With the development of Information and Communication Technology (ICT), big data is now becoming an important tool to carry on researches in many fields. In the domain of human geography, big data technology has gained more and more attention, and there are extensive studies carried out which could be categorized into three groups of research hotspots: the residential behavioral spatial pattern, the urban space and the regional structure. But few researches have explored the regional hierarchical spatial structure systematically based on the big data, and the relevant researches are primarily based on the survey data, which has a bottleneck towards the volume and the detailed micro-data acquisition. This study combined the internet big data mined from internet with the GDP and the spatial traffic network data, etc. to identify the regional spatial structure. The gathered data was categorized into three categories: the point data, the line data and the polygon data. They are used to specify the urban overall capacity and the intensity of interactions between city pairs and the serving area respectively. Based on the obtained data, an algorithm was developed to identify the hierarchical regional structure. This method was applied to the Beijing-Tianjin-Hebei region. And a hierarchical regional structure was demonstrated by a multi-way tree created with this algorithm. The established multi-way tree identifies a regional urban ranking system in detail and can be of great help to decision makers in delineating the regional spatial policies. The results shows that Beijing which has the highest overall capacity becomes the core city (root node) and has the largest serving area, but the matured secondary cities around Beijing are still expected to share Beijing's serving functions, and there is a development inequality existing between the north and south part of this region. This study provides a good reference to researches in the domain of human geography with the application of big data.
As a "bridge" connecting the information of various fields, such as politics, economy, society and culture, address is of significance in the exchange of multi-source information under the environment of big data. To solve the existing problems of address coding, such as the lack of location information, the coarse grain size, and the poor stability and readability, this paper proposes three principles for the design of new address coding models, including the independence and stability, the simplicity of realization, and the decipherability. Then, a multi-scale spatial location coding model for address is created, which is realized based on the global subdivision theory. This model takes the global subdivision grid as the basic unit, and builds the mapping rules from the address’s location to a set of grids. Furthermore, the structure of the spatial location code is provided in details. The spatial location code is developed by giving a unique sign to each of the address subdivision grid, which is the deformation of the global subdivision grid. It is described by a one-dimensional and fixed-length array, which is composed by the following four elements: the first one is C0, which is named as the Location Code; the second one is L, which is named as the Level Code, and two half span codes are the last elements. The deciphering method of the spatial location code is analyzed based on three aspects: location, region and relation. It shows that the spatial location coding model for address has presented good independence, stability, readability and scalability. Finally, this paper discusses the applications and prospects of the spatial location coding model for address, by taking the logistics industry as an example. Generally, the proposed model has presented strong theoretical and practical values for the construction of smart city.
Automatic and efficient regional three-dimensional (3D) geological modeling based on the field geological survey data is the key issue of the nationwide 3D geological mapping nowadays. Based on the fundamental geological survey elements such as the attitudes, boundaries and sections, a method for transforming the two-dimensional (2D) geological maps and route section maps into the 3D geological models was proposed in this paper. The geological bodies that are both constrained and non-constrained by route sections were taken into consideration in this method. For the geological bodies that are constrained by sections, through the spatial geometrical processing, such as boundary regional division and polyline affine transformation, the underground boundary could be estimated using the surficial boundary constrained by the section lines. Then, based on the Coons surface, the side surfaces of the geological body were constructed by filling the areas between the top and bottom boundaries and the section lines. Afterwards, the top and bottom surfaces of the geological body were constructed by the constrained Delaunay triangulation network and the topologically consistent regional geological model was finally constructed. Based on this workflow, a 3D geological modeling practice was carried out in the domestic 1:25 000 regional geological survey experiment area. In the experiment, some typical and complex geological structures, including the volcanic edifice, stratum, rock and faults were efficiently reconstructed.
