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  • Orginal Article
    ZUO Yao,WANG Shaohua,ZHONG Ershun,CAI Wenwen
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    The development of Internet technology has given birth to the explosive growth of various information in recent years. The traditional data processing method cannot matched with the rapidly improving performance of computer hardware and more efficient methods are needed to process the numerous data. High performance computing technologies, including Parallel cluster computing technology and distributed network technology, bring hints to the solution of these problems. In practice, there are three major distributed computing systems, namely Hadoop, Spark and Storm. Hadoop improves computational performance by introducing MapReduce distributed computing framework, while Spark make full use of computer memory to store data based on Resilient Distributed Datasets(RDD), which has a more rapid reading and writing functions of data . The Storm does not directly collect data. It realizes the data transmission and processing using network nodes. Nowadays, how to take advantage of the improvement of computational performance brought by the development of new hardware architecture to solve the long existing data intensive, computational intensive and communication intensive problems has become a topical issue in the field of GIS studies. In this paper, reviewing current research progress of high performance GIS, we examine and discuss about the algorithm of high performance GIS, parallel GIS computing, memory computing and core computing and give some prospective on the future development of high performance GIS, which provide a reference for the development of high performance GIS system. In addition, the development of the Internet technology and cloud computing is continuously boosting the popularity of GIS cloud computing and big data technology. In this context, domestic and foreign GIS platform vendors have launched their own cloud GIS platform, such as ArcGIS10.4 developed by ESRI and SuperMap 8C by SuperMap, to give support to cross-platform, parallel computing, 64-bit computing, distributed systems and other technologies.

  • Orginal Article
    LIU Jingyi,XUE Cunjin,FAN Yanguo,KONG Fanping,HE Yawen
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    For dealing with the raster datasets, most of the traditional clustering methods are based on the thematic attribute, which separate the integrities of spatial and thematic characteristics. However, the current clustering methods considering both spatial and thematic characteristics still have great problems such as complicated clusters, computational complexities and many input parameters, etc. Thus, this paper presents a Raster-oriented Clustering Method with Space-Attribute Constraints, named RoCMSAC. The core idea of RoCMSAC uses the spatial contiguities and the connectivity of raster datasets to redefine the similarity measure criterion. The RoCMSAC consists of three steps, i.e. the cluster generation with the homogeneous attributes, the cluster merging with the spatial contiguities and the cluster merging with the spatial vicinities. Finally, the feasibility and effectiveness of the algorithm are validated with the datasets of sea surface temperature in Pacific Ocean. The clusters from RoCMSAC are compared with those from K-Mean and DDBSC. The results show that: (1) RoCMSAC can detect any grid cluster with the complicated shape, which needs less time and fewer input parameters; (2) The clusters from RoCMSAC obtain both the proximity in spatial domain and the homogeneity in attribute one.

  • Orginal Article
    LIU Yang,GUAN Qingfeng
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    Landscape metrics have been widely used to quantitatively evaluate the spatial patterns of landscapes and to analyze the temporal dynamics of landscapes and their effects. However, when dealing with massive amounts of data, the calculation of landscape metrics requires large amount of computing time and extremely large memory size, which greatly decreases the feasibility in real-world applications. This paper presents a parallel algorithm to improve the performance of landscape metric calculation. First, the classical two-pass connected component labeling (CCL) algorithm was modified: (1) the calculations of some basic geometrical metrics of patches, such as areas and perimeters, are integrated into the second pass of the data for the calculation of landscape metrics; and (2) continuous patch IDs are generated along the second pass, to reduce the overheads for re-labeling. Then, a parallel algorithm consisting of a master process and a set of worker processes is designed and implemented using the C++ programming language and Message Passing Interface (MPI). In our parallel algorithm, the whole spatial domain is decomposed into multiple sub-domains and assigned to a set of concurrent processes. Each process uses the modified CCL algorithm to identify the patches within its assigned sub-domain and calculates the basic geometrical metrics of the patches. After gathering and merging the basic metrics from other processes, the master process calculates the final landscape metrics. The experiments using the land-use dataset of California showed that the computing time of landscape metrics was largely reduced using multiple processes. In conclusion, our parallel algorithm provides a high-performance solution for landscape metric calculation using massive and high-resolution datasets.

