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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)
    A Review of Urban Environmental Assessment based on Street View Images
    Liying ZHANG, Tao PEI, Yijin CHEN, Ci SONG, Xiaoqian LIU
    Journal of Geo-information Science    2019, 21 (1): 46-58.   DOI: 10.12082/dqxxkx.2019.180311
    Abstract1069)   HTML32)    PDF (766KB)(704)      

    Urban environmental assessment research has traditionally adopted a method based on field survey, which is difficult to evaluate on a large scale and refined scale. Street view image has a wide coverage, can provide street-level landscape and intuitively reflect the city facade information, and have the advantage of lower cost than on-site data collection, so it provides a large sample data source and new research ideas for urban environmental assessment. Different from the sky view of remote sensing image and the user interaction data of geo-tagged social media, street view image is more focused on recording stereoscopic sectional view of the city street level from the perspective of people, which can represent scenes seen or felt from the ground on a fine scale, so it is more suitable to replace on-site observation of urban environmental assessment. The continuous breakthrough of artificial intelligence technology and its application in various fields make it possible to conduct urban environmental assessment research based on street view image on a wide range of spatial scales. In this paper, we first described and compared three categories of data sources commonly used in urban environmental assessment including street view image, remote sensing image and geo-tagged social media data, and summarized the advantages of street view image in urban environmental assessment. Then we classified the methods used in urban environment assessment based on street view image into the following four categories : methods based on image analysis, statistical analysis, artificial intelligence and spatial analysis. Next, from the urban physical, social, economic and aesthetic environment, we summarized the research and application of street view image in urban environmental assessment. Finally, we pointed out the innovations, limitations and future research directions of the urban environmental assessment based on street view image. On one hand, the application of artificial intelligence represented by deep learning promotes the research progress of urban environmental assessment on large-scale and fine-scale. On the other hand, in the era of big data, the integration of data source represented by street view image, remote sensing image, and geo-tagged social media data will help promote urban environmental assessment research from multiple perspectives and multi-level.

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    Cited: CSCD(3)
    Spatiotemporal Characteristics of Urbanization in China from the Perspective of Remotely Sensed Big Data of Nighttime Light
    Ting MA
    Journal of Geo-information Science    2019, 21 (1): 59-67.   DOI: 10.12082/dqxxkx.2019.180361
    Abstract961)   HTML32)    PDF (7825KB)(621)      

    The rapid growth of nation's economy has driven the unprecedented pace of urbanization in China over the past several decades. Urbanization process is a complicated geographical phenomenon involving human-nature interactions, such as population aggregation, land use change, infrastructure construction and eco-environmental changes. Hence, an understanding of the spatiotemporal dynamics of urban development is increasingly important for a variety of issues including research, planning, management and policy decision making. Owing to a spatially and temporally explicit manner of sensed information with respect to the magnitude of socio-economic activity related to urban development, the recent emergence of satellite-derived nighttime light data provides new means for investigating urban patterns and urbanization processes. In the present study, four kinds of quantitative information, including the spatial lighting area, temporal turning point, the spatial transformation of different types of lit areas and the velocity of spatial disperse of nighttime lightings signals, have been obtained and quantitatively analyzed based on time series of big data of annual composite products of nighttime light radiances during the period 1992-2013 from the Defense Meteorological Satellite Program (DMSP). Analysis results reveal the spatiotemporal patterns of China's urbanization over the past 22 years from the perspective of remotely sensed big data of artificial nighttime lighting signals in context of the spatial expansion, the distribution of urbanization onset time, the evolution of spatial structure and the urbanization velocity. This study can provide new insights into the understating of the fundamental spatiotemporal features of the rapid urbanization process in the present-day China using the remotely sensed big data of observed anthropogenic nighttime lighting signals.

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    Cited: CSCD(1)
    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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    Multi-level Spatial Distribution Estimation Model of the Inter-regional Migrant Population Using Multi-source Spatio-temporal Big Data: A Case Study of Migrants from Wuhan during the Spread of COVID-19
    LIU Zhang, QIAN Jiale, DU Yunyan, WANG Nan, YI Jiawei, SUN Yeran, MA Ting, PEI Tao, ZHOU Chenghu
    Journal of Geo-information Science    2020, 22 (2): 147-160.   DOI: 10.12082/dqxxkx.2020.200045
    Abstract1792)   HTML115)    PDF (12593KB)(618)      

    Previous researches have paid little attention to the multi-level spatial distribution dynamic estimation of the inter-regional migrant population. Preventing the spread of COVID-19 is the most urgent need for society now. Before the closure of Wuhan on Jan 23, 2020, more than 5 million people had left Wuhan to other regions. A better understanding of the destinations of those people will assist in the decision making and prevention of the coronavirus spread. However, few studies have focused on the dynamic estimation of multi-level spatial distribution of inter-regional migrant populations. In this study, by using multi-source spatiotemporal big data, including Tencent location request data, Baidu migration data, and land cover data, we proposed a dynamic estimation model of multi-level spatial distribution of inter-regional migrant population, and further characterized the spatial distribution of the population migrating from Wuhan to other regions of Hubei Province. The results showed that: (1) During the Spring Festival, the average ratio between the number of population increase in the rural areas and the total population change was 124.7% in the prefecture-level cities in Hubei Province. At least 51.3% of the population moving from Wuhan to prefecture-level cities has flowed into rural areas; (2) the spatial distribution of migrants among cities and counties in Hubei Province exhibits a 3-ring structure. The 1st ring is core area of disease, ncludes Wuhan and its surrounding areas, which are mainly characterized by population outflows. The 2nd ring is primary focus area, includes Huanggang, Huangshi, Xiantao, Tianmen, Qianjiang, Suizhou, Xiangyang and parts of Xiaogan, Jingzhou, Jingmen, Xianning, where the total population and the population in rural areas increased significantly during the Spring Festival. The 3rd ring is the secondary focus area, includes Yichang, Enshi, Shennongjia, and parts of Jingzhou and Jingmen, which are located in the western part of Hubei Province and are mainly characterized by a small inflow of population. We suggest higher attention to those rural areas of the counties located in the 2nd ring to better control and prevent the coronavirus spread. The research was completed in 2-3 days, showing that big data can quickly respond to major public safety events and provide support for decision-making.

