joined as a graduate student on a research team at the University of Washington, Seatle USA. I worked under the supervision of professor Sandor Veress at the Department of Civil Engineering. At that time, it was the Vietnam war, and many veterans were suffering various injuries. Therefore, there were several research projects with joint research between the Swedish Medical Center, the Veterans Administration Hospital, and the University of Washington in Seattle dealing with orthopedics. I was already a graduate of the Rural and Surveying Engineering Department at the Technical University of Athens and a licensed surveyor in Greece. Because of my qualifications, I was involved in two such research projects. One was to perform external measurements and mapping the human body using closerange photogrammetry with simultaneous exposures of overlapping photographs. The other was internal measurements and mapping the human body using X-ray photogrammetry with simultaneous exposures of overlapping X-ray images from two anodes. The first project was based on the theory of Dr. Ernest M. Burgess, M.D., head of the Swedish Medical Center: by knowing the shape of a below-the-knee amputated leg in two extreme macules states of contract and relax position, he could design a prosthesis for better physiologic support. The other project used X-rays to map and monitor the hip joint replacement and also study the patella movement.