8.4 Examples of Big Data Application
in Agro-Geoinformatics
In the past several years, the Center for Spatial Information Science and Systems
(CSISS) at George Mason University has worked on a number of research projects
dealing with agro-big data for supporting the agricultural decision-making. The
examples of such projects are briefly described below.
8.4.1 Agro-Sensor Web
CSISS has contributed significantly to the development of geospatial sensor web
technology to coordinately and purposely collect agro-geodata from in situ, airborne,
and satellite-based imagery and nonimagery sensors (Di 2007). Spatially distributed
heterogenous sensors monitoring the temperature, sound, vibration, pressure,
motion, or pollutants in agricultural fields are interconnected through standard
interfaces into a sensor network (Chen et al. 2009). The term sensor web was brought
up in the early years of this century by the National Aeronautics and Space
Administration (NASA) Sensor Web Applied Research Planning Group (Delin
and Jackson 2001; Di et al. 2010). The OGC and ISO made joint Sensor Web
Enablement (SWE) efforts on formulating the standards and protocols to enable the
interoperability within the sensor web (Di 2007). CSISS developed a general
purpose sensor web framework to integrate and harmonize diverse sensor network,
store disparate sensor datasets, and meet the diverse requirements of concurrent
distributed users (Di 2007) and a series of multipurpose sensor web-related web
services including SOS (Sensor Observation Service), SPS (Sensor Planning Service), CSW (Catalogue Service for the Web), WFS-T (transactional Web Feature
Service), and WCS-T (transactional Web Coverage Service) (Chen et al. 2009). To
efficiently process the data from sensor web, a OGC WPS (Web Processing Service)
compliant service was built on CSISS’ cloud computing platform, GeoBrain Cloud
and Apache Hadoop (Zhang et al. 2019; Chen et al. 2012). The processing tasks
were linked together to realize real-time geoprocessing of observations from live
sensors to connect sensor web with decision-makers and provided them with sharable problem-solving knowledge (Sun et al. 2012a, b, 2013; Sun and Yue 2010). To
make the sensor observation more accessible and interoperable, OGC WCS was
combined with SOAP interface to facilitate the retrieval of observations about
agricultural fields (Sun et al. 2016a). To make the agro-big data from sensor
observations more discoverable and comparable, a whole set of solutions for
enabling search of big data was developed (Gaigalas et al. 2019; Sun et al. 2019a).
The searching strategy takes two steps to reduce the burden on web-based catalogs:
the first step is to search the desired sensors, and the second step is to search
observation data granules of a specific sensor. The granule search, which takes the
most resources and time, is designed to be on demand to minimize the unnecessary
8 Big Data and Its Applications in Agro-Geoinformatics
151
in Agro-Geoinformatics
In the past several years, the Center for Spatial Information Science and Systems
(CSISS) at George Mason University has worked on a number of research projects
dealing with agro-big data for supporting the agricultural decision-making. The
examples of such projects are briefly described below.
8.4.1 Agro-Sensor Web
CSISS has contributed significantly to the development of geospatial sensor web
technology to coordinately and purposely collect agro-geodata from in situ, airborne,
and satellite-based imagery and nonimagery sensors (Di 2007). Spatially distributed
heterogenous sensors monitoring the temperature, sound, vibration, pressure,
motion, or pollutants in agricultural fields are interconnected through standard
interfaces into a sensor network (Chen et al. 2009). The term sensor web was brought
up in the early years of this century by the National Aeronautics and Space
Administration (NASA) Sensor Web Applied Research Planning Group (Delin
and Jackson 2001; Di et al. 2010). The OGC and ISO made joint Sensor Web
Enablement (SWE) efforts on formulating the standards and protocols to enable the
interoperability within the sensor web (Di 2007). CSISS developed a general
purpose sensor web framework to integrate and harmonize diverse sensor network,
store disparate sensor datasets, and meet the diverse requirements of concurrent
distributed users (Di 2007) and a series of multipurpose sensor web-related web
services including SOS (Sensor Observation Service), SPS (Sensor Planning Service), CSW (Catalogue Service for the Web), WFS-T (transactional Web Feature
Service), and WCS-T (transactional Web Coverage Service) (Chen et al. 2009). To
efficiently process the data from sensor web, a OGC WPS (Web Processing Service)
compliant service was built on CSISS’ cloud computing platform, GeoBrain Cloud
and Apache Hadoop (Zhang et al. 2019; Chen et al. 2012). The processing tasks
were linked together to realize real-time geoprocessing of observations from live
sensors to connect sensor web with decision-makers and provided them with sharable problem-solving knowledge (Sun et al. 2012a, b, 2013; Sun and Yue 2010). To
make the sensor observation more accessible and interoperable, OGC WCS was
combined with SOAP interface to facilitate the retrieval of observations about
agricultural fields (Sun et al. 2016a). To make the agro-big data from sensor
observations more discoverable and comparable, a whole set of solutions for
enabling search of big data was developed (Gaigalas et al. 2019; Sun et al. 2019a).
The searching strategy takes two steps to reduce the burden on web-based catalogs:
the first step is to search the desired sensors, and the second step is to search
observation data granules of a specific sensor. The granule search, which takes the
most resources and time, is designed to be on demand to minimize the unnecessary
8 Big Data and Its Applications in Agro-Geoinformatics
151