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    Mapping Paddy Rice in the Hainan Province Using both Google Earth Engine and Remote Sensing Images
    Shen TAN, Bingfang WU, Xin ZHANG
    Journal of Geo-information Science    2019, 21 (6): 937-947.   DOI: 10.12082/dqxxkx.2019.180423.
    Abstract1033)   HTML42)    PDF (10741KB)(574)      

    Rice is one of the main grain crops in China and East Asia, including China. The annual yield of rice has a significant influence on domestic livelihood. Therefore, timely and accurate assessment of rice distribution information is crucial for forecasting rice yields and optimize the allocation of agricultural resources. Remote sensing (RS) images can provide time series surface spectral, and other electro-magnetic, dynamics over a large-scale land surface, which are commonly used for large-scale crop monitoring. However, routine rice classifying strategies provided by the RS images during key growth stages, require spectral patterns at high frequency. This method appears to be impractical within South China, as the number of high quality RS images are difficult to obtain due to cloud contamination caused by the hot and wet weather. A combination of various RS images of rice classification from multi-platforms provide an indirect way of reducing the revisit period in routine rice classification, thus enabling successful crop mapping in cloudy regions. However, this causes difficulty with data manipulation and storage, especially when conducting classification work at province or large area levels. To address these issues, this research utilizes Google Earth Engine (a cloud-based geospatial analysis platform running on the Google server)to collect online optic RS data and micro-wave RS data at diverse resolutions for rice mapping. A distribution map of paddy rice at 10-m spatial resolution in the Hainan Province in 2016 was made by using the combined methods of random forest (RF) classification and a pattern-matching strategy based on conjunct features extracted at monthly level and histogram value distribution. Results showed this method was suitable for rice mapping in Hainan and could show clear feature divergence between the different land surface cover types. Spatial distribution results corresponded well with the actual edges of the field, along with texture information. The rice classification result of the Hainan Province was validated using sample points captured on the ground and achieved overall accuracy of 93.2%, indicating reliability for practical application. Overall, the automatic rice classifying strategy was able to map paddy rice with high efficiency and sufficient accuracy in the Hainan Province, and could be applied to other vast areas.

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    Cited: CSCD(2)
    Random Forest Classification of Landsat 8 Imagery for the Complex Terrain Area based on the Combination of Spectral, Topographic and Texture Information
    Huijuan MA, Xiaohong GAO, Xiaotian GU
    Journal of Geo-information Science    2019, 21 (3): 359-371.   DOI: 10.12082/dpxxkx.2019.180346
    Abstract615)   HTML9)    PDF (20667KB)(560)      

    Random forest classification has become an effective method in remote sensing classification of machine learning. It is of great significance to combine the Landsat satellite data and random forest method to obtain long time series data in the complex terrain areas and to explore its land use/land cover change. Based on the multi-spectral data of landsat8 OLI satellite, this paper adopted the random forest classification method to classify the land use types of Huangshui basin complex topography areas in Qinghai province. According to the characteristics of complex terrain areas, the study area was divided into different geographical regions. The topographic parameters were then selected, and the optimal feature collection was constructed by extracting spectral and texture information of Landsat8 data. The objective of this papers was to explore the applicability of random forest methods in land use classification on the complex topographic regions. The results showed that RFC classification with the landsat8 OLI data can be well used to obtain the land use types in the Huangshui basin. The combination of spectral, topographic, and texture information performed differently in different areas. In the middle and high mountain areas, the combination of spectral and topographic information can obtain the best results in the random forest classification with the overall accuracy of 91.33% and Kappa coefficient of 0.886. In the shallow mountain areas and valley plain, however, the random forest classification can obtain the best results by combining spectral, topographic, and texture information with the overall accuracy of 92.09% and 87.85% and Kappa coefficient of 0.902 and 0.859, respectively. Using the random forest algorithm to optimize the selection of texture feature combination can extract the land use type information quickly and ensure its accuracy. Random forest classification combined multi-source information can be used effectively to classify land use types, which can provide some enlightenment and reference values for the renewal of land use status and the development of social economy in the study area.

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    Cited: CSCD(7)
    Geographic Similarity: Third Law of Geography?
    ZHU Axing, LV Guonian, ZHOU Chenghu, QIN Chengzhi
    Journal of Geo-information Science    2020, 22 (4): 673-679.   DOI: 10.12082/dqxxkx.2020.200069
    Abstract2063)   HTML79)    PDF (1329KB)(545)      

    Laws, in expressing the relationships that existed in the world, are powerful ways for people to understand and communicate human understandings. In this paper through the comparison of laws in geography and those well accepted laws in physics (namely Newton's Laws), we concluded that the laws in geography also fit the definition of "law" albeit the laws in geography are different from the laws in physics in how they are generated and how they are expressed. We further compared the geographic similarity principle or the Third Law of Geography as suggested by Zhu et al (Annals of GIS, 2018,24(4):225-240) with the existing laws of geography from the perspectives of broadness, independence and applicability and found that the geographic similarity principle has the similar broad implications in geography as the other two laws but it is fundamentally different from the other two. It solves problems in geographic analysis that the other two were found to be insufficient. We thus believe that geographic similarity principle would serve a great candidate of the Third Law of Geography.

