Geographic features change detection has became a vital component of the national geographical information 12th Five-Year-Plan and the national geographic general survey. In web pages, billions of geographic feature changes were contained, especially in government official websites, news homepages, social portals and etc. The web pages of these websites update frequently, which could provide the latest data for geographic infor-mation change detection. Considering the complex characteristics of the web geographic information description, this paper did some valuable achievements. First of all, the geographic information knowledge base was established by summarizing the geographic information words and phrases, which could give the great supports to geographic information semantics change detection. Then, the web geographic information was obtained using two kinds of web crawler technologies. Combining the Google Custom Search crawler and general topic crawler, the web geographic information obtainment could be more complete in both scope and depth. Thirdly, the geographic information was parsed and extracted from the web text, which showed users the related features, place names, times and attributes. Last but not least, the prototype system was finally developed and the results were analyzed. The experiments indicated that the accuracy of related web pages obtainment and features change detection were over 74% and 70% respectively. In addition, the results of geographic information change detection highly relied on the integrity of knowledge base, which need to be completed further. Moreover, the uncertainty and fuzziness of web geographic information also limited the change detection results. Therefore, the web page based geographic information change detection could be a supplementary method of geographic information change detection. Combining the traditional surveying detection and remote-sensing imagery detection methods, it could solve the problems of continuous updating and timely updating of geographic information efficiently.
As a kind of spatial relationship, spatial similarity relationship is still in its initial research stage. Research works of similarity assessment of spatial group objects will improve spatial relationship's theory, deepen spatial cognition and raise the level of spatial data intelligent and automated handling. Focus on the spatial polygon groups, this paper proposed a novel computational model of geometry similarity measurement between spatial polygon groups. The conceptual neighborhood network of topological relationship was utilized to define topological relationship similarity between polygon groups. A suitable "dimensionality reduction" approach was utilized to process different polygon objects into line group. Then, the directional mean was utilized to define direction relationship between line groups, namely direction similarity of polygon groups. Finally, the circular variance was utilized to define distance relationship between line groups, namely distance similarity of polygon groups. Combined with the length, average length, area, average area, density and compactness of polygon groups, the final step established a computational model of similarity to comprehensively measure the geometry similarity of polygon groups. The computation model considered both geometry features and spatial relationship features of polygon groups, and a proper weights distribution was taking into account at the same time. Two experiments were taken out to verify the model. The experimental results showed that the computed similarity is consistent with ground object features and intuitive cognition of human beings.
Due to complex data structure, complicated spatial relationship and massive data volume, distributed spatial query is a time-consuming processing, which will cause high transmission and processing cost. Query processing method in traditional distributed database cannot satisfy the demands of query in distributed geospatial database. Therefore, new query methods in distributed geospatial database need to be studied. In this paper, the distributed spatial join query processing is deeply studied based on the existing optimizing methods of the conventional query processing in traditional distributed database, and a series of transformation rules of relational algebra expression based on cross-border topological join optimization rules are proposed. The processed query tree is optimized by equivalent transformation after data localization. The global optimized method of distributed spatial join query for different fragments is studied. The global spatial query can be transformed into some local fragments joins effectively. The spatial join query is processed in the local area, avoiding the data transmission of spatial data among data nodes during the processing of query, so that the query performance can be improved. To improve the efficiency of the method, some new concepts were put forward, including query merged tree and execution plan tree, which can optimize the executing path of query plan. For example, by adjusting the executing order, some processes with low cost execute first, and the time-consuming processes execute based on the result set generated by the previous processes so as to reduce the process of time-consuming parts and resolve the problem of high cost of query processing to improve the performance of distributed spatial query. The experiment based on the vector data of China shows our methods can reduce the cost of the spatial join and data transmission among the nodes, and the performance improve 28.5%, which demonstrates that our methods outperform the traditional methods in terms of both algorithm complexity and the running time.
Land-use map generalization may cost much time for cartographers, so scientists constantly hope to find some methods automatically and have made a lot of efforts, but got few progresses. Right now, people mainly focus on the theory and algorithms of generalization. In fact, the topological relation does great restriction during map generalization. Firstly, while beginning work, operation types of generalization may be chosen according to topological relations between the parcels and other limits (such as Land classification neighborhood), e.g., they may be aggregated when they are adjacent, or amalgamated when disjointed, or reduce to points when inclusive. Moreover, based on the theory of point set topology and representation of Intersection Model, which is well known in GIS, we draw some topological rules from generalization of parcels and linear features of land-use map. E.g., when a long parcel (road or river) is generalized to be a linear feature, its neighboring parcels must be extended synchronously to make sure their topological relations unchangeable. Finally, the possible applications of these topological rules are discussed, as follows: These rules can be applied to check exactly the topological relationships between objects in land-use map before or after generalization, and used as the foundation of generalization algorithms. However, there still are many problems to be studied and improved in future work to make these rules more strictly. We also suggest making topological rule database apart from the algorithms, as rules are unalterable for some time while algorithms are various.
