Zeqiang Chen

Cloud Computing Enabled Web Processing Service for Earth Observation Data Processing

The OpenGIS Web Processing Service (WPS) can process both simple and complex geospatial tasks including Earth Observation tasks. As the requirements of Earth Observation data, algorithms, calculation models, and daily life become increasingly complicated; WPS needs to provide high-performance service-oriented computing capability. This paper proposes a cloud computing enabled WPS framework for Earth Observation data processing. It consists of a client layer and a WPS layer, which further consists of a WPS server layer and a cloud computing layer. The cloud computing environment is based on the open-source software Apache Hadoop. The three layers of the proposed cloud computing enabled WPS are outlined, followed by a workflow that processes a users task using these three layers. Then technological implementation details are explained. An experiment processing Moderate Resolution Imaging Spectroradiometer (MODIS) data shows that WPS can be enabled in a cloud computing environment.

A Flexible Data and Sensor Planning Service for Virtual Sensors Based on Web Service

How to achieve a flexible data and sensor planning service to schedule, plan, and empower diverse sensors and heterogeneous data ordering systems is a big challenge. In this paper, a service-oriented framework of data and sensor planning service for virtual sensors is proposed. The framework includes an Open Geospatial Consortium (OGC)-compliant Sensor Planning Service (SPS), a Web Notification Service (WNS), a Sensor Observation Service (SOS), and virtual sensors. There are two important key technologies in this framework, namely a flexible SPS middleware and an asynchronous message notification mechanism. The flexible SPS middleware, based on a configuration file and standard interfaces, is adopted to integrate virtual sensors into a sensor Web. A WNS-based asynchronous notification middleware is used to inform the user of the status of a task that may need midterm or long-term actions. The framework has been successfully demonstrated in application scenarios for Simplified General Perturbations Satellite Orbit Model 4 (SGP4) and Earth Observation System ClearingHOuse (ECHO). The results show that the proposed method has the following improvements over the existing SPS implementation: a uniform planning service for more satellites, a seamless connection with data order systems, and a flexible service-oriented framework for virtual sensors.

An Efficient Method for Near-Real-Time On-Demand Retrieval of Remote Sensing Observations

Recent advances in sensor web geospatial data capture, have led to the generation of large numbers of real-time or near-real-time observations and measurements. As the magnitude of Web Coverage Service (WCS) and Sensor Observation Service (SOS) becomes increasingly large, a major problem is how to make fast and fully robust use of such data services in a Web-ready sensors environment. A new method is proposed, Real-time Coverage Service (RCS), for serving observational data based on the integration of WCS and SOS. The RCS method hides the complexity of a series of information models and service interfaces in the Earth Observation (EO) and Sensor Web world, allowing near-real-time on-demand access to geospatial observations. The core components-dynamical schema transformer and automatic information extractor-are designed and implemented based on service middleware technology. The Observations & Measurements (O&M) schema of SOS and coverage schema of WCS are matched by schema transformer dynamically. The coverage information is extracted from a SOS “GetObservation” operation by an information extractor and served by a WCS “GetCoverage” operation on-demand. Experiments on the feasibility of Earth Observing-1 (EO-1) Hyperion data retrieval for the RCS method and OGC Sensor Web SOS method were conducted and their efficiency compared. The results show that the proposed RCS has architecture that is more robust and performs more efficiently than the SOS method.

A capability matching and ontology reasoning method for high precision OGC web service discovery

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type, version, time, space, and scale has become a challenge in the integration of geospatial information services. A new method for retrieving Open Geospatial Consortium (OGC) Web Service (OWS) that deals with this challenge using page crawling, link detection, service capability matching, and ontology reasoning, is described in this paper. Its major components are distributed OWS, the OWS search engine, the OWS ontology generator, the ontology-based OWS catalog service, and the ontology-based multi-protocol OWS client. Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutchs method. In addition, the precision is much higher. Moreover, this proposed method can carry out complex OWS reasoning-based queries. It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

Active on-demand service method based on event-driven architecture for geospatial data retrieval

Timely on-demand access to geospatial data is necessary for environmental observation and disaster response. However, traditional service methods for acquiring geospatial data are inefficient and cumbersome, which is not beneficial for timely data acquisition. In these service methods, data are obtained and published by managers and are then left to users to discover and to retrieve them. To solve this problem, we propose an event-driven active on-demand data service method, for which a prototype based on sensor web technologies is demonstrated. First, we select a subset of observed properties as the attributes of an observation event of a data service system. Event-filtering technologies are then employed to find the data desired by users. Finally, the data that meet the subscription requirement are pushed to subscribers on time. The aims of the implementation of the method are to test the suitability of the observation and measurement (O&M) profile for Earth observation and OGC event pattern markup language (EML) specification. We determined the attributes of observation events according to the requirement of the data service and encoded observation event information using the OGC Observations and Measurements specification. We encoded the information under filtering conditions using the OGC Event Pattern Markup Language specification. We implemented a data service method that is based on event-driven architecture via a combination of some sensor web enablement services. Finally, we verified the feasibility of the method using MODIS data from the forest fires that occurred on February 7, 2009, in Victoria, Australia. The results show that the proposed method can achieve actively pushing the desired data to subscribers in the shortest possible time. O&M profiles for Earth observation and EML are suitable for the metadata encoding of observation events and the encoding of subscription information respectively. They match well for the data service in the system. Highlights? The design of the conceptual framework of the proposed method. ? The modeling of observation events and their encoding. ? The encoding of filtering conditions that are applied to observation events. ? The design and implementation of the event-driven process of data service. ? The design and implementation of a data service based on EDA.