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    Monitoring and Assessment of the Eco-Environment Quality in the Sanjiangyuan Region based on Google Earth Engine
    CHEN Wei, HUANG Huiping, TIAN Yichen, DU Yunyan
    Journal of Geo-information Science    2019, 21 (9): 1382-1391.   DOI: 10.12082/dqxxkx.2019.190095
    Abstract1072)   HTML96)    PDF (15525KB)(543)      

    The Sanjiangyuanregion is known in China forits fragile and sensitive terrestrial ecosystem. Degradation of its ecosystem is often irreversible, to which remote sensing based monitoring has much to offer. However, due to the cloudy and other adverse meteorological conditions there, obtaining cloudless Landsat imagery of the same season in the large region remains a big challenge. In this study, based on the Google Earth Engine platform (GEE), an image composite method was applied and 3766 tiles of Landsat TM historical images were employed to generate the same seasonal clear imagery with the lowest cloud possible composited at the pixel level. With the help of parallel cloud computing ability in GEE, a remote sensing ecological index (RSEI) was calculated directly and efficiently in GEE to reflect regional eco-environment quality. Specifically, we monitored and assessed the eco-environment quality of the Sanjiangyuan region in Qinghai Province during 1990-2015. Results show that: (1) During 1990-2000, the eco-environment quality dropped quickly (RSEI average value dropped quickly from 0.588 to 0.505), and his region suffered from mainly mild degradation; (2) During 2000-2015, degradation of the eco-environment quality slowed down toward stabilization and the eco-environment quality showed upgrade tide from 2015, and the areas of mild degradation significantly decreased. Moreover, the eco-environment quality in this region showed a spatial gradient of west-to-east degradation. Our findings provide comprehensive information for improving the eco-environment of the Sanjiangyuan region, and demonstrate the potential of using GEE for monitoring and assessing eco-environment quality at large scales.

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    Cited: CSCD(1)
    The Intelligent Processing and Service of Spatiotemporal Big Data
    LI Deren
    Journal of Geo-information Science    2019, 21 (12): 1825-1831.   DOI: 10.12082/dqxxkx.2019.190694
    Abstract1001)   HTML70)    PDF (11258KB)(539)      

    The intelligent processing and service of spatiotemporal big data is an important application and development opportunity of Geo Spatial Information Science, which is centered on surveying and mapping, remote sensing and geographic information technology. The development, main characteristics and mining methods of spatiotemporal big data are comprehensively discussed in this paper; Then automatic matching, change detection and intelligent decision-making of intelligent processing technologies based on spatiotemporal big data are introduced; On this basis, the "3S" socialized applications from earth observation to human observation are discussed; Finally, the current situation, development goal, key technologies, and application prospects of PNTRC based on spatiotemporal big data are introduced. Many practices have proved that in the age of big data and artificial intelligence,facing on the massive multi-source and heterogeneous spatiotemporal big data, focusing on the construction of automation, real-timized, intelligence, popularization and socialization, the innovation and development of Geo Spatial Information Science will have a bright future!

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    Cited: CSCD(2)
    Analysis on Spatial Distribution Pattern of Beijing Restaurants based on Open Source Big Data
    Xiaoyu XU, Mei LI
    Journal of Geo-information Science    2019, 21 (2): 215-225.   DOI: 10.12082/dqxxkx.2019.180437
    Abstract700)   HTML24)    PDF (15964KB)(535)      

    Using big data to analyze the spatial pattern of urban service facilities has become a new research hotspot, and catering industry is a typical representative of urban service industry. Therefore, it is of great significance to study the spatial layout of urban catering industry through open source big data. The restaurants in a city can be abstracted as point objects in the geographical study , and clustering analysis is a classical data mining method that quantificationally identifies geographical clustering among objects. In this paper, Beijing is selected as the research area, and the data of 153 895 restaurants in are obtained by using web crawler technology. The density-based CFSFDP clustering algorithm (clustering by fast search and find of density peaks) is adopted here to analyze the geographical clustering characteristics of catering industry in terms of spatial distribution density and per capita consumption level. This approach, which is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities, can recognize clusters regardless of their shape and the dimensionality of the space in which they are embedded, so more accurate spatial analysis results can be obtained. The results show that: (1) the spatial pattern of Beijing restaurants is imbalanced, which generally presents the characteristics of multi-center spatial distribution, and the agglomeration degree of restaurants decreases with the distance increase from the main urban area which are regarded as the core. Besides, the restaurants hot spots mainly circles around important business centers, tourist attractions as well as residential areas, and extends along the traffic line evidently. (2) The catering stores with different per capita consumption levels have the characteristics of hierarchical system. That is to say, there are the number of high-grade restaurants is few, and mainly concentrated in the commercial centers, financial centers and famous tourist attractions in Dongcheng district, Xicheng district, Chaoyang district and Haidian district, while the number of middle and low-grade restaurants is much more and their spatial distribution are more scattered. (3) The density and price of restaurants accords with consumption level of consumers. At the same time, this paper also analyses the factors influencing the spatial distribution pattern of catering clusters by combining the two indicators of spatial agglomeration characteristics and consumption level, in order to provide useful reference of urban commercial spatial layout for the government planning departments.

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    Cited: CSCD(2)
    Impact of Weather Condition on Intra-Urban Travel Behavior: Evidence from Taxi Trajectory Data
    Chaogui KANG, Xuan LIU, Xinyue XU, Kun QIN
    Journal of Geo-information Science    2019, 21 (1): 118-127.   DOI: 10.12082/dqxxkx.2019.180122
    Abstract1238)   HTML44)    PDF (10744KB)(514)      

    Weather conditions have a substantial impact on urban residents' daily travel activities. They usually determine the travel demand within a specific spatial location by land use type, as well as the route selection strategy between a pair of travel origin and destination. This information is crucial for stakeholders including urban dwellers, city planners and transport managers to optimize urban mobility, facility allocation and transportation resilience. In this paper, we apply spatiotemporal statistics, multiple linear regression and clustering analysis on taxi data and weather records of Wuhan City, China to understand the spatiotemporal characteristics of residents' travel demand and taxi drivers' route selection under different weather conditions. As a result, the dominant weather condition factors influencing residents' travel activities are revealed on space and time. First, taxi demand is more vulnerable to weather changes on weekdays than weekends. It is negatively proportional to the increasement of rainfall, temperature and wind speed. Second, at city scale taxi demand decreases along with raining on weekdays while the demand increases on weekends. In particular, the short-distance travels increase while medium- and long-distance travels decrease. Third, taxi demand is more vulnerable to weather changes within the urban area than the suburban area. On rainy days, medium-distance travels within the urban area decrease, whereas short-distance travels within the suburban area increase. Fourth, taxi demand on residential area increases, whereas the demand on commercial area decreases on rainy days. Last, taxi drivers are found to prefer the shortest path on sunny days and the fastest path on rainy days. Those research results can assist urban planners and municipal managers to enhance their understanding of urban residents' mobility pattern and their spatiotemporal dynamics more deeply.