  • Orginal Article
    CAO Jinzhou,TU Wei,LI Qingquan,CAO Rui
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    Urban space and the behavior of human activities constantly interact with each other. Investigation on distribution of aggregated human activities and spatio-temporal change benefits data-driven policy-making in urban planning and urban governing. In the era of big data, with the development of information and communication technologies, it is possible to collect city-scale data with high resolution in space and time by various location-aware devices and sensors. Exploration of spatial-temporal activities attracts a lot of attention. By taking about 10 million one-day tracking data of mobile phone users in Shenzhen, China as an example, this paper firstly identified their stay locations according to spatial and temporal rules to generate stay trajectory for each individual and recovered activity semantic information by labelling activity types for each stay locations. Then, the significant differences in patterns of distributions of stay locations and their activities were analyzed. Spatial and temporal distributions of different human activities were explored, respectively. The study shows that the distribution of stay locations and activities is obviously heterogeneous. The average number of stay locations of an individual per day is 2.1, while the average number of activities an individual engaged in per day is 3.4. This study furthermore suggests that different types of activities have temporal variance and spatial heterogeneity. The temporal distribution fluctuates significantly over 24 hours, which is in accordance with daily routine. The spatial distribution overall obeys “space power law”, and the spatial distribution of social activity, which has a faster-down tail, shows a more obvious pattern of spatial segregation than the other two activities. The study revealed the diversity and heterogeneity of spatial and temporal distribution of human aggregated activities in urban space, which is meaningful in analyzing human activities research and facilitating urban traffic optimization and urban planning.

  • Orginal Article
    LIU Penghua,YAO Yao,LIANG Hao,LIANG Zhaotang,ZHANG Yatao,WANG Haosong
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    Serious air pollution has recently aroused wide public concerns in China. The traditional method of quantitative remote sensing model is not only sophisticated but also inaccurate to fetch the exact PM2.5 data near the ground. Though the built-up ground monitoring stations can now provide sufficient PM2.5 observation data with high sampling frequency, there still exist many extreme outliers due to inevitable observation noise. Therefore, in this study, we adopted Kalman filter for optimal estimation of time-series of air quality data in 338 cities of China and comprehensively analyzed the spatiotemporal distribution pattern during the period of 2015. In our detailed analysis, we used DTW based K-Medoids clustering to classify cities into 4 levels according to their contamination degree, and utilized q statistic technique to evaluate the spatial stratified heterogeneity of PM2.5. The results show that by using Kalman filter, noise can be effectively reduced and value of PSNR can be significantly improved. In the study of temporal distribution, we found that PM2.5 followed a ‘U’ curve in yearly temporal distributions while daily temporal distributions obeyed a ‘W’ curve. PM2.5 density is much higher in winter than in summer in China, and spatial stratified heterogeneity is even more pronounced during the fall-winter stage. In the study of spatial distribution, it can be clearly seen that PM2.5 appears a ‘Dual-core’ pattern across China where concentration of PM2.5 spiked at Xinjiang and North China plain. In contrast, Xizang, Guangdong and Yunnan are more stable areas with excellent air quality, ranking first-tier nationwide.

