1. Long term monitoring and connection between topography and cloud cover distribution in SerbiaAleksandar Valjarević, Cezar Morar, Jelena Živković, Liudmyla Niemets, Dušan Kićović, Jelena Golijanin, Milena Gocić, Nataša Martić Bursać, Ljiljana Stričević, Igor Žiberna, Nikola Bačević, Ivica Milevski, Uroš Durlević, Tin Lukić, 2021, original scientific article Abstract: The use of weather satellite recordings has been growing rapidly over the last three decades. Determining the patterns between meteorological and topographical features is an important scientific job. Cloud cover analysis and properties can be of the utmost significance for potential cloud seeding. Here, the analysis of the cloud properties was conducted by means of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite recordings. The resolution of used data was 1 km2 within the period of 30 years (1989-2019). This research showed moderate changing of cloudiness in the territory of Serbia with a high cloudiness in February, followed by cloudiness in January and November. For the past three decades, May has been the month with the highest cloudiness. The regions in the east and south-west, and particularly in the west, have a high absolute cloudiness, which is connected with the high elevation of the country. By means of long term monitoring, the whole territory of Serbia was analyzed for the first time, in terms of cloudiness. Apart from the statistical and numerical results obtained, this research showed a connection between relief and clouds, especially in the winter season. Linear regression MK (Mann-Kendall test) has proven this theory right, connecting high elevation sides with high absolute cloudiness through the year.
Keywords: clowd cover, remote sensing, GIS, topography, statistics, trends
Published in DKUM: 18.10.2024; Views: 0; Downloads: 1
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2. Tilt correction toward building detection of remote sensing imagesKang Liu, Zhiyu Jiang, Mingliang Xu, Matjaž Perc, Xuelong Li, 2021, original scientific article Abstract: Building detection is a crucial task in the field of remote sensing, which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned. This is not beneficial to preserve semantic completeness of the building objects. Motivated by the above fact, we first propose a framework for detecting objects in a large-size image, particularly for building detection. The framework mainly consists of two phases. In the first phase, we particularly propose a tilt correction (TC) algorithm, which contains three steps: texture mapping, tilt angle assessment, and image rotation. In the second phase, building detection is performed with object detectors, especially deep-neural-network-based methods. Last but not least, the detection results will be inversely mapped to the original large-size image. Furthermore, a challenging dataset named Aerial Image Building Detection is contributed for the public research. To evaluate the TC method, we also define an evaluation metric to compute the cost of building partition. The experimental results demonstrate the effects of the proposed method for building detection.
Keywords: building detection, cost of building partition, deep neural network, remote sensing, tilt correction
Published in DKUM: 26.09.2024; Views: 0; Downloads: 1
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3. Vine canopy reconstruction and assessment with terrestrial lidar and aerial imagingIgor Petrović, Matej Sečnik, Marko Hočevar, Peter Berk, 2022, original scientific article Abstract: 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.
Keywords: precision agriculture, remote sensing, 3D point clouds, vineyard, canopy reconstruction, terrestrial lidar, aerial photogrammetry, manual defoliation
Published in DKUM: 15.07.2024; Views: 123; Downloads: 10
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4. 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, original scientific article Abstract: 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.
Keywords: Internet of Things, IoT, remote sensing, sensor integration, machine learning, ensemble method
Published in DKUM: 22.09.2023; Views: 514; Downloads: 31
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5. Remote sensing data and their use in topoclimatic studyMiroslav Vysoudil, 2007, original scientific article Abstract: 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.
Keywords: topoclimate, remote sensing, thermal imagery, land cover, DEM
Published in DKUM: 05.03.2018; Views: 1872; Downloads: 119
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