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  • 2018 Volume 20 Issue 1
    Published: 20 January 2018
      

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  • LV Guonian,YU Zhaoyuan,YUAN Linwang,LUO Wen,ZHOU Liangchen,WU Mingguang,SHENG Yehua
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    The arrival of information age and big spatio-temporal data age brings new demands for the ability of expression and analysis of cartography. The existing theories of cartography are difficult to adapt to the research needs above, and there are still some obvious deficiencies in the aspects of modeling elements, information processing and expression forms. Based on the concept of geographic scenario which is a new cartography, we discuss the connotation and characteristics of geographic scenario and describe the necessity and main technical approaches of transformation from maps to scenes. We also propose a data model, a calculation model and an expression model for the development of cartography in the new era. The data model can be broken through from the unified geometric algebra data model based on the integrated representation of geographic six-elements. The computational model can utilize the mathematical space which solves the multiple information on constructing the solving strategies of the corresponding mappings, associations and operators. Finally, we point out the development direction of the expression model which contains the scenario adaptive combination and multi-model presentation of spatio-temporal distribution, evolution processes and factor interactions.

  • LI Anbo,CHEN Ying,YAO Mengmeng,WU Saisong
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    Quantitative measurement for sensitive geometrical information of secret-related vector digital maps is the basis and precondition of achieving the quantitative evaluation of classification level. At present, there are rare related researches on how to measure the sensitive geometrical information of a map. Based on the findings of map information measurement, we focus on the measurement method of the sensitive geometrical information for secret-related vector digital maps. The methodology is composed of three phases. Firstly, we discuss the definition of map feature sensitivity and the sensitivity index of feature sets. Then, we aim to the automatic division of information-unit for point feature set, line feature set, polygon feature set and comprehensive feature set. The method of constructing voronoi diagram with general generators is used. Finally, based on length coefficient, area coefficient and angle coefficient, we propose the methods for figure complexity of line feature and polygon feature. The results demonstrate that it follows the principles of information theory, such as nonnegative, continuous and additive, and reasonably reflects some factors’ (map scale, features spatial distribu-tion, features figure complexity and features sensitivity coefficient) impact on the results. The results will support the quantitative evaluation for classification level of vector digital maps, and help to improve the theory and method systems of geographical information security supervision.

  • HOU Zhiwei,ZHU Yunqiang,GAO Ying,SONG Jia,QIN Chengzhi
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    Different countries or organizations publish different versions or language editions of geologic time scales. Problems of ambiguity, such as synonymy or near-synonym and polysemy or other kinds of semantic heterogeneity arise in those geochronological concept systems, along with the lack of semantic linkage among different concepts and data, have hindered people from understanding and using those concepts accurately. Also, this caused insufficient results of integration and retrieval of geo-data for users’ requirements. The ontology of geologic time proved to be an effective solution to these problems. However, current studies focus on international geologic time scales, and their content and formalizations are not fully applicable in China. In this paper, we present a new Geologic Time Scale Ontology which mainly focuses on Chinese geochronological and stratigraphic concept systems. It describes the formalizations of attributes, especially the temporal features and relations of those concepts. It adopts a modular method to build this ontology. In addition, we propose a design of a pilot system to study the utility of this ontology as the basis of a geoscience knowledge graph in knowledge retrieval. Furthermore, we implemented an approach of semantic geo-data retrieval in the pilot system which uses a hybrid strategy of fulltext and ontology-based search. Applications of knowledge graph and geo-data retrieval based on the abilities such as temporal reasoning of Geologic Time Scale Ontology, proved that the proposed researches are effective in resolving semantic heterogeneity issues in geoscience knowledge and data. They can not only facilitate discovery of geoscience knowledge but also achieve the function of semantic data retrieval more intelligently, comprehensively and accurately for the discovery of specified and relevant data.

