| Based on the theory and key technologies of cloud computing, this research firstly proposes a theoretical cloud-computing-based framework for high-performance processing of land resource services. The study also introduces solutions to some key issues such as distributed storage of spatial data, index creation and operation of spatial cloud services and high-performance parallel processing of spatial data. On the basis of the framework, this research establishes a cloud-computing-based application for managing land resource information named Cloud Service Platform of Land Resource (LRCSP). Using this platform, four experiments are performed for testing the efficiency of high-performance processing of land resource cloud services.Specifically, this research mainly includes three aspects as follows:1) A cloud-computing-based framework for managing land resources servicesThis study reviews basic theory and applications of cloud computing, focusing on basic architectures, key technologies and reference architectures of four enterprise cloud computing platforms. From the view of three disciplines, the author discusses definitions of cloud GIS and proposes a six-layer architecture for cloud GIS (physical layer, virtual layer, data source layer, support platform&service component layer and application layer), in which distribution strategy of computing nodes is of great concern. Based on characteristics of GIS services, the author introduces a GIS service model and designs cloud GIS service catalog and service interfaces which allow users to develop some simple programs. The High-performance processing model is a focus of this research. The author analyzes function decomposition in terms of atomic services and composition services, and data decomposition based upon the data structure of vector and raster data. One the basis of the above research, the framework for Cloud Service Platform of Land Resource (LRCSP) is proposed.2) Key technologies to implement LRCSRBased on key technologies of cloud computing, this work presents solutions to four main issues for cloud GIS. For distributed storage of spatial data, this research introduces a Grid-aided and STR-Tree-based partition (GASTRSDP) method for dividing vector data and a quadtree-index-based partition approach for partitioning raster data. In particular, this work puts forwards a data-partition-based algorithm for determining the optimal parallel processing strategy. In the research of job scheduling for virtual computing nodes, the author describes job scheduling algorithms in details and proposes a computational model for computing nodes. To solve the problem of map retrieving after the map has been partitioned and parallel stored, this study chooses a tile-caching-based strategy which can also be used to dynamically update land maps once land data are changed. When using parallel database and MapReduce to improve performance, the author designs a Complementary Model. By using the example of counting the instances of each feature's type of land-use in a layer, the research presents an algorithm for parallel statistics.3) Implementation of the platform and four experiments to test performance of LRCSP Three functional modules for client, land resources business and cloud resources management are established. Using a large quantity of vector and raster data, four experiments are conducted on cloud storage performance (parallel partitioning vector and raster data), map display performance (loading mass data of multiple computing nodes), high-performance processing (statistics on land use using MapReduce) and virtual load balancing (comparing the performance and consumptions of virtual nodes to those of Cluster System with common PCs that install only one Operation System). The results show that using cloud theory, methods and technologies to solve the problem of land resource management is promising and that the LRCSP is of high performance, flexibility and scalability, which can shed lights on application of cloud GIS.
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