ALBERT

Description: 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.

Description: To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.

Description: Remote sensing plays an important role in flood mapping and is helping advance flood monitoring and management. Multi-scale flood mapping is necessary for dividing floods into several stages for comprehensive management. However, existing data systems are typically heterogeneous owing to the use of different access protocols and archiving metadata models. In this paper, we proposed a sharable and efficient metadata model (APEOPM) for constructing an Earth observation (EO) data system to retrieve remote sensing data for flood mapping. The proposed model contains two sub-models, an access protocol model and an enhanced encoding model. The access protocol model helps unify heterogeneous access protocols and can achieve intelligent access via a semantic enhancement method. The enhanced encoding model helps unify a heterogeneous archiving metadata model. Wuhan city, one of the most important cities in the Yangtze River Economic Belt in China, is selected as a study area for testing the retrieval of heterogeneous EO data and flood mapping. The past torrential rain period from 25 March 2015 to 10 April 2015 is chosen as the temporal range in this study. To aid in comprehensive management, mapping is conducted at different spatial and temporal scales. In addition, the efficiency of data retrieval is analyzed, and validation between the flood maps and actual precipitation was conducted. The results show that the flood map coincided with the actual precipitation.

Description: Comprehensive surface soil moisture (SM) monitoring is a vital task in precision agriculture applications. SM monitoring includes remote sensing imagery monitoring and in situ sensor-based observational monitoring. Cloud computing can increase computational efficiency enormously. A geographical web service was developed to assist in agronomic decision making, and this tool can be scaled to any location and crop. By integrating cloud computing and the web service-enabled information infrastructure, this study uses the cloud computing-enabled spatio-temporal cyber-physical infrastructure (CESCI) to provide an efficient solution for soil moisture monitoring in precision agriculture. On the server side of CESCI, diverse Open Geospatial Consortium web services work closely with each other. Hubei Province, located on the Jianghan Plain in central China, is selected as the remote sensing study area in the experiment. The Baoxie scientific experimental field in Wuhan City is selected as the in situ sensor study area. The results show that the proposed method enhances the efficiency of remote sensing imagery mapping and in situ soil moisture interpolation. In addition, the proposed method is compared to other existing precision agriculture infrastructures. In this comparison, the proposed infrastructure performs soil moisture mapping in Hubei Province in 1.4 min and near real-time in situ soil moisture interpolation in an efficient manner. Moreover, an enhanced performance monitoring method can help to reduce costs in precision agriculture monitoring, as well as increasing agricultural productivity and farmers’ net-income.

Description: The provenance of observations from a Sensor Web enabled remote sensing application represents a great challenge. There are currently no representations or tracking methods. We propose a provenance method that represents and tracks remote sensing observations in the Sensor Web enabled environment. The representation can be divided into the description model, encoding method, and service implementation. The description model uses a tuple to define four objects (sensor, data, processing, and service) and their relationships at a time point or interval. The encoding method incorporates the description into the Observations & Measurements specification of the Sensor Web. The service implementation addresses the effects of the encoding method on the implementation of Sensor Web services. The tracking method abstracts a common provenance algorithm and four algorithms that track the four objects (sensor, data, processing, and service) in a remote sensing observation application based on the representation. We conducted an experiment on the representation and tracking of provenance information for vegetation condition products, such as the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI). Our experiments used raw Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce daily NDVI, weekly NDVI, and weekly VCI for the 48 contiguous states of the United States, for May from 2000 to 2012. We also implemented inverse tracking. We evaluated the time and space requirements of the proposed method in this scenario. Our results show that this technique provides a solution for determining provenance information in remote sensing observations.

Description: Realization of open online control of diverse in-situ sensors is a challenge. This paper proposes a Cyber-Physical Geographical Information Service-enabled method for control of diverse in-situ sensors, based on location-based instant sensing of sensors, which provides closed-loop feedbacks. The method adopts the concepts and technologies of newly developed cyber-physical systems (CPSs) to combine control with sensing, communication, and computation, takes advantage of geographical information service such as services provided by the Tianditu which is a basic geographic information service platform in China and Sensor Web services to establish geo-sensor applications, and builds well-designed human-machine interfaces (HMIs) to support online and open interactions between human beings and physical sensors through cyberspace. The method was tested with experiments carried out in two geographically distributed scientific experimental fields, Baoxie Sensor Web Experimental Field in Wuhan city and Yemaomian Landslide Monitoring Station in Three Gorges, with three typical sensors chosen as representatives using the prototype system Geospatial Sensor Web Common Service Platform. The results show that the proposed method is an open, online, closed-loop means of control.

