Chongcheng Chen

Parallel genetic algorithms on programmable graphics hardware

Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how fine-grained parallel genetic algorithms can be mapped to programmable graphics hardware found in commodity PC. Our approach stores chromosomes and their fitness values in texture memory on graphics card. Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on graphics processing unit in parallel. We demonstrate the effectiveness of our approach by comparing it with compatible software implementation. The presented approach allows us benefit from the advantages of parallel genetic algorithms on low-cost platform.

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001–2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.

Minimum spanning tree based spatial outlier mining and its applications

Spatial outliers are spatial objects whose nonspatial attributes are significantly different from the values of their neighborhoods. Detection of spatial outliers will provide the user with meaningful, interesting and potential information. Usually, algorithms for outlier mining on traditional business-oriented datasets are no longer applicable to spatial datasets. A new algorithm based on MST clustering is proposed in this paper to identify spatial outliers. The algorithm organically integrates the approach of minimum spanning trees and the density-based mechanism for outlier mining. Basic spatial structure characteristics of spatial objects are maintained by Delaunay Triangles and MST clustering is achieved by cutting off several most inconsistent edges. It turns out that the algorithm can find true spatial outliers, and it doesnt require any parameter for the algorithm be specified firstly. Experiments on real application problems indicate that the proposed algorithm is feasible and effective for identifying outliers from the large-scale spatial datasets.

A GIS-based forest visual simulation system

This paper reports on a visual simulation system that supports GIS-based realistic modeling and real-time rendering of forest scenes. Geometric models of trees are automatically generated according to inventory database and pre-designed template models. A combined image and geometry representation method for 3D tree model is given with a specific level of detail algorithm for ensuring real-time frame rates. We have tested and evaluated the system in applications of walkthrough simulation and forest fire visualization.

Effect of topography and accessibility on vegetation dynamic pattern in Mountain-hill Region

Knowledge of both vegetation distribution pattern and phenology changes is very important. Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression (GWR) framework in Fujian province, China. The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset from 2000 to 2010 was applied. Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle (ΔEVI). Candidate explaining factors included topographic conditions, accessibility variables and proportions of primary vegetation types. Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square (OLS) regression analysis. GWR analysis revealed that spatially, the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude, as a result of the various combinations of environmental factors, vegetation composition and also intensive anthropogenic impact. Apart from the continuously increasing trend of phenology magnitude with increasing altitude, the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased, even from strongly positive to negative, with increasing altitude or distance. Specially, the most rapid change of correlation coefficient between them was observed within a low elevation or close distance; less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range. Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.

Light interception efficiency analysis based on three-dimensional peach canopy models

Light interception capability is a critical factor affecting the growth, development, fruit yield and quality of fruit trees; thus, it is beneficial to cultivate optimal canopy types with high light interception efficiency. In this study, we present a quantitative method of analyzing light interception by tree canopies based on a virtual plant model. A detailed three-dimensional (3D) peach model with a natural growth shape was reconstructed and then the branches in the model were pruned to generate canopies with an open center form. These models were used to calculate the light interception and corresponding net photosynthesis. A solar radiation transfer model was used to determine the radiation intensity at the top of the canopy, and a ray tracing algorithm and turtle algorithm were utilized to simulate the spatial distribution of direct and diffuse radiation, respectively, in the tree canopy and obtain the photosynthetically active radiation (PAR) for each leaf. In the final step, we applied the photosynthesis model to calculate the canopy net photosynthetic rate. To compare the light interception efficiency among various plant canopy shapes, the net production rate at the whole-canopy scale and the average net photosynthetic rate per unit leaf area were calculated. The simulation results showed that peach canopies with an open center form provided better results compared with canopies with a natural form in terms of light penetration and air ventilation. Our method supports quantitative analysis of light interception and use efficiency for different types of canopy architectures at each time step and for individual leaf units. The approach was implemented in the interactive parametric individual 3D tree modeling software ParaTree. The extended ParaTree software is useful for fruit tree management applications because it provides an intuitive tool that can assist in tree pruning and design for ideal canopy architecture types.

GeoKSGrid: A geographical knowledge grid with functions of spatial data mining and spatial decision

Motivated by the lack of a geographical problem solving environment that is adequate to provide end users with reliable, open, distributed, and long lasting spatial data analyzing and knowledge discovery services, a novel geographical knowledge service platform — GeoKSGrid with functions of spatial decision support and distributed & parallel data mining is described in this paper. The basic concepts and state-of-the-art knowledge in grid research is discussed first. Then, the design of system architecture and the implementation of several most important modules of GeoKSGrid are illustrated. Finally, some demonstrative applications of the geographical knowledge services in real industry contexts is examined, combining with a brief interpretation of the processing results which confirm the practical value of the services and knowledge grid platform.