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    The Impact of UAV Remote Sensing Technology on the Industrial Development of China: A Review
    Lei YAN, Xiaohan LIAO, Chenghu ZHOU, Bangkui FAN, Jianya GONG, Peng CUI, Yuquan ZHENG, Xiang TAN
    Journal of Geo-information Science    2019, 21 (4): 476-495.   DOI: 10.12082/dqxxkx.2019.180589
    Abstract992)   HTML19)    PDF (3197KB)(496)      

    The drone is a data-driven air mobile agent in the future network environment, UAV remote sensing technology has become one of the leading industries for UAV applications. This paper introduces the development of UAV remote sensing technology within China and internationally, and there is a particular focus on the development of UAV remote sensing technology within China from the "10th Five-Year Plan" to the "13th Five-Year Plan" since the 21st century. It also focuses on the UAV remote sensing calibration field, the establishment of aerospace calibration field and application verification are also described, including the development of load and system technology of UAV remote sensing system. Secondly, it will introduce the industrial application of UAV remote sensing technology in the fields of national defense, land and ocean island reef mapping, geological disaster monitoring, and emergency rescue. At last, China's advancement in UAV remote sensing technology with regards to intelligent control of networking, accuracy and real-time metric basis, self-organizing redundant fault tolerance of load platform, remote sensing big data cloud processing technology, and the practical application of UAV remote sensing networking will also be discussed. The overall goal of the future development of UAV remote sensing is to establish an unmanned aircraft network observation system with rapid information acquisition capability, to realize the unmanned aircraft networking technology from the project level to the remote sensing industry. At the same time, it also lays a foundation for Chinese national strategic leap in becoming a strong power in remote sensing field.

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    Cited: CSCD(8)
    Spatial Structure and Population Flow Analysis in Chengdu-Chongqing Urban Agglomeration based on Weibo Check-in Big Data
    Bilin PAN, Jianghao WANG, Yong GE, Mingguo MA
    Journal of Geo-information Science    2019, 21 (1): 68-76.   DOI: 10.12082/dqxxkx.2019.180235
    Abstract916)   HTML23)    PDF (9709KB)(483)      

    With the rapid development of regional integration, nowadays the regional inter-city migration gets the more attention of the scholars at home and abroad. Micro-blog, as one of the most popular application in China, has become a hotspot of research in areas such as sociology and computer. Check-in, as one of Micro-blog's functions, can reflect the flow of inter-city population in real time. We used the crawler program to collect the research samples in the Chengdu-Chongqing urban agglomeration in January 2014. The information includes the Micro-blog's unique ID number, the grid coordinates of Micro-blog sending place, and the city code of the registered place, etc. By running this program, a total of 804204 valid Micro-blog check-in data weare obtained from the Chengdu-Chongqing urban agglomeration. Based on Micro-blog checking areas, this study analyzeds the spatial structure of the Chengdu-Chongqing urban agglomeration. And Wwe combined the micro-blog data with the traditional socioeconomic data, in order to analyze the impact factors of the regional migration. The results indicates that the spatial structure of micro-blog shows the characteristics of "many centers of dual-core" group in this area. There are only two cities whose micro-blog flows are more than 100,000. They are Chengdu and Chongqing, forming athe “dual-core”. The direction of Micro-blog flow is affected by administrative division, and the intensity of Micro-blog flow presents a certain grade difference. The network shows an obvious hierarchy, and it closely correlatesnnects with the actual social-economic area closely, such as GDP, population size and the strength of traffic connection. For Chengdu and Chongqing, its GDP ranksed first and second,1, 2 respectively, with athe population size all of greater than 7.59 million and both as a regional transport hubs, it makes their micro-blogWeibo flows areintensity in ranked 1st and, 2nd, places respectively. Lastly, there are still some differences between Micro-blog's space and the actual geographic space inof Chengdu-Chongqing urban agglomeration. In the background of the national Yangtze River Economic Belt and China's new urbanization, we put the network information into the geographical space. Actually In this paper we discovered the spatial network characteristics of Chengdu-Chongqing urban agglomeration, and then this paper pointeds out the influence of socioeconomic factors on Micro-blog cyberspace flow. Of course, there may still be other factors behind Micro-blog's cyberspace, which need to be explored and analyzed in the future.

