Three-dimensional GIS (3D GIS) is one of the primary and typical contents of GIS technology at present and in the future, which overcomes the constraints of representing 3D GIS spatial information in two-dimensional map, as well as provides a more effective decision-making support for people's daily life. This paper focuses on the research progress and its key technologies of 3D GIS, including the data model, database management and visual analysis. The pilot applications of 3D GIS in Wuhan are also illustrated. The entire 3D space of the city is represented by 3D GIS. Then construction of the large-scale city digitalization is enabled with the improvement of city management. Finally, the applications of 3D GIS for spatio-temporal information bearing engine and spatial intelligence in smart city and city safety are investigated.
Vector data overlay analysis methods, with the most difficult and tricky core issue of overlapping between polygons, are the basic analysis approaches for many spatial analysis algorithms in Geography Information System (GIS) software and also the basis for many advanced spatial analysis models and complex spatial analysis applications. The divide and conquer strategy, with the paradigm of divide-conquer-combine for problem solving, can reduce the cumulative effects of nodes of the polygon union results at average level. And, compared with classic snowball union strategy, it can accelerate the polygon union algorithm effectively. In this research, we took the polygon union algorithm as an example to describe a fast polygon union strategy named tree-like union method. Firstly, we analyzed the variation of the efficiency of the core operator, which is polygon union operation implemented by Vatti's algorithm, with different node number of polygons, and figured out that the potential performance bottlenecks and pitfalls of the snowball union strategy is the cumulative effect of the nodes existing in the union process. Then based on the idea of divide and conquer we proposed a new approach named tree-like union strategy and implemented a union algorithm for polygon clusters or layers to solve the problem in polygon union process. Finally, we introduced an efficiency evaluate model by which the available acceleration potentiality derived from the tree-like union strategy can be assessed conveniently for a group of polygons. Experimental results in this research shown that compared with snowball union strategy, the tree-like union strategy based on the idea of divide and conquer could lead to great reduction of time costs of polygon union algorithm. Furthermore, we found that the time cost of snowball union was about 26-folds than that of tree-like method when union 400 polygons, and the number reached about 926 when union 11 200 polygons. Therefore, it can be inferred that the accelerate effects brought by the tree-like union strategy could become more significant when dealing with larger polygon datasets. We supposed that the tree-like union strategy proposed in this research represents a certain degree of applicability in operations similar with polygon union algorithm, which could be a potential and practical optimization approach for vector data overlapping and other advanced spatial analysis algorithms which involved with polygon union operations.
Effective organization and management of vector geographic data is one of the key parts for spatial database application. The traditional vector geographic data service is usually based on magnetic disks and relational databases like Oracle Spatial. With the rapid development of wireless communication and mobile web technology, the performance of current vector geographic data service is declining dramatically under multi-user concurrent access, and can't meet the requirements of high performance and high concurrency. In order to improve the performance of vector geographic data service under multi-user concurrent access, we proposed a novel management approach of lightweight vector geographic data based on main memory database Redis. Redis is a main-memory lightweight key/value store. Its I/O performance is much better than traditional disk-based databases like Oracle and MS SQL server. At first, we analyzed Redis' key-value data model and data structure. Subsequently we designed a four level hierarchy organization structure of vector geographic database. We stored vector geographic data and its metadata based on Redis' plentiful data structures. Then, taking the grid spatial index as an example, we designed the storage method of spatial index and spatial query processing flow for Redis based on the hierarchy organization structure of vector geographic database. Our experimental results confirmed that compared to traditional relational spatial database-Oracle Spatial, our main memory style vector geographic data management approach greatly improves spatial query responding speed and its concurrent performance is excellent. The proposed approach can be used as a front end high performance cache of large spatial database or a high performance spatial indexes database.
