13.6 Conclusion
This chapter presented an overview of spatial and geo-statistical data characteristics,
acquisition, and processing methods in agriculture with some example applications
in Turkey. Quality and proper acquisition planning of spatial data are important for
the efficient use of agricultural information systems. As the integration level raises,
service level compliance to political and regional data and process standards enables
comparative performance reports as in the EU case of LPIS, IACS, and FADN
applications.
Parcel-level geospatial data services will be available all over the country in
Turkey when the number of agrometeorological monitoring stations reaches 1200
from 440. This monitoring station population depends on the limitation of real-time
interpolation errors and the sampling distribution of common crop types for
nowcasting and forecasting purposes.
Multilayer spatial data consisting of geo-tagged registry data, interpolated monitoring data, model-based computed maps, remote sensing data, cadastre maps, and
photogrammetric aerial images are increasingly used in agricultural geo-information
systems. They provide optimization of production planning, agricultural resource
management, irrigation scheduling, risk management, precision farming, basin-level
management, and handling of several administrative processes on integrated GIS
platforms. Not only the sensor characteristics as satellite imaging properties but also
geostatistical and geographical properties of the region are important planning
considerations for the feasibility of the spatial data acquisition in agriculture.
References
Demirkol, Ö. (2015). Geodetic works on acquisition of orthophoto under digitization of LPIS
project.
Erden, H., & Koçak, A. (2015). Agricultural inventory management system, Agrogeoinformatics
Conference, Turkey.
Gething, P. W. (2007). A local space–time kriging approach applied to a national outpatient malaria
data set. Computers & Geosciences, 33, 1337–1350.
Greenwalt, C. R., & Schultz, M. E. (1968). Principles and error theory and cartographic applications, ACIC Technical Report No:96, 89.
Heuvelink, B. M. (2014). Spatio-temporal interpolation of daily temperatures for global land areas
at 1 km resolution. Journal of Geophysical Research: Atmospheres.
Heuvelink, B., & Griffith, D. A. (2010). Space-time geostatistics for geography: A case study of
radiation monitoring across parts of Germany, Geographical Analysis, ISSN 0016-7363.
Kay, S., & Milenov, P. (2010). European Commission status of the implementation of LPIS in the
EU member states, Ispra, Italy.
Land Cover Classificitaion System. http://www.fao.org/docrep/003/x0596e/x0596e00.HTM.
Accessed 25 June 2018.
13 Spatial Data Usage in Turkish Agriculture
263
This chapter presented an overview of spatial and geo-statistical data characteristics,
acquisition, and processing methods in agriculture with some example applications
in Turkey. Quality and proper acquisition planning of spatial data are important for
the efficient use of agricultural information systems. As the integration level raises,
service level compliance to political and regional data and process standards enables
comparative performance reports as in the EU case of LPIS, IACS, and FADN
applications.
Parcel-level geospatial data services will be available all over the country in
Turkey when the number of agrometeorological monitoring stations reaches 1200
from 440. This monitoring station population depends on the limitation of real-time
interpolation errors and the sampling distribution of common crop types for
nowcasting and forecasting purposes.
Multilayer spatial data consisting of geo-tagged registry data, interpolated monitoring data, model-based computed maps, remote sensing data, cadastre maps, and
photogrammetric aerial images are increasingly used in agricultural geo-information
systems. They provide optimization of production planning, agricultural resource
management, irrigation scheduling, risk management, precision farming, basin-level
management, and handling of several administrative processes on integrated GIS
platforms. Not only the sensor characteristics as satellite imaging properties but also
geostatistical and geographical properties of the region are important planning
considerations for the feasibility of the spatial data acquisition in agriculture.
References
Demirkol, Ö. (2015). Geodetic works on acquisition of orthophoto under digitization of LPIS
project.
Erden, H., & Koçak, A. (2015). Agricultural inventory management system, Agrogeoinformatics
Conference, Turkey.
Gething, P. W. (2007). A local space–time kriging approach applied to a national outpatient malaria
data set. Computers & Geosciences, 33, 1337–1350.
Greenwalt, C. R., & Schultz, M. E. (1968). Principles and error theory and cartographic applications, ACIC Technical Report No:96, 89.
Heuvelink, B. M. (2014). Spatio-temporal interpolation of daily temperatures for global land areas
at 1 km resolution. Journal of Geophysical Research: Atmospheres.
Heuvelink, B., & Griffith, D. A. (2010). Space-time geostatistics for geography: A case study of
radiation monitoring across parts of Germany, Geographical Analysis, ISSN 0016-7363.
Kay, S., & Milenov, P. (2010). European Commission status of the implementation of LPIS in the
EU member states, Ispra, Italy.
Land Cover Classificitaion System. http://www.fao.org/docrep/003/x0596e/x0596e00.HTM.
Accessed 25 June 2018.
13 Spatial Data Usage in Turkish Agriculture
263