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    Evolution of the Multiple Accumulated Temperature Across Mainland China in 1961-2018 with the Gridded Meteorological Dataset
    BAI Lei, ZHANG Fan, SHANG Ming, SHI Chunxiang, SUN Shuai, LIU Lijun, WEN Yuanqiao, SU Chuancheng
    Journal of Geo-information Science    2021, 23 (8): 1446-1460.   DOI: 10.12082/dqxxkx.2021.200500
    Abstract147)   HTML3)    PDF (32794KB)(269)      

    Accumulated Temperature (AT) could affect plants' phonological period and crops' yield and spatial distribution. AT is usually obtained by extrapolation of surface observations. However, AT would have greater spatial uncertainties in regions where the surface observations are sparsely distributed with complex terrain. In recent years, there have been some gridded meteorological data with well spatial representation. If studies used these high spatial resolution gridded meteorological data to directly calculate AT, the problem mentioned above would be solved. This study used the gridded dataset (CN05.1) with high spatial resolution and long term time series from 1961-2018 to analyze the spatiotemporal changes of the four Accumulated Temperatures (ATs) in mainland China with the thresholds of ≥0 ℃, ≥5 ℃, ≥10 ℃, and ≥15 ℃, respectively. The gridded dataset was made using more than 2400 surface meteorological stations across mainland China and was well extrapolated by the plate spline method. The main conclusions are summarized as follows: ① In mainland China, the four ATs (≥0 ℃, ≥5 ℃, ≥10 ℃ and ≥15 ℃) have low-value areas in the Qinghai-Tibet Plateau, Tianshan Mountains in Xinjiang, and Northeast China, but high-value areas in South China. Their spatial patterns are similar to those of the 2-m air temperature. ② All four ATs show significant increasing trends, especially in Inner Mongolia and Northeast China. ③ Due to changes in the AT spatial trends, the area of tropical and subtropical regions, identified by a threshold of 10 ℃, have a significant increase. In contrast, the area of mid-temperate and cold-temperate regions have a significant decrease. ④ During 1961-2018, starting time of four ATs had significantly advanced while the ending time had significantly delayed in both regional and point scales. The interval period of temperature transition ranges of 0~5 ℃, 5~10 ℃, and 10~15 ℃’s starting time has more severe changes in the Loess Plateau and Inner Mongolia. For interval period of ending time, Central China Plain changes greatly. These significant changes would impact the farming plan, crop physiology, plant diseases, and insect pests. In the future, the gridded dataset with more high spatial resolution and longer time series could be used to study the changes of accumulated temperature under climate change.

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    A Dense Matching Algorithm for Remote Sensing Images based on Reliable Matched Points Constraint
    ZHANG Xin, WANG Jingxue, LIU Suyan, GAO Song
    Journal of Geo-information Science    2021, 23 (8): 1508-1523.   DOI: 10.12082/dqxxkx.2021.200660
    Abstract82)   HTML2)    PDF (73426KB)(244)      

    To avoid the problem of mismatches caused by initial matched points that may contain false matches during iterative dense matching based on corresponding points, a dense matching algorithm for remote sensing images based on reliable matched point constraint is presented. Firstly, to increase the number of initial matching points and expand the covering range of initial matching points, the initial set of matched points containing the matched Scale-invariant Feature Transform (SIFT) points and virtual corresponding points is constructed, where the virtual corresponding points are generated from the intersections of corresponding lines obtained by the line matching algorithm based on the matched SIFT points constraint. Secondly, the initial set of matched points is checked to remove the false matches using local image information and local geometry constraints in turn. This process first uses the local texture feature constraint constructed based on fingerprint information and gradient information to eliminate the mismatched points with low similarity, and then uses the local geometric constraint constructed by Delaunay triangulation to remove the false matches generated by similar textures, thereby obtaining the optimized set of reliable matched points. Finally, the Delaunay triangulation is constructed using reliable matched points, and the gravity center of the triangles satisfying the areal threshold is used as the matching primitive during the dense matching process. The matching based on the epipolar constraint and affine transformation constraint is performed iteratively to obtain the dense matching results. This paper used four sets of forward and backward viewing data of ZY-3 to perform parameter analysis experiment and comparative analysis experiment to prove the effectiveness of the proposed dense matching algorithm. The results of parameter analysis experiment show that the reliable matched points can be obtained when the weighted index, texture feature similarity threshold, and local geometric similarity threshold are 0.3, 0.95, and 0.85, respectively. The average matching accuracy of the reliable matched points on the four sets of data is improved by 19% compared with the initial matched point. Meanwhile, the results of comparative analysis experiment show that the dense matching algorithm based on the reliable matched point constraint can effectively avoid the error propagation, which has higher matching accuracy compared with the comparison algorithms selected in this paper. The average matching accuracy of the four sets of data is 95%. Therefore, the algorithm can obtain better dense matching results by effectively eliminating mismatched points.

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    Research on Three Dimensional City Model Data Partitioning and Distributed Storage
    LI Chaokui,YAN Wenying,YIN Zhihui,CHEN Guo
    Journal of Geo-information Science    2015, 17 (12): 1442-1449.   DOI: 10.3724/SP.J.1047.2015.01442
    Abstract1385)   HTML5)    PDF (5464KB)(1774)      

    With the rapid development of information acquisition technology, the geographic information data is increasing at the magnitude of terabyte every day. As an important content of 3D GIS, 3D city model data plays an important role in the construction of digital city and smart city. Because the data structure of 3D city model is complex and the data volume is huge, how to efficiently divide and store large amount of 3D city model data in order to meet the long-term management of data, the rapid visualization of data scheduling and the requirement of spatial assistant decision-making of 3D GIS system, has become a research hotspot in recent years. Previous data partitioning methods have caused the changes of zoning frequently in the data scheduling, which makes the update and management of data become more difficult. So, it is necessary to find out a more stable and universal data partitioning method. In this paper, based on the research of the shortcomings for the existing 3D city model data partitioning methods, we proposed the large scale map partition method based on topology relation model, and then we designed a unified name encoding scheme for the 3D models data after splitting. With the help of the powerful massive data organization and efficient multiple concurrent access function of the non-relational database MongoDB, a MongoDB sharded cluster server is constructed. The 3D city model data was used in unit division, and the rules modeling software City Engine was applied to processing the divided units, thus producing the 3D city model. Afterwards, MongoDB was used for data storage experiments. The results show that the large scale map partition method based on topology relation model is capable and sutable for the data partition of 3D city model, and the storage efficiency of the divided data is obviously improved. Moreover, the MongoDB database has a good stability on multiple concurrent access.

