Abstract
Modern technologies are often used in agriculture, which allow increasing yields while minimising production costs. Many tools are created to support farmers’ activities in the so-called precision agriculture consisting of adapting agrotechnical operations to changing conditions in different areas of the cultivated field. For field yield variability mapping and other precision agricultural applications is used high-resolution satellite imagery. This paper presents the possibility of monitoring the areas of crops specified by the user and informing about the hazards that appear thereby detecting areas showing vegetation degradation based on a series of satellite spectral and radar data recorded at specific points in time. This paper presents an example of the application of imaging performed by the Sentinel-1 and Sentinel-2 satellites as well as advanced imaging techniques and digital image processing for precision farming. The possibility of using satellite images to calculate indices NDVI and RVI4S1 that determine changes in vegetation in the study area was presented. Combined analysis of changes in these indicators in the analysed period may be performed to support the decision-making process and perform actions adequate to the identified situation. The research presented in this paper is conducted to determine the possibility of using combined, satellite spectral and radar data (so-called hybrid set) to detect areas of vegetative degradation in crops in the absence of an element of the spectral data series.
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Acknowledgement
The presented results were made as a part of the project “Development of an application detecting the vegetative degradation of crops based on the time series of satellite data – RPOP.01.01.00-16-0001/20”. The project is co-financed from The European Regional Development Fund under the Opole Voivodeship Regional Operational Programme (RPO WO) for the years 2014-2020, Action 1.1 Innovation in Enterprises.
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Tomaszewski, M., Gasz, R., Smykała, K. (2021). Monitoring Vegetation Changes Using Satellite Imaging – NDVI and RVI4S1 Indicators. In: Paszkiel, S. (eds) Control, Computer Engineering and Neuroscience. ICBCI 2021. Advances in Intelligent Systems and Computing, vol 1362. Springer, Cham. https://doi.org/10.1007/978-3-030-72254-8_29
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