The geophysical methods (gravity, seismic, electric etc.) are the major tools used in geological investigation and mapping. From the 2D geological mapping to the 3D geological mapping, the amount of data and work has rapidly increased. This requires some improvements in processing the geophysical data interpretation to promote and strengthen its role in the 3D geological mapping works. Since the geophysical data are recorded in forms of data grids, traditionally, people need to extract the geological information from various data grids acquired from different geophysical methods, and then manually integrate the information to construct a 3D geological model. This usually causes inconveniences and inefficiencies. Therefore, this study proposes a methodology of 3D geological mapping with the geophysical data grids. It firstly constructs some visualization models from different geophysical data grids. Then, it subsequently integrates these models for interpretation using the mapping rules adopted from the physical properties of rock samples measured in laboratory. And finally, it converts the interpreted visualization model into the 3D geological model. Based on this methodology, we implement the corresponding system which accomplishes the above processes efficiently. As an example, we presented a detailed description for constructing the 3D lithological model using the methodology mentioned above with the geological survey data acquired in the geological investigation of western Jungger, Xinjiang in China. The practical application demonstrates that the methodology has a high degree of automation and it can effectively solve the problems of 3D geological mapping in cases such as the lack of sufficient geological sampling information while having enriched geophysical data.
The existing finite element numerical simulation method of groundwater flow has some defects in the three-dimensional visual spatial analysis and the expression of numerical calculation process and simulation results. In order to solve this issue, the key steps of the finite element analysis process including the conceptual model construction, spatial discretization, hydrogeological parameters extraction and initial condition assignment are taken into consideration respectively. Based on the finite element method and 3D GIS platform, the method and technique framework of the groundwater finite element numerical simulation under 3D GIS are proposed with the supports of GIS spatial analysis algorithms and computer graphics theory. In addition to describe the technique framework, the core algorithms’ implementation details are given and the complete process of 3D GIS groundwater flow simulation is presented. The groundwater simulation example demonstrates that the proposed method and technique framework are capable of simplifying the finite element analysis process and improving the calculation efficiency of the model. The whole technique framework can be integrated into 3D GIS platform, and furthermore the visualization of simulation process and calculation results can be achieved eventually.
With the continuous development of computer technology, the purpose of map is more and more diverse, and the demand of map automation is increasingly high. As an important part of map, the automatic generation of symbols is also improved. Many researchers, including Jia Fenli, Tian Jiangpeng, and Peng Keman, have connected the symbol design with the semantic analysis based on the analogy between map symbols and natural language; proposed the concept of symbol-morpheme and associated it with the symbolic structures; and then realized the automatic generation of symbols on computer. However, the creation of symbol-morpheme combination and the reason behind this combination is still lack of good theoretical support and explanation. In order to solidify the theoretical basis of map symbol structure, this paper introduces the concept of image schema, and puts forward the theory of map symbol structure generation based on image schema. Through analyzing the cognitive mechanism of geographical entities, this paper illustrates the basic function of the image schema in forming the concepts of geographical entities and mental space. Secondly, the types of image schema used to represent the geographical entities are summarized and seven most basic image schema types are obtained. Finally, we summarize the graphic structure expression and the parametric description of each image schema by studying the relationship between the topological relationship and the image schema. In order to verify the correctness and the scientific nature of the theory for map symbol structure generation based on image schema, we conducted two experiments in this paper. The results of the experiments prove that the image schema plays a decisive role in the generation of map symbols and gives a good cognitive effect for the generated map symbols based on image schema.