  • XU Zhen,JING Yaodong,BI Rutian,GAO Yang,WANG Peng
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    Spatial association patterns include location patterns of spatial association which emphasize on spatial data and structure patterns of the spatial association, which emphasize on attribute data. However, traditional methods were based on traditional spatial data and used spatial predicates as the logic in the process of mining. This would lead to the following problems: Firstly, it relied on the boundaries of spatial phenomenon and didn’t take account in the area of spatial phenomenon. Secondly, the results were restricted strongly by the table of spatial predicate built before data mining. Based on The Tobler’s First Law of Geography, this research proposed a new method of extracting spatial association patterns without using spatial predicate. According to specific data content and data format, this method converted spatial data into grid data which has the same spatial coordinate and the same size of each grid. Then, the method used a smooth moving-mask to get the transaction database from the grid data. Apriori algorithm without self-connection of attributes was adopted to explore the latent association patterns in transaction database. Finally, an experiment was conducted to verify the accuracy of this method. The experiment data included the data of coal mining area, land use data, water system data and terrain data in Changhe basin of Jincheng City in Shanxi Province. In the experiment, the error of grid transformation of each data layer was controlled within 5% and the accuracy of transaction was verified in co-location pattern. Grid transformation generated 28 434 grids and the size of each grid was 64 meters. After setting cultivated land as main factor, there were 38 310 records in transaction database. Through the study on some association patterns with higher confidence, it showed that the results were consistent with the prior knowledge related to cultivated land in ore-agricultural area. Therefore, this method can effectively extract the meaning association patterns and improve the interestingness of the results. This method improves the degree of freedom of the data mining by setting different sizes of the grid, main factors and mask sizes. Based on grid data instead of traditional spatial data, this method doesn’t rely on the boundaries of spatial phenomenon and takes into account the area factor.

  • CHEN Zugang,YANG Yaping
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    The traditional correlation degree algorithms for geographic entities have many disadvantages, such as non-applicable for some kinds of geographic entities and some types of topological relations, and not considering the dependency of spatial scale that results in poor discernibility of data. In this study, a new algorithm is proposed which computes the spatial correlation degree according to the specified spatial scale which is represented by a spatial extent. Based on the first law of geography and the theories on spatial correlation degree put forward by Egenhofer, the equations of spatial correlation degree was obtained by analyzing the topological and metric relations between different kinds of geographical entities such as points, lines and polygons. By comparison, the algorithm in this study can compute the correlation degree between geographic entities of different types and topological relations, alter the correlation degree with the change of the specified spatial scale, which is consistent with the generic intuition of human beings. At last, we introduced an application of the algorithm by taking geospatial data retrieval as an example. Compared with the traditional retrieval methods based on keyword matching, our algorithms can improve the F1-measure in geographic information retrieval (GIR) and give the accurate scores of correlation degree so that the retrieval results can be ranked. The algorithm is an elementary research that can be applied in the research fields of GIR, scientific data discovery, data recommendation, linked data, and so on.

  • YU Mingming,ZENG Yongnian
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    The cellular automata model has become one of the important methods of urban spatial expansion simulation. However, the existing cellular automata model of urban expansion still has some shortcomings. The cell state setting is relatively simple. Differences and strength of land types conversion are not enough in the conversion rules. In this paper, the cellular state and conversion rules of multivariate structures are designed under the framework of cellular automata model, and the urban extended cellular automata model considering the difference and intensity of land conversion is proposed. In the calculation of the conversion probability of non-urban land to urban land, this model takes into account the probability of three aspects: (1) For the impact probability of topography, economic development and other factors of urban development on urban land expansion, we used the logistics approach to calculate this probability. (2) The impacts of land types of neighborhood cells on the convergence rates of central cells. We use the extended molar method to calculate this part of probability. (3) The conversion intensity of different types of non-urban land (i.e. cultivated land, woodland and bare land) into urban land. The calculation of this part is to get the conversion scale of different types of non-urban land into urban land during the period of the base year and the target year by simulation of the superposition of the land use data in this period. Then, we further determine the conversion intensity of the different types of non-urban land into urban land. Finally, the product of the above three probabilities is used as the probability of cell transformation. We used the conversion probability and the conversion threshold to determine whether the central unit would be converted into urban land in the next stage. The number of urban land cells would be increased after the iteration calculation. When the difference between the results of simulated urban land and the size of urban land of the target year was in a certain range, we stopped the simulation and get the final results. The results show that the proposed model can capture urban expansion in the study area with sgood adaptability. The accuracy of the simulation results is 68.66%, which is 4.25% higher than that of the cellular automata model based on the traditional logistics regression. The Kappa coefficient is 0.675.

