processing will be executed, and the file links will be responded promptly. The
WSDL of CDLService could be found at https://nassgeodata.gmu.edu/axis2/
services/CDLService?wsdl. More details and examples of data analysis services are
released on the developer guide Web page (CropScape 2017). Users and applications
can consume this service by sending HTTP GET/POST, or SOAP requests.
WPS is also utilized to develop geospatial processing services for crop loss
assessment. Four modules including EO analysis and processing, vulnerability,
loss assessment, and financial evaluation are wrapped in a WPS service for crop
loss assessment (Di et al. 2017). In particular, the EO analysis and processing
module, which consists of object-based classification and Bayesian net analyzer, is
used to generate the flooded acreage, crop types, and crop condition time-series data.
The loss assessment module is composed of a series of models such as the stagedamage model and depth-damage model. The financial evaluation model is used to
estimate the financial situation. Moreover, for the purpose of crop condition analysis
of time series data, some on-demand processes are implemented in RF-CLASS
based on WPS services, which are annual crop condition profile, multi-year crop
condition profiles, median crop condition profile of multiple years, cross-year crop
condition comparative profile, and combo chart of crop condition profiles against the
historical median. Figure 18.5 shows a use case of creating and retrieving crop
condition data for further analysis.
A set of geospatial services for the agricultural drought analysis in GADMFS are
implemented based on the existing enormous services from GeoBrain and
CropScape (Di 2004; Deng et al. 2012; Han et al. 2012; Peng et al. 2015). Specifically, over 100 Web services for manipulating raster, vector, and satellite image data
are implemented based on the Geographic Resources Analysis Support System
(GRASS) (Li et al. 2010). In addition, some agricultural drought monitoring and
forecasting services also have been implemented for further decision-making.
The DrawROI service provides visualizing for the drought patterns of a specified
area and time. The GetVCIStats service captures the mean value of VCI for the
selected AOI. The GetDroughtPercentageByStates service depicts the distribution of
drought severity groups for multiple specified states. The GetDroughtTimeSeries
service describes the time-series drought information of a selected state, county, or
ASD area. All these agricultural drought Web services could be easily invoked
through the HTTP GET/POST and SOAP request.
18.6 Conclusion
In this chapter, the design and implementation of geospatial data services for
agriculture are discussed through the aspects of agricultural data categories and
life cycle, the architecture of the service system, and implementation of service
functionalities. The CropScape system is used to demonstrate the interactive architecture framework for agriculture. The OGC standard compliant design makes the
system and services interoperable among different third-party applications,
18 Design and Implementation of Geospatial Data Services for Agriculture
399
WSDL of CDLService could be found at https://nassgeodata.gmu.edu/axis2/
services/CDLService?wsdl. More details and examples of data analysis services are
released on the developer guide Web page (CropScape 2017). Users and applications
can consume this service by sending HTTP GET/POST, or SOAP requests.
WPS is also utilized to develop geospatial processing services for crop loss
assessment. Four modules including EO analysis and processing, vulnerability,
loss assessment, and financial evaluation are wrapped in a WPS service for crop
loss assessment (Di et al. 2017). In particular, the EO analysis and processing
module, which consists of object-based classification and Bayesian net analyzer, is
used to generate the flooded acreage, crop types, and crop condition time-series data.
The loss assessment module is composed of a series of models such as the stagedamage model and depth-damage model. The financial evaluation model is used to
estimate the financial situation. Moreover, for the purpose of crop condition analysis
of time series data, some on-demand processes are implemented in RF-CLASS
based on WPS services, which are annual crop condition profile, multi-year crop
condition profiles, median crop condition profile of multiple years, cross-year crop
condition comparative profile, and combo chart of crop condition profiles against the
historical median. Figure 18.5 shows a use case of creating and retrieving crop
condition data for further analysis.
A set of geospatial services for the agricultural drought analysis in GADMFS are
implemented based on the existing enormous services from GeoBrain and
CropScape (Di 2004; Deng et al. 2012; Han et al. 2012; Peng et al. 2015). Specifically, over 100 Web services for manipulating raster, vector, and satellite image data
are implemented based on the Geographic Resources Analysis Support System
(GRASS) (Li et al. 2010). In addition, some agricultural drought monitoring and
forecasting services also have been implemented for further decision-making.
The DrawROI service provides visualizing for the drought patterns of a specified
area and time. The GetVCIStats service captures the mean value of VCI for the
selected AOI. The GetDroughtPercentageByStates service depicts the distribution of
drought severity groups for multiple specified states. The GetDroughtTimeSeries
service describes the time-series drought information of a selected state, county, or
ASD area. All these agricultural drought Web services could be easily invoked
through the HTTP GET/POST and SOAP request.
18.6 Conclusion
In this chapter, the design and implementation of geospatial data services for
agriculture are discussed through the aspects of agricultural data categories and
life cycle, the architecture of the service system, and implementation of service
functionalities. The CropScape system is used to demonstrate the interactive architecture framework for agriculture. The OGC standard compliant design makes the
system and services interoperable among different third-party applications,
18 Design and Implementation of Geospatial Data Services for Agriculture
399