1. Vine canopy reconstruction and assessment with terrestrial lidar and aerial imagingIgor Petrović, Matej Sečnik, Marko Hočevar, Peter Berk, 2022, izvirni znanstveni članek Opis: For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies.
Ključne besede: precision agriculture, remote sensing, 3D point clouds, vineyard, canopy reconstruction, terrestrial lidar, aerial photogrammetry, manual defoliation
Objavljeno v DKUM: 15.07.2024; Ogledov: 112; Prenosov: 3
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2. IoT and satellite sensor data integration for assessment of environmental variables: a case study on NO2Jernej Cukjati, Domen Mongus, Krista Rizman Žalik, Borut Žalik, 2022, izvirni znanstveni članek Opis: This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10−6 mol/m2.
Ključne besede: Internet of Things, IoT, remote sensing, sensor integration, machine learning, ensemble method
Objavljeno v DKUM: 22.09.2023; Ogledov: 512; Prenosov: 21
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3. Remote sensing data and their use in topoclimatic studyMiroslav Vysoudil, 2007, izvirni znanstveni članek Opis: This work demonstrates the potential of using current digital satellite raster data to study a topoclimate. Also presented are digital vector data. All data have been provided by the Canadian Center for Remote Sensing in Ottawa within the scope of the solution for the project titled “Environmental Consequences of Local Climatic Effects” (A Case Study: British Columbia). The model region represents the southwest part of British Columbia located between Vancouver and the Okanagan basin. The most valuable components of a topoclimatic research are altimetric data assisting in the calculation of a DEM, multi-spectral images assigned to appropriate land cover categories, and thermal images. Subsequent integration of the DPZ and vector data provides a powerful tool for solving tasks leading to a topoclimate description, potential climatic effects and in wider implications even for studies of their impacts on the living environment.
Ključne besede: topoclimate, remote sensing, thermal imagery, land cover, DEM
Objavljeno v DKUM: 05.03.2018; Ogledov: 1861; Prenosov: 115
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