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    Cited: CSCD(5)
    Analysis of Vegetation Coverage Change in Yunnan Province Based on MODIS-NDVI
    XIONG Junnan,PENG Chao,CHENG Weiming,LI Wei,LIU Zhiqi,FAN Chunkun,SUN Huaizhang
    Journal of Geo-information Science    2018, 20 (12): 1830-1840.   DOI: 10.12082/dqxxkx.2018.180371
    Abstract907)   HTML21)    PDF (10056KB)(461)      

    The monitoring of vegetation cover change is the basis of regional resource and environmental bearing capacity research. This paper estimates the vegetation of Yunnan Province from 2001 to 2016 by calculating the MODIS-NDVI vegetation index from 2001 to 2016, supplemented by trend analysis, and coefficient of variation. Next, the spatial and temporal variation characteristics of vegetation coverage and its distribution relationship with topographic factors are discussed in depth. Results are shown as follows: ① From 2001 to 2016, the vegetation coverage in Yunnan shows a significant increase, with a growth rate of 4.992%/10 a.② Spatially, the spatial pattern of vegetation coverage appears to be gradually decreasing from the south to the north and from the west to the east. The vegetation coverage is highest in the west and southwestern Yunnan and the lowest in the northwestern Yunnan. The stability of the vegetation coverage is characterized by increasing volatility from southwest to northeast; the increase of vegetation coverage in northeastern Yunnan was significantly better than other areas. The study region of the vegetation coverage change trend which was increased, basically stable and decreased, accounting for 49.53%, 43.76% and 6.71%, respectively.③ The area transfer matrix results of vegetation coverage in the three periods from 2001-2006, 2006-2011, and 2011-2016 all showed that the vegetation cover evolution area was larger than the degraded area, and the ratios of the two were 1.42, 1.63, and 2.0. It indicates that the vegetation coverage shows a continuous improvement trend in the study area. ④ The relationship between vegetation coverage and topographic factors in Yunnan Province shows that the average vegetation coverage increases first, then decreases, then increases, and then decreases with the increase in altitude; it increases first and then decreases with increasing slope; Changes have gradually decreased from north to south.

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    Cited: CSCD(11)
    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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    Comparisons of Spatio-temporal Fusion Methods for GF-1 WFV and MODIS Data
    Bo PING, Yunshan MENG, Fenzhen SU
    Journal of Geo-information Science    2019, 21 (2): 157-167.   DOI: 10.12082/dqxxkx.2019.180314
    Abstract650)   HTML5)    PDF (50160KB)(433)      

    Observations with high spatial resolution and frequency can better monitor land surface dynamics, such as urban heat island effect monitoring, normalized difference vegetation index, leaf area index assimilation. However, it is still difficult to acquire satellite data with high spatial and temporal resolution from one single satellite sensor. For example, Landsat TM/ETM+/OLI data at 30 m spatial resolution have been widely applied for various applications, however, their 16-day revisit-cycles limit their usage in dynamics monitoring; on the other hand, MODIS data can be widely used in land process monitoring at global or large-scale because of their daily revisit period, but the coarser spatial resolution of MODIS data cannot meet the fine-scale environment applications. The spatio-temporal fusion is an effective way to solve this trade-off problem. The spatio-temporal fusion methods can be grouped into linear model methods (STARFM, ESTARFM), unmixing methods (FSDAF, STDFA) and so on. These methods have been used to support various applications such as leaf area index assimilation, urban heat island effect monitoring, land surface temperature generating, crop types and dynamics mapping, land cover classification, and land surface water mapping. The GF-1 satellite was launched from China's Jiuquan Satellite Launch Center in April 2013. It carries two panchromatic cameras with pixel resolution of 2 m and two multi-spectral cameras with pixel resolutions of 8 m, and four wide-field view (WFV) cameras with 16 m pixel resolution. The WFV sensors capture ground features with four bands that cover the visible and near-infrared wavelength range and the swath width reaches 800 km when the four sensors are combined. The spatio-temporal fusion researches on GF-1 WFV are still insufficient, hence, in this study, we used four spatio-temporal fusion methods including STARFM, FSDAF, STDFA and Fit_FC to blend GF-1 WFV and MODIS data, and then the fusion validation and accuracies were analyzed for further researches.

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    Cited: CSCD(2)
    Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine
    LI Peilin, LIU Xiaoping, HUANG Yinghuai, ZHANG Honghui
    Journal of Geo-information Science    2020, 22 (3): 638-648.   DOI: 10.12082/dqxxkx.2020.190047
    Abstract1028)   HTML39)    PDF (24363KB)(415)      

    For assessing urbanization level and urban environment, the mapping of impervious surface has become a research hotspot. Compared with single-phase imagery, time series mapping can depict temporal trends, which is of great significance for monitoring urban expansion. Based on the Google Earth Engine platform, this paper calculated BCI and NDVI using Landsat TOA data from 2000 to 2017, and determined their thresholds by an adaptive iteration method to extract the initial impervious surface. Then, Temporal Consistency Check (TCC) was performed to make the time series of impervious surface more reasonable. Results show that: (1) Adding NDVI to both BCI and TCC improved the quality of impervious surface mapping. (2) The average accuracy of impervious surface mapping in this paper was 90.4%, and the average Kappa coefficient was 0.812. (3) The impervious surface area of Guangzhou downtown nearly doubled from 2000 to 2017 with a decreasing growth rate. (4)The newly developed impervious surface mainly concentrated on the relatively backward outskirts of Guangzhou downtown. (5) Elevation, road density, and shopping mart density were the main factors influencing the expansion of impervious surface.

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    Networked Mining of GDELT and International Relations Analysis
    Kun QIN, Ping LUO, Borui YAO
    Journal of Geo-information Science    2019, 21 (1): 14-24.   DOI: 10.12082/dqxxkx.2019.180674
    Abstract1535)   HTML24)    PDF (7314KB)(402)      

    The international relations are intricate and ever-changing since the 21st century, and have brought profound changes to the world's economy, security, and diplomacy. These changes have had a major impact on China's internal and external policies. A comprehensive and timely analysis of international relations and its changing characteristics has important reference value for China's economic and diplomatic development planning. The analysis of international relations has spatio-temporal characteristics, and it needs real-time processing. Thus, it needs to introduce the methods of spatio-temporal big data analysis to analyze international relations. Traditional mass media such as news, radio, etc. record all kinds of events happening in the world. It contains a wealth of information. Compared with social media data recording personal activities, it is more suitable for large-scale and long-term analysis of human society. The Global Database of Events Language, and Tone (GDELT) is a free and open news database which monitors news from print, broadcast, and online media in the world, analyzes the texts and extracts the key information such as people, place, organization, and event. This paper researches the network characteristics of GDELT based on theory of complex network and further analyze the relations between countries. Firstly, this paper constructs national interaction networks using GDELT, then analyze the interaction relationship between countries through network characteristic statistics, and finally detect the time series changes of the national conflict event interaction network. The results show that: (1)The National interaction network has scale-free characteristics, the interaction between countries is unevenly distributed from a global and local perspective. Very few countries have lots of interactions while most countries have very few interactions, and one country has lots of interactions with a few countries while a few interactions with most countries. (2) Sudden changes in the national interaction network of conflict events often indicates some significant national conflict events. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news media in the era of big data.