Parallel and Distributed Spatial Outlier Mining in Grid: Algorithm, Design and Application

There is an increasing interest in the field of parallel and distributed data mining in grid environment over the past decade. As an important branch of spatial data mining, spatial outlier mining can be used to find out some interesting and unexpected spatial patterns in many applications. In this paper, a new parallel & distributed spatial outlier mining algorithm (PD-SOM) is proposed to simultaneously detect global and local outliers in a grid environment. PD-SOM is a Delaunay triangulation (D-TIN) based approach, which was encapsulated and deployed in a distributed platform to provide parallel and distributed spatial outlier mining service. Subsequently, a distributed system framework for PD-SOM is designed on top of a geographical knowledge service grid (GeoKSGrid) developed by our research group, a two-step strategy for spatial outlier detection is put forward to support the encapsulation and distributed deployment of the geographical knowledge service, and two key techniques of the geographical knowledge service: parallel and distributed computing of Delaunay triangulation and the implementation of PD-SOM algorithm are discussed. Finally, the efficiency of the spatial outlier mining service is analyzed in theory, the practicality is confirmed by a demonstrative application on the abnormality analyzing of soil geochemical investigation samples from Fujian eastern coastal zone area in China, and the effectiveness and superiority of PD-SOM in a balanced, scalable grid environment are verified through the comparison with the popular spatial outlier mining algorithm SLOM, for the involvement of large amount of computing cores.

Vegetation distribution pattern along altitudinal gradient in subtropical mountainous and hilly river basin, China

Knowledge of vegetation distribution patterns is very important. Their relationships with topography and climate were explored through a geographically weighted regression (GWR) framework in a subtropical mountainous and hilly region, Minjiang River Basin of Fujian in China. The HJ-1 satellite image acquired on December 9, 2010 was utilized and NDVI index was calculated representing the range of vegetation greenness. Proper analysis units were achieved through segregation based on small sub-basins and altitudinal bands. Results indicated that the GWR model was more powerful than ordinary linear least square (OLS) regression in interpreting vegetation-environmental relationship, indicated by higher adjusted R2 and lower Akaike information criterion values. On one side, the OLS analysis revealed dominant positive influence from parameters of elevation and slope on vegetation distribution. On the other side, GWR analysis indicated that spatially, the parameters of topography had a very complex relationship with the vegetation distribution, as results of the various combinations of environmental factors, vegetation composition and also anthropogenic impact. The influences of elevation and slope generally decreased, from strongly positive to nearly zero, with increasing altitude and slope. Specially, most rapid changes of coefficients between NDVI and elevation or slope were observed in relatively flat and low-lying areas. This paper confirmed that the non-stationary analysis through the framework of GWR could lead to a better understanding of vegetation distribution in subtropical mountainous and hilly region. It was hoped that the proposed scale selection method combined with GWR framework would provide some guidelines on dealing with both spatial (horizontal) and altitudinal (vertical) non-stationarity in the dataset, and it could easily be applied in characterizing vegetation distribution patterns in other mountainous and hilly river basins and related research.

Massive Geo-spatial Data Cloud Storage and Services Based on NoSQL Database Technique

In recent years,how to implement a efficient storage management on massive geo-spatial data and ulteriorly web service for a broad variety of users,has becomes an increasingly hot issue in the field of geographical information science,with the explosive growth of Earth Observation System(EOS) data and the flourish of the new geography paradigm.A cloud storage system to provide distributed cloud-enabled storage management and services for massive geo-spatial data with an integrity of both vector and raster formats is proposed in this paper in the light of their intrinsic differences.Based on three-tier layer architecture,we put forward its implementation strategy and method of cloud storage management for raster and vector data respectively based on NoSQL database system,followed by a universal data access interface.The novel technolgies,which include distribute graph database-Neo4J and parralel graph compute framework on massive vector data storage and process were introduced.In our research,using the distributed file system-HDFS and the column family database-HBase as a container to store massive raster data with a distributed space index technique,and the distributed graph database system-Neo4J is used to store massive vector data in view of the constraints of ACID with a R-tree space index.Under the unified framework of Geographical Knowledge Cloud platform GeoKSCloud developed by our research group as a successor of GeoKSCloud,its core components — spatial data aggregation centre(GeoDAC) software has been in shape with aim to provide some distributed spatial data storage management and access services for all types of end users.A tesbed is established with serveral 5 physical nodes and accordingly 7 virtual nodes with different areas and operational systems.We carried out an elaborate comparison between GeoDAC and open source GIS software — PostGIS to validate vector data reading writing performance.The preliminary results indicated that,although GeoDAC has no accelerated write performance than PostGIS,but it gains significant powerful reading or spatial query performance than PostGIS.Inside GeoDAC,space-partitioned massive data is distributed on the cluster and spatial query operation is implemented in parallel,consequently an enhanced rate of spatial query is gained.The achieved techniques and system in our work will provide a variety of users a powerful tool for further in-depth processing and owns a broad application prospects.