  • PENG Chao,LIAO Yilan,ZHANG Ningxu
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    With the rapid development of Chinese urban construction, Chinese ozone (O3) pollution received serious attention gradually. The urban agglomeration is the main spatial carrier of the urbanization of the populous country. It is the main form of urbanization. By the end of March 2017, the State Council has approved six state-level urban agglomerations and proposed to optimize the urban agglomerations in the eastern region and cultivate the development of western urban agglomerations. On the other hand, the new division standard of urban scale is to take resident population of the city as the statistical caliber. The city is divided into five categories of seven files. In order to study the temporal and spatial distribution of O3 pollution in China and the relationship between O3 pollution and the city, the geographical detector and the evolution tree model were used to analyze O3 monitoring data from June 2014 to May 2017, totally 36 months. The results show that the level of O3 pollution in China is increasing, and it grows rapidly in 2017, O3 has become the second largest pollution factor after PM2.5, and it appears to be a staggered pollution status with PM2.5 in time. O3 polluted cities are concentrated in the urban agglomeration area, among which the Yangtze River Delta urban agglomeration, the Beijing-Tianjin-Hebei urban agglomeration, the Shandong Peninsula urban agglomeration and the Central Plains urban agglomeration are relatively prominent. Cities of O3 and PM2.5 not exceeding the standard are mainly concentrated in the Beibu Gulf urban agglomeration and the West Straits Urban Agglomeration. O3 pollution in the city of large population scale is more serious and has a negative impact on the surrounding cities.

  • HU Yunfeng,ZHAO Guanhua,Zhang QianLi
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    Spatial distribution data of the population with high-precision is the important data to study the law of the variation of population activity at small-scale. Remote sensing images of nighttime light have the unique ability of reflecting human social activities. Thus, they were widely used in spatial data mining of the socio-economic field. In this study, DMSP/OLS nighttime light data, NPP/VIIRS nighttime light data, resident population data and land use data were used as data sources. Then, we used these data to build the stepwise regression at county scale. The spatial distribution data of population in Sichuan and Chongqing area were established based on the stepwise regression model. Finally, we took the resident demographic data of the randomly selected 500 townships as the practical data to assess the accuracy of spatial distribution data of the population. The analysis shows that: (1) both of the two nighttime light data have high correlation with the population. The correlation coefficients are both above 0.76. The correlation of NPP / VIIRS night light data and population is higher than DMSP / OLS. The fitting model does not change the results. (2) There are many types of land use that are highly relevant to population. Farmland and woodland can also affect the spatial distribution of population. Thus, built area should not be considered as the only type of land use for building the population distribution model. (3) When the two nighttime lights were combined with LUC( Land Use/ Land Cover), the complex correlation coefficient (R2) of the stepwise regression model using DMSP / OLS nighttime light data and NPP / VIIRS night light data is 0.796 and 0.817 respectively, and the model fitting rate is higher. Compared with the results based on DMSP/OLS (1 km), the spatial resolution of population based on NPP/VIIRS nighttime light data increases to 500 m. The change of population density is more natural from the central city to the surrounding urban area, and the population distribution is more real. (4) When combined with LUC data, the results obtained with NPP / VIIRS nighttime light data were more accurate than DMSP / OLS nighttime light data, indicating that NPP / VIIRS nighttime light data is more suitable for the research of spatial distribution of population than DMSP / OLS.

  • YANG Fengshuo,YANG Xiaomei,WANG Zhihua,QI Wenjuan,LI Zhi,MENG Fan
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    The influence of geographical environmental factors on regional economic disparity has always been the focus and hot issue of scholars at home and abroad. Detecting the influence of each factor on regional economic disparity and scientifically revealing the mechanism of each factor will provide important guidance for the formulation of regional economic development strategy. Although there are many studies on that question, the previous studies were lacked of contrastive analysis of impact factors between poor counties and affluent counties in Jiangxi Province and also ignored the interactive and combined influence. To solve these problems, using multiple regression and the geographic detector, we synthetically detected the influence of terrain, landform, land use, traffic location and other factors on economic differences among typical poor counties and affluent counties in Jiangxi Province and analyzed the influence, interaction and instruction of various factors. The results show that: (1) The 9 selected geographical environment factors all have an impact on the economy of poor counties and affluent counties, and different factors have different influence and the dominant factors are also different between the two types of countries. (2) Poor counties are mainly affected by natural conditions and geographical location and affluent counties are mainly affected by resource abundance and traffic location. (3) Comparing the dominant factors between the two types of counties, we find that from the needy areas to the affluent areas, dominant factors gradually change from natural endowments and other uncontrollable factors to traffic, technology and other controllable factors. (4) According to the dominant factors of regional economic disparity, economic development should adapt to local conditions and implement different strategies on different county types. At the same time, paying attention to the improvement of enhanced interaction-factors will play obvious effect on the development of economy.