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    Cited: CSCD(3)
    Multi-scenarios Simulation of Urban Growth Boundaries in Pearl River Delta Based on FLUS-UGB
    WU Xinxin,LIU Xiaoping,LIANG Xun,CHEN Guangliang
    Journal of Geo-information Science    2018, 20 (4): 532-542.   DOI: 10.12082/dqxxkx.2018.180052
    Abstract1659)   HTML18)    PDF (22642KB)(770)      

    Arising from rapid growth of economy and population,urban sprawl has become a major challenge for sustainable urban development in the world. In order to assist urban planning, applicable methods and models are required to guide and constrain the growth of urban areas. Nowadays, urban growth boundaries (UGBs) has been regarded as a common tool used by planners to control the scale of urban development and protect rural areas which has a significant contribution to local ecological environment. However, existing models mainly focus on the delimitation of UGBs for urban development in single-scenarios. To date, there are rarely studies to develop efficient and scientific methods for delimiting the UGBs by taking the influences of macro policy and spatial policy into account. This paper presents a future land use simulation and urban growth boundary model (FLUS-UGB) which aims to delimit the UGBs for the urban areas in multi-scenarios. The top-down system dynamics (SD) model and bottom-up cellular automaton (CA) model are integrated in FLUS sub-model for simulating the urban growth pattern in the future. Furthermore, the UGB sub-model is developed to generate the UGBs that uses a morphological technology based on erosion and dilation according to the urban form produced by FLUS. This method merges and connects the cluster of urban blocks into one integral area and eliminates the small and isolated urban patches at the same time. We selected the Pearl River Delta region (PRD), one of the most developed areas in China, as the case study area and simulate the urban growth of PRD region from 2000 to 2013 for validate the proposed model. Then we used FLUS-UGB model to delimit the UGBs in PRD region of 2050 under three different planning scenarios (baseline, farmland protection and ecological control). The results showed that: (1) the model has high simulation accuracy for urban land with Kappa of 0.715, overall accuracy of 94.539% and Fom 0.269. (2) the method can maintain the edge details well in areas with high urban fragmentation and fractal dimension. This research demonstrates that the FLUS-UGB model is appropriate to delineate UGB under different planning policies, which is very useful for rapid urban growth regions.

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    Cited: CSCD(18)
    Research on Comprehensive Suitability Evaluation Method of Rice Planting Environment
    WANG Xingfeng, LI Daichao, WU Sheng, XIE Xiaowei, LU Jiaqi
    Journal of Geo-information Science    2021, 23 (8): 1484-1496.   DOI: 10.12082/dqxxkx.2021.200644
    Abstract102)   HTML2)    PDF (16655KB)(165)      

    Carrying out the layout of the rice planting industry in a specific area is an important content of scientifically formulating the regional agricultural planting industry plan, and the comprehensive suitability evaluation of rice planting environment is the premise of rice planting industry layout. This paper takes Pucheng County, Fujian Province, a good grain and oil Demonstration County in China as the research area. The Analytic Hierarchy Model was used to construct a rice planting suitability evaluation system with 21 indicators in five categories: soil conditions, site conditions, irrigation and drainage conditions, climate conditions and mechanical farming conditions. The evaluation system uses geological models, regression models and spatial interpolation methods to calculate and simulate the spatial distribution data of evaluation indicators to form a 5 m×5 m resolution evaluation index grid data set. The suitability index model was established by using experience index method to carry out comprehensive suitability evaluation of rice planting environment in fine scale. Analyzing the rice yield of the actual samples and the comprehensive suitability index of the rice planting environment, it was found that the two were significantly positively correlated, which verified the correctness and feasibility of the evaluation work of this study. Finally, the K-means attribute clustering method was used to identify the spatial pattern of multi-dimensional environmental suitability of rice planting in the research area. The results show that: ① The cultivated land area with high, relatively and moderately suitable rice planting in the study area accounted for 84.4% of the cultivated land area of the whole county, and the sub-suitable cultivated land only accounted for 15.6%. The overall suitability of cultivated land was relatively high. ② The comprehensive suitability for rice planting and the suitability of various indicators are higher in the type I cluster area. Type II cluster area have higher comprehensive suitability for rice planting, but the suitability of irrigation and drainage conditions is very low. The comprehensive suitability of rice planting in type III cluster area is relatively high, but the suitability of site conditions and soil conditions are lower. Type IV cluster area have low overall suitability for rice planting, and the lowest suitability for irrigation and drainage conditions. This study can provide a method for the evaluation of the suitability of rice planting, and provide a basis for Pucheng County to carry out agricultural planting planning more rationally and scientifically.

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    A Tentative Study on Knowledge Engineering for Virtual Geographic Environments
    LIN Hui,YOU Lan
    Journal of Geo-information Science    2015, 17 (12): 1423-1430.   DOI: 10.3724/SP.J.1047.2015.01423
    Abstract1093)   HTML5)    PDF (1401KB)(1555)      

    Geographic knowledge plays an important role in the researches and applications of Virtual Geographic Environments (VGE). Most researches about geographic knowledge engineering are still in the exploring stage. Geographic knowledge engineering for VGE is now a novel subject that has so far not been completely studied. As one component of the new generation of geographic information analysis, VGE has the typical features of multi-discipline, multi-collaboration, multi-interaction, multi-models and multi-sensing. It is urgent to systematically understand the features, mechanisms and key technologies in VGE knowledge engineering. This paper firstly reviewed the research status in knowledge engineering and geographic knowledge engineering from the domestic and abroad perspectives. Then, concepts are proposed regarding the geographic knowledge for VGE and VGE knowledge engineering. Furthermore, the typical features of geographic knowledge in VGE that differ from the common knowledge are discussed in depth. Focusing on the research direction and construction of VGE knowledge engineering in the near future, the key problems within four dimensions that must be resolved have been proposed and discussed. The tentative study of VGE knowledge engineering in this paper may provide a theoretical basis and reference for building the intelligent VGE system, which helps to promote the rapid transformation from the geodata to the geographic knowledge in VGE.

