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    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)
    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)
    Research on Multi-source Remote Sensing Information Fusion Application
    YUAN Jinguo, WANG Wei
       2005, 7 (3): 97-103.  
    Abstract717)      PDF (1310KB)(2644)      
    Multi-source remote sensing data fusion is the development trend of remote sensing technology in depth. This paper analyzes in detail algorithmic application characteristics of multi-source remote sensing data from three levels of pixel-based, feature-based and decision-based fusion processings. Take Fengning County for example, specific applications of remote sensing data fusion methods in information extraction are illuminated. The data used in this study is firstly pre-processed, then the principal components of Landsat TM data in 1999 are analyzed, the first three principal components account for 97.8% of the total information, the resulted image of inversed principal components transformation is clearer and has more abundant levels. To extract information from remote sensing image, we select the fusion image from Landsat TM pan and multi-spectral bands after principal components transformation, color composition scheme of bands 4, 3, 2 and bands 5, 4, 3, and vegetation index and greenness index after tasseled cap transformation are analyzed, the remote sensing image information fusion with DEM and spatial data of GIS database can also improve the accuracy of remote sensing information extraction. Problems to be resolved and future direction of multi-source remote sensing data fusion are put forward.
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    Cited: CSCD(11)
    Design of an Algorithm of Public Traffic Transfer Based on the Least Transfer
    FAN Xiaochun,ZHANG Xueying,LIU Xuejun,SHEN Qijun,FAN Xiaoming
       2009, 11 (2): 157-162.  
    Abstract454)      PDF (653KB)(2404)      
    At present there are two significant problems in the field of intelligent transportation systems,i.e.algorithmic efficiency and transfer routines.First of all,this paper describes route selection behaviors of passengers and the characteristics of city traffic networks,and then presents the public traffic network-transit matrix based on key stops.Secondly,based on the shortest path algorithm,a public traffic network-transit matrix and a non-transfer matrix are introduced to design the public traffic transfer algorithm.In this algorithm,the public traffic network transit matrix aims to decide which temp label notes are potential label notes,and non-transfer notes are always considered as the notes of the shortest path,in order to improve the performance of classical shortest path algorithm(Dijkstra).Finally,a case is used to evaluate the performance of this algorithm.The experimental results indicate that the proposed algorithm achieves better efficiency than the Dijkstra.And much more reasonable transfer frequency is obtained.It is believed that this algorithm can be used in general transit networks,especially high transfer-cost networks.
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    Cited: CSCD(5)
    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)
    Research on Human Mobility in Big Data Era
    LU Feng,LIU Kang,CHEN Jie
    Journal of Geo-information Science    2014, 16 (5): 665-672.   DOI: 10.3724/SP.J.1047.2014.00665
    Abstract1561)   HTML39)    PDF (873KB)(3579)      

    Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

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    Cited: CSCD(33)
    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)
    Review on Spatiotemporal Analysis and Modeling of COVID-19 Pandemic
    PEI Tao, WANG Xi, SONG Ci, LIU Yaxi, HUANG Qiang, SHU Hua, CHEN Xiao, GUO Sihui, ZHOU Chenghu
    Journal of Geo-information Science    2021, 23 (2): 188-210.   DOI: 10.12082/dqxxkx.2021.200434
    Abstract863)   HTML46)    PDF (12855KB)(450)      