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    Cited: CSCD(3)
    Building Extraction based on SE-Unet
    LIU Hao, LUO Jiancheng, HUANG Bo, YANG Haiping, HU Xiaodong, XU Nan, XIA Liegang
    Journal of Geo-information Science    2019, 21 (11): 1779-1789.   DOI: 10.12082/dqxxkx.2019.190285
    Abstract924)   HTML26)    PDF (34176KB)(399)      

    Automatic extraction of urban buildings has great importance in applications like urban planning and disaster prevention. In this regard, high-resolution remote sensing imagery contain sufficient information and are ideal data for precise extraction. Traditional approaches (excluding visual interpretation) demand researchers to manually design features to describe buildings and distinguishing them from other objects. Unfortunately, the complexity in high-resolution imagery makes these features fragile due to the change of sensors, imaging conditions, and locations. Recently, the convolutional neural networks, which succeeded in many visual applications including image segmentation, were used to extract buildings in high spatial resolution remote sensing imagery and achieved desirable results. However, convolutional neural networks still have much to improve regarding especially network architecture and loss functions. This paper proposed a convolutional neural network SE-Unet. It is based on U-Net architecture and employs squeeze-and-excitation modules in its encoder. The squeeze-and-excitation modules activate useful features and deactivate useless features in an adaptively weighted manner, which can remarkably increase network capacity with only a few extra parameters and memory cost. The decoder of SE-Unet concatenates corresponding features in the encoder to recover spatial information, as the U-Net does. Dice and cross-entropy loss function was applied to train the network and successfully alleviated the sample imbalance problem in building extraction. All experiments were performed on the Massachusetts building dataset for evaluation. Comparing to SegNet, LinkNet, U-Net, and other networks, SE-Unet showed the best results in all evaluation metrics, achieving 0.8704, 0.8496, 0.8599, and 0.9472 in terms of precision, recall, F1-score, and overall accuracy, respectively. Also, SE-Unet presented even better precision in extracting buildings that vary in size and shape. Our findings prove that squeeze-and-excitation modules can effectively strengthen network capability, and that dice and cross-entropy loss function can be useful in other sample imbalanced situations that involve high-resolution remote sensing imagery.

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    A Tentative Study on System of Software Technology for Artificial Intelligence GIS
    SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia
    Journal of Geo-information Science    2020, 22 (1): 76-87.   DOI: 10.12082/dqxxkx.2020.190701
    Abstract893)   HTML23)    PDF (12875KB)(381)      

    As the representative technology of Artificial Intelligence, deep learning has been the most exciting breakthrough technologies in big data analysis and other domains researches due to its novel data-driven feature representations learning, instead of handcrafting features based on domain-specific knowledge in traditional modeling. Driven by these technological developments. Artificial Intelligence plays a key role in the researches and applications of next-generation geographical information system software technology. Nevertheless, most researches about AI GIS are still in the stage of immature and preliminary exploration. As a method and technology for the novel architecture of GIS fundamental software, AI GIS is widely used in many earth science applications including remote sensing data analysis, water resources research, spatial epidemiology and environmental health. All these technologies are significantly improving capabilities of data processing of traditional GIS, and being able to extract geospatial information and characteristics from unstructured datasets such as street view or remote sensing imagery, texts. These applications are showing great value and developing potential of AI GIS. However, the existing research on the system of software technology of AI GIS is not comprehensive enough. A variety of AI GIS algorithms or models and their scenario-specific applications are commonly considered to be the most important topic. Few researchers have addressed the issues or theory of Artificial Intelligence GIS technologies system and software architecture. This paper presents and analyzes several levels of Geo-intelligence and discuss its relationships to AI GIS technology system , reviewed the research status in AI and GIS technologies from the domestic and abroad perspectives. Then, the system of software technology of AI GIS is proposed according to the relationships between Artificial Intelligence and GIS. This paper define the architecture of AI GIS into three parts including Geospatial Artificial Intelligence(GeoAI), AI for GIS, and GIS for AI. And concepts and examples for each parts of Artificial Intelligence GIS are also analyzed for illustration. Furthermore, in order to deeply explain and investigate the AI GIS software technologies architecture, this paper provide the example of the design and implementation of SuperMap AI GIS software architectures and production. Finally, this paper discusses the problems that need to be solved in the future development of GIS. The tentative study of AI GIS in this paper may provide a theory for establishing the fundamental GIS software technology architecture of Geo-intelligence, which would helps to promote the deep integration and development of AI and GIS technology, and make suggestions for further research about Geo-intelligence.