  • WANG Qiang,HUANG Chong,LIU Gaohuan,LIU Qingsheng,LI He,CHEN Zhuoran
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    The Landsat program began in 1972, providing valuable scientific data for recording surface dynamics. Landsat data is vulnerable to cloud and cloud shadow. Abnormal pixel values caused by cloud and cloud shadow affect scientific calculation. Cloud and cloud shadow detection is the first step to scientific research using remote sensing data. Newly established cirrus band in Landsat 8 OLI data has the capacity to provide cloud mask as quickly as possible, but the cloud shadow hasn’t been marked. A new method for cloud shadow identification in Landsat 8 imagery is proposed in this paper, based on the Landsat collection 1 level-1 quality assessment (QA) band. First, the cloud pixels are identified using cloud mask stored in QA band. Then, flood-fill transformation algorithm is applied to near-infrared (NIR) band and short-wavelength infrared (SWIR) band to identify potential cloud shadow. After this step, cloud shadow can be discriminated from bright features. However, it will be confused with the dark objects such as water bodies. It is necessary to remove water bodies from the potential cloud shadow. Iterative Self-organizing Data Analysis Technique (ISODATA) is further used to distinguish water from potential cloud shadow. Third, the solar elevation angle and the solar azimuth are employed to match the position of cloud and cloud shadow. The solar elevation influences the distance between cloud and cloud shadow, and the solar azimuth affects the relative direction of cloud and cloud shadow. Because the cloud level varies very much, the cloud shadow can be finally identified through matching of cloud and cloud shadow after several iterations of cloud altitude estimation. To assess the accuracy of cloud shadow identification, a new validation dataset “L8 Biome Cloud Validation Masks” is used to test the method. We applied the new method to five biomes (shrubland, barren land, snow/ice, urban area and wetland). The validation results demonstrated that the method performed well in different biomes with the overall accuracy of more than 87%. Especially, the new method achieved an overall accuracy as high as 94.48% in shrubland. In comparison with the Function of mask (Fmask) algorithm, our new algorithm needs fewer Landsat bands but achieves better results, especially in barren land and shrubland with accuracy of 87.99% and 94.48%, respectively (Fmask: 85.38% and 92.02%, respectively). The method proposed here simplifies the process of cloud shadow identification and cloud level estimation, making the QA band of Landsat 8 OLI more valuable. It has the potential to be further developed to produce cloud shadow mask product.