The suitability assessment of wildlife habitat is very important for wildlife management and protection. Niche model is the most commonly used presence-only based habitat suitability model, which cannot explicitly express the quantitative relationship between the suitability of wildlife habitat and the environmental factors, and would be insufficient to express the ecological effects of environmental factors on wildlife habitat use. In this study, a new giant panda (Ailuropoda melanoleuca) habitat assessment method based on geographical detector (Geogdetector) is proposed. A total of 8 environmental factors were selected for the suitability assessment of giant panda habitat, including the elevation, slope, aspect, topographic position index, distance from drainage system, vegetation type, staple food sources of bamboo, and distance from human settlements. Based on the initial habitat suitability index (HSI) input data calculated by Analytic Hierarchy Process (AHP) and MAXENT model respectively, we used four geographical detectors (the risk detector, factor detector, ecological detector, and interaction detector) to assess the relationship between the suitability of giant panda habitat and their environmental risk factors. Results show that the suitability assessment of giant panda habitat based on the geographical detector has relatively favorable precision and feasibility. (1) The MAXENT-Geogdetector model has higher level of performance on accuracy than the other three methods. The overall accuracy of the prediction results based on AHP, AHP-Geogdetector, MAXENT and MAXENT-Geogdetector are 85.6%、86.5%、91.3% and 94.2% respectively. The kappa coefficients are 0.699, 0.718, 0.821 and 0.882 respectively. The AUC values are 0.902, 0.928, 0.949 and 0.966 respectively. And the overlap ratios of the predicted distribution area to the actual distribution area are 63.66%, 61.30%, 76.70% and 90.10% respectively. (2) The proposed Geogdetector-based method well captures the ecological effects of environmental factors on the wildlife habitat use by indicating the quantitative relationship between the suitability of wildlife habitat and the environmental factors.
With the fast growing of vehicle population, parking is turning into an important element of urban management. The tension between the supply and demand of parking has caused a series of serious problems, such as the illegal parking, the increase of cruising time, the safety issues of cars and drivers, and the air pollution. From the spatial perspective, this paper examines the supply-demand relationship of the registered parking plots in Beijing in 2014. Specifically, the two-step floating catchment area method was used to analyze the situation and pressure of parking by comparing the supply of parking plots and the demand at a street level. The study shows that most of the urban centers face the big pressure on the parking demand, and 41.5% of the streets, 72.3% of the area and about 41.7% of the population in Beijing urban area have their accessibility to the registered parking spots to be less than 5 for every hundred people. The accessibility to the registered parking area is higher in the urban center than in the periphery area, and the lowest values are found to be located between the fourth-fifth south ring road, along the fifth ring road, along the third northwest ring road and the urban central axis, excluding the Donghua Gate Street. Higher accessibility is found to be in the financial street and the central business district, while Zhongguancun area does not have a high accessibility to registered parking areas. Future management planning should emphasize and increase the number of registered parking lots, especially in the key urban functional areas and in the old residential communities, with the consideration of adjusting parking fee standard to influence people’s transportation and parking behavior.
Calculating the ecological value of Jiangsu coastal region provides a significant guidance for making decisions regarding the scientific utilization of regional land and the optimized allocation of resources. According to the characteristics of land exploitation and the ecological services of Jiangsu coastal region, an ecological value-assessing indicator system was constructed by analyzing four primary types of land use, including farmland, urban industrial and mining land, woodland and coastal beach. Also, the assessment models used for calculating the ecological value of Jiangsu coastal region were constructed by incorporating the integrated equivalent factor method, the value evaluation method, the market valuation method, the expert evaluation method, the production cost method and the contingent valuation method. Based on a series of data, including the land exploitation data, the sown area, the output value, the unit price, the annual precipitation, and the discharge of waste water, waste gas and dust emission, the ecological value of land exploration in Jiangsu coastal region during 2011 was calculated. The results showed that: the unit ecological values of farmland in each city were similar, averagely being around 6000 yuan/hm2. The ecological value yielded by the urban and industrial land in Nantong is reaching up to -7720.68 yuan/hm2; meanwhile, the ecological value yielded by the urban and industrial land in Lianyungang was relatively smaller. The modified ecological value of woodland was considerably high, which is much greater than the ecological values of farmland and coastal beach. According to the area and mean ecological value of the four primary types of land use in the Jiangsu coastal region, it could be calculated that the total ecological value of Jiangsu coastal areas in 2011 was 10.386 billion yuan. From the multi-disciplinary perspectives, in 2011, the ecological values of farmland, urban industrial and mining land, woodland and coastal beach in Jiangsu coastal area were 6178.95 yuan/hm2, -5163.26 yuan/hm2, 16 438.42 yuan/hm2, and 8125.53 yuan/hm2 respectively, which were calculated based on the average value of three cities. From the perspective of different cities, in 2011, the ecological values of farmland, urban industrial and mining land, woodland, and coastal beach of Lianyungang city were 2.406, -0.376, 0.243 and 0.183 billion yuan respectively. The ecological values of farmland, urban industrial and mining land, woodland, and coastal beach of Yancheng city were 5.414, -1.107, 0.206 and 1.118 billion yuan respectively. The ecological values of farmland, urban industrial and mining land, woodland, and coastal beach of Nantong city were 2.635, -1.37, 0.007 and 1.027 billion yuan respectively. And the adding-up total ecological values of farmland, urban industrial and mining land, woodland, and coastal beach in Jiangsu province were 10.455, -2.853, 0.456 and 2.328 billion yuan respectively. Among them, it could be found that the total ecological value of woodland was relatively smaller, considering that it has a relative smaller area.