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    Cited: CSCD(9)
    Forest Fire Risk Rapid Warning Model based on Meteorological Monitoring Network
    LI Yu, ZHANG Liming, ZHANG Xingguo, WANG Hao, ZHANG Xingang
    Journal of Geo-information Science    2020, 22 (12): 2317-2325.   DOI: 10.12082/dqxxkx.2020.190799
    Abstract201)   HTML4)    PDF (6359KB)(170)      

    Forest fire occurs frequently and suddenly. Therefore, it is essential to carry out the rapid warning of forest fire danger for the reduction of the loss caused by forest fire and the promotion of sustainable development of forest resources. This paper designs an early-warning model based on GIS spatial analysis and visualization technology and the construction of real-time meteorological monitoring network using ground meteorological stations, which can achieve timely and rapid warning of forest fire danger. To build the model, this paper first determines the forest fire danger early-warning factors, which are the input parameters of the model. Secondly, a hierarchy model of the importance of early warning indicators is constructed to determine the weight of the early warning factors via using the AHP method and combining the analysis of early warning factors. Then, the thresholds and grade division criteria of the early-warning factor are determined according to the national, industrial, and local regulations for determining forest fire danger levels, which is suitable for the model. Finally, the Voronoi Diagrams are used to establish a meteorological monitoring network based on weather stations and real-time weather data. The Overlay Analysis technology is used to calculate the early warning result. Based on the model and real-time acquisition and processing of data, a rapid warning system for forest fires was constructed. This paper took Qinghai Province as the experimental area where the feasibility and applicability of the system were verified, which indicates that early warning of forest fire danger can be realized by the model comprehensively, accurately, and rapidly. Results show that: (1) According to the early-warning model, the real-time early-warning indicators which were set before, and real-time meteorological monitoring data, the early-warning signal can be sent in time, which can quickly realize early warning and timely response of forest fire risks at the county and forest farm levels; (2) Via introducing GIS visualization methods, the thematic map of forest fire risk spatial distribution can be generated by the model quickly, which is conducive to observe changes in early-warning levels visually. The rapid warning of forest fire risks has important guiding functions for effective prevention, interruption management, and prevention measures of forest fire, and has great significance for forest fire prevention work, protection of forest resources, and safety of human life and property.

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    An Improved RANSAC Algorithm for Point Cloud Segmentation of Complex Building Roofs
    LIU Yakun, LI Yongqiang, LIU Huiyun, SUN Du, ZHAO Shangbin
    Journal of Geo-information Science    2021, 23 (8): 1497-1507.   DOI: 10.12082/dqxxkx.2021.200742
    Abstract98)   HTML0)    PDF (14248KB)(114)      

    Roof model reconstruction affects the quality of building complete model reconstruction, and the segmentation quality of roof point cloud is of great significance for roof model reconstruction. Aiming at the problems of wrong segmentation and over segmentation in the traditional RANSAC algorithm, this paper proposes an improved RANSAC algorithm to redistribute the point cloud, considering the location information of the point cloud. The algorithm eliminates the non planar points temporarily, and selects three points from the planar points set as the initial samples in the way of R radius neighborhood to fit them. The distance between the remaining points in the neighborhood and the fitting plane is calculated, and the neighborhood meeting the threshold requirements is classified as an effective neighborhood, three points with the minimum standard deviation are selected as the initial model, RANSAC algorithm is used to segment the roof point cloud. Aiming at the misclassification phenomenon in segmentation results, the distance between misclassification points and patches is calculated by k-nearest neighbor algorithm, and then the misclassification points are reclassified, at the same time, the angleθ and the distance d between patches are considered to merge the over segmented patches, the Euclidean distance based clustering segmentation algorithm is used to analyze the connectivity of the merged patches. By using the distance from a point to a plane and the consistency of the normal vectors between the point and the plane, the non planar points are redistributed. In order to verify the effectiveness of the algorithm, three independent roofs of complex buildings in Helsinki area of Finland and six roofs of buildings in a residential area of Shanghai are selected as experimental data. In the first group of experiments data, the average accuracy of the segmentation of roof patch is 92.17%, and the highest accuracy is 93.18%. In the second group of experiments data, the average accuracy of the segmentation of the roof patch is 87.82%, and the highest accuracy is 94.44%. The average standard deviation of the distance between the points on all the segmentation patches and the corresponding best fitting plane is 0.030 m. According to the above two groups of experiments data, 78% of the buildings have no over segmentation, and the average accuracy is 90%. The experimental results show that the algorithm has a high accuracy in extracting the roof plane slice, which can suppress the over segmentation and has a good anti noise ability.

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    Achievements and Preliminary Analysis on China National Flash Flood Disasters Investigation and Evaluation
    GUO Liang,ZHANG Xiaolei,LIU Ronghua,LIU Yesen,LIU Qi
    Journal of Geo-information Science    2017, 19 (12): 1548-1669.   DOI: 10.3724/SP.J.1047.2017.01548
    Abstract1300)   HTML8)    PDF (12232KB)(333)      

    National Flash Flood Disasters Investigation and Evaluation project is the largest non-engineering projects in water conservancy industry since the establishment of new China in 1949. It is also the largest scale of general census on disasters background in flood management and mitigation fields. The whole project lasted for 4 years, covering 30 provinces, 305 cities and 2138 counties, with a total land area of 7.55 million km2 and a population of nearly 900 million. Through general census, on-site investigation, field measurement, hydrological analysis and calculation, and comprehensive evaluation methods, the spatial distribution, human settlement, underground situations, impacts, social and economic situations, hazard zoning, warning indicators and historical situations of flash flood disasters were collected. The storm flood characters in mountainous areas were also analyzed. The flood control ability of selected villages were assessed and the critical rainfall index of these villages were obtained. The hazard zones were finally identified, all of which provided a strong information support for flash flood early-warning and forecast and residential safety transfer. We systematically introduced the key focuses on the investigation and evaluation project of national flash flood disasters, made a general review on the collection of data and information, summarized thousands of investigation results and elements during this huge project. We also discussed the spatial pattern of these elements. Based on these survey data, the characteristics of flash flood disaster prevention areas, the human settlement features and spatial pattern of storm flood were further analyzed. Finally, flash flood prevention areas, population distribution, flash flood warning ability and historical flash flood disaster events were discussed. It was found that the national flash flood prevention areas, human settlement, historical flash flood events and warning ability appeared to be spatially consistent. They were mainly distributed along the transitional zone of Qinghai-Tibet plateau and Sichuan basin, the borders of Sichuan and Yunnan provinces, the Loess plateau zones, the Eastern coastal areas and the North China areas. Meanwhile, future application and analysis on diversified utilization of investigation and evaluation results of national flash flood disasters were proposed, providing a solid data foundation for flash flood monitoring and warning system, disaster management and mitigation researches, a better platform of technological promotions, in both flood management departments and other relevant fields.