  • Orginal Article
    HU Yecui,FU Ling,LI Qi
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    Urban growth boundary (UGB) is an effective tool to control urban sprawl. China has carried out the UGB delineation for 14 cities since 2014. UGB includes rigid boundary and elastic boundary. The rigid boundary is an external constraint line and the elastic boundary is the developing line of the interior. In this study, from the angle of the urban endogenous development motivation, we selected 6 types of influence factors such as nature, population economy, location, neighborhood, land use type and policy planning, totally 18 factors. We tried to use genetic algorithms, CA and BP neural network to establish the UGB model to define Beijing elastic boundary. From the angle of ecological carrying capacity of land, we selected terrain, topography, parks and water, land use status, urban land distance and nature reserves as the influence factors and we tried to use construction land suitability evaluation method to define Beijing rigid boundary. The results show that by using our urban growth boundary model to predict Beijing elastic urban growth boundary, the percent area match was 96%, kappa value is 0.812, the UGB Model accuracy is good and the prediction area of Beijing elastic UGB is 1738.98 square kilometers. Through the suitability evaluation, the rigid boundary area is 3297.01 square kilometers.

  • Orginal Article
    ZHOU Xun,FAN Zemeng,YUE Tianxiang
    2017, 19(4): 493-501.
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    According to the vertical zonality characteristic of vegetation type in Heihe River Basin, we established an analysis model of the vegetation distribution of Heihe River Basin at large scale based on Support Vector Machine algorithm. Kappa coefficient and the confusion matrix were used to validate the accuracy and performance of the model. The Overall Accuracy (OA) is 75.54% and Kappa coefficient is 0.66, indicating that this method was qualified to simulate vegetation distribution at regional scale. The results show that, semi-shrub, dwarf semi-shrub desert and temperate grasses-forbs meadow steppe have the highest simulation accuracy with OA of 90.20% and 90.02%, respectively. Vegetation types with large area such as semi-shrub and dwarf semi-shrub desert, shrub desert and Kobresia spp-forb high-cold meadows have much better accuracy than other vegetation types with small area. Artificial economic crops, desert vegetation types, and grassland and meadow are more sensitive to the chosen environmental factors. For shrub and arbor, simulation results differ among vegetation types. In the aspect of spatial distribution, upstream area with obvious distinctions in both vegetation types and environmental factors, has a better simulation results than middle and downstream area of Heihe River Basin, which are flat in terrain and have a small climate variation. Also, the simulation results of the upstream area have a higher degree of fragmentation in the landscape pattern.

  • Orginal Article
    CHEN Huiyan,LIAO Yilan,ZHANG Ningxu,XU Bing
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    Neural tube defects (NTDs) are congenital anomalies that occur in the central nervous system. NTD is one of the birth defects with the highest incidence. China has the world’s highest rate of NTDs. Moreover, Shanxi province which is a leading producer of coal in China, has the Chinese highest incidence of NTDs. Yuanping County is one of the cities with highest incidence of NTDs. In epidemiology, researchers often use data based on the spatial distribution of diseases. However, with the growing interest in detection of variation of temporal trend in different study units, spatial-temporal modeling has been developed in the epidemiological analysis. Recently, one of spatial-temporal models based on the theory of bayesian has been extensively applied to the analysis of spatio-temporal patterns in relation to given diseases. The main difference of Bayesian spatial-temporal model is that it offers a natural framework to combine information from neighbouring areas or periods and hence to make the estimated results more reliable. In this paper, we applied a Bayesian spatial-temporal model and incorporated a space-time interactions component to explore the spatial-temporal variation of NTDs. The incidences of NTDs in Yuanping County of Shanxi Province between 2007 and 2012 were selected to analyze the spatial-temporal variation. Firstly, we identified areas that belong to the hot spots, cold spots or neither, and then studied the temporal trends of each area. Results show that the incidence rates of NTDs in Yuanping County is still very high. There is 1 hot spot, none cold spot and 17 areas that are neither hot spots nor cold spots. As a whole, the risk of NTDs in Yuanping County is slowly decreasing. The single hot spot has a slower decreasing trend compared to the overall decreasing trend in Yuanping County. Four areas which are neither hot spots nor cold spots show a faster decreasing trend. The rest of thirteen areas show the same decreasing trend as the whole. This paper identified the space-time variation and trends of NTDs in Yuanping County, which can help to study the potential factors and control measures of NTDs. Also, we provide scientific basis for the government to prevent the occurrence of NTDs.

