MODIS and Landsat observations and CDL, and spot check for prevented planting
claims.
The implementation of RF-CLASS utilized Web service standards and specifications, mainly used OGC specifications. WMS, WFS, WCS, and Sensor Observation
Service (SOS) (Na and Priest 2007) are used to access different kinds of data, CSW
is used for data cataloging and discovery. WPS for data processing is used to
implement the processes and algorithms for crop loss assessment functions. These
standard services are instantly consumable by any standard compliable Web client.
The RF-CLASS system adopted matured algorithms and models; supported the
estimation of flooded crop acreages, crop damage, and flood frequency products;
and greatly enhanced the post-flood crop loss assessment and crop insurance policy
formulation.
12.4 Conclusion
In this chapter, the general research is proposed to show the human society’s real
needs in agricultural knowledge discovery, as well as the strengths and limitations of
current agricultural data and application systems. The capabilities of spatial and
temporal monitoring systems are analyzed via three example systems. Through the
aspects of data sources and products, system functionalities, interoperable standardization, and implementation, these operational systems are presented to show great
advantages on agricultural decision-making and policy formulation in a big data era.
References
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
department of agriculture, national agricultural statistics service, cropland data layer program.
Geocarto International, 26, 341–358.
Brown, S., Gerlt, S., Wilcox, L. (2011). The Value of the 2011 Crop Production Loss from the Birds
Point-New Madrid Floodway Levee Breach. FAPRI-MU Report 06-11, Food and Agricultural
Policy Research Institute, University of Missouri, USA, (pp. 6).
Cutter, S. L., & Emrich, C. (2005). Are natural hazards and disaster losses in the US increasing?
EOS. Transactions of the American Geophysical Union, 86, 381–389.
de La Beaujardiere, J. (2006). OpenGIS
® web map server implementation specification. Open
Geospatial Consort Inc OGC 06–042.
Deng, M., Di, L., Han, W., et al. (2011). The development of a web-service-based on-demand
global agriculture drought information system. In AGU fall meeting abstracts (p. 08).
Deng, M., Di, L., Yu, G., et al. (2012). Building an on-demand web service system for global
agricultural drought monitoring and forecasting. In Geoscience and remote sensing symposium
(IGARSS), 2012 IEEE international (pp. 958–961). IEEE.
Deng, M., Di, L., Han, W., et al. (2013). Web-service-based monitoring and analysis of global
agricultural drought. Photogrammetric Engineering and Remote Sensing, 79, 929–943.
Di, L. (2016). Big data and its applications in agro-geoinformatics. In 2016 IEEE international
geoscience and remote sensing symposium (IGARSS) (pp. 189–191).
230
L. Hu and P. Yue
claims.
The implementation of RF-CLASS utilized Web service standards and specifications, mainly used OGC specifications. WMS, WFS, WCS, and Sensor Observation
Service (SOS) (Na and Priest 2007) are used to access different kinds of data, CSW
is used for data cataloging and discovery. WPS for data processing is used to
implement the processes and algorithms for crop loss assessment functions. These
standard services are instantly consumable by any standard compliable Web client.
The RF-CLASS system adopted matured algorithms and models; supported the
estimation of flooded crop acreages, crop damage, and flood frequency products;
and greatly enhanced the post-flood crop loss assessment and crop insurance policy
formulation.
12.4 Conclusion
In this chapter, the general research is proposed to show the human society’s real
needs in agricultural knowledge discovery, as well as the strengths and limitations of
current agricultural data and application systems. The capabilities of spatial and
temporal monitoring systems are analyzed via three example systems. Through the
aspects of data sources and products, system functionalities, interoperable standardization, and implementation, these operational systems are presented to show great
advantages on agricultural decision-making and policy formulation in a big data era.
References
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
department of agriculture, national agricultural statistics service, cropland data layer program.
Geocarto International, 26, 341–358.
Brown, S., Gerlt, S., Wilcox, L. (2011). The Value of the 2011 Crop Production Loss from the Birds
Point-New Madrid Floodway Levee Breach. FAPRI-MU Report 06-11, Food and Agricultural
Policy Research Institute, University of Missouri, USA, (pp. 6).
Cutter, S. L., & Emrich, C. (2005). Are natural hazards and disaster losses in the US increasing?
EOS. Transactions of the American Geophysical Union, 86, 381–389.
de La Beaujardiere, J. (2006). OpenGIS
® web map server implementation specification. Open
Geospatial Consort Inc OGC 06–042.
Deng, M., Di, L., Han, W., et al. (2011). The development of a web-service-based on-demand
global agriculture drought information system. In AGU fall meeting abstracts (p. 08).
Deng, M., Di, L., Yu, G., et al. (2012). Building an on-demand web service system for global
agricultural drought monitoring and forecasting. In Geoscience and remote sensing symposium
(IGARSS), 2012 IEEE international (pp. 958–961). IEEE.
Deng, M., Di, L., Han, W., et al. (2013). Web-service-based monitoring and analysis of global
agricultural drought. Photogrammetric Engineering and Remote Sensing, 79, 929–943.
Di, L. (2016). Big data and its applications in agro-geoinformatics. In 2016 IEEE international
geoscience and remote sensing symposium (IGARSS) (pp. 189–191).
230
L. Hu and P. Yue