Alexander Lavrov

Feasibility of forest-fire smoke detection using lidar

The feasibility and fundamentals of forest fire detection by smoke sensing with single-wavelength lidar are discussed with reference to results of 532-nm lidar measurements of smoke plumes from experimental forest fires in Portugal within the scope of the Gestosa 2001 project. The investigations included tracing smoke-plume evolution, estimating forest-fire alarm promptness, and smoke-plume location by azimuth rastering of the lidar optical axis. The possibility of locating a smoke plume whose source is out of line of sight and detection under extremely unfavourable visibility conditions was also demonstrated. The eye hazard problem is addressed and three possibilities of providing eye-safety conditions without loss of lidar sensitivity (namely, using a low energy-per-pulse and high repetition-rate laser, an expanded laser beam, or eye-safe radiation) are discussed.

Design of committee machines for classification of single-wavelength lidar signals applied to early forest fire detection

The application of committee machines composed of single-layer perceptrons for the automatic classification of lidar signals for early forest fire detection is analysed. The patterns used for classification are composed of normalised lidar curve segments, pre-processed in order to reduce noise. In contrast to the approach used in previous work, these patterns contain application-specific parameters, such as peak-to-noise ratio (PNR), average amplitude ratio (AvAR) and maximum amplitude ratio (MAR), in order to improve classification efficiency. Using this method a smoke signature detection efficiency of 93% and a false alarm percentage of 0.041% were achieved for small bonfires, using an optimised committee machine composed of four single-layer perceptrons. The same committee machine was able to detect 70% of the smoke signatures in lidar return signals from large-scale fires in an early stage of development. The possibility of using a second committee machine for detecting fully developed large-scale fires is discussed.

Eye-safe lidar measurements for detection and investigation of forest-fire smoke

The problem of eye safety in lidar-assisted wildland fire detection and investigation is considered as a problem of reduction of the hazard range within which the laser beam is dangerous for direct eye exposure. The dependence of this hazard range on the lidar characteristics is examined and possible eye-safety measures discussed. The potential of one of the cheapest ways of providing eye safety, which is based on placing the lidar in an elevated position and using a 1064-nm laser beam with increased divergence, is also investigated experimentally. It is demonstrated that a lidar system operating with wider beams maintains its ability to detect smoke plumes efficiently. Providing eye-safe conditions allows scanning of the internal 3D structure of smoke plumes in the vicinity of fire plots. Examples are given as layer-by-layer smoke concentration plots on the topographic map.

Simple eye-safe lidar for cloud height measurement and small forest fire detection

A simple and robust eye-safe lidar was developed on the basis of a rangefinder optical scheme comprising an Er:glass laser which generates 8 mJ pulses of 1540-nm radiation with the pulse repetition rate of 0.17 Hz and a 38-mm-diameter telescope. Reliable measurements of the cloud height up to 3700 m and early forest-fire detection with a range of 3000 m were experimentally demonstrated. Theoretical estimations indicate that using an optical scheme built around a 10 Hz Er:glass lasers and 150 mm light gathering optics early forest fire detection in a range up to 6500 m can be achieved.

Low-cost active optical system for fire surveillance

Detection of smoke plumes using active optical sensors provides many advantages with respect to passive methods of fire surveillance. However, the price of these sensors is often too high as compared to passive fire detection instruments, such as infrared and video cameras. This article describes robust and cost effective diode-laser optical sensor for automatic fire surveillance in industrial environment. Physical aspects of the sensing process allowing to simplify the hardware and software design, eventually leading to significant reduction of manufacturing and maintenance costs, are discussed.

Neural Network Based Recognition of Smoke Signatures from Lidar Signals

The automatic recognition of smoke signatures in lidar signals collected during very small-scale experimental forest fires using neural-network algorithms was studied. An algorithm for pre-processing of raw lidar signals is proposed, which selects suspicious backscattering peaks and makes them unbiased and scale independent. The resulting patterns can be successfully classified as corresponding to alarm or no-alarm conditions by a neural-network algorithm based on a simple one-neuron structure (perceptron). In the case of an alarm, the pre-processing algorithm provides the location of the smoke plume. Five algorithms selected from the literature, and one that was specially developed, all with learning rate adaptation, were used for training the perceptron. Their efficiencies and statistical properties were compared. The best perceptron classifier presented an efficiency of 97% in the classification of smoke-signature patterns and a false alarm rate of 0.9%.

Evaluation of oil spills by laser induced fluorescence spectra

A low cost modular system for automatic oil spill detection, based on laser induced fluorescence light detection and ranging (LIF LIDAR) technology, which may be installed aboard watercraft and used for intensive surveillance of harborages, rivers, channels, and coastal waters, is described. First experimental results obtained with the developed LIF LIDAR detector prototype in the laboratory conditions are reported.

Application of rangefinder for small forest fire detection

Detection of fire smoke plume with a compact cheap rangefinder based on 905 nm laser diode (2 μJ pulse energy, slashed oh 2 cm telescope and 720 m solid-target range) is demonstrated. Reliable detection of small experimental fires (20×25 m2 fire plot, burning rate of ~3 kg/s) is achieved for the range of about 255 m. A theoretical model of the mixing of burning products with air in the wind, based on three-dimensional system of Navier-Stokes equation and commercial software PHOENICS, is developed. The model predicts 220 m range of smoke detection by the rangefinder, indicating good agreement between the theoretical and experimental data. On the basis of this theoretical model an estimation of the smoke detection efficiency for a longer range (20 km for solid targets) instrument, based on a 1540 nm laser with a pulse energy of 8 mJ and a 4 cm telescope, is made. The obtained smoke-detection range estimation, 6 km, indicate that more powerful rangefinders can be used not only in shot-range applications, such as fire detection in premises, tunnels and storage yards, but in more demanding areas, such as wildland fire surveillance, as well.

Active methods of early forest fire detection

A method of automated early fire detection based on the light detection and ranging (lidar) technology is presented. Specific lidar configurations and their application to forest and industrial-environment fire surveillance are discussed.

Automatic recognition of smoke-plume signatures in lidar signal

A simple and robust algorithm for lidar-signal classification based on the fast extraction of sufficiently pronounced peaks and their recognition with a perceptron, whose efficiency is enhanced by a fast nonlinear preprocessing that increases the signal dimension, is reported. The method allows smoke-plume recognition with an error rate as small as 0.31% (19 misdetections and 4 false alarms in analyzing a test set of 7409 peaks).