Shaobo Zhong

Soil moisture retrieval from MODIS data in Northern China Plain using thermal inertia model

Soil moisture plays an important role in surface energy balances, regional runoff, potential drought and crop yield. Early detection of potential drought or flood is important for the local government and people to take actions to protect their crop. Traditionally measurement of soil moisture is a time‐consuming job and only limited samples could be collected. Many problems would be results from extending those point measurements to 2D space, especially for a regional area with heterogeneous soil characteristics. The emergency of remote‐sensing technology makes it possible to rapidly monitor soil moisture on a regional scale. Thermal inertia represents the ability of a material to conduct and store heat, and in the context of planetary science, it is a measure of the subsurfaces ability to store heat during the day and reradiate it during the night. One major application of thermal inertia is to monitor soil moisture. In this paper, a thermal inertia model was developed to be suitable in situations whether or not the satellite overpass time coincides with the local maximum and minimum temperature time. Besides, the sensibilities of thermal inertia with surface albedo and the surface temperature difference were discussed. It shows that the surface temperature difference has more effects on the thermal inertia than the surface albedo. When the temperature difference is less than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will lead to a big fluctuation of thermal inertia. When the temperature difference is more than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will cause a small change of thermal inertia. The temperature difference should be larger than 10 Kelvin degrees when the thermal inertia model is selected to derive soil moisture or other applications. Based on this thermal inertia model, the soil moisture map was obtained for North China Plain. It shows that the averaged difference between the soil moisture values derived from MODIS data and in situ measured soil moisture data is 4.32%. This model is promising for monitoring soil moisture on a large regional scale.

Risk analysis for the highly pathogenic avian influenza in Mainland China using meta-modeling

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in Mainland China for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.

Preliminary Study on Unsupervised Classification of Remotely Sensed Images on the Grid

Grid is a new technology. With corresponding middleware it can give strong computing power. In this paper we mainly discuss the middleware technology and architecture used in remote sensing image classification algorithm. Because unsupervised classification middleware is the key of the classification middleware algorithms, we study the alternant-unsupervised middleware and put forward a non-alternant unsupervised middleware scheme. Based on this scheme, main factors which effect the performance of non-alternant unsupervised classification are analyzed.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.

Experience of Remote Sensing Information Modelling with Grid Computing

In this paper, we focused on the remote sensing information modeling and determination using Grid computing platform. We have underdone the experiments using remotely sensed images for thermal inertial modeling in Condor system that is one of the Grid Projects existed nowadays worldwide. We divided remote sensing data into several parts and run them on Condor pool and on one single machine. From these tests, the relationship among the work efficiency of image processing in Condor system and the number of the separated parts of image and the number of machines in this system is presented. Given a certain number of machines, a most efficient image size is existed among varies sized images. Besides, the possible causes of the longer put-off in this process are given, and some possible methods to resolve this problem are also presented. It is feasible to use Grid computing system such as Condor to process remote sensing data. And if the postpone problem can be resolved, the work efficiency of Grid systems will be high. Even with so many problems, it is a good thing that Grid systems do many things for you during all of us are in sleep. Our next major task will concentrate on realizing an arithmetic that can read and divide remote sensing images based on image size and number of machines in Grid system automatically, and transfer results back to the submitted machine as a whole data file.

Reconstruction of 3D Curvilinear Wireframe Model from 2D Orthographic Views

An approach for reconstructing wireframe models of curvilinear objects from three orthographic views is discussed. Our main stress is on the method of generating three-dimensional (3D) conic edges from two-dimensional (2D) projection conic curves, which is the pivotal work for reconstructing curvilinear objects from three orthographic views. In order to generate 3D conic edges, a five-point method is firstly utilized to obtain the algebraic representations of all 2D projection curves in each view, and then all algebraic forms are converted to the corresponding geometric forms analytically. Thus the locus of a 3D conic edge can be derived from the geometric forms of the relevant conic curves in three views. Finally, the wireframe model is created after eliminating all redundant elements generated in previous reconstruction process. The approach extends the range of objects to be reconstructed and imposes no restriction on the axis of the quadric surface.

Data-Parallel method for georeferencing of MODIS level 1b data using grid computing

Georeference is a basic function of remote sensing data processing. Geo-corrected remote sensing data is an important source data for Geographic Information Systems (GIS) and other location services. Large quantity remote sensing data were produced daily by satellites and other sensors. Georeferenceing of these data is time consumable and computationally intensive. To improve efficiency of processing, Grid technologies are applied. This paper focuses on the parallelization of the remote sensing data on a grid platform. According to the features of the algorithm, backwards-decomposition technique is applied to partition MODIS level 1B data. Firstly, partition the output array into evenly sized blocks using regular domain decomposition. Secondly, compute the geographical range of every block. Thirdly, find the GCPs triangulations contained in or intersect with the geographic range. Then extract block from original data in accordance with these triangulations. The extracted block is the data distributed to producer on Grid pool.

A remote sensing application workflow and its implementation in remote sensing service grid node

In this article we describe a remote sensing application workflow in building a Remote Sensing Information Analysis and Service Grid Node at Institute of Remote Sensing Applications based on the Condor platform. The goal of the Node is to make good use of physically distributed resources in the field of remote sensing science such as data, models and algorithms, and computing resource left unused on Internet. Implementing it we use workflow technology to manage the node, control resources, and make traditional algorithms as a Grid service. We use web service technology to communicate with Spatial Information Grid (SIG) and other Grid systems. We use JSP technology to provide an independent portal. Finally, the current status of this ongoing work is described.

Explore disease mapping of hepatitis b using geostatistical analysis techniques

This paper presents the application of Exploratory Spatial Data Analysis (ESDA) and Kriging from GIS (ArcGIS8.3) in disease mapping through the analysis of hepatitis B in China. The research shows that geostatistical analysis techniques such as Kriging and ESDA have a good effect in disease mapping. Kriging methods can express properly the spatial correlation. Furthermore, unlike model-based methods, which largely depend on assumption for disease data, the Kriging method is more robust for the data. So it can be used more widely and is more operational. What’s more, the Kriging method may be adapted to interpolate nonstationary spatial structure. This can expand its application more largely. At last, the Kriging method can estimate the uncertainty of prediction while many deterministic methods cannot do so. In conclusion, it is an effective operational procedure to gain a deep insight into the disease data through ESDA before mapping disease using the Kriging method.

Java-Based grid service spread and implementation in remote sensing applications

Remote sensing applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as remote sensing applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node’s Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a remote sensing application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of remotely sensed data.