The laser control method for thin oil films on a water surface is considered, based on measure ments of first order and second order derivatives of the reflection coefficient. Mathematical modeling shows that the method allows the detection of thin oil films with a high reliability, and measurement of oil films of a thickness from 0.1 to 10 µm with a an error of less than 30% at an RMS noise of 0.3%. The error decreases with a decrease in the film thickness and is less than 3% in the 0.1-2 µm thickness range in most cases.

This study demonstrates the potential of the multispectral lidar method to monitor the forest ecosystem under the forest canopy. The mathematical modeling results of forest territories elements classification on the created neural network using experimentally measured reflection coefficients are presented. It is shown that the neural network provides a high probability of correct classification for the forest ecosystem elements classification task (when using lidar measurement data about the height of the forest ecosystem elements). Laser pulse sounding at two wavelengths in near infrared spectral range 1064 and 2030 nm and the created neural network provide the probabilities of correctly classify the undergrowth of green broadleaved and coniferous trees, swamps and soils more than 0.84 and the probability of incorrect classification less than 0.08.

Statistical modelling of the correct detection and false alarm probabilities has been implemented to identify dominant (needle-leaved or broadleaved) tree species through laser sensing in the UV and NIR spectral bands. It is shown that the laser method of monitoring at 355 nm and 2100 nm wavelengths allows sensing dominant needle-leaved or broadleaved tree species with a probability of correct detection close to one and a probability of false alarm ~ second decimal places. The laser method using two eye-safe sensing wavelengths in the UV and NIR spectral bands can be used for airborne forest monitoring.

The optical reflection method is considered for detection of the forest areas where coniferous or broadleaved trees are dominant. Statistical modelling of correct detection and false alarm probabilities for identifying dominant (coniferous or broadleaved) tree species by the two-spectral reflection method has been conducted. It has been shown that monitoring enables us to identify dominant (coniferous or broadleaved) tree species with correct detection probability close to 1 and false alarms probability ~ second decimal places for the temperate climate zone at the wavelengths of 532 and 1540 nm or 532 and 1480 nm. As to the subtropical climate zone, due to a great variety of reflection spectra of vegetation, a selection of the spectral detection bands for reliable identification of dominant coniferous or broadleaved tree species is possible only for specific forestlands where the number of evergreen broadleaved and coniferous tree species is relatively small.