As a new surface modeling method, the HASM model can improve the spatial surface simulation significantly. However, the slow solution speed limited the wider application, at the same time, there is little analysis about how to select proper HASM algorithm for practical application because so many HASM algorithms were put forward in the past years. In order to improve the HASM model on speed, in this paper we improved the HASM-AD algorithms. Firstly, index information of sample points were added into the solution process, which could improve the solution speed greatly. At the same time, the computation of the first fundamental coefficients, the second fundamental coefficients and the Christoffel symbols of the second kind were moved to the calculation process of the independent units, which could decrease the memory demand. Mathematical test show that the improved HASM-AD indeed andvance the speed and meanwhile save the EMS memory. At last, in order to test the accuracy and speed advantage of the improved HASM-AD, the improved HASM-AD was applied into the rainfall distribution simulation of the China. The simulation result shows that the improved HASM-AD not only improves the simulation accuracy, but also improves the speed signicantly. For example, the HASM-PCG, another HASM algorithm used widely for its highe solution speed and accuracy, takes 1920 second, while the improved HASM-AD only takes 4 second and meanwhile abtain a better simulation result. So the HASM-AD indeed advance the solution speed greatly and it is proper to applied this algorithm into the large scale simulation of spatial variable.
Research of urban growth has focused on the two-dimensional flat space, while the development of the modern city is three-dimensional. So the development and changes of the modern urban space could not be accurately described by two-dimensional method. Therefore, the research of three-dimensional urban growth has great significance to the future development of the city. Based on the theory of self-organization in urban development, the urban growth simulating model using three-dimensional cellular automata (3DCA) which proposed by Bengguigui was improved. The center distance parameter and the traffic distance parameter were added to the model. And new three-dimensional urban growth models were set up: the center distance model and the transport distance model. The two establishment steps of the model are as follows: Firstly, described the calculation methods and the economic interpretations of all the model parameters. Secondly, gave the potential development function of three-dimensional urban growth and the transition rules of the cellular state. The three-dimensional urban growth simulating experiments based on the given models was developed by NetLogo 3D. NetLogo 3D is a programmable modeling environment for natural and social phenomena simulation, which could show simulating results both in two dimension and three dimension view. The experiment results show that the three-dimensional urban growth simulating with the models proposed in this paper is more approximate to the reality city extension progress than Bengguigui's model, and also prove that the urban development is a self-organized process.
Construction of 3D geological models is a very efficient way to create a good understanding of geological features. It is a comprehensive method to geologists, geophysics engineers and GIS experts who sometimes need to visualize an area to accomplish their researches. Geological drill hole records have been widely used as the main data sources in 3D geological model construction approaches. However, since the original drill hole sampling points are limited and maldistributed, it is difficult to construct 3D geological models that not only show favorable visual effect but also reflect accurately the geological structure of surveying regions. To solve this problem, this paper proposes a 3D geological model construction approach based on virtual boreholes. Firstly, according to the spatial distribution of original drill holes, positions of interpolated points are determined self-adaptively and Kriging interpolation algorithm is applied to the construction of interpolated virtual boreholes. Secondly, according to the pinch-out rules, the pinch-out virtual boreholes are constructed, and the improved self-adaptive butterfly subdivision algorithm is applied to the construction of subdivided smooth virtual boreholes. Finally, with the original drill holes, the interpolated virtual boreholes, the pinch-out virtual boreholes and the subdivided smooth virtual boreholes, 3D geological models of survey regions can be constructed, matching geostatistics patterns, being C1 geometric continued, and showing favorable visual effect. Differing from the traditional modeling methods that regard virtual boreholes as complements to expert knowledge, the proposed method introduces virtual boreholes into each step of the construction of three-dimensional geologic models as the intermediate elements of the core algorithm, simplifying the realization process of the modeling algorithm effectively, and ensuring the stability and efficiency of the algorithm.
There often exist contradictories between different sea uses at adjacent sea areas. So it is needed to carry out an automatic method to detect the sea use change based on the marine functional area classification and marine environmental protection requirement in China. According to the sea water quality standards of the marine functional zones, this automatic detection method firstly computes the different values of distance and time between any two classifications of the above water quality standards, then establishes the buffer detection algorithm and the historical sea use detection algorithm between them respectively. The buffer detection algorithm could detect two different types of sea use in the same time but the different spatial places, then judging whether there are contradictories between these two types. The historical sea use detection algorithm could detect two different types of sea use in the same spatial place but the different times, then judging whether there are contradictories between these two types. Based on the above two detection algorithms, Microsoft Visual C# and the geographic information system development component ESRI ArcGIS Engine, the automatic detection function of new marine parcel adding and original marine parcel changing has been built. At last, we took Shanghai marine function zoning data as a case, and proved that as one of the core functions of marine cadastral spatial information management system, this method can realize automatic detection function of sea use.