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    Cited: CSCD(1)
    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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    Spatio-temporal Pattern of Chinese Economy Development based on Nightlight Data
    Xiang LI, Jiang ZHU, Xiangdong YIN
    Journal of Geo-information Science    2019, 21 (3): 417-426.   DOI: 10.12082/dqxxkx.2019.180569
    Abstract587)   HTML8)    PDF (14839KB)(371)      

    Knowledge on the spatio-temporal pattern of economy development can effectively help inform policy on economy. Most current studies on spatio-temporal pattern in economic development mainly rely on statistical data. However, these statistical data have disadvantages such as lacking of consistently statistical standard and low spatial resolution. These shortcomings prevent the use of statistical data to accurately describe the real pattern of economic development. Nightlight data covers the most surface on the earth, and it is available with free of charge. Moreover, the nightlight data is highly related to socio-economic activities, so it can be used as a proxy variable to study human activities. Based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) nightlight data, three methods including gravity center, standard deviation ellipse, and local Moran'I were used in this study to explore the spatio-temporal pattern in Chinese economic development at different scales. The results showed that: (1) Chinese economic gravity center moved to the southeast from 2003 to 2013, but the moving distance was reduced year by year. These results indicated that there existed an economic gap between eastern region and inland region, but the gap was reduced gradually in these periods. The ellipse's extent of standard deviation in Chinese economy expanded, but its oblateness decreased from 2003 to 2013. This implies that the total volume of Chinese economy continued to rais, however, the spatial pattern became locally aggregated gradually. Besides, the direction angle of Chinese economy's standard deviation ellipse deflected to the east in these periods, which agrees with the result that the economic gravity center moved to the southeast. (2) High-high and low-low clustered areas were the two most obvious features of Chinese economy.

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    Assessing the Impacts of China's Road Network on Landscape Fragmentation and Protected Areas
    HUANG Mengna,MA Ting
    Journal of Geo-information Science    2019, 21 (8): 1183-1195.   DOI: 10.12082/dqxxkx.2019.190059
    Abstract498)   HTML24)    PDF (16196KB)(366)      

    Road construction often leads to landscape fragmentation and damages ecosystem functions, such ecological impacts and consequences have been widely studied in the field of road ecology and geospatial analysis. This paper aims at exploring the influences of the road network in China. With spatial data analysis and the data set of nationwide roads in 2015, we characterized the landscape fragmentation patterns caused by paved roads, estimated the impacts on protected areas, and then explained the relationship between the degree of impacts and multiple environmental variables. The results show that: (1) The area affected by the paved roads in China have reached 10% of the terrestrial areas. The land surface have been cut into over 30 000 patches. The number of small patches is numerous, and the number of large patches is less. The extent of land surface fragmentation presents obvious east-west differentiation, and the spatial pattern of the roadless patches is similar to the population distribution and economic development level. (2) About 58% of the protected areas suffer from road influences. The degree of influence increases as the level of establishment of the protected areas decreases. The national parks have been interfered stronger than the unprotected areas. (3) The main human activity factors were positively correlated with the degree of disturbance on the protected areas, and the size of protected areas and topographic factors were negatively correlated with the degree of interference. Small areas, low level protected areas, plain areas, climate-friendly protected areas are susceptible to road disturbances and are in a state of being affected seriously by human activities. Therefore, China's road construction should achieve a balance between social development and ecological protection. Road disturbances are affected by natural and human factors, which should be considered comprehensively in the study of relevant impact mechanisms and the formulation of environmental protection policies.

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    Determining the Distribution of Unmanned Aerial Vehicles Airports for the Emergency Monitoring of Floods in China
    Ming LU, Xiaohan LIAO, Huanyin YUE, Shifeng HUANG, Chenchen XU, Haiying LU, Yiqin BAI
    Journal of Geo-information Science    2019, 21 (6): 854-864.   DOI: 10.12082/dqxxkx.2019.180177
    Abstract666)   HTML25)    PDF (7507KB)(357)      

    Frequent flood hazards affect large areas and cause great losses, which have posed a serious threat to economic development and people's lives and properties. Unmanned Aerial Vehicles (UAVs) have proven to be useful in monitoring disaster status and providing decision-making support for emergency rescue, because they are able to arrive at floods area timely, obtain flood images and videos quickly, low-risk to operate and flexible to carry out different sensors. The important roles of UAV in emergency rescue have been widely recognized. However, the lack of available UAV resources nearby at the sudden onset of floods seriously limits the capability of UAVs' rapid response to flood disasters. For addressing this challenge, a multi-UAVs remote sensing observation network is highly required, and now has been planned in China to enhance our ability of emergency response. Key problems include where and how to allocate UAVs resources such that the UAVs can reach destination timely when floods occur. To help close this gap, we proposed to build a number of UAV airports in China to establish a remote sensing observation network of UAVs. Field stations of Chinese Academy of Science (CAS) were considered as the potential locations of UAV airports because of their extensive geographical distribution and good cooperation with the UAV application and regulation research center of CAS. With the available flood risk prevention data, administrative division data, CAS field stations data and the UAV database as data sources, we created a fishnet of 0.5° multiply 0.5° to discretize administrative divisions and regarded the central points of these grids as potential demand points, and then calculated the importance of UAV to those demand points based on their flood risk prevention level. Based on this analysis, a Maximum Covering Location Problem (MCLP) model was adopted to determine the optimum stations for UAV airports and a cost-effectiveness curve was used to determine the optimum number of UAV airports. In the end, 81 field stations were selected from 268 field stations, thereby ensuring that UAV airports would be allocated near flood-prone areas and most floods in China could be monitored with UAVs within two hours, which is critical for saving lives and reducing losses. The construction of UAV airport networks will surely contribute to an integrated disaster emergency observation system combining satellite, airplane, UAV and ground observations in China. Additionally, the methods and results in this study can serve as a basis for building a more comprehensive national UAV remote sensing observation network.