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    Cited: CSCD(7)
    Using UAVs Remote Sensing for Population and Distribution of Grazing Livestock in the Source Region of the Yellow River
    LIU Shuchao, SHAO Quanqin, YANG Fan, GUO Xingjian, WANG Dongliang, HUANG Haibo, WANG Yangchun, LIU Jiyuan, FAN Jiangwen, LI Yuzhe
    Journal of Geo-information Science    2021, 23 (7): 1286-1295.   DOI: 10.12082/dqxxkx.2021.210075
    Abstract82)   HTML0)    PDF (2776KB)(59)      

    The source region of the Yellow River has a unique ecosystem and biological resources, which is an important water conservation area and ecological barrier in China. In recent years, the traditional husbandry in this area faces the development problems of overgrazing, grassland degradation, seasonal imbalance by the increase in the population of grazing livestock. It is important to scientifically grasp the situation of grazing livestock, we used UAVs to investigate the population and distribution of grazing livestock (yaks, Tibetan sheep and horses) in Maduo County. According to the library of UAV image interpretation of yaks, Tibetan sheep and horses, visual interpretation was carried out. Five methods were used to estimate the population of grazing livestock in Maduo County, and the relationship between distribution of livestock and environmental factors was analyzed by selection index. The results showed that: (1) Yaks, Tibetan sheep and horses were found in 9 of 14 UAV flight strips in April 2017, and the grazing livestock were all located in the cold season grassland. A total of 1351 yaks, 2405 Tibetan sheep and 19 horses were found. In the cold season, the densities of yaks, Tibetan sheep and horses were 4.12, 7.34 and 0.06 per km2, respectively. (2) According to the estimation method of five kinds of livestock, it is the most accurate to estimate the livestock quantity in Maduo County based on the grassland in cold and warm seasons. In 2017, there were 70 800 yaks, 102 200 Tibetan sheep and 12 000 horses, and the error of estimating the population of yaks, Tibetan sheep and horses were -0.93%, 2.27% and -13.23% respectively. (3) The environmental factors of the three livestock, which tended to slope was less than 12°, the grassland coverage was more than 0.6, the distance from residential area was less than 1 km, the water source was less than 3km, the road is more than 3 km. Yaks and Tibetan sheep were mainly group activities, and horses usually were not large clusters. UAVs remote sensing has great potential in animal husbandry, and provides new ideas for studying the characteristics and balance of grazing livestock in pastoral areas

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    Application of Geo-information Science and Technology in Poverty Alleviation in China
    HU Shan, GE Yong, LIU Mengxiao
    Journal of Geo-information Science    2021, 23 (8): 1339-1350.   DOI: 10.12082/dqxxkx.2021.200631
    Abstract437)   HTML10)    PDF (3790KB)(63)      

    Through various exploration and practice of poverty alleviation, China has embarked on a path of poverty alleviation with Chinese characteristics, which has greatly reduced the number of rural poor people and significantly improved the living standard in poverty-stricken areas. For a long time, the monitoring of socioeconomic and environmental conditions in poverty-stricken areas is based on all kinds of statistical data, reports, paper files, etc., based on administrative units, lacking effective and accurate spatial location information. With the rapid development of geo-information science such as Remote Sensing (RS) and Geographic Information System (GIS), the real-time and efficient capture and calculation ability of spatial information greatly improves the efficiency and decision support level of poverty alleviation. This paper expounds the contributions of geo-information science on China's poverty alleviation from the following aspects:① monitoring and evaluation of natural resources and environment in poverty-stricken areas based on multi-source geospatial data; ② monitoring, early warning, and management of natural disasters in poverty-stricken areas; ③ analysis of poverty causing factors and poverty prediction; ④ decision support system for targeted poverty alleviation based on the mechanism of targeted poverty alleviation. China aims to eradicate absolute poverty in 2020, so the application of geo-information science in poverty alleviation will mainly focus on the establishment of monitoring and assistance mechanism to prevent poverty returning and alleviate the relative poverty. Moreover, under the background of rural revitalization, using geo-information science and technology to promote rural infrastructure information construction will be the focus of the next step.

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    A Brief Introduction of the Southeastern Qinghai-Tibet Plateau Glacier Wonder
    Journal of Geo-information Science    2015, 17 (11): 1412-1417.   DOI: 10.3724/SP.J.1047.2015..01412
    Abstract538)   HTML2)    PDF (6262KB)(1176)      
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    On Space-Air-Ground Integrated Earth Observation Network
    LI Deren
       2012, 14 (4): 419-425.   DOI: 10.3724/SP.J.1047.2012.00419
    Abstract1001)      PDF (1423KB)(3301)      

    Space-Air-Ground integrated earth observation network (SAGIEON) is not only the most promising high-tech area, but also a fundamental infrastructure closely related to national security and economic/social development. Firstly, the scientific concept, key technologies, current situation and tendency of SAGIEON are comprehensively represented. Secondly, an integrated data processing system for native remote sensing satellites is introduced, including its objectives and key technologies. Thirdly, the connotation of generalized spatial information grid are proposed on the basis of the above mentioned discussions. Finally, some conclusions are drawn. For the propose of providing fast, precise and real-time spatial information service to everyone, it is very important to carry out research on the theories and technologies of SAGIEON.

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    Cited: CSCD(22)
    On Geographic Knowledge Graph
    LU Feng,YU Li,QIU Peiyuan
    Journal of Geo-information Science    2017, 19 (6): 723-734.   DOI: 10.3724/SP.J.1047.2017.00723
    Abstract1559)   HTML29)    PDF (5515KB)(1902)      

    Web texts contain a great deal of implicit geospatial information, which provide great potential for the geographic knowledge acquisition and service. Geographic knowledge graph is the key to extend traditional geographic information service to geographic knowledge service, and also the ultimate goal of the collection and processing of implicit geographic information from web texts. This paper systematically reviews the state of the arts of the researches on open geographic semantic web, geographic entity and relation extraction, geographic semantic web alignment, and knowledge graph storage methods. The pressing key scientific issues are also addressed, including the quality evaluation of geospatial information collected from web texts, geographic semantic understanding, spatial semantic computing model, and heterogeneous geographic semantic web alignment.

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    Cited: CSCD(11)
    Full Three-Dimensional GIS and Its Key Roles in Smart City
    zhu qing
       2014, 16 (2): 151-157.   DOI: 10.3724/SP.J.1047.2014.00151
    Abstract1481)      PDF (3709KB)(3902)      

    Three-dimensional GIS (3D GIS) is one of the primary and typical contents of GIS technology at present and in the future, which overcomes the constraints of representing 3D GIS spatial information in two-dimensional map, as well as provides a more effective decision-making support for people's daily life. This paper focuses on the research progress and its key technologies of 3D GIS, including the data model, database management and visual analysis. The pilot applications of 3D GIS in Wuhan are also illustrated. The entire 3D space of the city is represented by 3D GIS. Then construction of the large-scale city digitalization is enabled with the improvement of city management. Finally, the applications of 3D GIS for spatio-temporal information bearing engine and spatial intelligence in smart city and city safety are investigated.