The annual and seasonal variability of Arctic sea ice extent was analyzed using of the sea ice extent data from 1989 to 2014, which was offered by the America National Snow and Ice Data Center. According to the data, it can be found that the Arctic sea ice extent decreases with an amplitude about 59 100 km2 annually. Moreover, the fastest decline occurs in summer, while the slowest decrease appears in winter. In addition, the Arctic sea ice extent shows a relatively stable seasonal variation, which exhibit the same thawing and freezing period. Arctic sea ice extent reaches its maximum value in March, undergoing the ice thawing period from April to September. It has the minimum value in September. Subsequently, it is the ice freezing period from October to March. During the summer, the sea ice reveals some featured characteristics, such as the fast thawing and sudden freezing. The Arctic sea ice thaws quickly in July and August, and freezes fast in October and November. The freezing and thawing process maps of the Arctic sea ice were made by ArcGIS software. Then, the freezing and thawing processes of the sea ice were discussed in detail. The results show that the Arctic sea ice mainly freezes and thaws in various marginal seas, including the Bering sea, Okhotsk, Beaufort Sea, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea, Hudson B. and Baffin Bay. Finally, according to the sea surface temperature and air temperature data, the relationships among Arctic sea ice extent, sea surface temperature and air temperature were preliminarily discussed here. The results show that a change of the Arctic sea ice which affects the sea surface temperature may cause a variation of the air temperature. However, the seasonal changes of sea ice extent occur later than the seasonal changes of sea surface temperature and air temperature. The relationships among Arctic sea ice extent, sea surface temperature and air temperature in Chukotskoye More were analyzed using the sea surface temperature data and air temperature data derived from the ship-based observations. The data show that when the sea ice extent gets closer to the North Pole, the sea surface temperature and air temperature will be lower. When the sea ice extent gets closer to the land, the sea surface temperature and air temperature will be higher.
Synthetic Aperture Radar Interferometry (InSAR) time series analysis technique gradually becomes an important approach to obtain the long-term trend of surface deformation. It chooses a series of SAR images and establishes the corresponding model to extract the surface deformation. Currently, the InSAR time analysis technique is capable to improve the decorrelation and atmospheric delay in InSAR, therefore it has been widely used in many fields, including the urban ground deformation caused by the exploitation of underground resources, the mining field surface deformation caused by exploitation of mineral and natural gas, and the natural surface deformation caused by earthquake, volcano or landslide. Among different practical cases, selecting an appropriate method to capture the high coherent target is the fundamental step in getting the accurate and reliable surface deformation information. In this paper, we analyze the theoretical models of Permanent Scatterers (PS) and Distributed Scatterers (DS) and the differences of characteristics between the PS and DS point types. And we summarize the advantages and disadvantages of different high coherent target selection methods from the perspectives of SAR echo signal and InSAR time series analysis. Dispersion of amplitude is the most widely used method and the phase analysis method is more suitable for further selecting the points of PS. For the method of signal-to-clutter ratio, it is more applicable to select the PS candidates. The correlation coefficient method is only adaptive to a few SAR data. And for DS selection, the improved average temporal coherence method is more representative than the dispersion of amplitude difference method and the average temporal coherence method. In addition, this paper chooses some of the western part of Altyn Tagh Fault as the study area. And we verify the feasibility of the typical PS and DS selection methods to the real surface of the study area. The experimental results show that the DS method is more applicable than the PS method in non-urban area, and we obtain the deformation rate of Altyn Tagh Fault as well. Through these analyses, we provide the theory basis for selecting the appropriate methods in different research applications within different geographical areas.
