8.5 Conclusion
Agro-geoinformatics deals with collecting, managing, and analyzing agriculturalrelated geospatial data, which are domain-specific big data. Through the adoption
and adaptation processes, the general big data technologies are very useful in agrogeoinformatics but cannot meet all technology needs in dealing with agro-big data.
The development of agro-big data-specific technology is a necessary supplement to
the adoption of general big data technology. The combination of adoption of general
big data technology and development of agro-big data-specific technology proves to
be a good strategy for applying big data technology in agro-geoinformatics.
References
Borthakur, D. (2007). The hadoop distributed file system: Architecture and design. Hadoop Project
Website, 2007(11), 21.
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(5), 341–358.
Bronson, K., & Knezevic, I. (2016). Big data in food and agriculture. Big Data & Society, 3(1),
2053951716648174.
Chen, N., Di, L., Yu, G., & Min, M. (2009). A flexible geospatial sensor observation service for
diverse sensor data based on web service. ISPRS Journal of Photogrammetry and Remote
Sensing, 64(2), 234–242.
Chen, Z., Chen, N., Yang, C., & Di, L. (2012). Cloud computing enabled web processing service
for earth observation data processing. IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 5(6), 1637–1649.
Delin, K. A., & Jackson, S. P. (2001). Sensor web: A new instrument concept. In ‘Sensor web: A
new instrument concept’ (International Society for Optics and Photonics) (pp. 1–9).
Deng, M., Di, L., Yu, G., Yagci, A., Peng, C., Zhang, B., & Shen, D. (2012). Building an
on-demand web service system for global agricultural drought monitoring and forecasting. In
Building an on-demand web service system for global agricultural drought monitoring and
forecasting (pp. 958–961). IEEE.
Deng, M., Di, L., Han, W., Yagci, A., Peng, C., & Heo, G. (2013). Web-service-based monitoring
and analysis of global agricultural drought. Photogrammetric Engineering & Remote Sensing
(PE&RS), 79(10), 929–943.
Di, L. (2004). Distributed geospatial information services-architectures, standards, and research
issues. The International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, 35(Part 2), 187–193.
Di, L. (2007). Geospatial sensor web and self-adaptive Earth predictive systems (SEPS). In
Geospatial sensor web and self-adaptive Earth predictive systems (SEPS) (pp. 1–4).
Di, L., & Yang, Z. (2014). Foreword to the special issue on agro-Geoinformatics—The applications
of Geoinformatics in agriculture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4315–4316.
8 Big Data and Its Applications in Agro-Geoinformatics
159
Agro-geoinformatics deals with collecting, managing, and analyzing agriculturalrelated geospatial data, which are domain-specific big data. Through the adoption
and adaptation processes, the general big data technologies are very useful in agrogeoinformatics but cannot meet all technology needs in dealing with agro-big data.
The development of agro-big data-specific technology is a necessary supplement to
the adoption of general big data technology. The combination of adoption of general
big data technology and development of agro-big data-specific technology proves to
be a good strategy for applying big data technology in agro-geoinformatics.
References
Borthakur, D. (2007). The hadoop distributed file system: Architecture and design. Hadoop Project
Website, 2007(11), 21.
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(5), 341–358.
Bronson, K., & Knezevic, I. (2016). Big data in food and agriculture. Big Data & Society, 3(1),
2053951716648174.
Chen, N., Di, L., Yu, G., & Min, M. (2009). A flexible geospatial sensor observation service for
diverse sensor data based on web service. ISPRS Journal of Photogrammetry and Remote
Sensing, 64(2), 234–242.
Chen, Z., Chen, N., Yang, C., & Di, L. (2012). Cloud computing enabled web processing service
for earth observation data processing. IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 5(6), 1637–1649.
Delin, K. A., & Jackson, S. P. (2001). Sensor web: A new instrument concept. In ‘Sensor web: A
new instrument concept’ (International Society for Optics and Photonics) (pp. 1–9).
Deng, M., Di, L., Yu, G., Yagci, A., Peng, C., Zhang, B., & Shen, D. (2012). Building an
on-demand web service system for global agricultural drought monitoring and forecasting. In
Building an on-demand web service system for global agricultural drought monitoring and
forecasting (pp. 958–961). IEEE.
Deng, M., Di, L., Han, W., Yagci, A., Peng, C., & Heo, G. (2013). Web-service-based monitoring
and analysis of global agricultural drought. Photogrammetric Engineering & Remote Sensing
(PE&RS), 79(10), 929–943.
Di, L. (2004). Distributed geospatial information services-architectures, standards, and research
issues. The International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, 35(Part 2), 187–193.
Di, L. (2007). Geospatial sensor web and self-adaptive Earth predictive systems (SEPS). In
Geospatial sensor web and self-adaptive Earth predictive systems (SEPS) (pp. 1–4).
Di, L., & Yang, Z. (2014). Foreword to the special issue on agro-Geoinformatics—The applications
of Geoinformatics in agriculture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4315–4316.
8 Big Data and Its Applications in Agro-Geoinformatics
159