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    Cited: CSCD(27)
    Effect of Spectral Transformation Processes on the PLSR Models of Soil Nitrogen
    QIAO Xingxing,FENG Meichen,YANG Wude,SUN Hui,GUO Xiaoli,SHI Chaochao
    Journal of Geo-information Science    2016, 18 (8): 1123-1132.   DOI: 10.3724/SP.J.1047.2016.01123
    Abstract770)   HTML0)    PDF (1794KB)(845)      

    Spectral transformation is an essential pre-treatment technique as it can eliminate the effects of background and noise, and it plays a vital role in extracting spectral features′ information and constructing the optimal model. In order to explore the effects of different spectral transformation methods on the accuracy of the PLSR model in monitoring soil nitrogen and determining the optimal spectral transformation, the raw spectrum was transformed with respect to fifteen transformation algorithms and the correlations between each pair of transformed spectrum and soil nitrogen were analyzed. Furthermore, the performances of the PLSR models in monitoring the soil nitrogen based on different transformed spectra were evaluated. The results showed that, for cases involving the first or second-order differential reprocessing transformations, the correlation coefficient between the soil nitrogen and the relevant transformed spectrum increased more significantly than with the raw spectrum, especially when applying the transformation algorithms of square root (T8 and T11) and logarithm (T6 and T12) firstly. Also, fewer optimal factors for these pre-treatments were needed and selected to achieve the threshold of 98% in explaining the dependent variable. Moreover, the first-order differential reprocessing of the square root of raw spectrum (T8) had a higher accuracy (R2=0.985022, RMSEC=0.000132; R2=0.9853, RMSEV=0.000162, Fn=6) for the calibrated model and the validated model respectively, after the comprehensive evaluation of the predicting performance and the complexity of different models. Finally, the first-order differential reprocessing of the square root of raw spectrum (T8) was determined as the recommended transformation method to evaluate the soil nitrogen. In addition, the first-order and second-order differential of the logarithm of raw spectrum (T6 and T12), the first-order differential of the logarithmic reciprocal of raw spectrum (T7), the first-order differential of raw spectrum (T9), as well as the second-order differential of the square root of raw spectrum (T11) could also be considered and chosen as alternatives. The study would provide some theoretical techniques and references to the evaluation of soil nitrogen and spectrum processing.

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    Cited: CSCD(5)
    Geographic Knowledge Graph for Remote Sensing Big Data
    WANG Zhihua, YANG Xiaomei, ZHOU Chenghu
    Journal of Geo-information Science    2021, 23 (1): 16-28.   DOI: 10.12082/dqxxkx.2021.200632
    Abstract853)   HTML51)    PDF (1365KB)(343)      

    Due to the temporal and spatial heterogeneity of the complex earth's surface, the traditional idea of developing new intelligent interpretation algorithms to solve the remote sensing geoscience cognition based on the features of remote sensing images has hit the bottleneck in terms of accuracy and geographic usage when analyzing remote sensing big data. To overcome the bottleneck, we proposed the Geographic Knowledge Graph (GKG) that based on the geographic knowledge to analyze the remote sensing big data, which is inspired by the recently proposed Knowledge Graph from the geographic perspective. It expands the concept of the geographic knowledge and classifies the geographic knowledge into three levels: Data knowledge, conception knowledge, and regularity knowledge. Then, it represents and connects all geographic knowledge in Graph by nodes and edges and realizes the feedback iteration and update between different levels of the geographic knowledge. This representation enables GKG to perform well at knowledge inquiring, reasoning, calibration, and expanding. How to construct multiscale high-dimension geo-entities and how to connect different levels of the geographic knowledge with heterogeneous features are two key technologies. These functions make GKG promising in refining existing geographic knowledge in the era of remote sensing big data, promoting remote sensing interpretation accuracy and geographic usage, and promoting the development of geoscience.

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    Review on the Simulation of Non-Point Source Pollution in the Hilly Region of Southern China
    GAO Huiran,SHEN Lin,LIU Junzhi,ZHU Axing,QIN Chengzhi,ZHU Liangjun
    Journal of Geo-information Science    2017, 19 (8): 1080-1088.   DOI: 10.3724/SP.J.1047.2017.01080
    Abstract678)   HTML0)    PDF (1874KB)(494)      

    With the increase of economic development, water quality degradation caused by non-point source pollution has become a serious problem in the hilly region of southern China. It is hard to set up controllable experimental environment at the watershed scale due to the complexity of non-point source pollution processes. Model simulation has become an effective way to facilitate watershed management and planning. Related studies on the simulation of non-point source pollution have been conducted in this region. However, few works have been done to summarize the outcomes of shortcomings of these studies and to point out the future research directions. Firstly, this paper analyzed the physical mechanism of non-point source pollution and regional characteristics such as special land features, human activities in this region and pointed out that the simulation methods of non-point source pollution in this region should meet the following demands: (1) coupling multiple watershed processes such as hydrology, soil erosion, plant growth and the migrating and transforming of non-point source pollutants; (2) spatially fully distributed in order to express the spatial heterogeneity of non-point source pollutant loading, and describe the migration and transformation routes of pollutants explicitly in this region; (3) taking the special land features and human activities into consideration which have important effects on the process of non-point source pollution. Then, based on the above demands, this paper summarized the current studies on the aspects of migration routes modeling and the representation of special land features and human activities in this region, and analyzed the problems of existing methods for non-point source pollution modeling that applied in the hilly region of southern China. On the aspect of spatial discretization, current methods cannot accurately describe the spatial heterogeneity of non-point source pollution processes, and the modeling of pollutant transport routes is limited to semi-distributed approaches which can’t describe the exchange relationship of material and energy among adjacent spatial units at the hillslope scale. On the aspect of describing the regional characteristics, some watershed processes that are special in this region are absent in the current models. At last, future research directions were discussed on the following aspects: (1) Strengthen the description of the special landscape features, and explore the method of spatial discretization that suitable for hilly region of southern China; (2) Improve the construction of the fully-distributed migration routes of non-point source pollutants; (3) Conduct comprehensive representation of special land features and human activities in the fully-distributed non-point source pollution model. This paper aims to provide references to the simulation of non-point source pollution in the hilly region of southern China, which can then serve as an effective tool for scientific watershed management.