Image classification is a popular research topic in the field of remote sensing. This technology has been widely used in environmental protection, military, urban planning, and other fields. Interfering by the massive feature information of remote sensing image, applying the reasonable feature selection approach in the progress of image classification becomes critical for improving the efficiency and accuracy of classification. This paper extracts the image feature data from the ZY3 satellite multispectral image of Huainan region, and studies the mRMR (minimal-Redundancy-Maximal-Relevance) feature selection method. This algorithm has a simple core principle and low requirement of data. The core problem of this algorithm is the computation of mutual information. The mRMR algorithm is initially applied in the field of bioscience, such as the gene expression analysis, and it is not widely used in the field of remote sensing. This research uses three methods (the binary discretization, histogram method and F-statistic) to realize the computation process of mRMR algorithm. And two classifiers (the C5.0 decision tree and k-nearest neighbour) are used for the classification based on three types of feature selection results and the total feature information. Moreover, the visual interpretation is used to verify the image classification results from these different methods. The study shows that the results produced by different mRMR computation processes are distinct regarding to different classifiers. In terms of efficiency, all methods can improve the efficiency of C5.0 and KNN. The classification efficiency is increased by 36.84% for C5.0 and by 72.05% for KNN. In terms of accuracy, all method can maintain the accuracy of C5.0 while improve the accuracy of KNN. The total classification accuracy and Kappa coefficient are increased for C5.0 by 0.60% and 0.80%, respectively. The total classification accuracy is increased by 4.34% and the Kappa coefficient is increased by 7.90% for KNN. In summary, the feature selection method based on the mRMR algorithm is effective in the procedure of multispectral image classification.
The effects of changing grain on landscape metrics is an important topic in landscape ecology studies. The original data in the most previous studies were derived from moderate/low resolution data and how the high-resolution data could affect the grain effect has not yet been well investigated. Therefore, we selected the land cover datasets produced from SPOT 5 imagery (with a spatial resolution of 2.5 m) in three watersheds located in the south-eastern coastal region of Fujian Province, China. We examined the behaviors of 28 landscape metrics in varying the range of grain size varing from 2.5 m to 150 m, where the grains coarser than 2.5 m were aggregated through majority filters. We then compared the differences between scaling functions fitted with the data from 2.5 m to 150 m and 30 m to 150 m. The results show that the effects of changing grain on 28 landscape metrics are obvious in the three watersheds examined. The responses of the metrics to changing grain size can be divided into four categories, while previous studies reported only three categories. The newly category is named as TypeⅡ including eight metrics that behaved split-up predictable responses with scale inflexion at 5 m, 7.5 m or 10 m respectively. This indicates that the high-resolution data can reveal more detailed effects of changing grain on landscape metrics. The results also show that the scaling functions for ED, SHAPE_MN, CONTIG_MN and AI, are sensitive to the spatial resolution of the raw data. Those scaling functions obtained from the moderate-resolution data may not be applicable to estimating the landscape metrics for grains finer than 10 m.
