Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzzines significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN (triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.     

Integrated data processing method for GPS and INS field test over Rocky Mountain

The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.     

On generalised and specialised spatial information grids: are geo-services ready?

Abstract

After the set-up of a spatial data infrastructure (SDI) and a national information infrastructure (NII) in many countries, the provision of geo-services became one of the most important and attractive tasks. With the integration of global positioning system (GPS), geographic information system (GIS) and remote sensing (RS), we can, in principle, answer any geo-spatial related question: when and where what object has which changes? An intelligent geo-service agent could provide end-users with the most necessary information in the shortest time and at the lowest cost. Unfortunately there is still a long way to go to achieve such goals. The central component in such geo-services is the integration of the spatial information system with a computing grid via wire- and wireless communication networks. This paper will mainly discuss the grid technology and its integration with spatial information technology, expounding potential problems and possible resolutions. A novel categorising of information grids in the context of geo-spatial information is proposed: generalised and specialised spatial information grids.     

China Digital Ocean Prototype System

Abstract

Digital Ocean is a new research domain of Digital Earth. Because of the spatio-temporal, three-dimensional (3D) and intrinsically dynamic nature of ocean data, it is more difficult to make a breakthrough in this domain. The construction of the China Digital Ocean Prototype System (CDOPS) pushes Digital Ocean a step forward from its operation as a mere concept to its achievement as a realistic system. In this paper, the technical framework of the CDOPS is discussed, including its data, function, and application layers. Then, two key technologies are studied in detail that will enable the construction of the 3D ocean environment and the visualization of the ocean model output data. Practical demonstrations show that the CDOPS provides a technical reference for the development of Digital Ocean. This paper is based on an ongoing research project of the development of CDOPS that aims at the facilitation, integration, sharing, accessing, visualization, and use of the ocean data and model computing data from the Digital Earth perspective.     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.     

Enrichment of topographic road database for the purpose of routing and navigation

With growing demand on multi-purpose or multi-modal navigation, the route calculation needs to traverse semantically enriched road networks for different transportation modes. Currently, operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the underlying geo-data that are separately maintained in different spatial databases. To overcome this limitation, a novel approach has been proposed to integrate the routing-relevant information from different data sources, which involves three processes: (1) automatic matching to identify the corresponding road objects between different datasets; (2) interaction to refine the automatic matching result; and (3) transferring the routing-relevant information from one data-set to another. In process (1), the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets, which has revealed a high matching rate and certainty. However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent. The remaining unmatched or wrongly matched objects are treated in process (2), with the help of a series of interaction tools. On the basis of refined matching results after the interaction, process (3) is dedicated to automatic integration of the routing-relevant information from different data sources.     

Populating digital earth: improving access to Chinese remote sensing data for terrestrial applications

Global change has a significant impact on the lives of humankind. Earth observation can help to better understand our earth and cope with global change. With the availability of more reliable environmental data sets, digital earth is becoming a popular way to monitor the Earth and provide information to researchers and decision makers on environment protection, disaster mitigation, and social benefits. Therefore, accessing data with lowering costs is essential for digital earth. Nevertheless, there are big challenges in ensuring the feasibility of access to Chinese remote sensing data. This paper outlines some of the main challenges in realizing data sharing, provides an analysis of the core reasons leading to these challenges, and proposes recommendations to overcome the challenges. Amongst the main challenges are differences in data policy to gain access to satellite data, diverse data formats, and delivery mechanisms. The major challenge for the decision makers is to define a more open policy and for the scientist the challenge is to implement these polices for the benefit of all. This paper proposes that governments should adopt policies encouraging more open distribution and access to their data, in order to generate an improved digital earth with increased benefits to human society.     

Performance optimization of grid aggregation in spatial data warehouses

The problem of storage and querying of large volumes of spatial grids is an issue to solve. In this paper, we propose a method to optimize queries to aggregate raster grids stored in databases. In our approach, we propose to estimate the exact result rather than calculate the exact result. This approach reduces query execution time. One advantage of our method is that it does not require implementing or modifying functionalities of database management systems. Our approach is based on a new data structure and a specific model of SQL queries. Our work is applied here to relational data warehouses.     

Challenges and strategies for the visual exploration of complex environmental data

ABSTRACT

In this opinion paper, we, a group of scientists from environmental-, geo-, ocean- and information science, argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal data. We discuss the challenges that appear such as synthesis of heterogeneous data from various sources, reducing the amount of information and facilitating multidisciplinary, collaborative research. We argue that to fully exploit the potential of visual data exploration, several bottlenecks and challenges have to be addressed: providing an efficient data management and an integrated modular workflow, developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and education. We are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.     

Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets

Abstract

Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items.

We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper.