Equal classes play an important role in the generation and optimization of query plans. In order to reduce the search space of query plan, Ingres often takes two attributes that connected by a non-equal spatial topology relation operator as an equal class, and puts a spatial join operator between them to the bottom of the query plan tree. After filtering the spatial topology relation operator, the database system will have fewer data to proceed in the following operators, and produce higher efficiency. However, if take them as equal classes, just as in Ingres, it often results in building some errors in the spatial query plans especially for some queries with multiple spatial joins. Therefore, the non-equal spatial relation operators are not an equal relation. This paper analyzes the reason why non-spatial topology relation is not an equal relation and puts forward to use the spatial constrained pair instead of equal classes. The paper also gives a definition of a spatial constrained pair, which is an attributes pair connected by a non-equal spatial relation operator, or the spatial column of a table and the KEY column of its spatial index table. Spatial constrained pair is a sub-concept of equal class. It could adopt some heuristic strategies of equal class on building query plan except for transitivity. The paper explores a database implementation about the spatial constrained pairs in Ingres. Taking consideration of a query with multiple spatial joins, this paper conducts two tests. One takes the attributes pairs connected by a non-equal spatial relation operator as equal classes; the other one takes them as spatial constrained pairs. If follow the heuristic strategies of equal classes, it will produce some errors in the procedure of plan generation. However, if follow the heuristic strategies of spatial constrained pairs, it could help the system find the best query plan.
Geo-tagged video contains location information, and it is critical for true geographic representation. The geospatial representation of geo-tagged video is the key feature for the integration of video and GIS. Regarding to the disadvantage of geo-tagged representation methods for video objects with monotone spatial semantic information, a geographic representation framework for geo-tagged video objects is proposed. On the basis of extending OGC specifications for geographic information, this paper defined the respective objects in 7 types from 3 categories to describe the spatial information on two levels, including the video frame and video clip. The 3 categories include: (1) the video positions (point) to represent the location and attitude as the camera taking shoots; (2) the video trajectories (line) to portray the track of the video clip; and (3) the video field of view in plain view (polygon) or 3D (solid) space to describe the spatial extent of the video scene. The framework consists of the main spatial objects including the point, line, polygon and solid. It is more competent for demonstrating video spatial information. Meanwhile, the framework supports different levels of video data, such as the video frame and video clip. It achieves the loosely-coupled and perfectly-integrated integration of video and GIS, which does not need to alter the data structures. This paper discussed the data acquisition methods for the spatial information of video frames or clips in detail, which take use of the GPS receiver and 3D digital compass. We also developed 9 tables and defined their relations for the logical model to realize the geographic representation of geo-tagged video objects, and we analyzed the data visualization and retrieval methods by taking them as the application cases. The results show that the geographic representation framework for geo-tagged video extends the current spatial database standard. It is easy to implement and applicable in geographic visualization, video retrieval and spatial analysis or data mining.
Building is an important part of city. With the development of Smart City, 3D building model and its applications are going further from the external into the internal environment. Building engineering data contain rich information about building components throughout architecture design and construction process. They are good sources for building modeling and related applications. In this paper, building engineering data are divided into unstructured data, semi-structured data and structured data according to the data development status. The research status of information extraction from unstructured data and structured data are summarized from different aspects, including object recognition methods, extracted contents and application aims. Furthermore, the specification of layers and geometric primitive features for semi-structured data are studied based on the national and industry standards. In conclusion, the standardized layer information reduces the complexity of semi-structured data and assists the process of object recognition and information extraction. With its abundant sources and lower complexity, semi-structured data is becoming an important data source for the construction of building database in Smart City.
Over the past decade, WebGIS has been widely adopted in various applications to visualize and share geospatial information over the Internet. To address the Internet transmission problems regarding large-volume vector data, streaming transmission based on streaming media transmission protocol is proposed. This paper focuses on the organization mode and the streaming transmission mechanism of vector data, and a service framework for vector data streaming transmission is put forward. The framework consists of server-side vector data preprocessing, streaming progressive transmission, client-side vector data reconstruction and application. A vector data structure is designed which is taken to be an independent group storage. Each group is a separate transmission unit, and the grouped features can be handled immediately after they arrive at the client side. This structure can support the data structure of point, polyline, polygon and other basic geometrics and abide by the OpenGIS standard encoding specification. The server-side preprocessing divides the originally stored vector data into several groups for progressive transfer. Referencing to the multimedia model, we propose an RTP-based streaming transmission schema on the basis of analyzing the packet headers of the RTP and RTCP. The RTP payload format is called vector data stream (VDS), and it is composed of a stream header and a stream body. The combination of RTP method with UDP for streaming transmission has better transmission efficiency than the XML-based WFS for web mapping applications. The error control method and security mechanism we proposed make up UDP's unreliable connection issue. The results are compared with WFS using 1:100 million Chinese basic geographic databases. The comparison reveals that the transfer size of WFS is larger than VDS and the transfer time of streaming transmission is approximately half of WFS's. Thus, the outline of a large-volume vector data map could be viewed quickly based on the proposed mechanisms and algorithms. The experimental results demonstrate the technical feasibility and usability of this approach.
Mental representations of spatial knowledge are organized hierarchically. This should be introduced to route guidance in order to reduce the cognitive workload of drivers, and to increase drivers′ satisfaction and wayfinding success probability. The most commonly used hierarchical spatial reasoning based route planning methods take the hierarchical characteristic of road network into consideration, but the road design grade used in these methods does not conform to human′s hierarchy recognition of road network. In this paper, we introduce the complex network analysis methods, and take use of topological structure measures to express roads′ hierarchical characteristic. And on this basis, we propose a novel route planning method. The planned routes are compared to the taxi driving routes in reality and the travelling time decrements are calculated by comparing these routes to the distance shortest route. The experimental results indicate that, the routes that are planned using our method are more rational and optimal than the distance shortest routes, dynamic time shortest routes, road grade based time shortest routes and dynamic betweenness centrality hierarchy based routes, and are equivalent to the empirical taxi driving model based routes. Moreover, our method does not need the support of floating car system, hence it is more practical for promotion and application.
To study the urban heat island (UHI) effect of Beijing with long time series data, MODIS 8-day land surface temperature product from 2005 to 2014 is used to obtain the information about Beijing heat island, and LANDSAT-8 data on October 2, 2014 is used to get the distribution of Beijing imperious surface. The distance-weighted imperious surface cohesion density is calculated, Beijing’s main urban construction area is extracted based on city cluster algorithm, and a boundary is determined around the main urban construction area with an area of approximately 150% to the main urban construction area. We calculate the mean temperature difference between the main urban construction area and the boundary area, and regard it as UHI intensity. Then, we analyze the intra annual changes of UHI intensity, rank UHI intensity according to the land surface temperature and the month average of UHI intensity, perform statistical analysis on the frequency of UHI intensity level, and analyze the spatial distribution of UHI intensity level and its correlation with urban construction aggregate density. The results show that: (1) the dependency of UHI intensity on the boundary temperature showing a regulated pattern on the intra annual variations of UHI effect: counterclockwise distribution during the day and clockwise distribution at night; (2) in the daytime, the spatial distribution of UHI intensity level frequency appears to be level-2 and level-3, and the south-central high incidence area is featured by the level-3 heat island intensity during spring and summer; while at night, it has little correlation with construction cohesion density, but shows a cyclic characteristic increasing from the periphery to the center; (3) the imperious surface cohesion density make a notable effect on the frequency of different UHI level during the day with a positive correlation, while the effect tends to weaken when the imperious surface cohesion density is higher than 50%.
The availability of precipitation data with high spatial resolution is critical for several applications, such as hydrology, meteorology and ecology. The Tropical Rainfall Measuring Mission (TRMM) data sets can provide effective precipitation information, but at a coarse resolution (0.25°). Therefore, it is very necessary to improve its resolution. The existing TRMM-downscaling methods tend to use ordinary linear regression (OLS), which is known as a global model. However, it ignores the local characteristics. In this paper, the relationship between TRMM and Normalized Difference Vegetation Index (NDVI) was explored by using a local regression analysis approach that is known as geographically weighted regression (GWR). The relationship was used to construct the precipitation downscaling model, which then produces 1 km downscaled precipitation data. The OLS model and GWR model were tested for the data of Northeast China from 2000 to 2010. The accuracy of the downscaled data was validated by the observed precipitation data from 93 meteorological stations located in the study area. Some conclusions can be drawn from our study: (1) there is a strong correlation between TRMM data and the observation data obtained from meteorological stations (R = 0.9172). Overall, the TRMM precipitation is higher than the observed data at all stations. (2) Two downscaling methods were applied in this study, and the results show that the downscaled precipitation based on GWR model produces better results. It produces better R values and the reduced RMSE. Thus, the GWR model is more suitable for the spatial downscaling of TRMM. (3) The correlation coefficient between the downscaled precipitation based on GWR model and the observed data is ranging between 0.44 and 0.97, and its spatial distribution is disperse. (4) The downscaled precipitation data improves the spatial resolution (from 0.25° to 1 km), which can better reflect the characteristics of the precipitation in the study area. It could provide more accurate and realistic precipitation data for the studies at small scales.
Hydrological analysis module supported by GIS extracts information about the hydrological characteristics of research area by using Digital Elevation Model. In this paper, Zhengzhou-Luoyang region is selected as the research area. And Peiligang cultural period, early Yangshao cultural period, later Yangshao cultural period and Longshan cultural period are selected as the time series. The paper uses hydrological analysis to get the information of drainage basins in Zhengluo region, which is proved to be viable and rarely used in previous researches about the relationship between prehistoric settlement sites and drainage basins. According to the information of drainage basins, this paper analyzes its relationship with the spatial distribution of settlement sites in Zhengluo region. Besides, the relationship between the area of the drainage basin and the growth rate of its settlement sites, and the relationship between the area of the drainage basin and the number of its settlement sites are studied in the paper. The above relationships are clearly displayed in a digital way using the correlation analysis. And the results suggest that there is a significant positive correlation between the area of drainage basins and the number of settlement sites. There is also a positive correlation between the area of drainage basins and the change trend of settlement sites. When studying the characteristics of spatial and temporal distribution of settlement sites in each drainage basin, it can be found that during Peiligang cultural period, the distribution density of settlement sites in the eastern region is greater than the western region. And among the other three cultural periods, the distribution density of settlement sites in the central and western region is greater than the eastern region. Due to the different modes of production, there is a great difference among the residential environment within which the settlement sites are located. As time goes on, people′s dependence on drainage basins is gradually decreasing with the change of the modes of production. Therefore, the research on the relationship between prehistoric settlement sites and drainage basins extracted using the hydrological analysis is highly significant.
Ruminant livestock accounts for the major proportion of methane emissions within the agricultural sector. In China, cattle dominates the livestock due to its huge population and large size in comparison to sheep. The latest studies have paid little attention to the spatial variations and seasonal changes of the livestock methane emission factors, though a lot of direct measurements and modeling estiamtions have been made to improve the quality of the national inventories. In this study, we analyzed the spatial variarions and seasonal changes of the methane emission factors of cattle by studing the spatio-temporal differences in the body weight for 46 cattle species, the feeding and diet, and the draft and milk production in different places of China are also discussed . The Tier 2 equations of IPCC (2006) were used to calculate the methane emission factors from both the enteric and the manure management emissions. The calculation showed that the enteric emission factor was general low in summer (4.48 kg head-1 month-1) and high in other seasons, while the emission from manure management was high in summer (0.44 kg head-1 month-1) and low in other seasons. Spatially, northwestern China has a higher enteric methane emission factor (71.0 kg head-1 year-1) than southwestern China (55.2 kg head-1 year-1). The methane emission factor from manure management was low (0.1 kg head-1 year-1) in Tibetan plateau and high (5.4 kg head-1 year-1) in southeastern China.
Target classification and recognition (TCR) of high resolution remote sensing image is an important approach of image analysis, for the understanding of earth observation system (EOS), and for extracting information from the automatic target recognition (ATR) system, which has important values in military and civil fields. This paper reviews the latest progress and key technologies between domestic and international remote sensing image TCR in optical, infrared, synthetic aperture radar (SAR) and synthetic aperture sonar (SAS). The main research levels and the contents of high resolution remote sensing image TCR are firstly discussed. Then, the key technologies and their existing problems of high resolution remote sensing image TCR are deeply analyzed, from aspects such as filtering and noise reduction, feature extraction, target detection, scene classification, target classification and target recognition. Finally, combined with the related technologies including parallel computing, neural computing and cognitive computing, the new methods of TCR are discussed. Specifically, the main framework includes three aspects, which are detailed in the following. Firstly, the predominant techniques of high resolution remote sensing image processing are discussed based on high performance parallel computing. And the hybrid parallel architecture of high resolution remote sensing image processing based on Apache Hadoop, open multi-processing (OpenMP) and compute unified device architecture (CUDA) are also presented in this paper. Secondly, application prospects of TCR accuracy promotion are analyzed based on a thorough study of neuromorphic computing, and the method of multi-level remote sensing image target recognition based on the deep neural network (DNN) is introduced. Thirdly, the model and algorithm of big data uncertainty analysis for remote sensing images are discussed based on probabilistic graphical model (PGM) of cognitive computing, and the multi-scale remote sensing image scene description is given based on hierarchical topic model (HTM). Moreover, according to the related research of multi-media neural cognitive computing (MNCC), we discuss the development trend and research direction of TCR for remote sensing images big data in the future.
DMSP/OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light images can objectively reflect the intensity of human activities; therefore they were widely used in a variety of fields for urban remote sensing. However, the raw night-time dataset cannot be used directly in these researches due to the lack of inflight calibration, thus it needs to be further corrected. There are two problems existed in the long-time series of DMSP/OLS night-time light image dataset that should be addressed in the image correction procedure. First, every image in the raw night-time light image dataset cannot directly compare with each other due to the issue of discontinuity; second, there is a pixel saturation phenomenon existed in every image of the raw night-time light image dataset. In order to solve these problems, a method based on invariant region was proposed. This method included the intercalibration, the saturation correction, and the continuity correction procedures among all the images from the raw images dataset. All the night-time light images of China, which were extracted from the raw images dataset, were corrected using this method. Finally, this correction method was evaluated by analyzing the relationships between the night-time light images and the corresponding gross domestic product (GDP) data and the corresponding electric power consumption data respectively. Through the analysis toward the evaluated results, two main conclusions were acquired. One was that this method had solved the problem of discontinuity in the raw image dataset; the other one was that this method could reduce the pixel saturation phenomenon that existed in every images of the raw night-time light image dataset. However, this method has not completely solved the problem of pixel saturation. How to perfectly solve this problem is the core issue for future research on night-time light data application.
With the application of high resolution remote sensing image, the object-oriented technology has been developing rapidly in high resolution remote sensing image information extraction. Many scholars are interested in conducting research and application in this field, but the study of ZY-1 02C satellite imagery is rare. This paper uses the method of combining object-oriented classification and DEM data, to extract the wetland information of ZY-1 02C satellite image, and explores the application of this method in wetland information extraction from ZY-1 02C satellite imagery. It is of great significance to the research on the application of domestic satellite in wetland monitoring and protection. The results showed that: (1) ZY-1 02C satellite imagery has relatively high resolution, rich spatial information and large local heterogeneity. In addition, its spectral characteristics among various types of wetland are similar. The proposed object-oriented remote sensing image information extraction considers both the image’s spectral information and spatial information. It is applied to ZY-1 02C satellite imagery for the wetland information extraction, and the precision of its classification result is significantly higher than the pixel-based method. (2) This article has explored the method of combining object-oriented classification and DEM. The DEM data was taken as an additional band of image and was associated with the three original bands from the image to be involved in the segmentation, and the segmented objects are classified. The confusion between marsh and grass lands during the extraction is reduced, and the accuracy of wetland type classification is further improved. This method is suitable for wetland information extraction from high resolution remote sensing image. (3) The information extraction results based on object-oriented classification and DEM showed that, the precisions of paddy fields, water body, marsh and tidal flats are 97.44%, 86.96%, 88.46% and 88.46% respectively, which satisfy the wetland monitoring and protection requirements of 02C remote sensing image.
Cloud and shadow contained in satellite images usually causes inaccuracy in water extraction and brings difficulty to time-series analysis of water resources. In this paper, we took HJ-1 as the primary data and ZY-3 data as the auxiliary data to extract water information and monitor water distribution changes. To find the potential location of water, we randomly selected a series of check points using ZY-3 data. Based on the uniformly distributed check points calculated from the ZY-3 data, we examined the integrity of the water extraction result of HJ-1 data. If the extraction result was not integrated, we supplemented it with the water extraction result from images that are obtained within an adjacent time period, according to a check points based strategy. These supplemented extraction results in different periods formed a completed time-series dataset that provided a reliable way to monitor the water resources and distribution changes. The experiment was carried out on analyzing the Anhui section of Huaihe River watershed, and its results showed that this method took full advantage of the images within adjacent time. Even if the image quality was poor, this method managed to produce a more integrate result in comparison with the use of a single HJ-1 image. The randomly calculated check points minimized the manual intervention, thus provided an accurate and effective way to monitor the time-series of water resources. In the study area, 8295 check points were extracted. The results revealed the water resources of the study area in the rainy seasons (July and August) were more abundant than dry seasons (March and April) in 2013; especially, many temporary water bodies had been detected in the south of the watershed in the rainy seasons. The total water area had increased 22.1% in the rainy seasons compared to the dry seasons.
Steel industry is one of the dominant sources of pollution. Illegally constructed steel enterprises have been emerging for a long period. It brings enormous pressure to the ecological environment. Using remote sensing technology to monitor the violate behavior, is very helpful to enhance the capability of environmental monitoring department and strengthen environment security. Taking Shijiazhuang city as a research area, this study analyzed the feasibility of steel company inspection based on remote sensing technology, and presented the minimum requirements of image geometric resolution for different assignments. For this purpose, firstly, the representative steel enterprises of different sizes were selected. The identification characteristics of these steel companies were analyzed using remote sensing techniques and were used to format the interpretation knowledge base. Secondly, the feasibilities of identifying recognition features for different sized enterprises were analyzed, with remote sensing images at different geometric resolutions. Finally, the minimum requirements of image geometric resolution for different detection assignments were suggested according to their features. The results are indicated as follows: (1) the blast furnace, gas tank, electricity dust removal facility and material storage field were the primary recognition features of steel enterprises using remote sensing. (2) To identify the illegal extensions and constructions built without permission, the minimum requirements of image geometric resolution are 2 m panchromatic image for large-size company, 1 m panchromatic image for medium-size company and 0.5 m panchromatic image for small-size company. (3) To identify the constructions built without permission in sensitive area, the minimum requirements of image geometric resolution are 2 m panchromatic image for large-size company, 2 m panchromatic image plus 5 m multispectral image for medium-size company, and 0.5 m panchromatic image plus 5 m multispectral image for small-size company. (4) To identify the constructions built without permission in non-sensitive area, the minimum requirement of image geometric resolution is 5 m multispectral image for companies of all sizes.
Vegetation indices are frequently used in remote sensing applications. Nevertheless, vegetation monitoring based on vegetation indices may be affected by different soil background conditions. The aim of this study is to investigate the relationships between vegetation abundance and vegetation indices, and evaluate the performance of vegetation indices along with different values for soil-adjusted factors in vegetation-feature extractions. Changting county of Fujian, a typical reddish soil erosion region in southern China, was taken as a test site for the study. Three subtest sites were selected to represent the low, moderate and dense vegetation coverage areas, respectively. After converting the original digital numbers of the ALOS image to at-satellite reflectance, vegetation indices including the normalized difference vegetation index (NDVI), the modified soil-adjusted vegetation index (MSAVI) and the soil-adjusted vegetation index (SAVI) with soil adjustment factor (L) values of 0.25, 0.5, 0.75 and 1 were calculated. The vegetation features were then extracted from the above vegetation-enhanced images and compared to high resolution images to assess their accuracies. The results suggest that SAVI gives a better performance in both low and moderate vegetation coverage area while using a L value of 0.75 for low vegetation coverage area and 0.5 for moderate vegetation coverage area, with the overall accuracies of 76.26% and 80.65%, respectively. NDVI gives a better performance in the dense vegetation coverage area with an overall extraction accuracy of 84.01%. Whereas MSAVI does not perform well in any of the three selected test sites. The soil adjustment factor L of SAVI has significant influence on the accurate extraction of vegetation information. As increasing L from 0 to 1, the ability of the SAVI in detecting sparse vegetation is gradually enhanced, however its ability in detecting vegetation information in shaded slope areas is decreased.