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    Cited: CSCD(1)
    Massive Geo-spatial Data Cloud Storage and Services Based on NoSQL Database Technique
    CHEN Chong-Cheng, LIN Jian-Feng, TUN Xiao-Zhu, WU Jian-Wei, LIAN Hui-Qun
       2013, 15 (2): 166-174.   DOI: 10.3724/SP.J.1047.2013.00166
    Abstract1044)      PDF (4208KB)(1397)      

    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.

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    Cited: CSCD(23)
    Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data
    LIU Xiang, LIU Rong-Gao, CHEN Jing-Meng, CHENG Xiao, ZHENG Guang
       2013, 15 (5): 734-743.   DOI: 10.3724/SP.J.1047.2013.00734
    Abstract847)      PDF (416KB)(3012)      

    Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.

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    Cited: CSCD(43)
    Extraction of Summer Crop in Jiangsu based on Google Earth Engine
    Zhaoxin HE, Miao ZHANG, Bingfang WU, Qiang XING
    Journal of Geo-information Science    2019, 21 (5): 752-766.   DOI: 10.12082/dqxxkx.2019.180420
    Abstract1105)   HTML36)    PDF (14005KB)(759)      

    Jiangsu province, with 13 municipalities and located in the east of China, is an important part of the Yangtze river delta economy belt. The temperature is appropriate and the rainfall is moderate. Jiangsu province enjoys a moderate climate, which is suitable for the agricultural development. Winter wheat is distributed throughout the whole province, whereas the planting structure of winter rapeseed is complex and mainly scattered in Southern Jiangsu. As reported by the State Statistics Bureau, the total planting area of winter wheat and winter rapeseed in Jiangsu ranked the fifth and seventh in China, respectively, during the last 10 years. Fast obtaining the precise planting area of these two crops in Jiangsu is crucial for the agricultural development. Remote sensing classification based on local host can obtain spatial distribution of crops with high accuracy, but is time-consuming. With the development of geographical big data, cloud platform, and cloud computation, the Google Earth Engine (GEE), a global scale geospatial analysis platform based on the cloud platform, has brought new opportunities for rapid remote sensing classification. Based on the GEE cloud platform, a time-saving method of obtaining the spatial distribution of winter wheat and winter rapeseed by use of sentinel-2 data in Jiangsu was proposed. First, 119 sentinel-2 images without cloud were obtained using the GEE in Jiangsu. The time interval was set from March 1 to June 1, 2017, and the space area was Jiangsu province. Based on the spatio-temporal information, the 119 remote sensing images were mosaicked and clipped. Secondly, remote sensing indices, texture, and terrain features were calculated respectively, and the original features were extracted. The original feature space was optimized by an algorithm named Separability and Thresholds (SEaTH algorithm). Finally, four classifiers including naive Bayes, support vector machine, classification regression tree, and random forest were tested and evaluated by the average assessment accuracy. The spatial distribution information of winter wheat and winter rapeseed were obtained quickly. The following conclusions are drawn: (1) the GEE can quickly complete pre-processing of cloud-masking, image-mosaicking, image-clipping, and feature extraction, which is superior to the local processing. (2) The distance values of J-M that are higher than 1 and rank top two highest can reduce the number of features from 28 to 11 and effectively compress the original feature space. (3) With the combined training of spectral, texture and terrain features, the average assessment accuracy of naive Bayes, support vector machine, classification regression tree, and random forest was 61%, 87%, 89% and 92%, respectively.

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    Cited: CSCD(3)
    Analysis on the Trade Networks of the Belt and Road Countries and Regions under Large Scale Shipping Data
    SUN Tao,WU Lin,WANG Fei,WANG Qi,CHEN Zhao,XU Yongjun
    Journal of Geo-information Science    2018, 20 (5): 593-601.   DOI: 10.12082/dqxxkx.2018.180066
    Abstract741)   HTML2)    PDF (1554KB)(528)      

    Among the 65 countries along the Belt and Road, 46 countries have registered ports of entry. At the same time, the trade by maritime shipping account for more than 75% of the total international trade. In order to fully understand the shipping trade in the countries and regions along the Belt and Road and assess the trade relations between countries and regions along the Belt and Road, we selected data which depicts the shipping history movements of the countries along the Belt and Road in the year of 2016 for study in this paper. Firstly, based on the method of rule determination, we excavated the Stop-port events of ships. By use of the ports in the countries of the Belt and Road as the main nodes, and the inter-port cargo transactions events as the edges, we have built the Belt and Road international shipping trade network. Based on this, the following network structure analyses of trade networks were conducted: (1) basic attributes analysis of the Belt and Road trade network, including network connectivity, degree distribution and average shortest path; (2) calculation of network node centrality, mainly using Eigenvector Centrality to evaluate the centrality of nodes in the trade network; (3) Using the concept of community mining in social network mining as the reference, and using the Fast Unfolding algorithm to discover the community of trading network. It can be seen that the trade between the countries and regions along the Belt and Road is intricately interwoven. By analyzing the degree distribution of nodes in the trade network, it can be clearly seen that there are small-world networks within the Belt and Road trade network. Further, Turkey, Russia and China are the three most influential counties in terms of the ports influence. By analyzing the results of the community detection, five major trade communities were identified. The distribution of these communities is basically in line with the geographical distribution. However, there are still some countries that are affected by special trade practices and their communities have broken regional restrictions. By building the trading network under large scale ship data, we evaluated the node's influence and analyzed the structure of the trade network more clearly on the basis of network analysis, and we hope this paper can help to better implement the Belt and Road Initiative strategy.

