Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing
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 respect...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-08-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/6/8/238 |
| _version_ | 1818136513773305856 |
|---|---|
| author | Tomáš Řezník Vojtěch Lukas Karel Charvát Karel Charvát Zbyněk Křivánek Michal Kepka Lukáš Herman Helena Řezníková |
| author_facet | Tomáš Řezník Vojtěch Lukas Karel Charvát Karel Charvát Zbyněk Křivánek Michal Kepka Lukáš Herman Helena Řezníková |
| author_sort | Tomáš Řezník |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T09:41:36Z |
| format | Article |
| id | doaj.art-e43bea560bfd48d6b7efee514794259a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-11T09:41:36Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e43bea560bfd48d6b7efee514794259a2022-12-22T01:12:40ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-08-016823810.3390/ijgi6080238ijgi6080238Disaster Risk Reduction in Agriculture through Geospatial (Big) Data ProcessingTomáš Řezník0Vojtěch Lukas1Karel Charvát2Karel Charvát3Zbyněk Křivánek4Michal Kepka5Lukáš Herman6Helena Řezníková7Department of Geography, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicDepartment of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University in Brno, 613 00 Brno, Czech RepublicWirelessinfo, Cholinská 1048/19, 784 01 Litovel, Czech RepublicWirelessinfo, Cholinská 1048/19, 784 01 Litovel, Czech RepublicWirelessinfo, Cholinská 1048/19, 784 01 Litovel, Czech RepublicDepartment of Geomatics, Faculty of Applied Science, University of West Bohemia, 301 00 Pilsen, Czech RepublicDepartment of Geography, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicLesprojekt – služby s.r.o., Martinov 197, 277 13 Záryby, Czech RepublicIntensive 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.https://www.mdpi.com/2220-9964/6/8/238precision farmingmachinery telemetrywireless sensor networkremote sensing |
| spellingShingle | Tomáš Řezník Vojtěch Lukas Karel Charvát Karel Charvát Zbyněk Křivánek Michal Kepka Lukáš Herman Helena Řezníková Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing ISPRS International Journal of Geo-Information precision farming machinery telemetry wireless sensor network remote sensing |
| title | Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing |
| title_full | Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing |
| title_fullStr | Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing |
| title_full_unstemmed | Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing |
| title_short | Disaster Risk Reduction in Agriculture through Geospatial (Big) Data Processing |
| title_sort | disaster risk reduction in agriculture through geospatial big data processing |
| topic | precision farming machinery telemetry wireless sensor network remote sensing |
| url | https://www.mdpi.com/2220-9964/6/8/238 |
| work_keys_str_mv | AT tomasreznik disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT vojtechlukas disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT karelcharvat disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT karelcharvat disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT zbynekkrivanek disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT michalkepka disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT lukasherman disasterriskreductioninagriculturethroughgeospatialbigdataprocessing AT helenareznikova disasterriskreductioninagriculturethroughgeospatialbigdataprocessing |