    The COVID-19 pandemic is the most serious global public health event since the 21 st century, and has become a hot topic concerned by different disciplines. According to the bibliometric analysis, more than 13,000 papers related to the COVID-19 have been published since the beginning of the pandemic. Related researches include not only the pathogenic mechanism of the virus and the development of specific drugs and vaccines from the medical and biological perspectives, but also the non-pharmaceutical prevention and control methods for the pandemic. The latter is the focus of this paper, in which the research progress on the pandemic is discussed from six aspects: detection of transmission relationships, spatiotemporal pattern analysis, prediction models, spread simulation, risk assessment, and impact evaluation. The research on the detection of transmission relationship mainly includes the detection of cluster cases and transmission relations, among which individual trajectory big data have become the key to research. The progress of the analysis of spatiotemporal patterns of the pandemic shows that the spatiotemporal distribution of the pandemic has significant temporal and spatial heterogeneity, and the spatiotemporal transmission presents typical network characteristics. The prediction of the pandemic mainly relies on dynamic models scaling from macro to micro, in which the non-negligible impact of population migration makes the human flow big data become one of the key elements of model prediction accuracy. In the study of epidemic spread simulation, the focus is on evaluating the effects of controlling measures such as traffic restrictions, community prevention and control, and medical resources allocation through simulation methods. Results show that traffic interruption and community control measures are the most effective means among non-pharmaceutical interventions at present, and the guarantee and reasonable deployment of medical resources are the basis for pandemic prevention and control. After the pandemic is controlled under the effective measures, the resumption of work and production must be in an orderly manner. The research on pandemic risk assessment currently focuses on biological factors, natural factors and social factors. As to biological factors, researches show that the underlying disease and the male (due to their high mobility) are related to a higher risk of infection. Among natural factors, temperature, precipitation and climate have limited influence on the spread of the pandemic. As to social factors, human mobility, population density, and differences in medical conditions caused by social inequity have significant influences on the infection rate. Regarding the impact of the COVID-19 pandemic, we mainly focus on three aspects: the public psychology, natural environment and economic development. Specifically, the impact of the pandemic is mainly negative on the public psychology and economy, and positive on the natural environment. In conclusion, big data especially individual trajectories and population big data are indeed pervasive in research of non-pharmaceutical intervention. To prevent and control the major outbreaks, the intersection of multiple disciplines and the collaboration of personnel in different fields are indispensable. Although a great progress has been made on various aspects such as the effect of controlling measures and the influencing factors of the pandemic, the spatial traceability, precise prediction and future impact of the pandemic are still unsolved problems.

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    Analysis of Anthropogenic Heat Discharge of Urban Functional Regions Based on Surface Energy Balance in Xiamen Island
    LIU Jiahui,ZHAO Xiaofeng,LIN Jianyi
    Journal of Geo-information Science    2018, 20 (7): 1026-1036.   DOI: 10.12082/dqxxkx.2018.170450
    Abstract801)   HTML5)    PDF (15149KB)(569)      

    Anthropogenic heat discharge not only constitutes the cause of urban heat island (UHI) formation, but also is an important indicator related to energy consumption. It is important to analysis the magnitude and variation of anthropogenic heat discharge in order to mitigate UHI effect and improve energy efficiency. This paper examined the spatio-temporal variation of anthropogenic heat discharge in the Xiamen Island, China using Landsat TM data and meteorological data. First, the anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model. Then, the urban functional regions derived from IKONOS data were combined with the anthropogenic heat discharge. The results indicate that the anthropogenic heat discharge in different types of urban functional regions reaches the maximum in summer and the minimum in spring. The anthropogenic heat discharge of industrial area was higher than those in the other regions for all seasons. The high anthropogenic heat discharge occurred in the old industrial bases in the west of Xiamen Island. In traffic area, high anthropogenic heat discharge was observed in the Changan Road, Jiahe Road, Chenggong Avenue, Xianyue Road, North Hubin Road-Lvling Road, South Hubin Road-East Lianqian Road. In residential area, high anthropogenic heat discharge was observed in the old town. The high anthropogenic heat discharge occurred in the large single buildings in commercial and public area. Overall, the anthropogenic heat discharge in the western part of Xiamen Island was higher than that in the east. The differences of spatial and seasonal distribution were closely related to land cover types, population and the degree of economic development. Moreover, the density and height of the buildings and materials of land cover change the amount of anthropogenic heat discharge by affecting other surface fluxes. This paper brings a more microscopic perspective by analyzing the spatio-temporal variation of anthropogenic heat discharge in different urban functional regions to study urban thermal environment and energy utilization, as well as to provide a theoretical basis for promoting urban sustainable development.

