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In the last decade, in Romania, the management of large and very diverse agricultural areas from the point of view of the relief has been imposed especially the for the case of permanent grasslands. These ecosystems that are in balance due to the interdependence between the biotic and abiotic factors should be exploited according to correct management practices, supervised so that their level of production and quality should not be damaged. With the help of computer systems, the management and exploitation plan was established for 10-year periods corresponding to the period of renting these areas to livestock breeders. The present study aims at using the aerial monitoring with drone systems, the aerial surveillance of the correct implementation of management practices contractually assumed by the users and the breeders respectively. Thus, it is analyzed the quadratic processing of the surfaces, the compliance with the limits of rented areas, the observance of the load with animals per hectare (livestock unit LU) as well as of other management elements imported for maintaining and improving the condition of the permanent grasslands of Romania.

One of the most demanded assets nowadays is energy. Over many options to generate it, humankind must seek sustainable ways; therefore, renewable energies must be empowered. Moreover, wind provides great benefits, granting uncalculated power at our disposition. Since this task is executed by big structures, their maintenance represents a difficult task for human efforts to achieve, because the height requires much support, effort and time to accomplish. A Remotely Piloted Aircraft System (RPAS) can be adapted to perform surveillance on different types of surfaces, presenting a comfortable way to execute it in dangerous and difficult to access spaces, providing safer methods, and bringing experience of qualified workers and technology together. This paper will provide methods for generating a trajectory over Wind Turbine blades, relying on the specification of a Phantom 4. The main objective is to establish a path over this structure, based on the measurements of a specific model and incorporating all of the blades surface. The results were satisfying once the precision was inside the allowed deviation. Nevertheless, some issues might be improved such as the velocity between waypoints and the polynomial selected to define the trajectory.

One of the most demanded assets nowadays is energy. Over many options to generate it, humankind must seek sustainable ways; therefore, renewable energies must be empowered. Moreover, wind provides great benefits, granting uncalculated power at our disposition. Since this task is executed by big structures, their maintenance represents a difficult task for human efforts to achieve, because the height requires much support, effort and time to accomplish. A Remotely Piloted Aircraft System (RPAS) can be adapted to perform surveillance on different types of surfaces, presenting a comfortable way to execute it in dangerous and difficult to access spaces, providing safer methods, and bringing experience of qualified workers and technology together. This paper will provide methods for generating a trajectory over Wind Turbine blades, relying on the specification of a Phantom 4. The main objective is to establish a path over this structure, based on the measurements of a specific model and incorporating all of the blades surface. The results were satisfying once the precision was inside the allowed deviation. Nevertheless, some issues might be improved such as the velocity between waypoints and the polynomial selected to define the trajectory. [ABSTRACT FROM AUTHOR]

Machine learning methods are the most popular approaches for carrying out classification in remote sensing studies. Of the methods available, random forest (RF) is the one most often used due to its high predictive performance. The objective of this study was to assess the predictive performance of RF in identifying (classifying) mangrove species in an arid environment using two cameras: one conventional (visible part of the light, RGB), the other specialized (Green, Red, Near-infrared, GRN). The RGB and GRN bands were used with derived vegetation indexes (for each camera), the canopy height model (derived from photogrammetry), and distance to water (derived from raster analysis) to classify the study area in eight classes (including three mangrove species) using RF. Results suggest only slight differences in predictive performance (validation) between the products derived from the GRN and RGB cameras, the accuracy values ranged from 0.58 to 0.77 and from 0.53 to 0.72 for RGB and GRN, respectively. The most important variables were the distance to water and canopy height model for both cameras, followed by specific bands and vegetation indices. The study concludes that conventional cameras mounted in commercial drones can be used efficiently to identify mangrove species in arid environments when the classification model uses physical variables of the species (tree height) and the system (distance to water). Results of this study can be applied to describe spatial distributions by species in small or large patches of mangroves in arid environments, thus improving our ecological knowledge of this ecosystem. • High predictive performance on the classification of the study area. • Little differences in the predictive performance between RGB and GRN. • Conventional cameras can be efficiently used to identify mangroves species. • Canopy height model and distance to water were the most important variables. [ABSTRACT FROM AUTHOR]