Improved methods for determining the leaf surface index using unmanned aerial vehicles to determine the productivity of phytocenoses

Abstract

In landscape and biogeographic studies, the assessment of the photosynthesis process, which affects the possibility of determining the productivity of phytocenoses, calculating the growth of phytomass, is of particular relevance. The use of unmanned aerial vehicles (UAVs) to evaluate the leaf surface index (LAI) is gaining more and more scope over time due to their low price, high operational efficiency and accuracy. The basis for determining LAI is a model for the slit fraction, theoretically estimated using the Bera-Booger-Lambert law. The possibility of determining the LAI of vegetation using a UAV, on board of which either a lidar or a multispectrometer can be installed, is analyzed. In the first problem, LAI is determined by calculating the logarithm of the slit function (fraction) multiplied by the cosine of the scanning angle and divided by the attenuation coefficient. The second problem uses the existing correlations between known vegetation indices and LAI. Empirical statistical regression models may be suitable for determining LAI after determining various vegetation indices. Based on the results of multispectral measurements, it was found that the LAI determination technique based on measuring the intensity of rays that passed through the crown of plants leads to highly noisy estimates. For this reason, it was decided to use the slit fraction (GF), while using the experimentally established fact that when multiplying the logarithm of the vegetation index by the height of the crown, this correlation is significantly enhanced. To increase the reliability of the obtained LAI values, it is proposed to use the average integral value of this indicator, calculated by composing and calculating an optimization variational problem containing an additional restrictive condition. At the same time, it is possible to solve both problems to the maximum, i.e. it becomes possible to increase the signal-to-noise ratio of the calculated value of the LAI index. In both optimization calculation procedures, there are generalized indicators that have different physical meanings.