Lakes, especially the inland lakes, are sensitive to global climate change, which are the indicator of environmental variation. The area of lakes can reflect local climate change information. Thus, the rapid and accurate monitor of the dynamic change of the lake area is of great significance to analyze regional ecological environment. Based on MODIS data, this study used ESTARFM to simulate the Landsat data which are unavailable after 2000, and utilized two types of water index assisted by DEM data to analyze the dynamic area change of Siling Co Lake in Tibet from 1976 to 2014. Then, we analyzed the reasons of lake area change and its respond to climate change using the meteorological data acquired by six adjacent meteorological stations from 1976 to 2014. Conclusions can be made according to the results as the following statements. (1) The Landsat-like data acquired by ESTARFM was consistent to the real Landsat data in water information extraction, whose determination coefficient can reach a value of greater than 0.93. So, the fused data can be applied to extract the information of lakes. (2) Siling Co kept expanding from 1976 to 2014, the area of which increased approximately 711.652 km2, which is 42.36% larger. The average annual growth was about 18.728 km2, and the largest annual increase was up to 55.954 km2. The whole process of lake area change can be divided into three stages: the smooth change, the rapid change, and the smooth change again. The northern region changed most obviously, extending northward for about 22.812 km2. From 2003 to 2005, the southern region was integrated with Ya Gencuo Lake, and then they expanded together. (3) The snow-ice melting water supply caused by global warming might be the main reason for lake spread, and the decrease of wind speed was the secondary factor. However, the amount of precipitation and sunshine duration were poorly related to the lake area change.
The Nanji wetland of Poyang Lake is a typical wetland of the water-carrying type. It is quite difficult to accurately extract the information of wetland landscape from remote sensing images with the traditional information extraction approaches due to the complicated hydrology conditions and arduous field verification, and moreover, the mud flat, swamp and infested water (schistosomiasis) is widely distributed in the region. This study chose GF image as the data source, with the auxiliary data of digital elevation model (DEM), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) to ensure the accuracy of information extraction. And it uses the object-oriented classification method to extract the landscape information of the Nanji wetland, which achieved some reasonable classification results indicating in the following part; (1) Based on the object-oriented classification of domestic GF satellite remote sensing images, which contains the spectral, spatial structure and texture features, this study makes a comprehensive utilization of the multi-source data in the classification calculation, and the precision of its classification result is significantly higher than the object-oriented classification of single-source; (2) The object-oriented method of multi-source remote sensing data has better distinguish ability for mixed pixels. It obtains a higher overall accuracy of 94.3275% and a Kappa coefficient of 0.9324, which indicates a distinctive high degree of accuracy and reliability. It has effectively solved the classification problem in extracting the wetland landscape of water-carrying; (3) This method makes up the deficiency in the object-oriented classification method of single-source remote sensing image, and it acts as an important reference and has the practical significance for effectively extracting the information of water-carrying wetland landscape through the domestic GF remote sensing image. Finally, we put forward some issues to be resolved and illustrated the future research direction of the object-oriented classification of multi-source data.
Soil moisture is a key factor in the dynamics of hydrological cycle, and it also plays an important role in the ecological environment, especially in terms of an arid area. Microwave remote sensing technology is an effective technique that has been used to extract the soil moisture. However, the impact that vegetation imposes on the process and result of soil moisture retrieval is so influential that cannot be ignored. Therefore, it is necessary to establish a soil moisture retrieval model for the arid area with vegetation being taken into consideration. By taking the Uxin Banner of Inner Mongolia as a case study, the main objective of this paper is to develop different soil moisture retrieval models that are suitable for surfaces that are covered by sparse vegetation in the arid area based on Radarsat-2 and TM remote sensing data. The NDVI and NDWI indices extracted from the TM data have been used to calculate the vegetation water content. And subsequently, the impact of vegetation to the backscattering is removed by applying the water-cloud model. Furthermore, according to the characteristics of the vegetation in the study area, an improved algorithm based on the AIEM model is proposed to retrieve the soil moisture under different surface roughness parameters and polarization modes (VV and HH). Different from the existing algorithms which only utilize the backscattering coefficient within a single treatment for the soil moisture inversion, the improved algorithm comprehensively utilizes the backscattering coefficient before and after applying the correction which is conducted by the water-cloud model. After comparing the inversion results with the field in situ data, the results show that the improved soil moisture inversion algorithm based on the vegetation characteristics has presented a better adaptability. The soil moisture inversion model