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    Cited: CSCD(3)
    A GIS-based Analysis of the Shapes of Provincial Boundaries of China
    SHUAI Fangmin, WANG Xinsheng, ZHU Chaoping, YU Ruilin, ZHANG Hong, SUN Yanling
       2008, 10 (1): 34-38.  
    Abstract587)      PDF (1102KB)(1068)      
    The fractal dimensions,shape indices and compactness of the boundaries of 32 provinces(regions) of China have been calculated in this paper based on GIS software.The results show that the provinces with irregular shapes calculated with the method of fractal dimensions are provinces of Fujian,Zhejiang,Guangdong,Chongqing,Guizhou,etc.The provinces with regular shapes are in the order of Hainan,Xinjiang,Inner Mongolia,Tibet,Taiwan,Shanxi,etc.The provinces with large fractal dimensions are almost all located in southern China,which show obvious differences between the North and the South.The provinces with irregular shapes based on shape index come out as below: Hebei,Inner Mongolia,Shaanxi,Chongqing,Ningxia,and those with irregular shapes include Zhejiang,Guangxi,Hunan,Qinghai,Hainan and other provinces.Although no obvious differences can be shown the same as fractal results,yet some differences between the East and the West are identified.Those with irregular shapes based on compactness are in the order of Fujian,Gansu,Guangdong,Inner Mongolia,Chongqing,and other provinces with regular shapes are Shanghai,Xinjiang,Shanxi, Taiwan,Tibet and other provinces,which also show marked differences between the East and the West.Those with complex shapes(edge complex and greater fragmentation) are mostly concentrated in the eastern and coastal areas;and the other provinces with compact shapes are mainly concentrated in western regions,such as Xinjiang,Tibet,Qinghai.Between eastern provinces and western provinces,there are greater differences in the shape features.This paper studies the reasons and concludes that the root cause is the geographical and climate environment and historical human characteristics,and preliminarily analyzes the effects of the traffic,and other organizations impacted by the features.Meanwhile,the planar provincial outline can also reflect the transport connectivity and development level of inter-and intra-provinces.Quantitative study of the provincial space form is one of the important contents of research in regional geography and urban/regional planning.It is also an important direction of the regional research.
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    Cited: CSCD(2)
    Study on the Influence of Urban Building Density on the Heat Island Effect in Beijing
    GE Yaning,XU Xinliang,LI Jing,CAI Hongyan,ZHANG Xuexia
    Journal of Geo-information Science    2016, 18 (12): 1698-1706.   DOI: 10.3724/SP.J.1047.2016.01698
    Abstract1210)   HTML2)    PDF (2166KB)(793)      

    "Heat island effect" is one of the main features of modern urban climate. In this paper, we have obtained the information for regions with different building densities in Beijing by using the artificial visual interpretation based on the high resolution remote sensing images, and then analyzed the relationship between the urban building density distribution and the urban heat island effect and its change pattern based on the land surface temperature data obtained by remote sensing inversion. The results show that the medium density building regions are the primary type within the fifth ring road in Beijing, and the area proportion of which is 23.5%. The distribution of the high density building regions is slightly less than the medium density building regions, and the area proportion of which is 12.01%. There are evident differences among the distributions of regions with different building densities within different ring roads. The high density building regions are mainly distributed within the second ring road, while the medium density building regions are commonly distributed in the whole area. The medium density building regions and the low density building regions are mainly distributed within the second-third ring road area. The overall area of the high-rise building regions is very small, and the high-rise building regions are mainly distributed within the second-third ring road area as well as the third-fourth ring road area. The relationship between the land surface temperature and the building density for the urban building regions is significantly positive, in which the higher density of the urban buildings, the higher average land surface temperature it will reach. The average temperature of the high density building regions in Beijing reached 30.5 ℃. The contribution of the high-rise building regions to the heat island intensity is small, and the average temperature of the high-rise building area is 28.32 ℃, which is 2.18 ℃ lower than the high density building regions. The distribution pattern of the average temperature for regions with different building densities among different ring roads is approximately the same. The differences of the average temperature among regions with different building densities within the second ring road are the smallest, while the average temperature of the high density building regions is obviously higher than other building density regions within the second-third ring road area and the third-fourth ring road area, and the average temperature of the high-rise building regions within the fourth-fifth ringroad area is the lowest, which is 28.09 ℃. Taking the change of heat island intensity between 2010 and 2015 into consideration, only the heat island intensity in the high-rise building regions has a weakening tendency, in which the intensity of the heat island has reduced by 0.07 ℃. While the heat island intensity in the high, medium and low density building regions have an enhancing tendency, the heat island intensity in the high density regionshas the largest growth, which had an increase of 0.56 ℃. Heat island effect is one of the most representative ecological environment problems in the process of urbanization, and the intensity of urban construction has an important impact on the urban heat island effect. Base on many researches, an appropriate reduction of the urban building density can effectively ease the occurrence of urban heat island effect.

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    Cited: CSCD(4)
    Setting Parameters and Choosing Optimum Semivariogram Models of Ordinaty Kriging Interpolation ——A case study of spatial interpolation to January average temperature of Fujian province
    WU Xuewen, YAN Luming
       2007, 9 (3): 104-108.  
    Abstract832)      PDF (688KB)(3621)      
    This article discusses about the thereunder of choosing the optimum semivariogram models and setting the key parameters based on ARCGIS and GS+software from characteristics and laws of data through understanding the ordinary Kriging interpolation theory, and carries through an in-depth exploratory spatial data analysis taking the spatial interpolation to January average temperature of Fujian province as an example, using the obtained parameters and semivariogram models to simulate the spatial distribution of January average temperature of Fujian province. The aticle offers a clear way for reasonable spatial interpolation.
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    Retrieval of Cloudtop Properties from MODIS Data
    LIN Lin, HUANG Sixun DU Huadong
       2006, 8 (2): 106-109.  
    Abstract508)      PDF (744KB)(1318)      
    Cloudtop properties constitute one of the important cloud properties. Cloudtop properties include cloudtop pressure, cloudtop temperature and cloud effective emissivity. One of the greatest current uncertainties in Global Climate Models is the role of clouds. The CO 2 slicing algorithm is one of the important methods for retrieval of cloudtop properties. It is founded based on the difference in atmospheric absorptions due to CO 2 between two spectrally neighboring channels in the wings of CO 2 absorption band with a resolution of 1km. This technique has been applied to HIRS data for about 20 years. Since Earth Observing System (EOS) platform launched in December 1999, the Moderate Resolution Imaging Spectroradiometer(MODIS) has been the first sensor to have CO 2 slicing bands at high spatial resolution. This paper mainly describes the theoretical model of CO 2 slicing and it' s applications. An analysis of the characteristics of the method and the estimate of errors will also be discussed. Finally, research issues are recommended for future studies.
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    Cited: CSCD(6)
    Spatiotemporal Point Process:A New Data Model, Analysis Methodology and Viewpoint for Geoscientific Problem
    FEI Tao, LI Ting, ZHOU Cheng-Hu
       2013, 15 (6): 793-800.   DOI: 10.3724/SP.J.1047.2013.00793
    Abstract1061)      PDF (1321KB)(2698)      

    The gridding computation is a major model in current geoscientific research due to its simplicity in organizing data resources. However, because the gridding computation equally distributes computational resources, it brings redundancy to the computational process and neglects catastrophe points in geoscientific phenomena, which might overlook the important patterns and bring more uncertainties to the research result. To overcome this weakness, this paper proposes to use the spatial point process model in geoscientific research. The spatial point process model is used to model spatial point based geoscientific phenomenon, also is applied to most of the other geoscientific processes (because they can be transformed into spatial point processes). In this regard, the spatial point process is not only a data model, but also an analysis tool for geoscientific problems. Moreover, it provided a new angle of view for observing geoscientific problems. To extract patterns from point process data, the authors propose the frame of multilevel decomposition of spatiotemporal point process. This frame is similar to the basic idea of signal decomposition. We first assume that any point data set is the overlay of an unknown number of homogeneous point processes. Then, the points are transformed into a mixture probability density function of the Kth nearest distance of each point. After that, the optimization method is used to separate clustering points from noise. Finally, the patterns are extracted using the density connectivity mechanism. The theory can be used to any type of point process data. It can be considered as the "Fourier transform" of point process data.

