Multispectral lidar method for monitoring the forest ecosystem under the forest canopy

Abstract

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.