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    Cited: CSCD(2)
    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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    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)
    GIS-based Assessment on Eco-vulnerability of Jiangxi Province
    FAN Zhewen,LIU Musheng,SHEN Wenqing,LIN Liansheng
       2009, 11 (2): 202-208.  
    Abstract467)      PDF (1102KB)(1302)      
    Research on eco-environment vulnerability assessment contributes to ecological environmental conservation and development.Taking Jiangxi Province as a case study,this research has developed the index system of eco-vulnerability from the perspective of the meaning and the cause of eco-vulnerability and in accordance with the eco-environment situation of Jiangxi Province.The indicators were weighted by the principle of the spatially principal component analysis.With the support of GIS,the integrated index evaluation was applied in assessing eco-vulnerability.The result shows,in 2005,the moderate vulnerable area dominates and shares 85.36% of total land area in Jiangxi Province;the mild and intensive vulnerable land area occupy 14.64% and 0.002%,respectively.And the vulnerability of Jiangxi Province was distinct-spatially distributed.In general,Jiangxi Province belongs to the moderate vulnerable area.Taking county as assessment unit,the results show that the integrated vulnerable degree lies between 20 and 40 in 7 counties which belong to mild vulnerable zone: Nanchang County,Xinjian,Jinxian and De'an around Poyang Lake Plain;and Xiajiang,Taihe and Ji'an located in Jitai Basin;and the other 82 counties or cities of Jiangxi Province with index of 40-60 are moderate vulnerability.The eco-vulnerability of Jiangxi Province widely and apparently varies in spatial distribution.Mountain areas in upstream of the five rivers have more intensive eco-vulnerable environment,while Jitai basin and Poyang Lake Plain are the contrary,and the hilly areas in middle and lower parts of the five rivers fill in with the mild vulnerability.The conclusion of this research is that the vulnerable environment is put down to the natural conditions here,and the stresses from human activities play as catalyst to intensify the vulnerability.
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    Cited: CSCD(23)
    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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    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)
    Fast Extraction of Built-up Land Information from Remote Sensing Imagery
    XU Hanqiu, DU Liping
       2010, 12 (4): 574-579.  
    Abstract963)      PDF (612KB)(2076)      
    The fast expansion of urban built-up land and accompanied sharp decrease in farm land have made timely monitoring of landuse changes become more important than ever before.The ability to monitor the built-up land dynamics in a cost-effective manner is highly desirable for local communities and decision makers alike.Fortunately,satellite remote sensing technique offers considerable promise to meet this requirement.Although the use of remote sensing technique in the monitoring of land use changes has become more and more popular and satellite imagery has been frequently used to discriminate built-up lands from non-built-up lands for the last few decades,the extraction of built-up land information from remote sensing imagery is still not an easy task due largely to the heterogeneous characteristics of the built-up land.Among many techniques developed for the extraction of built-up land information,the index-based built-up index(IBI) was created based on three existing thematic indices rather than original multispectral bands.The use of the three thematic indices-soil-adjusted vegetation index(SAVI),modified normalized difference water index(MNDWI) and normalized difference built-up index(NDBI)-greatly help the delineation of built-up land features in remote sensing imagery,because these three indices represent three major landuse components,which are vegetation,water and built-up land,respectively.Therefore,the IBI can significantly enhance built-up land information while suppressing background noise.Consequently,the built-up land can be effectively extracted from the IBI image with high accuracy.In order to quicken image processing,this built-up extraction technique has been programmed to form an easy-use module using the ER Mapper scripting language.The module was further integrated in the ER Mapper package by adding a button to the manual bar.This allows users to automatically perform the extraction procedure with high accuracy just in a few minutes.
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    Cited: CSCD(14)
    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)
    Analysis and Visualization of Multi-dimensional Characteristics of Network Public Opinion Situation and Sentiment: Taking COVID-19 Epidemic as an Example
    DU Yixian, XU Jiapeng, ZHONG Linying, HOU Yingxu, SHEN Jie
    Journal of Geo-information Science    2021, 23 (2): 318-330.   DOI: 10.12082/dqxxkx.2021.200268
    Abstract317)   HTML8)    PDF (10736KB)(344)      