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    Cited: CSCD(2)
    Revealing the Behavioral Patterns of Different Socioeconomic Groups in Cities with Mobile Phone Data and House Price Data
    GUAN Qingfeng, REN Shuliang, YAO Yao, LIANG Xun, ZHOU Jianfeng, YUAN Zehao, DAI Liangyang
    Journal of Geo-information Science    2020, 22 (1): 100-112.   DOI: 10.12082/dqxxkx.2020.190406
    Abstract707)   HTML27)    PDF (27769KB)(350)      

    The spatial distribution characteristics and activity patterns of urban populations play essential roles in studies of spatial isolation, optimizing urban resource allocation, and so on. Because of the sensitivity of population activity data and socioeconomic data, previous studies focus mostly on the macro level. They have difficulties in dividing the socioeconomic status and quantitatively analyzing human mobility regulation. In recent years, geospatial big data, such as the mobile app data, provide us with a rare opportunity to analyze the human activity of urban internal problems. In this study, we constructed a fine-grained activity portrait of mobile phone users based on the mobile phone signaling data of Shenzhen residents, and coupled the high-resolution Shenzhen house price distribution data to achieve accurate division of people by their economic levels. Then, we extracted six activity indicators, which include the number of active locations, activity entropy, moment of inertia, travel time, travel distance, and travel speed, to quantify the spatial distribution and analyze the activity patterns of people at different economic levels. The results reveal the correlation between mobility and socioeconomic status. The distribution of people's activities at different economic levels in Shenzhen was related to the economic development of each administrative region. The results also demonstrated that three activity indicators (moment of inertia, travel distance, travel speed) were positively related to the economic level. Residents across different socioeconomic classes exhibited different travel patterns. Likely because the rich people live in the southwest of Shenzhen, but their work locations have more self-selectivity. This leads to the distribution of home and work locations in different administrative districts and the home-work distance of high-economic people are larger than others. For the other three activity indicators (number of active locations, activity entropy, travel time) that reflect the similar pattern of activity between different socioeconomic status, we found that people were mainly concentrated in living and working locations on weekdays. These locations share activities on weekdays for people at different socioeconomic levels. The socioeconomic status does not affect the number of daily activities nor the scheduling of activities. This study provides necessary data and policy guidance for government and urban planners.

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    Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine
    Kai LIU, Liheng PENG, Xiang LI, Min TAN, Shugong WANG
    Journal of Geo-information Science    2019, 21 (5): 731-739.   DOI: 10.12082/dqxxkx.2019.180354
    Abstract802)   HTML20)    PDF (17787KB)(348)      

    Remote Sensing technologies have been widely used in the investigation and dynamics monitoring of mangrove forests. However, problems remained that severely hinder the precise description and deep understanding of mangrove forests' dynamics. The problems include difficulties in remotely sensed data acquisition, the heavy workload of data preprocessing, and the lengthy time period in long time series monitoring. Based on Google Earth Engine (GEE), a cloud platform of remotely sensed data processing, this study used raw images of Landsat series satellites to produce an inter-annual mostly-cloudless (cloud coverage less than 5%) image collection of top-of-atmosphere reflectance (TOA). Then, classification rules were established based on three infrared-band TOAs (NIR, band near infrared; SWIR1, band shortwave infrared 1; SWIR2, band shortwave infrared 2) and three indices (NDVI, normalized difference vegetation index; NDWI, normalized difference water index; NDMI, normalized difference moisture index). Next, four land cover types, i.e., mangrove, mangrove-shrimp pond, impervious surface-bare land, and water body, were classified for mapping our case study area of Ngoc Hien, Vietnam from 1993 to 2017. Finally, the inter-annual land cover maps were used to analyze the characteristics of mangrove dynamics. The results showed that the long time-series inter-annual change monitoring of mangroves in cloudy and rainy regions can be implemented satisfactorily on the GEE platform. The image classification had an overall accuracy of over 80% for 86% of the study years, indicating that our proposed thresholds-based approach can effectively extract mangroves and mangrove-shrimp ponds. Through the analysis of inter-annual changes, the change process of mangroves in this region was depicted in details: it first increased, then decreased, and later, increased again. The correlation between the area changes of mangroves and mangrove-shrimp ponds was accurately detected to be negative. The inter-annual change monitoring of mangroves reduces the uncertainty of researching mangrove evolution processes, and quantifies in more details the conversions between mangroves and other land cover types. In so doing, the impacts of economic development, policies, and other factors on mangrove dynamics can then be assessed.

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    Extraction of Loess Dissected Saddle and Its Terrain Analysis by Using Digital Elevation Models
    XUE Kaikai,XIONG Liyang,ZHU Shijie,TANG Guoan
    Journal of Geo-information Science    2018, 20 (12): 1710-1720.   DOI: 10.12082/dqxxkx.2018.180358
    Abstract1057)   HTML4)    PDF (25489KB)(346)      

    Dissected saddle, as an important terrain control point, is the result of the struggle statue between positive and negative terrains. The width of the dissected saddle ranges from only a few meters to about twenty meters, which represents the critical stage of gully capture. That is, the headward erosion of gullies on both sides of watersheds are going to erode and dissect the boundary of the watershed. Thus, the division of positive terrains and the connection of negative terrains are achieved. The typical dissected saddle is located in the loess landform in the Loess Plateau, also known as the loess dissected saddle. This dissected saddle could act as an important indicator for distinguishing the extent between loess interfluve area and loess gully area during the landform evolution process. In this paper, taking the typical loess landform as an example, and on a basis of DEM data and remote sensing images, the semi-automatic extraction of dissected saddles is conducted. The terrain characteristics of these extracted dissected saddles, i.e., slope, relief, depth of cut, were then calculated based on the DEM data. Moreover, the spatial pattern of the dissected saddle was summarized. The experimental results show that the dissected saddles are distributed at the boundary of the main stream and perpendicular to the widest part of the main channel, indicating an obvious terrain controlling effect. The quantity and distribution of dissected saddles determine the development and the shape of the watershed to some extent. The results of slope, relief, and depth of cut for dissected saddles are all larger than that for the normal saddles. At the same time, the value of the high-hierarchical watershed is greater than the value of the lower-hierarchical watershed, which reflects that the dissected saddle has characteristics of strong erosion and high surface fragmentation. In summary, the dissected saddle is highly eroded by the channel, which could help to demonstrate the development stage of the loess landform. Along with the development of the landform, the dissected saddle could be regarded as a symbol, indicating the development of the loess landform has reached the metaphase of the landform evolution process.

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    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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