To satisfy the requirement of simultaneous compression along with real-time collection for line vector data, this work proposes an innovative algorithm with the characteristics of high compression rate and low distortion. Cumulative Offset Based Real-time Compression Algorithm (CORC-Algorithm) has outstanding performance in the perception of right direction and offset distance. CORC-Algorithm proposes fast discovery method of cumulative changeable point, cumulative changeable inflection point and cumulative offset distance critical point. The CORC-algorithm can also be efficient in discovering all types of bending extreme points and continuous offset extreme points even in the condition of high tolerance threshold. The algorithm has time complexity of O(N) and space complexity of O(1) when reducing compression distortion and completing the zero delay synchronization compression. By comparing with vertical distance algorithm and subsection Douglas Peucker compression algorithm, we focus on experiments by collecting line vector data at the timing and distance strategy with different tolerance threshold. The experiments show that CORC algorithm has great advantages in terms of real-time, compression and distortion by comparing with vertical distance algorithm and subsection Douglas Peucker compression algorithm. CORC-Algorithm can achieve the universal lower distortion under the same compression ratio. The maneuverability of CORC-Algorithm is effective and stable for having low effect of tolerance threshold. Because of its excellent performance in real-time compression, CORC-algorithm has a wide application in the real-time location monitoring field of traffic, tourism, adventure, rescue, and entertainment.
With the frequent occurrence of abnormal climatic events in recent years, social economic and people's life are impacted more and more seriously. Meteorologists have found that ocean climate has an effect on land climate, such that the EI NINO can lead abnormal precipitation events on some land regions. Therefore, it is very critical to study the associations between ocean and land climate factors. At present, some researchers have done a series of work about this aspect and several representative methods have been proposed. The eigenvalue statistics and traditional sequential association rules mining are two main methods. However, the former is sensitive to noise and not suitable for huge amounts of data, while the latter dose not fully consider the correlation and multi-scale properties hidden in the climate time series data. In view of this, a method based on multi-constraints is proposed to discover sequential association rules between individual ocean and land climate factors in this paper. First, we took both time correlation and spatial correlation into account and a hierarchical clustering method with the consideration of spatial proximity is employed to find climate zones for the land climate factor. In this way, we not only preserve the effective information in the data, but also make the raw data simpler by removing time correlation and spatial correlation. Second, the land and ocean climate sequences are discretized based on domain knowledge and a series of events are also extracted. These are further used to construct the transactions mining table. Finally, a new method, which utilizes multiple constraints, is developed to mine sequential association rules. We only focus on the associations between ocean climate indices and abnormal land climate events, such as flood and drought. As a matter of fact, we need the frequent rules which can describe a law to a certain extent. A practical example is used to explore the relationships between each climate index and unusual precipitation events in China, and the results obtained are very consistent with the actual situation. This to a large degree illustrates that the method proposed in this paper is rational. In addition, we also gain some unknown knowledge that can provide some information for meteorologists. Based on the information, meteorologists can study the internal mechanism deeply. Also, the information can guide the government to make related policy decisions. In summary, the method in this paper takes the spatial correlation, time correlation and multi-scale characteristics into account effectively, while considers multi-constraint to deal with climate problems more accurately. By experiments, it is proved that our method is correct and valid.
In the area of location service, the location information of user based on context-aware play an important role in analyzing, processing and sending location information service. That is, if using the context about the environment around the user well, we could analyze the request of the user better and provide the most appreciate service to the users in time which could indeed meet the users' request. However, how to store and manage context information, we have not a uniform model and standard yet. Moreover, there is no specific model designed for the location context information of user. This paper applies the 5-ary model which is designed to express context information to propose a new method specifically for the location information based on context-aware of the user. The model is: User Context (< User >,< Time >,< Location >, < Surroundings >,< Demand >). This model could state the five key information elements, that is user information, location information, time, surroundings information and user demand information. These five elements are direct and independent. Moreover, they could be acquired easily. The user information may include name, sex, job, major of the user and so on. The location is made up with two parts: textual address and coordinate. The coordinate information is usually gathered by GPS. The surrounding information is about weather and temperature which could be received from the relevant website. And the demand information is text which is used to describe the user's demand or request, mostly about restaurant, shopping, entertainment and so on. Then, store the users' location context information into a database in the way of the 5-ary to improve the query speed of data. In the end, through extracting the keywords in the demand information with TF-IDF method, we can conclude the inclination of users. Based on time and location information, we could also acquire some initial conclusions including the trend of demand information from users, the analysis of user behavior, hotspot analysis and the analysis of users' interest.
Human population distribution is an important parameter to science research, but the security of population data leads to the dilemma that many researchers cannot acquire the population distribution figures, which is of significance in their work. To solve this problem, this paper concludes the rules of population distribution pattern and determines the statistics data, administration divisions, evaluation, and the distance to coast, city center and river, as the factors of this pattern. Based on this pattern, an algorithm is built to simulate the spatial distribution of population by the distance calculating, reclassifying and weighted calculating. The algorithm is transformed into work flow model which simulated the population spatial distribution of Fujian in 2011 and is improved in the way how the data was classified and how the edge between counties was manipulated. After extracting slope information from the DEM and calculating the distance to county centers, city centers, rivers and the coast, the model classified the slope DEM and distance data into different types by the 1/4 Standard Deviation (to decide how many categories should be classified) and Quantile (to decide the range of every classifications). Then, a general effect cost raster was worked out. Following the summary of the maximum and minimum of the cost in each county, the premier allocation of population was reckoned upon the normalized effect cost raster. However, the edge between counties and the discontinuous between raster cells lead to the improvement of classify method and the interpolation of statistics data. This is improved by the continuous classify method, the calculation of effect cost and weighted population density. The simulated result shows population gathers around cities, along the coast and rivers, and on plain areas. What is more, the data inside counties emphasis its center effects and the data at the edge is connected with adjacent counties in a continuous way, which is more likely to comply with the population distribution in reality. It is about 89% and 84% of the total number of the counties whose errors of the total population and average population density in each county are below 40%. Compared to the average population density of each county, the simulation method that considers terrain is more suitable for researches in other fields and is more understandable.
Data adopted was the official near the ground surface (975hPa) CO2 concentration L4B retrieval data products of GOSAT-the world's first Greenhouse Gases Observing Satellite "Breathing No." - with a passive infrared detector (TANSO) from June 2009 to May 2010, which was jointly developed by Japan Aerospace Exploration Agency (JAXA), Japanese Environment Agency (MOE) and the Japan Institute for Environmental Studies (NIES). Based on the ArcGIS Geostatistical Analytical Method, authenticity inspection was processed by comparing the data mentioned above with the ground observation data of the Global Atmospheric Background Station (Waliguan), in order to analyze Chinese CO2 concentration spatial and temporal variations near the ground surface. The results show that: (i) Chinese CO2 concentration near the ground surface is concentrated and has a high value in the East, a low value in the West; (ii) Chinese CO2 concentration has an obviously seasonal variation characteristic that monthly average concentration has a largest amount in April (spring) and a smallest in July (summer); (iii) Combining with 2009 Chinese population density, total energy consumption (coal), GDP and other auxiliary data from the China Statistical Yearbook 2012, we find that the reasons of Chinese CO2 spatial and temporal distribution near the ground surface are various, and cannot be attributed easily to human or nature.
In a period of rapid urbanization, the urban fringe changes most in the spatial structure and changes fastest in land-use with the urban expansion process, being characteristic in transitivity, dynamics and complexity. Studying on spatial evolution of urban fringe areas is of great significance for understanding urbanization and optimizing urban space structure. Based on summarizing the historical origins of Haidian District space development, this paper empirically analyzes the space expansion of urban fringe in Haidian District since the 1980s, by using RS / GIS technology. Combined with systems theory, it approaches to explore the factors which affect the space evolution of urban fringe in Haidian District. This study finds that, the spatial development of Haidian District has a unique geographical advantage and cultural origins, which is constantly moving and changing, under the effect of economy, population, transportation, policy and other factors combined, featured in irregular distribution along the ring road, more blurring boundary, overall moving to the northwest, annual growth rate first increased and then decreased while total area is increasing, expanding outwards mainly from 1995 to 2007 while internal filled mainly in other phases of development, regional differences such as spatial distribution and expansion intensity of different townships having different characteristics. This paper empirically verifies technical feasibility of using RS/GIS techniques, combined with systems theory to study issues related to the urban fringe. With the use of RS/GIS techniques for data collection and analysis, it improves scientificity and credibility of the research owing to a relatively objectivity, and providing direct support to the analysis on spatial evolution characteristics and influencing factors.
The research of forest ecosystem service value is very important for the construction of green GDP, the formulation of Ecological Compensation Standards, the improvement of people's environmental protection consciousness, and so on. With the development of GIS (Geographic Information System) and RS (Remote Sensing), more and more researchers apply them to the estimation of ESV (Ecosystem Service Value), so as to realize a dynamic, fast, and space discrepant estimation of ESV. In this paper, based mainly on satellite imagery, geo-spatial data, site record, and combined with GIS and RS technology, we firstly calculated NPP, evapotranspiration, soil conservation amount of Zhejiang's forest ecosystem in 2010. Then, we calculated the value of carbon sequestration and oxygen release based on NPP and Afforestation Cost Method, the value of organic production based on NPP and Energy Substitution Method, the value of water conservation based on precipitation, evapotranspiration, and Shadow Project Approach, the value of nutrient recycling based on NPP and Shadow Pricing Method, and the value of soil and water conservation based on soil conservation amount and Opportunity Cost Approach. In the end of this research, the monthly variation of ecosystem service value is analyzed. The analysis results showed that: based on the technology of GIS and RS, we can estimate the value of forest ecosystem service value dynamically and fast. In 2010, the total quantity of different ESV provided by forest ecosystem of Zhejiang Province varied in different months. The biggest one from January to June and December is the value of water conservation; however, the biggest one from July to November is the value of carbon sequestration and oxygen release. Different kinds of Ecosystem Service Value changed regularly from month to month; the monthly variation of carbon sequestration, oxygen release, organic production, nutrient recycling, and soil conservation could be fitted with downwards parabola; the monthly variation of water conservation could be fitted with upwards parabola. The research of this paper has important significance for the establishment of the green GDO accounting system, monthly ecological compensation of the returning farmland to forest project, the improvement of people's environmental consciousness, etc.
In this paper, we analyzed the influence of city development on the regional water conservation function in Wuhan City Circle. Taking the factors of vegetation, meteorology, terrain, soil and man-made into account, we respectively selected vegetation cover, precipitation, evaporation, slope, slope length, soil thickness and coverage types as the assessment indicators of water conservation function importance. Based on a combination of the expert judgment matrix and AHP, the index system was built. Furthermore, we applied RS and GIS spatial analysis technology to analyze the dynamic change and the spatial distribution of each factor importance on the water conservation function, and then comprehensively assessed the function importance. The results showed that the intensity of the water conservation function importance increased first and then decreased from 2000 to 2009 in Wuhan City Circle, the area of the most important water conservation function increased from 9283 to 16 131 km2, but then reduced to 11 258 km2, the area of the general important water conservation function increased dramatically, especially from 2005 to 2009 it grew by 1.1 times. The Northeast of the city circle and in the Southeast of the hilly areas were much more important, and the factor of human activities that caused the changes in vegetation cover and coverage types played an important role on the water conservation function in short time. Recently, lots of unreasonable activities, such as deforestation, grass destruction, and farming around the lake turned out. Because of a significant reduction in water area, forest and grass owning to urban expension, the water conservation function changed. The main reason why the water conservation function declined between 2005 and 2009 in Wuhan City Circle was that human being explored land irrationally which directly made a reduction in vegetation cover, and in areas of lakes and wetlands.
The spatial and scale characteristics of metropolitan areas make an effect on the expansion of cities and the coordinated development between areas in a certain extent. Firstly, based on fractal theory, this paper used the cluster dimension and correlation dimension to analyze the spatial characteristics of the Urumqi urban system. Secondly, utilizing social and economic statistical data of 2007 and 2011, combined with the Zipf model of fractal theory, and through comparison with other Chinese metropolitan areas, this paper used grade-scale dimension to analyze the change of fractal characteristics related to grade-scale of the Urumqi urban system. The results showed that the Urumqi urban system has already formed the self-organization of space fractal characteristics. It presents a state of agglomeration, with Urumqi as the center. The cities within the system keep a close relation and strong interactions. The grade-scale difference of the total retail sales of social consumer goods showed a trend of widening, and the factor of the non-agricultural population, GDP and general budget revenue showed the opponent trend in 2007-2011. In all, through the analysis we can see that the spatial system of the Urumqi Metropolitan Area has been formed, though it's still in an immature condition. The center city of Urumqi plays an important role that it developments fast and strong itself, though, its leading role to the surrounding cities is weak and the radiation effects dose not act well. In short, the grade-scale and spatial structure need a further improvement and optimization so as to promote a coordinated regional development.
The South China Sea accounts for two-thirds of ocean land in China. It is not only the natural barrier of China's national security and important port and strategic channel, but also an area of the important energy, resource base and the prevalent geopolitical economic problems in our country in the future. With network technology and GIS technology, this study focus on spatial distribution and correlation analysis of the development trend on disputes of the South China Sea for providing auxiliary decision support. Results show that: (1) the method of document research and web crawler can quickly and efficiently obtain geography data about historical disputes in the South China Sea. GIS can carry on the space-time changes to geographic data in depth; (2) In terms of the time distribution of data, disputes of the South China Sea present a significantly increase over time, and in individual years appear a spate of major of the South China Sea issue; (3) In terms of spatial distribution, disputes in the South China Sea about Vietnam and the Philippines are much more, while other neighboring countries are relatively less; And (4) the claimed areas by countries such as Vietnam, the Philippines and Malaysia are mainly located in the Nansha Islands. They distributed clearly, and overlapped partially. This study also reveals the availability of the network technology and GIS technique, enriches the ways, and provides new ideas and methods for historical geography research. At present, the preliminary analysis of the historical disputes has been completed. In the next step of historical geography study, we will mainly focus on independent events in disputes of the South China Sea, from a deeper level to reveal the change situation of the geography of history events, in order to provide support for the decision involved in the South China Sea.
Over-exploitation of resources resulting in environment severely damaged, making the resources and environmental carrying capacity declined significantly. Research on carrying capacity of resources and environment has become popular and a trend in ecological environment research. Moreover, it has great significance to economic activity of human society. Regarding resource and environment as a complex ecological system, this article established the integrated index system of resources and environmental carrying capacity based on multi-level structure of target layer-criterion layer-index layer, which contains three criteria level indicators, eight integrated indicators and the integration of a series of basic indicators index system with the resources and environmental carrying capacity as the target layer. Through a comprehensive analysis of the linkages between indicators and their contribution to the overall goal, several integrated indicators model, socio-economic development level model, resources carrying capacity model, environmental vulnerability degree model, resources and environmental carrying capacity composite index model was constructed. Regarding grid which distributed evenly and continuously with the same size as the basic unit of analysis, a systematic and quantitative techniques methodology was formed through the use of GIS technique. In addition, calculation of index data in grid cells and visualization of the resources and environmental carrying capacity was achieved. The results reflected the continuity and gradual change of spatial distribution of resources and environmental carrying capacity, improved the mutability generated by administrative divisions, i.e. the unit of analysis. By the empirical analysis of Yanggao County in Shanxi Province, the validity and operability of the model and method was verified due to the consistency of the results with the actual situation.
Geoscience is a science of data-intensive research, whose process is dependent on the data resources and information platform. Especially for earth system science, in order to understand how the earth runs as a whole and to reveal the driving force of global environmental change, it is urgent to implement data sharing. Early geoscience data sharing services are primarily of data concentration and archiving by the government behaviors, which has many prominent problems such as imbalanced load of data service, simplex pattern of integrated data, indistinctive effect of data services, etc. We aimed to set up a new geoscience research environment that every researcher is both the user and the contributor of geoscience resources. With the present of Web2.0 and the emergence of cloud computing technology, data sharing mode has undertaken great changes. This paper presented the concept model, function structure and technology system of earth system science data sharing based on cloud computing, and built a prototype system of "geoscience data sharing services based on cloud service". This system embodies a transition of service mode by Infrastructure as a Service (IaaS), Data Resources as a Service (DaaS) and Data Software as a Service (SaaS). It ensures data security and realizes the Data Access Load Balancing based on the master-slave data storage technology. In the cloud service system of data sharing environment for geosciences research, everyone is both data producer and user. It can solve the data sharing contradiction between "user and data" by providing data publishing, data needs publishing, data finding/sharing, data needs finding/feedback and other functions. Finally, through the study of earth system science based on cloud computing and the key technique of data sharing we have built a prototype system to validate the framework. The cloud service system can improve the sharing and utilization of resources, enhance the on-demand service mode and provide an extensive and compatible scientific data sharing environment.
Currently, it is one of the most challenging issues to discover and organize diverse distributed geospatial services for geosciences problem resolving, knowledge innovation and sharing. These services include geospatial data services, geospatial analysis services and geospatial data mining services, etc. Facing this challenge and considering the key points of geospatial information processing, knowledge discovery and sharing, in this paper, the concept of geospatial knowledge cloud is depicted, and a novel cloud-based geographical knowledge service platform named as GeoKSCloud is proposed. Based on cloud computing technology, GeoKSCloud tries to create a unified framework to aggregate a broad variety cross-node and cross-platform geospatial services for end-users. With aim to deal with the compute-intensive and data-intensive challenge of geospatial data processing, the platform adopts the idea of virtualization to construct a scalable computation environment. Five main components of data aggregation, service management, geosciences problem solving, platform control and portal are designed to provide functions of services registry, discovery, composition, execution and data integration. Moreover, supported by natural language understanding, ontology and data visualization technology, the platform offers intelligent reasoning and visualization tools to help users to perform problem solving task more efficiently. The key technologies associated with platform realization are discussed, which includes massive geospatial data cloud storage and management technology, knowledge service management and composition technology, and intelligent geospatial problem solving technology, et al. Finally, a use case of historical seismic influence field analysis is proposed to demonstrate the interoperation of platform components, and the representative user interfaces of platform are illustrated. The case study reveals that GeoKSCloud could reduce the complexity and overhead of geosciences problem solving by coordinating multiple distributed and heterogeneous services.
At present, all kinds of large-scale spatial databases have been developed in China, which adopt the international standard of map dividing method and coding scheme. Compared with the common database, the volume of spatial database are massive in terms of spatial location, which heavily hinder the efficiency and effectiveness of data processing. Thus, how to manage massive spatial data effectively and efficiently has been the problem urgently to be solved. In recent years, with the rapid development of geographic information system (GIS) technology, spatial database management efficiency is greatly improved, which has become an important tool for spatial database management, analysis, acquisition, processing, storage, updating and maintenance. In order to satisfy the need of production and management of spatial data, this paper discuss a method of map dividing of spatial database, functions of common spatial database management and processing operation are integrated in the spatial database map dividing tools. The main functions of the spatial database map dividing tool are to visualize the storage status of spatial data, representing topological relationships, projection, spatial reference, various map scale, feature type and geometry type, as well as automatically drawing the border of standard map according to different scale. In addition, the extra information of the spatial data could be retrieved and displayed by feature type, geometric type of spatial data, which traditional method of map dividing could not achieving. Furthermore, based on the design of the logical structure of data storage, and to realize the optimization strategy of spatial data storage and retrieval, this method provided massive date retrieval technique and parameterized scheme for spatial database management, the application of map dividing in Management System for spatial database of Shanxi province shows that it provides an effective and efficient method of the manipulation and management of spatial database.
HJ-1A/B satellite, designed for observing the environment of water, atmosphere, and ecosystem, was launched successfully on September 6th, 2008 from Taiyuan Satellite Launching Centre. Its products have been widely used in many application areas, such as agriculture, forestry, land use, ecological environment and natural disasters, and greatly improved the ability of environment and disaster monitoring. There is a 4-band CCD camera boarded on both HJ-1A and HJ-1B platforms with the wavelength range from visible to near-infrared spectrum. Different bands need to be combined together while retrieving the application products. The error of Band-to-Band Registration (BBR) is a significant parameter which can impact the image quality and product accuracy. Therefore, calculating the BBR of CCD is important for enhancement of its usage. In this paper, an approach based on ground features is presented to compute BBR of HJ-1A/B CCD. In our approach, Xin'an reservoir located in Dongying City, Shandong Province is selected as the target area, within which several water and dam strips are interlaced. In satellite images, the responses of water and dam have a strong contrast feature, which forming continuous peaks and troughs. There are three steps in our algorithm. Firstly, we used the reservoir observation images in each band of CCD sensors to extract the pixel reflectance and pixel position to calculate the real position of the ground measurements. There are three procedures in second step, data preprocessing, radiometric calibration, and atmospheric correction. Finally, according to the deviations in the image of each band, the BBR was calculated in both along scan and along track directions. With several study cases, that the BBR of HJ-1A CCD1 in both along track and along scan directions is about 0.2 pixel for all bands except the band 2 whose along track BBR reaches 0.36 pixel and along scan BBR reaches 0.55 pixel; the BBR of HJ-1B for all bands is almost between 0.2 to 0.3 pixel.
Marine oil spill detection has become a worldwide issue. Traditional oil spill detection algorithm only depended on the spectral or texture has low detection accuracy. This paper presents a new method of the oil spill detection based on spectral and texture from optical remote sensing data. The spectral feature is oil slick sensitive band of the optical remote sensing data and the texture feature is got by gray level co-occurrence matrix. The model used support vector machine method to establish the spectrum and texture oriented oil spill detection method with these features. This paper used the MODIS optical remote sensing data to detect the oil spill in China Bohai Sea in 2006. The oil spill information can be shown on this band clearly because the variance of the sea water spectrum is less than the contrast of oil-water spectrum in the MODIS band 2, so we called the MODIS band 2 is the oil slick sensitive band, which we selected as the spectral feature. We obtained eight texture eigenvalues of oil slick and sea by gray level co-occurrence matrix in the MODIS band 2. Then three texture eigenvalues include mean, contrast and correlation were selected by analysis. Based on the selected sample, the detection accuracy was up to 91.32% and the Kappa coefficient is 0.7125. In contrast with spectral and texture integration Maximum Likelihood method and SVM method only use the spectral feature, the detection method we proposed is superior to these methods. The results showed that the method to combine the spectral feature and textural feature of MODIS data can effectively extract the oil slick in the Bohai Sea, and has a strong ability to suppress noise.
Land cover/use change in Taizhou City is studied in this paper. Due to human or natural factors, land cover/use constantly changes. With the development of remote sensing technology, change detection is one of the important applications on resource and environment remote sensing. In this paper, we propose a Change Vector-PCA analysis method based on CVA to extract change information in the study area. First, we compute the relative difference between t1 and t2 remote sensing image of Taizhou to obtain the difference image, then employ PCA to this difference image and select principal components for the extraction of change information. Next, the image objects are obtained by multi-scale segmentation combining with the spectral and spatial characteristics and suitable segmentation scale. And, the change information is extracted automatically with the rules derived from a classification tree on the basis of Change Vector-PCA analysis of the objects. Last, we have an accuracy evaluation according samples which reaches the mapping requirement. The reason of missed or false detection is that some land cover types is significantly affected by the reason, such as farmland, grass. The result shows that Change Vector-PCA based on objects is more superior than the traditional methods based on pixel in terms of utilizing the spatial information and avoiding "salt" noises; and this method can compare several principal components transformed by multi-band difference change information. Change Vector-PCA combines the advantage of CVA and PCA. It is significant in improving the accuracy and automation level for change detection.
Compared with traditional methods for eco-environmental assessment, the method based on remote sensing imagery is more convenient and does not require huge amounts of statistical data. Satellite remote sensing has the advantages of efficient, economical, macro and dynamic monitoring of land surface and therefore is an excellent way in providing data for eco-environmental assessment. Scholars have deeply studied how to extract ecological environment factors from remotely sensed data. In this paper, normalized difference vegetation index (NDVI), land surface temperature, slope, aspect, land use and wetness index were selected as evaluation factors, and the eco-environmental quality of Ruoergai County of Sichuan Province in the years of 2007, 2009 and 2012 is assessed based on multi-sources remote sensing images (TM, HJ1B CCD1/CCD2, ASTER GDEM, and MODIS) and GIS technology. Firstly, the evaluation factors were extracted from remote sensing images. Secondly, based on principal component analysis (PCA) method, the weight of each factor was determined and used to calculate the eco-environmental quality index of Ruoergai County with the comprehensive index evaluation model. Thirdly, according to the eco-environmental quality index, the study area is divided into four regions that are marked with four different degrees which corresponds to excellent quality, good quality, general quality and poor quality. Finally, the spatial analysis was integrated into the eco-environmental quality assessment. The result shows: (1) the global eco-environmental quality of Ruoergai County is at the level of good quality, for all of the average quality indexes of 2007, 2009 and 2012 are around 60 which represents good quality. (2) For a given year, the average and minimum values of eco-environmental quality index in September are both larger than that in May. (3) The ecological environment in the east region and middle region is better, while the northwest region is worse than others.
Snow cover is one of the active components of the cryosphere. Snow cover has a very important impact on the natural environment and human activities. Snow parameters (snow area, snow depth and snow water equivalent) inversion has practical significance to hydrological models and climate change research. However, the accuracy of snow depth inversion of remote sensing in the forest area should be further improved at present. Northeast is one of China's largest natural forest areas and important seasonal snow areas. This paper used L1 level brightness temperature data and L2 level snow water equivalent data of Microwave Radiation Imager (MWRI) mounted on FY3B satellite, and used field snow depth data in Northeast typical forest regions. Chang algorithm, NASA 96 algorithm and FY3B operational inversion algorithm were validated and analyzed. The results showed that, in Northeast typical forest regions, the retrieved snow depth of Chang algorithm and NASA 96 algorithm had large fluctuations. The performance of NASA 96 algorithm was better than Chang algorithm and FY3B operational inversion algorithm when fractional forest cover (f) was 0.6 or less, because the root mean square error value of NASA 96 algorithm was smaller than the other two algorithms. However, NASA96 algorithm had serious distortion when f was bigger than 0.6. Considering the pure forest pixel (f=1), Chang algorithm underestimated the snow depth of 47%. When f was 0.3 or less, FY3B operational inversion algorithm is better than Chang algorithm. On the whole, FY3B operational algorithm was relatively stable, and FY3B operational algorithm had higher accuracy compared with Chang algorithm and NASA 96 algorithm.
Fossil fuel shortages and environmental pollution problems are worsening, while globally, countries are actively seeking alternative energy sources. As an important renewable energy, bio-fuel (including bio-fuel ethanol and biodiesel) has attracted extensive attention of governments and enterprises throughout the world. Development and utilization of energy plants is one effective way to deal with energy issues and global change, and accurate information resource potential and the spatial and temporal important distribution is a foundation for resource planning and related research. The main objective of this paper is to establish an operational method for comprehensive evaluating biodiesel potential of Pistacia chinensis in China by combining GIS with life cycle analysis (LCA). Geographic Information System (GIS) is adopted as a tool to process spatial data from different sources, and establish the integrated multiple factors model. Meanwhile, LCA is used to evaluate the GHG emission from Pistacia chinensis plantation, yield acquisition, production, and product use through to post-processing. First, the potentiality, degree and spatial distribution of marginal land resources suitable for Pistacia chinensis planting are determined. Next, LCA-based analytical model of GHG emission reduction potential of biodiesel produced from Pistacia chinensis is established. By this means, the analysis and appraisal of maximum net GHG emission reduction potential of biodiesel from Pistacia chinensis can be achieved, thus offering technical methods and typical cases for analysis of development potentials and environmental benefits of biofuels derived from energy plants. The results indicate that total area of marginal land exploitable for development of Pistacia chinensis is about 19.90 million hm2, which is made up of the suitable area 7.10 million hm2 and the fairly suitable area 12.79 million hm2. And the total greenhouse gas emission reduction potential is 2.55 million tons per year.
Grasslands are important renewable resources in ecosystems of arid areas. As one of the important components of prairie ecosystems, aboveground biomass is the key indicator of health status of the prairie ecosystems. Comparing to the significant limitations of the traditional method of biomass, the satellite remote sensing provides a unique effective and efficient means in biomass monitoring and assessment. In this paper, we proposed a retrieval methodology for herbaceous vegetation biomass based on the vegetation structure characteristic and Michigan Microwave Canopy Scattering (MIMICS) model using the ASAR and TM data. A two-layer canopy reflectance model (ACRM) was used to inverse the Leaf Area Index (LAI) which can be easily retrieved from the optical remote sensing data. Then LAI was used to get the number of plants per unit area. The aboveground biomass served as an input parameter was input into the adaption of MIMICS model which adopted for characterizing the backscatter from herbaceous vegetation by deleting the scatter component associated with ground-trunks. Then, based on established equations which used the dual-polarized radar data, the aboveground biomass was calculated using the lookup table. The method was applied to retrieve the biomass of Wutumeiren prairie, Qinghai Province. Results indicated that the method was of the operational potential in aboveground biomass of the herbaceous vegetation in arid region. And a good accuracy of the biomass retrieval was achieved (R2=0.8562, RMSD=0.6263). Finally, we analyzed the error sources of biomass estimation using this method. The sources of error might be come from two aspects, i.e. error of the input model parameters, and the ill-posed inversion problem, for which, the mean value was used as the inverse results when the solution is not unique.
