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Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing

Department of Geography, Faculty of Science, Masaryk University, 611 37 Brno, Czech Republic
Department of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University in Brno, 613 00 Brno, Czech Republic
Wirelessinfo, Cholinská 1048/19, 784 01 Litovel, Czech Republic
Department of Geomatics, Faculty of Applied Science, University of West Bohemia, 301 00 Pilsen, Czech Republic
Lesprojekt – služby s.r.o., Martinov 197, 277 13 Záryby, Czech Republic
Author to whom correspondence should be addressed.
Academic Editors: Milan Konecny and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(8), 238;
Received: 1 May 2017 / Revised: 19 July 2017 / Accepted: 2 August 2017 / Published: 6 August 2017
PDF [1799 KB, uploaded 6 August 2017]


Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains. View Full-Text
Keywords: precision farming; machinery telemetry; wireless sensor network; remote sensing precision farming; machinery telemetry; wireless sensor network; remote sensing

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Řezník, T.; Lukas, V.; Charvát, K.; Charvát, K.; Křivánek, Z.; Kepka, M.; Herman, L.; Řezníková, H. Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing. ISPRS Int. J. Geo-Inf. 2017, 6, 238.

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