    At the beginning of 2020, COVID-19 epidemic swept across China, and the development of COVID-19 attracted extensive attention from all sectors of society. Social media platform is an important carrier of online public opinion. In the process of epidemic prevention and control, it is very important to analyze the characteristics of network public opinion comprehensively and accurately. Firstly, from the perspective of spatiotemporal correlation between public opinion ontology and object, we construct a multi-dimensional analysis model of network public opinion during the epidemic period. We obtained the network public opinion data related to the covid-19 epidemic in multiple media platforms from January 17 to March 17, 2020. Secondly, from the perspective of epidemic spread, the spatial and temporal evolution and semantic characteristics of network public opinion in Wuhan, Hubei and the national scale are explored by comparative study and Spearman correlation coefficient. Finally, we use HowNet sentiment dictionary and emotional vocabulary ontology to analyze public opinion sentiment, and use interactive information chart to visualize the above results. The results show that: (1) The characteristics of time changes of public opinions are basically the same in Wuhan, Hubei province and China. There is a positive correlation between the number of daily public opinions and the number of new cases per day. With the rapid spread of the epidemic, the number of daily public opinions continues to increase. As the epidemic is gradually brought under control, the number of daily public opinions has shown a tortuous downward trend. (2) There is a positive correlation between the spatial distribution of public opinion data and the distribution of epidemic situation. The spatial distribution of the number of public opinions is similar to the distribution of the epidemic situation, and the areas with a large number of public opinions are mostly areas with severe epidemics. Changes in public opinions are spatially related to the development of the epidemic. (3) During the epidemic, the neutral sentiment of online public opinions was the most. Compared with forums, WeChat and Weibo, news platforms have a more positive overall sentiment. (4) At different stages of the development of the epidemic, the emotional characteristics of Weibo hot search data are quite different. The mood changed from anxiety in the early stage of the epidemic to excitement in the mid-term. And as the epidemic is gradually brought under control, emotions have also stabilized. Generally speaking, there are more positive emotions than negative emotions. Research shows that the multi-dimensional analysis model proposed in this article can visually show the public opinions situation, public opinions focus, and emotional changes at multiple scales during the epidemic.

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    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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    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)(1771)      

    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)
    Construction of Technical System for National Urban Ecological Environment Comprehensive Monitoring
    WANG Qiao, ZHAO Shaohua, FENG Hong'e, WANG Yu, BAI Zhijie, MENG Bin, CHEN Hui
    Journal of Geo-information Science    2020, 22 (10): 1922-1934.   DOI: 10.12082/dqxxkx.2020.190488
    Abstract566)   HTML18)    PDF (12176KB)(316)      

    More and more people have paid attention to the severe problems of urban ecological environment in recent years, such as air pollution in key urban agglomerations, water pollution, urban black and odorous water, risk of drinking water source, urban heat island, soil pollution, municipal solid waste, and so on. As a vital part of environment protection, with the rapid urbanization, the monitoring of urban ecological environment is becoming more and more important and the demand is getting higher and higher. Many studies have documented the monitoring of urban ecological environment at home and abroad, however, these works are discrete and unsystematic. There is a lack of general technical system in China, including key technology system, index system, and technical standards. The integrated space and ground monitoring is very urgent and necessary, and it is badly need to establish its technical system to guide and normalize the development of comprehensive monitoring of urban ecological environment. Given the national demand, this work (1) designs and constructs the technical system framework, index system framework, and standard system framework of urban ecological environment comprehensive monitoring from three aspects: urban polluted gas, water quality, and ecological resource; (2) puts forward the series concerned key technologies, gives the current monitoring status and accuracy of main indies of urban ecological environment; (3) on the untangling basis of key science problems, in combination with the characteristics of remote sensing data and the needs of national ecological environment monitoring, the study subsequently designs the operational application scheme of ecological environment comprehensive monitoring, gives the main monitoring emphasis of urban polluted gas, water quality, and ecological resource, plots the application scheme which includes the region demonstration, application products and services based on the constructed information service platform of urban ecological environment comprehensive monitoring, and provides the application examples of theme maps of PM2.5, urban black and odorous water, and urban island effect. The work will provide important support for the state and local government monitoring and management in urban ecological environment.