  • YANG Yanjun,TIAN Qingjiu,ZHAN Yulin,TAO Bo,XU Kaijian
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    Multi-spectral remote sensing classification is strongly affected by spatial resolution while the classification accuracy is not necessarily improved by the increase of the spatial resolution. There exists an optimal resolution for each geographical entity, corresponding to its intrinsic spatial and spectral characteristics. Despite of many existing efforts, it is still far from clear how spatial resolutions affect classification accuracy. In recent studies, texture feature has been widely used as an effective factor to increase the classification accuracy of multi-spectral remote sensing. As an important characteristic of spatial information, texture feature is closely linked with morphology and distribution of objects. It may greatly increase classification accuracy in some cases that the same object has different spectra or different objects have the same spectrum. However, large uncertainties still exist in the effects of texture feature on classification for various objects at different spatial scales. This paper presents a case study implemented in Jining, Shandong Province to examine the impacts of spatial resolution and texture features on Multi-spectral Remote Sensing Images Classification using the Chinese Gaofen-1 (GF-1) satellite data. The GF-1 satellite was successfully launched on April 26, 2013, which was equipped with two types of sensors. One is the wide field view sensor (WFV sensor); the other is the panchromatic and multispectral sensor (PMS sensor) which can acquire panchromatic images at 2 m spatial resolution and multispectral images at 8 m spatial resolution. First, we carried out radiometric calibration, atmospheric correction and precise geometric rectification for original images. Then, we conducted data fusion based on Gram-Schmidt transformation and performed the expansion of spatial scales for the establishment of the spatial series of the reflectance (2~10 m at an interval of 1 m, 10~90 m at an interval of 10 m). Second, we generated the classification results using three popular approaches, i.e., the Maximum Likelihood Classification (MLC), the Support Vector Machine (SVM) and the Artificial Neural Network (ANN). Third, after the calculation of texture features of 2 m and 8 m for reflectance images, separately, the Principal Components Analysis (PCA) method was used for texture features selection. The data combining key features with corresponding multi-spectral bands were classified based on the ANN approach. Finally, we evaluated the classification accuracies using the confusion matrix. Our final regression analysis suggested an optimal spatial resolution of 5 m for the multi-spectral classification, implying that the optimal selection of the spatial resolution is not affected by the spectral information of multi-spectral remote sensing images. Further analysis of changing trend of accuracies along with spatial resolution showed a sharp decrease when the spatial resolution is coarser than 20-30 m. The results of the impacts of texture feature suggested that, compared with the classification by spectral information, the accuracies of winter wheat, architecture, forest and water were increased by 1.49%, 1.51%, 4.94% and 1.54% at 2 m resolution, and 2.95%, 10.95%, 5.91% and 5.14% at 8 m resolution, respectively, when the texture features were introduced. We concluded that, compared with the classification accuracy of the spectral information, considering texture feature effects may improve the classification accuracies, to varying degrees, at different spatial resolutions, especially when an appropriate resolution was chosen. Our findings are practically helpful for the optimal selection of spatial resolution for multi-spectral remote sensing classification. In the next step, we will further examine the impacts of spatial resolution and texture features at larger scales. In addition, the impacts of texture features at different phenological stages will also be investigated.

  • CHEN Luwan,HAN Ling,WANG Wenjuan,QIN Xiaobao
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    Soil moisture is a key factor in the energy and water balance of the earth's surface, and it also plays an important role in the ecological environment. Soil moisture inversions based on Synthetic Aperture Radar (SAR) have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms cannot quantify the uncertainty of soil moisture inversions. Uncertainty of surface roughness is the main factor that causes uncertainty of SAR-retrieved soil moisture. Most of the existing studies focused on the uncertainty of single roughness parameter (correlation length), and seldom directly studied the uncertainty of surface combined roughness. The uncertainty was usually estimated by probability distribution of model parameter values in existing studies. Then, the probability distribution was propagated through the inversion process. Finally, the probability distribution of soil moisture inversion was obtained. The uncertainty was quantified by using skewness, kurtosis, and interquartile range in this paper. First of all, the range and distribution of the measured soil moisture data and roughness data in sampling area were counted and analyzed. Input values and scope of the AIEM model parameters were obtained. Then, effective correlation length was calculated by using the LUT (look up tables) method based on the measured soil moisture data and backscattering coefficients, and the effective combined roughness was obtained. The nonlinear relationship between the effective combined roughness and backscattering coefficients was constructed. By adding different levels of Gaussian noise to surface combined roughness, the uncertainty propagating of surface combined roughness in the process of retrieved soil moisture was studied, and the uncertainty of soil moisture retrieval was quantitatively analyzed. For each Gauss noise level, 1000 effective combined roughness sampling values with noise were obtained. By using the nonlinear relationship between the effective combined roughness and the backscattering coefficient, the backscattering coefficients corresponding to the sampling value of each effective combined roughness were derived. The soil moisture was obtained by using the empirical equation of soil moisture inversion. The skewness, kurtosis and interquartile range of the effective combined roughness and the inversion results were calculated. By using the AIEM (Advanced Integrated Equation Model) model and the limited range of input parameters, a large number of simulated data were obtained. The effective combined roughness of the simulation was introduced into different proportion error according to the initial value, and the soil moisture was obtained by the empirical equation of soil moisture inversion. Furthermore, according to the response characteristics between RMSE (Root Mean Square Error) of retrieved soil moisture and the error range of combined roughness, the error control range that meets the inversion accuracy requirement was obtained. The experimental results of sample area show that kurtosis range is -0.1984 to 1.2501, the deviation range is 0.0191 to 0.6791, and interquartile range is 0.0018 to 0.0167 when gaussian noise standard deviation range of composite roughness is 0 to 0.045. Also, these three quantitative indexes increase with the increase of combined roughness gaussian noise. Soil moisture inversion values tend to be concentrated near mode, and the tendency to underestimate soil moisture is more obvious than the overestimation tendency. The error range of combined roughness should be controlled within a certain range of the initial value to meet the inversion accuracy requirement, and it is negatively related to the incident angle. The error control range is suitable for bare soil with low surface roughness and low sparse vegetation coverage area.