  • Orginal Article
    SUN Jianguo,WEI Fang
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    It is important to comprehensively assess regional ecosystem health, which can offer scientific basis for the development of the policy and measures of protecting ecological system and the coordination and sustainable development of the ecosystem. Restricted by factors such as difficulty of obtaining data sources, carrying out the ecosystem health research from the township (town, street) scale is still few. In this paper, the pressure-status-response model is used in the ecosystem evaluation. There were 15 indicators selected to build an ecosystem health assessment system suitable at the township (town, street) scale. It includes GDP energy intensity, point density ofmonitoring and prevention of geological disasters, biological abundance index, the first national census geography results of Lanzhou region, the 2015 statistical yearbook of Lanzhou city and three counties and five districts, Landsat8, HJ-CCD, OMI remote sensing images as the main data source. Also, based on the statistical scale reduction to some indices such as the per capita water use and environmental protection investment accounting for GDP consumption, taking the township (town, street) as the evaluation unit, entropy method was used to evaluate health status of the ecological system of Lanzhou City. to The coordination degree of pressure-status-response was also analyzed. The results indicated that: (1) the ecosystem health showed a transition from poor status of downtown to better status of suburbs. From the township (street) scale, the "unhealthy" is 13.5%, the "sub-health" is 28.8%, the "healthy" is 51.3% and the "very healthy" is 6.2%. (2) From the aspect of coordination, 59% of township (town, street) is moderate coordination area, majorly distributed in the outskirts of the city and the streets of An Ning District. The remaining 41% streets are high coordination area and low coordination area, accounting for 25% of the land area, mainly distributed in the streets of the city and scattered on some towns of the outskirts. The results of this study will provide a scientific basis for the fine management, rational use and protection of land resources in the city of Lanzhou.

  • Orginal Article
    YE Yu,QIN Jianxin,HU Shunshi
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    This study used 9 scenes of Landsat TM / ETM images from 1991-2015 in Changsha to retrieve the land surface temperature of built-area of Changsha City and extract the land use/cover types. Combined with the related data, the characteristics of temporal and spatial variation of urban heat island (UHI) effects in Changsha City and the relationship between UHI and urban land use/cover change were analyzed. The results show that the extent of UHI in Changsha increased with the expansion of built-up area, and the spatial and temporal evolution of heat island was consistent with the trend of built-up area. During 1991-1996, the UHI in the east of Changsha city developed rapidly. In 1996, the eastern heat island area increased by 53.54 km2. Then, the UHI extended to the west with the increased area of 39.88 km2 as a result of the extension of city built-up area between 1996-2003. In 2003-2007, the UHI extended to the west and south rapidly, during which the heat island area increased about 33.55 km2. After 2007, the urban area and UHI of Changsha began to expand all-around. In addition, great changes have occurred in the land use/cover types of Changsha. Large area of green land was changed into building land and farmland, which greatly affected the spatial distribution of the land surface temperature (LST). Temperature difference between different land types was reduced significantly. The great capacity of heat absorption of water was obviously reflected. Construction land and bare land made great contributions to the LST.

