The purpose of the study is to analyze the possibilities of forest areas hyperspectral monitoring. The mathematical modeling of the forest territories elements classification on the created neural network using experimentally measured reflection coefficients is presented. The simulation results show that hyperspectral monitoring in the spectral range of 400-2400 nm allows for classification of forest elements (different species of deciduous and coniferous trees, deadwood, swamps, water bodies, soils without vegetation, different types of mosses and lichens, post-fire areas, different shrubby plants) with the probability of correct classification of more than 0.73 and the probability of incorrect classification of less than 0.037. The use of additional information from the laser altimeter allows to significantly improve the classification. The created neural network, using hyperspectral monitoring data and lidar data on the height of trees, provides the probabilities of correct forest area elements classification of more than 0.8 and the probabilities of incorrect classification of less than 0.025.
The paper presents a capability analysis of the laser reflection method for remote monitoring of the forestland condition and species composition. A mathematical simulation using the spectral libraries of plant reflection coefficients shows that monitoring of forestland condition and species composition can be based on the laser method. Laser sounding at wavelengths 355, 1540, 2030 nm or 532, 1540, 2030 nm allows us to monitor the forestland condition and species composition with a probability of correct detection close to one and a probability of false alarms ~ second decimal places.
The paper provides a capability analysis of optical sensors for remote monitoring of vegetation condition in visible (VIS) and near infrared (NIR) bands. Mathematical modelling based on the spectral libraries of vegetation reflection coefficients shows that a hyper-spectral sensor with narrow spectral channels (or a laser sensor) allows us to detect the vegetation under adverse conditions with correct detection probability close to one and false alarm probability ~ second decimal places both in VIS and NIR bands below 1 μm and in the near infrared band above 1.4 μm. Data sharing in various spectral bands enables enhancing measurement reliability.
The paper presents a comparative analysis of efficiency to detect vegetation under adverse conditions using a passive optical method and a laser reflection one at the eye-safe sensing wavelengths. A mathematical simulation based on the spectral library of the reflection coefficients of vegetation shows that the laser reflection method (at two eye-safe wavelengths in the NIR band or in the NIR and UV ones) can be good advantage for vegetation monitoring. Sensing at the eye-safe wavelengths of 1.54 and 2.03 μm or 2.03 and 0.355 μm allows us to detect vegetation under adverse conditions with a probability of correct detection close to one and a probability of false alarm ~ second decimal places.
We investigate the possibilities to use a laser reflection method for vegetation monitoring at eye-safe sensing wavelengths. A mathematical simulation involving spectral libraries of the vegetation reflection coefficients shows that the laser method (at the eye-safe sensing wavelengths in the ultra-violet and the near infrared spectral bands) can be accepted as a basis for vegetation monitoring. Laser sensing at the wavelengths of 2 and 0.38 μm or of 2 and 0.355 μm allows us to detect vegetation under adverse conditions with a probability of correct detection close to one and a probability of false alarm ~ second decimal places.
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