Meixia Deng

BPELPower-A BPEL execution engine for geospatial web services

The Business Process Execution Language (BPEL) has become a popular choice for orchestrating and executing workflows in the Web environment. As one special kind of scientific workflow, geospatial Web processing workflows are data-intensive, deal with complex structures in data and geographic features, and execute automatically with limited human intervention. To enable the proper execution and coordination of geospatial workflows, a specially enhanced BPEL execution engine is required. BPELPower was designed, developed, and implemented as a generic BPEL execution engine with enhancements for executing geospatial workflows. The enhancements are especially in its capabilities in handling Geography Markup Language (GML) and standard geospatial Web services, such as the Web Processing Service (WPS) and the Web Feature Service (WFS). BPELPower has been used in several demonstrations over the decade. Two scenarios were discussed in detail to demonstrate the capabilities of BPELPower. That study showed a standard-compliant, Web-based approach for properly supporting geospatial processing, with the only enhancement at the implementation level. Pattern-based evaluation and performance improvement of the engine are discussed: BPELPower directly supports 22 workflow control patterns and 17 workflow data patterns. In the future, the engine will be enhanced with high performance parallel processing and broad Web paradigms.

Web-service-based Monitoring and Analysis of Global Agricultural Drought

It is of great importance and an urgent demand to enable operational and near real-time monitoring and analysis of global agricultural drought at desirable spatial and temporal resolutions. Traditional approaches and existing systems are not able to meet the demand because of big-data and geoprocessing-modeling challenges. The latest advances in Web service, geospatial interoperability and cyberinfrastructure technologies and the availability of near real-time global remote sensing data have shown potential to address the challenges and meet the demand. This paper presents a Web service approach to building the Global Agricultural Drought Monitoring and Forecasting System ( GADMFS), an open, interoperable, and on-demand geospatial Web service system, for meeting the demand. The big-data and geoprocessingmodeling issues in providing complete agricultural drought information are resolved in GADMFS through improved data-, service- and system-level interoperability and servability. GADMFS is able to overcome major limitations of current drought information systems in the world and better support decision making with improved global agricultural drought data and information dissemination and analysis services.

Building an on-demand web service system for Global Agricultural Drought Monitoring and Forecasting

There is an urgent need but remains a very challenging problem to provide worldwide users with timely, on-demand, and ready-to-use agricultural drought data and information. The latest advances in Web service, geospatial interoperability and cyber-infrastructure technologies and the availability of near real-time global remote sensing data promise a solution to the problem. This paper presents the research study of the state of the art, methodology and approaches to building an open, interoperable, on-demand Web service system for global agriculture drought monitoring and forecasting. The implemented system, named as Global Agriculture Drought Monitoring and Forecasting System (GADMFS), overcomes most limitations of current agriculture drought information systems in the world and significantly improves global agriculture drought monitoring, prediction and analysis with advanced geospatial Web service technology.

Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

ABSTRACT A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R2 = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R2 = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.

Interoperable access of remote sensing data through NWGISS

This paper describes the interoperable, personalized, on-demand data access and services (IPODAS) of remote sensing data provided by NASA HDF-EOS Web GIS Software Suite (NWGISS). NWGISS is a web-based, multiple OGC-standard compliant data distribution system. Currently, NWGISS consists of following components: a Web Map Server, a Web Coverage Server, a Catalog Server, a Web Coverage Client, and a Toolbox. Those components can work either independently or collaboratively. Executables of all NWGISS components are free to any users.