  • Orginal Article
    ZHANG Jing,JIANG Wanshou
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    LiDAR point cloud and optical imagery are different types of remote sensing data source. They have some unique merits, respectively, that are complementary to each other. Integrating these two dataset has significant value in many applications. However, as the existence of various error sources, point cloud and optical imagery are usually misaligned. For the purpose of further integrated processing, the registeration of point cloud and imagery is a preliminary step which will align them into a unified geo-reference frame. Although after decades of research, this registration problem is far from solved. This paper gave a detailed survey of registration between point cloud and optical images. To obtain thorough understanding of this problem, a general mathematical paradigm for the registration was established firstly. By analyzing the mathematical paradigm, we indicated three main difficulties in this registration problem, and then definitely divided the whole workflow of registration into three key parts which are named: (1) the acquisition of corresponding observations, (2) the selection of transformation models; (3) the optimization of unknowns. Afterwards, we reviewed a series of representative registration methods from the above three aspects. In the acquisition of corresponding observations, the existing methods were classified into area-based method, feature-based method and multiple-view geometry based method. In the stage of transformation models selection, frequently-used models were classified into sensor-based models and empirical models. In the unknowns’ optimization part, two principal optimization methods termed local optimization and global optimization were introduced and the general usages of these optimization in registration were described. Furthermore, we summarized the mentioned registration methods and gave a detailed comparison and analysis including the advantages / shortcomings and the applicable scope. At last, the trends of registration development were forecasted.

  • Orginal Article
    WEI Chunyang,XU Dandan,DONG Kaikai,LIU Zhaoli
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    With the widespread application of multispectral sensors, the spectral response characteristics of features have been increasingly used to extract the surface information. Due to the complexity of surface conditions and limitation of spectral responses, spectral methods of indicating the average size, spatial distribution, spatial heterogeneity and pattern information are insufficient. Therefore, considerable attentions have been paid by researchers to mine the spatial pattern of remote sensing images. Previous studies have found that there is a corresponding relationship between semi-variogram parameters and scene parameters. Researchers can extract the surface parameters using semi-variogram parameters. With the understanding of this relationship, variogram analysis methods are widely used to quantify the surface pattern parameters, including the average scale extraction, periodic pattern detection, spatial heterogeneity and spatial anisotropy, extraction of image information of best scale selection, and texture analysis in pattern analysis of remote sensing images. Although the analysis methods of variogram play an important role in the above-mentioned application fields, the analysis of spatial pattern of remote sensing images based on the variogram is mostly limited to the qualitative description levels, and lacks precise quantitative description and analysis, which restricts the further application of the variogram analysis method. The main problem is the lack of understanding about the inherent mechanisms of the analysis of pattern variogram of remote sensing images. This should be the future direction in the research. This paper reviews the application of variogram analysis method in the field of remote sensing pattern analysis over the past two decades. The advantages and disadvantages of the method are summarized, which can provide a reference for the effective application of the variogram in the analysis of remote sensing image pattern.

  • Orginal Article
    WANG Xuecheng,YANG Fei,GAO Xing,LI Li
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    Extraction of damaged forest range caused by ice-snow frozen disaster is good for knowing the relevant regional disaster information in time, and it can provide scientific support for disaster prevention and protection of forest resources and ecosystem. We extract pre-disaster plant NDVI reference value and the threshold of normal change with the time series data of 2001-2008. We attained the results of the spatial distribution of Hunan forest disturbed by ice-snow frozen disaster with NDVI data in 2008. The NDVI threshold method can make up for the defect of traditional method based on single-temporal images, which doesn’t take the normal change of vegetation index into consideration. The NDVI threshold method helps extracting different normal change threshold for each pixel, causing the results extracted by NDVI threshold method is more objective and reasonable. Contrasted with the results extracted by the traditional method, the percentage of damaged forest according to two methods have significant difference at county level, although the rates forest disasters with two methods are all 34.74% (the real rate of forest disaster is 35.3%) at provincial level. The forest snow disaster is mainly distributed in southern Hunan province and less in northern Hunan province using the NDVI threshold method, but the results using traditional method is contrary compared to the NDVI threshold method. According to field survey data, the spatial distribution of forest snow disaster using the method of NDVI threshold is more closed to real results compared to traditional method and its extracting accuracy is higher. Therefore, the NDVI threshold method is more suitable for extracting the spatial distribution information of forest snow disaster at large regional scale.

