Анализ возможностей мультиспектрального оптического метода мониторинга лесных территорий

Abstract

The paper analyzes possibilities of the multispectral remote optical method in monitoring the forest areas. Results of mathematical simulation are provided of classification of the forest areas elements in the created neural network using experimentally measured reflection of the forest vegetation coefficients. It is demonstrated that the created neural network ensures high probability of correct classification within the classification problem (according to the multispectral remote optical monitoring data) of the forest probed areas. The selected spectral probing channels in a wide spectral range of ~ 400--2400 nm and the created neural network used seven spectral channels in the visible and in the near infrared spectral range, as well as the active laser sensor to measure the trees height. They provided a probability of correct classification of the forest areas elements (green deciduous trees, green coniferous trees, dry deciduous and coniferous trees, swamps, pastures with different vegetation cover and different types of soils) of more than 0.74 and the probability of misclassification of the forest areas elements of less than 0.08. The multispectral remote optical method could be used in operational monitoring of the vast forest areas from an aircraft (light aircraft or unmanned aerial vehicle)