The effect of corn–soybean rotation on the NDVI-based drought indicators: a case study in Iowa, USA, using Vegetation Condition Index

Satellite remote sensing has become a popular tool to analyze agricultural drought through terrestrial vegetation health conditions using the normalized difference vegetation index (NDVI). Drought monitoring techniques using remote sensing-based drought indices assume that vegetation conditions vary year-to-year due to prevailing weather conditions (e.g., precipitation and temperature), and current conditions are evaluated based on the deviation from the long-term statistics such as mean, minimum, or maximum. However, the rotation between agricultural crops (e.g., corn and soybeans) implies that this assumption may not hold, as each crop type may have distinct phenological variability across the growing season. In this study, the effect of crop rotation between corn and soybeans on the accuracy of the NDVI-based agricultural drought monitoring was investigated in Iowa, USA. The vegetation condition index (VCI), which is derived from NDVI, was selected to demonstrate the impact of crop rotation. The standard precipitation index (SPI) and official crop yield statistics were used as independent validation of the drought information acquired by these indices. The results suggested that the NDVI alone was not able to distinguish drought-related vegetation stress from vegetation changes caused by crop rotation between corn and soybeans. It was found that the integration of land cover with NDVI greatly improved the agricultural drought information obtained by the VCI over the crop-rotated agricultural fields in Iowa.

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana, USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.

Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects From Remote Sensing Images

It still remains a big challenge to accurately identify the geospatial objects with well-regulated outlines within remote sensing (RS) images such as residential buildings, factory storage buildings, highways, local roads, cars, and planes. In this paper, a novel spatial feature index, which is named regular shape similarity index (RSSI), is defined to address the challenge. It represents the ratio between the area of an object and its minimum bounding shape area. The application of RSSI in identifying objects with different shapes is discussed, and its capability is found to be a great supplement to the existing spatial feature hierarchy. An approach combining RSSI with object-based image analysis (OBIA) technology is proposed for image object extraction. A Web service for RSSI calculation is developed and integrated into a Web OBIA system. In the system, four experiments extracting factory storage buildings, residential buildings, roads, and planes, respectively, are conducted on three large-scale high-resolution RS images. In each experiment, two tests, i.e., one using traditional spatial features and the other using RSSI, are performed and compared. The results show that RSSI improves the accuracy of regular object extraction.

Cloud- and Agent-Based Geospatial Service Chain: A Case Study of Submerged Crops Analysis During Flooding of the Yangtze River Basin

More intelligent construction of geospatial service chains and more efficient execution of such service chains remain major challenges in distributed geospatial analysis. This study addresses these challenges using a Cloud- and agent-based approach for automatic and intelligent construction of a geospatial service chain in the Cloud environment. A spatial agent infrastructure comprising fundamental services and an agent interface is designed, implemented, and deployed. Our approach involves a strategy for selecting and aggregating appropriate agents and Web-processing services (WPS) by evaluating their availability. This strategy ensures successful construction of a geospatial service chain in the Cloud environment, even when there is a lack of requisite geospatial services in the system. Moreover, the method can significantly increase the speed of a service chain in distributed environments and retains high stability when more requests are submitted over various network conditions. This is because the computing mobility and intelligence of the agent help to avoid transfer of large volumes of spatial data and keep the load balanced during construction and execution of the service chain. A prototype system for analysis of submerged crops during flooding of the Yangtze River basin demonstrates the advantages of our approach over existing methods.

Vegetation index based technique for global agricultural drought monitoring

Droughts occurring every year all over the world have great impacts on human society, nature, and the global economy for example in declining crop yields, reduction of water supplies, and distressed vegetation. Satellite data have been widely used in drought monitoring. Vegetation condition is an excellent indicator of agricultural drought and can be quantified by the Normalized Difference Vegetation Index (NDVI). One way to detect agricultural drought is to quantify it through calculation of drought indices, such as the Vegetation Condition Index (VCI). For this purpose, we have developed an agricultural drought portal under the name of Global Agricultural Drought Information Services System (GADISS), which is interoperable with the Global Earth Observation System of Systems (GOESS) and follows Web standards for geospatial data recommended by the Open Geospatial Consortium (OGC), on the top of remote sensing drought monitoring. This study investigates the performance of VCI drought maps, which is also scientific base of GADISS, against annual grape production. Taking severe drought in the Aegean region, Turkey in 2007 as an example, the VCI drought index derived from 8-day NDVI satisfactorily detect drought. It is also validated with the fluctuations of annual grape production capacity. The results suggest that agricultural droughts have negative impacts on grape production rate per tree, and they can be operationally monitored by VCI over a geographic region for an extended period of time.