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    The Mass Innovation Era of UAV Remote Sensing
    LIAO Xiaohan,ZHOU Chenghu,SU Fenzhen,LU Haiying,YUE Huanyin,GOU Jiping
    Journal of Geo-information Science    2016, 18 (11): 1439-1448.   DOI: 10.3724/SP.J.1047.2016.01439
    Abstract1365)   HTML3)    PDF (14391KB)(822)      

    The contemporary development of science and technology reduced barriers to applying unmanned aerial vehicles (UAV) and remote sensors. Meanwhile, public participation triggered an explosive growth of innovative applications in the field of UAV remote sensing. Therefore, UAVs have become a common means of scientific research. Under some circumstances, the UAV remote sensing data can be used to substitute for the satellite remote sensing data. In this study, the authors firstly systematically summarized both of the features of times and characteristics of science and technology of UAV remote sensing. Then, the authors introduced several fundamental applications including earthquake relief, surveying and mapping of islands and reefs, Antarctic scientific expedition, accurate farmland management, etc. Thirdly, the authors put forward some future directions from the aspects of the stimulation of restructuring of remote sensing data supply, the promotion of comprehensive perspective in geography research as well as the necessity of planning of UAV remote sensing testing sites. In particular, a concept of UAV remote sensing data carrier was proposed.

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    Cited: CSCD(20)
    Visualization of the Epidemic Situation of COVID-19
    YING Shen, DOU Xiaoying, XU Yajie, SU Junru, LI Lin
    Journal of Geo-information Science    2021, 23 (2): 211-221.   DOI: 10.12082/dqxxkx.2021.200301
    Abstract500)   HTML19)    PDF (19128KB)(305)      

    The COVID-19 epidemic has extremely attracted our attentions and lots of maps and visualization charts were created to represent and disseminate the information about COVID-19 in time, which exactly became a key role for the public to acquire and understand the quantitative information and spatial-temporal information of COVID-19. The paper analyzed the dimension of data for COVID-19 and processing levels about them, then divided the COVID-19 visualization into three types, that is 1-order visualization, 2-order visualization and multi-order visualization for COVID-19, based on direct data or indirect data of COVID-19 with the corresponding visualization methods, characteristics and information transmission Shortcomings and weakness of visualization methods for COVID-19 were analyzed in details, from the aspects of multiple scale unit in spatial data statistics, max value dealing in data classification, also many key design points were described including color connotation in disease visualization, the influences of area / unit size in visualization, symbol overlapping, multiple-scale heat maps and labels in statistical tables. The paper indicated the visualization traps of COVID-19, such as misuse of visual effects and excessive visualization, and reasonable abilities of COVID-19 visualization including map-story narrative methods and visualization pertinence for specific problems should be considered sufficiently to provide the references for cartographers to design the maps and for readers to understand the maps.