Vegetation condition indices for crop vegetation condition monitoring

NDVI maps have been proven valuable in providing a spatially complete view of crops vegetation condition, which manifests disastrous events such as massive flood and drought. It is virtually impossible to obtain from ground survey data. This paper uses NASA MODIS 250m resolution, daily surface reflectance data for crop condition monitoring. The NDVI provides an absolute metrics for vegetation condition. However, a relative measurement of the current vegetation condition against a reference vegetation condition is critical for understanding, interpreting and quantifying the current vegetation condition. In this paper, a new NDVI based vegetation condition index is presented to measure the vegetation condition with respect to the “normal condition”, which is characterized by historical average. The proposed new vegetation condition index is empirically compared with several other vegetation indices to evaluate its effectiveness. Its advantages and utility for crop vegetation condition measurement are evidenced by the preliminary results.

FLCNDEMF: An Event Metamodel for Flood Process Information Management under the Sensor Web Environment

Significant economic losses, large affected populations, and serious environmental damage caused by recurrent natural disaster events (NDE) worldwide indicate insufficiency in emergency preparedness and response. The barrier of full life cycle data preparation and information support is one of the main reasons. This paper adopts the method of integrated environmental modeling, incorporates information from existing event protocols, languages, and models, analyzes observation demands from different event stages, and forms the abstract full life cycle natural disaster event metamodel (FLCNDEM) based on meta-object facility. Then task library and knowledge base for floods are built to instantiate FLCNDEM, forming the FLCNDEM for floods (FLCNDEMF). FLCNDEMF is formalized according to Event Pattern Markup Language, and a prototype system, Natural Disaster Event Manager, is developed to assist in the template-based modeling and management. The flood in Liangzi (LZ) Lake of Hubei, China on 16 July 2010 is adopted to illustrate how to apply FLCNDEM in real scenarios. FLCNDEM-based modeling is realized, and the candidate remote sensing (RS) dataset for different observing missions are provided for LZ Lake flood. Taking the mission of flood area extraction as an example, the appropriate RS data are selected via the model of simplified general perturbation version 4, and the flood area in different phases are calculated and displayed on the map. The phase-based modeling and visualization intuitively display the spatial-temporal distribution and the evolution process of the LZ Lake flood, and it is of great significance for flood responding. In addition, through the extension mechanism, FLCNDEM can also be applied in other environmental applications, providing important support for full life cycle information sharing and rapid responding.

Real-Time On-Demand Motion Video Change Detection in the Sensor Web Environment

Detecting motion-based video change, such as different types of motion video or applications using different change detection algorithms in a Web system, is difficult. This paper designs and implements architecture for a real-time or near real-time on-demand motion video change detection system using the Sensor Observation Service (SOS) and the Web Processing Service (WPS) in the Sensor Web environment. Real-time or near real-time includes sensors that obtain motion video data, SOS provides motion video data to WPS and WPS processes this data. Three solution methods are introduced: the GetObservation operation of SOS by transaction, dynamical interaction between SOS and WPS, and WPS real-time or near real-time processing. On-demand means that a developer can choose different motion video change detection algorithms under different applications or different conditions. For this purpose, a flexible, standards-based and service-oriented WPS architecture is designed, which consists of three layers: the WPS interface layer, the field interface layer and the implementation layer. To test the proposed approach, a video change detection case of monitoring a road situation is shown, which was a demonstration for Open Geospatial Consortium Web Service phase 7. The results demonstrate that the proposed approach is feasible.

A Semantic Registry Method Using Sensor Metadata Ontology to Manage Heterogeneous Sensor Information in the Geospatial Sensor Web

Efficient information management and precise discovery of heterogeneous sensors in the Geospatial Sensor Web (GSW) are a major challenge. Intelligent sensor management requires a registry service to store and process sensor information efficiently. In this paper, we propose a Sensor Metadata Ontology (SMO) to achieve a unified semantic description for heterogeneous sensors that is used to express sensor semantics. Through mapping between the sensor registry information model and the SMO, the sensor metadata could be stored with semantic information for the registry. The framework of a Sensor Semantic Registry Service (SSRS) has been successfully implemented for the registration and discovery of heterogeneous sensors. The results of GEOSENSOR-SSRS experiments show that the proposed semantic registry method can be used to enable sharing in an open distributed sensor network as well as to promote accuracy and efficiency of discovery.

ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture

The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.