Description: The timely sharing and interoperation of multi-source cross-sectoral information is an important issue for a Geographic Information System (GIS). To study this issue, a real-time and open GIS model called GeoSensor is proposed in this work. GeoSensor integrates the real-time GIS model, real-time computation framework, and Open Geospatial Consortium services. This paper illustrates the system architecture and the implementation methods of the GeoSensor. One of the methods developed is the conceptual mapping of a real-time GIS data model to open GIS models and services and a real-time computation framework. The other method developed is the integration of open GIS services, a real-time computation framework, and hybrid databases. The GeoSensor was tested in a case study of building a smart river. In the case study, a comprehensive sensor web was constructed and integrated with the real-time information on rainfall, beacon, channel, sediment, and water levels derived from space-based sensors, air-borne sensors, and underground sensors from multiple sectors in the Yangtze River basin. The GeoSensor manages the comprehensive sensor web with 32 types of sensors and more than 10 billion observation records. Three application systems were developed based on the GeoSensor to manage flood control, hydropower production, and navigation of the Yangtze River. The results of the three application systems show that the real-time and open system improves the management efficiency of the Yangtze River. This system is promising for managing smart rivers.

Description: The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods are time consuming when applied to large floodplains, high-resolution DEMs are not always available, and remote sensing imagery cannot be used alone to predict inundation. In this article, a new model for the highly accurate and rapid simulation of floodplains, called “RFim” (real-time inundation model), is proposed to simulate the real-time flooded area. The model combines remote sensing images with in situ data to find the relationship between the inundation extent and water level. The new approach takes advantage of remote sensing images, which have wide spatial coverage and high resolution, and in situ observations, which have continuous temporal coverage and are easily accessible. This approach has been applied in the study area of East Dongting Lake, representing a large floodplain, for inundation simulation at a 30 m resolution. Compared with the submerged extent from observations, the accuracy of the simulation could be more than 90% (the lowest is 93%, and the highest is 96%). Hence, the approach proposed in this study is reliable for predicting the flood extent. Moreover, an inundation simulation for all of 2013 was performed with daily water level observation data. With an increasing number of Earth observation satellites operating in space and high-resolution mappers deployed on satellites, it will be much easier to acquire large quantities of images with very high resolutions. Therefore, the use of RFim to perform inundation simulations with high accuracy and high spatial resolutions in the future is promising because the simulation model is built on remote sensing imagery and gauging station data.

Description: Rapid urban growth in major cities of a country poses challenges for sustainable development. Particularly in Africa, the process of rapid urbanization is little understood and research is mostly limited to single cities. Thus, this study provides a comprehensive comparative analysis of the growth and spatial patterns of urban development in the three major cities of Ethiopia (Addis Ababa, Adama, and Hawassa) from 1987 to 2017. Also, the applicability of diffusion and coalescence theory on the evolution of these cities has been tested. Remote sensing and GIS technologies were combined with spatial metrics and morphological analysis was employed to undertake this study. The result revealed that all the studied cities experienced accelerated growth in the urbanized areas, but the cities with a larger initial urbanized size were associated with lower expansion rates. Differences in extent and direction of expansion in each city were mostly related to physical features, urban master plans, and policies, with an increase in the irregularity and dispersion of urban growth, representing strong evidence of urban sprawl. The spatiotemporal analysis confirmed that the urbanization processes of Addis Ababa and Adama were consistent and Hawassa city diverged from expectations based on diffusion and coalescence theory. In general, large cities with strong economic growth in a country fail to effectively control the scattered nature of urban growth, thus requiring aggressive policy intervention. The approach used in this study permits a deeper exploration of urban development patterns and the identification of priority areas for effective urban planning and management.

Description: This study introduces an improved algorithm for the real-time path planning of Unmanned Air Vehicles (UAVs) in a 3D large-scale battlefield environment to solve the problem that UAVs require high survival rates and low fuel consumption. The algorithm is able to find the optimal path between two waypoints in the target space and comprehensively takes factors such as altitude, detection probability, and path length into account. It considers the maneuverability constraints of the UAV, including the safety altitude, climb rate, and turning radius, to obtain the final flyable path. Finally, the authors test the algorithm in an approximately 2,500,000 square meter area containing radars, no-fly zones, and extreme weather conditions to measure its feasibility, stability, and efficiency.