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    Research on Knowledge Acquisition and Design of Inference Engine for ALCGEIS
    JIA Zelu, LIU Yaolin
       2006, 8 (1): 67-72.  
    Abstract311)      PDF (1081KB)(1368)      
    With the establishment of market economic system and the practice of using land institution, integrated evaluation of the quality grade of agricultural land has been a very important task in land administration at present and in the future. Classifying and grading of agricultural land is a very comprehensive task, the results of it not only connect with many natural factors, but also relate to the economic factors, society factors and many other factors. Moreover, in practice it involves analysis and handling huge amount of data and pictures, so the calculation task is very gigantic. Agricultural Land Classifying & Grading is a problem, which can not be described with mathematics completely, but needs to be resolved with experience and knowledge. Using a great deal of data accumulated in the past reasonably and integrating the expert knowledge adequately to classify & grade agricultural land, setting up Agricultural Land Classifying & Grading Expert Information System (ALCGEIS) with intellectualized analysis and decision function to improve the precision of classifying and grading results of agricultural land and to raise the efficiency of work are very necessary. Building a system based on Artificial Intelligence (AI) is also the goal and developing orientation in the future research. This paper presents knowledge acquisition and design approaches and principles of inference engine for Expert System (ES), and probes basic structure of ALCGEIS, knowledge classification, knowledge acquisition and algorithms of program realization, basic control strategies of inference engine and algorithms of program realization as well as search strategies of reasoning knowledge. In addition, the author designs the inference engine of ALCGEIS. And taking the structure of inference engine as an example, the author designs methods of reasoning rules as well as reasoning algorithms, and elaborates organization structures of reasoning knowledge.
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    Cited: CSCD(1)
    Simplified Computation of Atmosphere Downward Radiance and Impact of Surface Reflectance Characteristic on Atmospheric Downward Radiance Effect
    ZHU Li, GU Xingfa, WANG Qiao, CHEN Liangfu, YU Tao
       2008, 10 (5): 638-644.  
    Abstract575)      PDF (1475KB)(1306)      
    Atmospheric downward radiance effect should be considered in the retrieving surface temperature and validation of thermal infrared bands data.In the field of thermal infrared remote sensing the computation of the effect is often simplified under two assumptions: one is the Lambertian reflection of the surface;another is the isotropic downward thermal radiance of atmosphere.The simplified computation method of atmosphere downward radiance has been researched,and the effect of Lambertian reflection assumption on the atmosphere downward radiance effect and at-pupil radiance of remote sensing sensors has been analyzed in the theory.Then atmosphere profiles and land surface temperature of a year were input to MODTRAN4 to calculate the atmospheric downward radiance effect and analyze the utmost error caused by two assumptions in erlianhaote region of Inner Mongolia of China.The result showed:(1) the error was up to 4 degree if the atmospheric downward radiance effect was ignored for such an inner land region as erlianhaote and the proportion of the effect would increase with the increase of satellite view angle.(2)The assumption of the isotropic downward thermal radiance of atmosphere was not appropriate and we can choose the downward radiance in an optimal angle to be equal to the average of hemispheroidal downward radiance.And the optimal angle was 57° for erlianhaote.(3)The assumption of Lambertian reflection of the surface was appropriate for calculating downward radiance effect.
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    Cited: CSCD(3)
    Cognitive Transformation from Geographic Information System to Virtual Geographic Environments
    LIN Hui, HU Mingyuan, CHEN Min, ZHANG Fan, YOU Lan, CHEN Yuting
    Journal of Geo-information Science    2020, 22 (4): 662-672.   DOI: 10.12082/dqxxkx.2020.200048
    Abstract534)   HTML23)    PDF (10496KB)(372)      

    Since the beginning of 1960s, Geographic Information System (GIS) has been advanced in the analysis of geographic information and the services generated from it. Yet the rate of demands from geographers and large engineering projects continues to accelerate in the multi-dimensional geographic process simulation and the assessment of simulation results before those projects carried out. The set of increasing demands gives the Chinese scholars a sense of direction to explore the emerging concept Virtual Geographic Environments (VGEs) over the subsequent decades. In a broad sense, the VGEs is a collective term for all geographic environments except the real geographic environment while in the narrow sense, the virtual geographic environment can be considered as a computer-generated digital geographic environment in which the complex geographic systems are perceived and cognized by means of multi-channel human-computer interaction, distributed geographic modeling and simulation, and cyberspace geographic collaboration. From the very beginning, this paper elaborates on the transformation from the understanding of GIS to VGEs. In the second place, the evolution process of VGEs is analyzed including its current developing stage and a series of challenges it faced with. Aimed at facilitating the research on geoscience in the context of advanced technologies and accumulated geospatial information, this paper describes the new perspectives of VGEs research as followed: geographic space based on VGEs cognitive research, VGEs and experimental geography, virtual geographic cognitive experimental methods, and VGEs and geographic knowledge engineering in the context of big data. It can be foreseen that the study of VGEs is gradually moving towards an open, group-participated, collaborative scientific research paradigm. This is also a true reflection of the development trend of scientific research in the field of geography.

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    The Spatial Evaluation of Urban Ecological Security Pattern Based on Subjective and Objective Analysis
    CHENG Peng,HUANG Xiaoxia,LI Hongga,LI Xia,ZHANG Lin
    Journal of Geo-information Science    2017, 19 (7): 924-933.   DOI: 10.3724/SP.J.1047.2017.00924
    Abstract744)   HTML0)    PDF (17585KB)(452)      