  • ZHANG Qian,XIN Xiaozhou,ZHANG Hailong,LI Yue,LI Xiaojun,YI Chuanxiang
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    Solar energy is recognized as one of the most promising new energy sources because it is abundant, clean, and environmentally friendly. Photovoltaic power generation is one of the main ways to use solar energy resources. Site suitability analysis is necessary to be carried out before constructing a photovoltaic plant. China is one of the most abundant countries in solar energy and has the largest installed photovoltaic capacity in the world. In this paper, we use remote sensing technology to obtain the spatial and temporal distribution of solar energy resources, and adopt the multi-criteria evaluation model to evaluate the site suitability of large-scale photovoltaic power plants in China. We provide scientific basis for site selection of photovoltaic power plants. This study took into account five criteria that affect the suitability, total solar radiation, stability of sunshine hours, distance from major roads, distance from major towns and slope direction, setting constraining conditions for elevation and land cover types to define areas that are not allowed, not suitable for construction of photovoltaic power plants. Multi-source data Synergized Quantitative remote sensing production system (MuSyQ) radiation products, DEM, road network data, nighttime light data and land cover maps were used to get the layers. MuSyQ radiation products were used to derive two indicators to assess solar energy resources according to the meteorological industry standard of the People's Republic of China, i.e., the total solar radiation and sunshine hours. Remotely sensed nighttime light data was used to extract densely populated pixels such as towns where electricity is needed. In this paper, we used the analytic hierarchy process to determine the relative importance of each factor in the analysis model. We used GIS overlay analysis to obtain the site suitability result, which was divided into five categories as "low suitable", "moderate", "suitable", "very suitable" and "constrained areas". The results show that the suitable area of Chinese northwestern region accounts for 53.0% of the national suitable area, 47.3% of very suitable area and the cumulative installed photovoltaic capacity in northwestern region accounts for 45.6% of the country. There is no clear linear relationship between the cumulative installed photovoltaic capacity and the size of the suitable area as well as the very suitable area for constructing photovoltaic power plants. East China is economically developed and has a large demand for electricity. Even if its suitable area accounts for 1.9% of the national suitable area, its installed photovoltaic capacity is more than 12 million kilowatts which accounts for 19.2% of that of the country. Very suitable areas are best places for building photovoltaic power plants and the photovoltaic power generation potential is 5 times more than the national power generation in 2016. Moreover, the installation limit and the subsidy policy made by the government have played a certain point to site selection of photovoltaic power plants. Finally, more work is needed to study the relationship between micro factors and suitability model for the sake of gaining a better evaluation of the spatial suitability of photovoltaic power plants.

  • LIU Kaisi,GONG Huili,CHEN Beibei
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    :In this paper, we choose the Beijing Metro Line 6 as the study area. The land subsidence is obtained by using PS-InSAR technique, and the spatial distribution characteristics of subsidence in the study area are analyzed. We further combine hierarchical entropy method to locate the typical section of Line 6. The severity and uneven of subsidence is also analyzed. The main conclusions are as follows: (1) The rate of subsidence from west to east along the line 6 is gradually increasing, and annual maximum subsidence rate is 77.2mm/a, which appears in the ChangYing-CaoFang section; (2) The integrated entropy is divided by the JinTaiLu Station, the west side has a small entropy (less than 0.5), and the east side is close to 1 or greater than 1. The subsidence of the east side of JinTaiLu Station is serious. It is pronounced uneven subsidence. (3) In the three sections of JinTaiLu - ShiLiPu, QingNianLu - DaLianPo, HuangQu - CaoFang, the subsidence severity is affected by the annual subsidence rate, slope, curvature radius, and the correlation is strong.