  • Orginal Article
    QI Wenjuan,YANG Xiaomei
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    Due to differences in zonal and vertical distribution of growth environment, there could be "foreign body with same spectrum" phenomenon of different vegetation types in mountain and plain areas, resulting in false land cover classification. To avoid such misclassification, we defined vegetation boundaries between mountain zone and plain zone before interpretation of land cover types. This research is carried on based on clustering analysis of remote sensing images, spatial analysis of GIS and technology support of mathematical statistics analysis. The study area is located in the northern region of Duchang County, Jiangxi province. Based on the remote sensing images of high resolution GF_1, 3 kinds of terrain factors, which are area absolute elevation, relief and terrain landform position, division of mountain vegetation and plain vegetation are completed. Experimental results show that terrain factors combining with GF_1 image will achieve division accuracy of 99.47% and 96.28%, respectively. Compared to the calculated results of pure terrain factors, the accuracy of boundary extraction increased by more than 40% and 5%, respectively. Compared to experiments based on pure remote sensing image classification, the accuracy increased by nearly 25% and 23%, respectively. Besides, if compared with the 1:250,000 geomorphologic map, the accuracy is increased by nearly 15% and 23%, respectively. This research proves that using GF_1 images and terrain factor together to calculate vegetation boundary of plain and mountain will obtain results with an accuracy which can meet the requirements of interpretation of high resolution of remote sensing images of land cover.

  • Orginal Article
    HU Wenqiu,SU Fenzhen,WANG Wuxia,FENG Xue
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    This study was designed to reveal how land use change in Halong City in Vietnam during three different periods from 1973-2014. The land use data in 1973, 1988, 2003 and 2014 based on the remote sensing and land-use thematic maps were established with man-computer interactive image processing methods. Combined with GIS method and analysis model of land use change, we quantitatively analyzed the spatial-temporal change of land use and briefly described the characteristics of land use change in three different historical periods in Halong City since the Founding of Vietnam. The social and historical background is as follows: the first period was from the Founding of Vietnam to the beginning of Vietnam's Renovation and Opening up. The second period was from the beginning of Vietnam's Renovation and Opening up to implementing the policies of the socialist market economy, and the final period was from the beginning of socialist market economy policies up to now. In this paper, we explored the spatial-temporal variation of land use from the rate of land use change, the degree of land use, and the direction of land use transformation. Results suggested that: (1) forests shrank by 26.3% since the Founding of Vietnam. Urban area and Industrial warehouse space expanded 4.3-fold in total. In 2014, farmland and Mangrove accounted for only 3.6% and 1.3%, respectively. In the meantime, Aquaculture accounted for 5.5%. (2) Before the periods of Vietnam's Renovation and Opening up, Urban area and Industrial warehouse space was the mainly change, both increased about 4%, which mainly concentrated in Hòn Gai district. Forests and Farmland decreased 8.1% and 3.2%, respectively. Under the guidance of economic policy "heavy industry, light industry, agriculture", with struggling in economic stagnation, the whole pattern of land use had not obviously change in Halong City. (3) From Vietnam's Renovation and Opening up to implementing the policies of the socialist market economy, Urban area and Industrial warehouse space expand 3-fold and 2-fold than the past 15 years, respectively. Urban construction gradually became gentle in Hòn Gai district and B?i Cháy district. Forests decreased to 52.2%. Aquaculture appeared and it increased about 2.8% in 1988-2003 and caused the decrease of mangroves. With adjusting the guidance of economic policy, the process of urbanization was accelerating in Halong City. The spatial pattern of land use became fragmentation, and urban area construction became more balanced in both of Hòn Gai district and B?i Cháy district. (4) Since implementation of socialist market economy system, forests sharply reduced to 40.0%. Mangrove and Farmland reduced about 3% and 2.5%, respectively. Meanwhile, Urban area, Industrial warehouse space and Aquaculture increased about 8.6%, 4.4% and 2.7%, respectively. The spatial pattern of land use had transformed from forests into artificial construction land. Both sides of coasts on the bay mouth were all covered by urban land. Farmland and Aquaculture were marginalized.