    Ecological security is one of the main goals to the reconstruction of urban ecological civilization and an important foundation for the sustainable development of urban economy. Therefore, it is significant to evaluate the ecological security pattern of urban for urban planning. This paper selected several typical indexes, made a comparative analysis between objective analysis and subjective analysis method and established an evaluation system of ecological security pattern. To verify the effectiveness of the evaluation system, this paper made an evaluation of the ecological security pattern for the study area using the evaluation system. The result shows that: (1) The Tanglang mountain, Merlin mountain, Yinhu mountain, ecological corridors of Dashahe park and Xiangsilin park all have a low level of security pattern of the value of ecosystem service and ecological sensitivity. (2) The ecological corridors of Dashahe Park and Xiangsilin Park and the dams of Merlin and Changlingpi have a complete biodiversity with a low level of security pattern of the protection of biodiversity and ecological sensitivity. (3) On the basic line of ecological control, the area of the low level and lower level of ecological security pattern increased. Also, the area of the intermediate-level ecological security pattern had a decrease in the area outside the line. On the whole, the high-level ecological security pattern area was substantially constant. (4) The percentage of high level and intermediate level of security pattern is the same in the result made by taking the objective analysis method and the result by taking the subjective analysis method. The former percentage of low-level security pattern is less than the latter. In spite of the difference, they show the similar tendency and results.

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    Cited: CSCD(4)
    Analysis of Slope Length Extracted from Grid-based Digital Elevation Model in ArcGIS Environment
    JIN Bei, LIU Xuejun, ZHEN Yan, LI Haoshu
       2010, 12 (5): 700-706.  
    Abstract1406)      PDF (888KB)(1655)      
    Slope length,which describes the distance of flow path from origin point,is often a vital terrain parameter in soil and water conservation,soil erosion and environmental assessment studies.A common method of obtaining estimates of slope length is based on flow directions from grid-based Digital Elevation Model(DEM) in Geographical Information System(GIS) software.This method leads to slope length estimates with variable accuracy,which depends on DEM error,DEM resolution,algorithms of flow direction and distance measurement.Error and resolution are determined for a given DEM,leaving only algorithms of flow direction and distance measurement.This thesis focuses on the effect of these two uncertain factors on slope length which is derived from DEM.Based on a series of rigorous calculation on a DEM of simulated gully network,the paper finds that the model of slope length in ArcGIS software is not suitable to calculate the slope length because the flow direction derived from D8 is deterministic and this makes the flow route become discontinuous.Although the error by which distances are calculated over raster structures is less than 8.9% and can be improved by statistical methods such as distance transforms,any method that used to low this error is meaningless if the flow direction is wrong.In order to test these conclusions,we applied non-cumulative slope length algorithm in the platform of ArcGIS for two different typical DEMs,including a sample data in ArcGIS and a 5m grid cell DEM of Jiuyuangou drainage basin in North Shaanxi Loess Plateau.Comparing with the lengths obtained from contour map,the former conclusions are verified.So,it is necessary to develop a new flow direction algorithm with vector character in order to improve the estimate accuracy of slope length derived from grid DEM.
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    Cited: CSCD(9)
    Rule-based Approach to Semantic Resolution of Chinese Addresses
    ZHNAG Xueying, LV Guonian, LI Boqiu, Chen Wenjun
       2010, 12 (1): 9-16.  
    Abstract917)      PDF (1656KB)(2009)      
    A geographic information system(GIS) integrates hardware,software,and data for capturing,managing,analyzing,and displaying all forms of geographically referenced information.Addresses are one of the most popular geographical reference systems in natural languages.Address geocoding is considered as the most effective approach to bridging the gap between business data in management information systems(MIS) and GIS,which supports geospatial information visualization and spatial analysis.Chinese address geocoding faces three significant problems,i.e.address models,address resolution and address matching,because of the un-standardization of Chinese place names and the shortage of national address databases.Address resolution aims to automatically split address strings in natural language into address units without semantic incompletion.It plays a fundamental role in address models and address matching.Previous research focuses on rule or gazetteer based approaches,which are easily implemented but with poor coverage and performance.In theory,Chinese address resolution is similar to word segmentation in Chinese natural language processing.Based on the investigation of large-scale Chinese place names and address syntactic patterns,this paper identifies primary and secondary general characters that represent a variety of address units.And then an address numerical representation method is presented to induce syntactical rules of Chinese addresses.Finally,we develop an RBAI algorithm for implementation Chinese address resolution and illustrate an example.The experimental results indicate that the proposed approach can achieve satisfactory efficiency and effectiveness for large-scale data processing,the accuracy ratio over 92% and the processing rate over 2,800 items per second.The proposed approach and system can be extended to such fields as land management,asset management,city plan,public security,postal system,taxation,public health management and other location-base services.
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    Cited: CSCD(28)
    Spatio-temporal Dynamics of the Impacts of Rainstorm Disaster on Crop Growing Using Multi-satellites Remote Sensing
    SU Yali,GUO Xudong,LEI Liping,WANG Xiaofan,WU Changjiang
    Journal of Geo-information Science    2018, 20 (7): 1004-1013.   DOI: 10.12082/dqxxkx.2018.180065
    Abstract645)   HTML9)    PDF (13706KB)(433)      