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    Cited: CSCD(6)
    Application of Divide-and-Conquer Method and Efficiency Evaluation Model in the Fast Union Algorithm of Polygons in GIS
    FAN Junfu, MA Ting, ZHOU Chenghu, ZHOU Yuke, XU Tao
       2014, 16 (2): 158-164.   DOI: 10.3724/SP.J.1047.2014.00158
    Abstract1014)      PDF (3379KB)(1051)      

    Vector data overlay analysis methods, with the most difficult and tricky core issue of overlapping between polygons, are the basic analysis approaches for many spatial analysis algorithms in Geography Information System (GIS) software and also the basis for many advanced spatial analysis models and complex spatial analysis applications. The divide and conquer strategy, with the paradigm of divide-conquer-combine for problem solving, can reduce the cumulative effects of nodes of the polygon union results at average level. And, compared with classic snowball union strategy, it can accelerate the polygon union algorithm effectively. In this research, we took the polygon union algorithm as an example to describe a fast polygon union strategy named tree-like union method. Firstly, we analyzed the variation of the efficiency of the core operator, which is polygon union operation implemented by Vatti's algorithm, with different node number of polygons, and figured out that the potential performance bottlenecks and pitfalls of the snowball union strategy is the cumulative effect of the nodes existing in the union process. Then based on the idea of divide and conquer we proposed a new approach named tree-like union strategy and implemented a union algorithm for polygon clusters or layers to solve the problem in polygon union process. Finally, we introduced an efficiency evaluate model by which the available acceleration potentiality derived from the tree-like union strategy can be assessed conveniently for a group of polygons. Experimental results in this research shown that compared with snowball union strategy, the tree-like union strategy based on the idea of divide and conquer could lead to great reduction of time costs of polygon union algorithm. Furthermore, we found that the time cost of snowball union was about 26-folds than that of tree-like method when union 400 polygons, and the number reached about 926 when union 11 200 polygons. Therefore, it can be inferred that the accelerate effects brought by the tree-like union strategy could become more significant when dealing with larger polygon datasets. We supposed that the tree-like union strategy proposed in this research represents a certain degree of applicability in operations similar with polygon union algorithm, which could be a potential and practical optimization approach for vector data overlapping and other advanced spatial analysis algorithms which involved with polygon union operations.

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    Cited: CSCD(1)
    A Study on the Expressing Techniques of Battlefield Situation Evolution and Variation Based on GIS and its Application
    ZHANG Xin, ZHANG Lili, CAO Guofeng, ZHONG Ershun
       2006, 8 (4): 80-83.  
    Abstract621)      PDF (625KB)(935)      
    This paper studies the dynamic expression techniques about the evolution and variation of battlefield situation and presented a new dynamic expression technique using symbol integrated with symbol action, the symbol action includes movement action,modification action,blink action,zoom action,show or hide action,property change and symbol change. Furthermore, one method was discussed to make the expression more vividly using script control, which includes scene command,speed command,jump command,pause command and playing sound command. The prototype was developed to validate the feasibility and reusability of the presented technique which can solve many key problems in the evolution and variation of the battlefield situation.
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    Cited: CSCD(5)
    Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data
    DENG Min, CAI Jiannan, YANG Wentao, TANG Jianbo, YANG Xuexi, LIU Qiliang, SHI Yan
    Journal of Geo-information Science    2020, 22 (1): 41-56.   DOI: 10.12082/dqxxkx.2020.190491
    Abstract1400)   HTML51)    PDF (11720KB)(620)      

    Multi-modal spatio-temporal analysis is aimed at discovering valuable knowledge about the spatio-temporal distributions, associations and revolutions underlying the multi-modal geographic big data. It is a core task of the pan-spatial information system, and is expected to facilitate the study of relationship between human and space. With emerging opportunities and challenges in an era of geographic big data, we systematically summarized four main methods for spatial-temporal analysis based on previous study, including spatio-temporal cluster analysis, spatio-temporal outlier detection, spatio-temporal association mining and spatio-temporal prediction. We discussed the challenges when applying the four methods in multi-scale modeling, multi-view fusion, multi-characteristic cognition, and multi-characteristic expression for spatial-temporal analysis. First, two types of scales (including data scale and analysis scale) are of great importance in the spatio-temporal clustering task. Given the data scale, the best analysis scale for detecting spatio-temporal clusters can be determined using a permutation test method by evaluating the significance of clusters. Second, in the spatio-temporal outlier detection method, the cross-outliers in the context of two types of points are known as the abnormal associations between different types of points and the validity of cross-outliers is assessed through significance tests under the null hypothesis of complete spatial randomness. Third, in the spatio-temporal association mining method, the multi-modal distribution characteristics of each feature quantitatively described in the observed dataset are employed to construct the null hypothesis that the spatio-temporal distributions of different features are independent of each other, and then the evaluation of spatio-temporal associations is modeled as a significance test problem under the null hypothesis of independence. Finally, in the spatio-temporal prediction model, the effects of multiple characteristics of spatio-temporal data (e.g., spatio-temporal auto-correlation and heterogeneity) on the prediction results are fully considered using a space-time support vector regression model. These methods can reveal the geographic knowledge in a more comprehensive, objective, and accurate way, and play a key role in supporting the smart city applications, such as meteorological and environmental monitoring, public safety management, and urban facility planning. For example, the spatio-temporal clustering method can be used to identify the meteorological division, the spatio-temporal outliers can contribute to the detection of the abnormal distribution of urban facilities, the spatio-temporal association mining method can help discover and understand the relationship among different types of crimes, and the spatio-temporal prediction method can be employed to predict the concentration of air pollutants.

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