    The heavy rain may induce flood disaster inundating crop lands while the long period of the continues heavy rainfall may strongly affect the growth of crops even not evolving into a flood disaster. The impact of heavy rainfall on the growth of crops is a gradual process due to the long period of time needed for soil to become saturated. Multi-satellites remote sensing observations can capture and characterize the ground conditions over large area in multiply time. To develop the potential applications of the multi-satellites remote sensing observations, this paper proposes a method of extracting dynamic information of heavy rain disaster and its impacts on the growth of crops using multi-satellites data including Terra/MODIS, Landsat and Sentinel. We implemented the application of the proposed method in the studying area around Chaohu lake, where the heavy rainfall started from the end of June and continued to August in 2016, and a heavier rain in July brought about the flood in large crop areas. The beginning period and the duration of the heavy rainfall, leading to the impacts on the growth of crops, were identified using a dynamic threshold method of multi-temporal NDVI derived from MODIS. Based on this information, the area with crop fields impacted by heavy rainfall and flooded were obtained. On the other hand, the dynamic information of flooded lands was extracted by using Landsat observing data in July and Sentinel observation data in August, respectively. These results provide more accurate area of flooded crop fields, and can be used to modify the area derived from MODIS although they have only few temporal data available. In conclusion, multi-satellites remote sensing, as one of the tools for monitoring and assessing the influences of heavy rainfall, can obtain the dynamic information of the heavy rainfall impacts on the growth of crops in addition to flooded land area and the recovery of the farmland, which provide the supporting scientific data for the assessment of the loss caused by the disaster and making the disaster relief policy.

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    Cited: CSCD(2)
    Development and Prospect of GIS Platform Software Technology System
    SONG Guanfu, CHEN Yong, LUO Qiang, WU Mengyao
    Journal of Geo-information Science    2021, 23 (1): 2-15.   DOI: 10.12082/dqxxkx.2021.210015
    Abstract539)   HTML43)    PDF (6405KB)(276)      

    As an important part of the IT system, every advancement of GIS technology is closely related to the rise of the latest IT technology. With the development and application of cloud computing, big data, artificial intelligence and other technologies, nowadays GIS basic software has formed five major technology systems. The big data GIS technology increases the storage management, analysis, processing and visualization of spatial big data, enriching the connotation of spatial data. Artificial intelligence GIS technology enables GIS to enhance the analysis and prediction capabilities of GIS models by combining AI related algorithms. At the same time, the two empower each other. While enhancing GIS capabilities, AI also has spatial analysis and visualization capabilities and expands Its scope of application. The new 3D GIS technology realizes the integration of 2D and 3D GIS and the integration of multi-source heterogeneous data. It promotes 3D GIS from outdoor to indoor, from the macro to the micro. Distributed GIS technology breaks through the limitations of data types and resource capacity. The performance of GIS software is improved by orders of magnitude. It makes highly available and highly reliable GIS applications possible. Cross-platform GIS technology enables GIS software to run on different types of CPU structures and operating systems, meeting the increasingly diverse needs of multi-terminal applications. The five technologies complement each other, and they further expand the capabilities and application scenarios of GIS basic software. Taking SuperMap GIS as an example, this article introduces the specific content of the five GIS technology systems in detail and explains the difficulties and innovations of each technology. Finally, this article uses the hype cycle to divide the development stages of the five major technology systems and discusses the future development trend of GIS technology.

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