Recently, the air quality is deteriorating continuously, do serious harm to human health. As one of the major primary pollutants produced by human activities, real-time monitoring of the spatio-temporal characteristics of NO2 and its content can make a big difference to the air quality studies and public safety warning. This research studied the OMNO2e data between January 2005 and December 2010 from the Ozone Monitoring Instrument and the human society factors in China, explored the spatio-temporal characteristics of NO2 in China and the anthropogenic influences on it. The results showed that: the average NO2 column concentration in China has a significant upward trend from 2005 to 2010, the total column concentration in the atmosphere increased by 0.61×1015 molec/cm2, with an average annual growth rate of 2.47%; while the tropospheric column concentration increased by 0.63×1015 molec/cm2, with an average annual growth rate of 5.69%. The NO2 column concentration in most areas of China increased rapidly, and the growth rate in eastern China was much greater than the less developed regions in western China. The spatial distribution of NO2 in China also shows a significant difference. In general, it presents the characteristic of higher in the east than that in the west. Besides, NO2 column concentration in winter was significantly higher than that in summer in eastern China, but in western, the highest value appears in summer. We also studied the relationship between every season's spatial distribution of TropNO2/Tot-NO2 and the human footprint. The result showed a good consistency between them: the correlation coefficients between TropNO2/TotNO2 and human footprint is maximum in winter, about 0.59; and its minimum value appears in summer, about 0.48. And the number of samples is 56166×2, indicated that the influences from human activities were maximal in winter and minimum in summer. The correlation coefficients between car ownership and TropNO2, gross regional production and TropNO2 had also been calculated. We found that the coefficient between car ownership and TropNO2 is 0.95, with a significance test at 95 percentile of confidence level, and the correlation between gross regional production and TropNO2 also very strong. The increasing of NO2 in China has remarkable relevance with human activities and social economic development.
After the occurrence of an earthquake, different surface features at different seismic intensity areas are exposed to individual degrees of damage. The quick obtainment of accurate seismic intensities and their spatial distribution is an important basis and parameter for the post-earthquake emergency rescue. To construct seismic intensity rapid reporting system as a cloud service applying cloud computing technology and provide scalable, configurable, transparent seismic influence field analysis application service, will be a new technology and trend for the development of seismic intensity rapid reporting systems. Based on a geographical knowledge cloud service system, namely GeoKSCloud, which is developed innovatively and independently by our research team, this paper carried out the design and implementation of a cloud service system for seismic influence field analysis. By taking Fujian Province as the study area, seismic intensity attenuation relationship and peak ground motion acceleration site correction method suitable for Fujian area are firstly analyzed, and then the working mechanism and workflow-based service composition of their cloud service are proposed. To implement the cloud service, related atomic services are realized, service-oriented packaged and published on GeoKSCloud system (such as seismic intensity attenuation calculation service, PGA site correction service, seismic site property obtaining service for grid points, service for coordinate transformation along the seismic attenuation axis direction for grid points, et al.), data services for supporting spatial datasets are published as well, and also a Web GIS based cloud service portal are developed and integrated with GeoKSCloud. Finally, an analysis case of seismic influence field analysis is carried out on the constructed cloud portal, with the offshore 7.5 earthquake occurred in Quanzhou area in 1604 as the real case. The peak motion accelerations (including bedrock PGA and ground PGA) for all equally spaced grid points within Fujian Province are calculated via the cloud service proposed. Based on the PGA values of the grid points, the spatial distribution of PGA values covered Fujian Province was also interpolated and further comparative analyzed with the actual survey data. The results show that, the seismic influence field analysis cloud service presented in this paper, which has taken account of the site-specific amplification effecting, can provide more precise seismic influence spatial distribution than the traditional method which consider only the seismic intensity attenuation relationship. The cloud service system with distinct advantage features of cloud service, such as scalable, configurable and transparent, would provide a common technology and a support service platform for intensity rapid reporting system research and application, and also give an innovative operation pattern for public service of seismic intensity rapid reporting.
Assessment of eco-environmental vulnerability can be taken as a strategic basis for the comprehensive improvement on a degraded system. North Shanxi Province was a constituent part of farming-pastoral ecotone in north China, was characteristic of vulnerable under integrated impact of natural and human factors. In this paper, based on remote sensing data and geographic information system software, combined with spatial principal component analysis and analytic hierarchy process (AHP), an integrated method was used to evaluate the eco-environmental vulnerability of North Shanxi Province. The results show that the vulnerability of the eco-environment in North Shanxi Province is characteristic of imbalance in general under integrated influences of natural and human factors. The northeast part is evaluated as the most vulnerable region, which occupy 33.1% of the total studied area. The southwest part is the slightly and moderately vulnerable areas, which occupy 49.1% of the total studied area. And the lightly vulnerable areas scatter throughout the entire region. Totally, the moderately and lightly vulnerable areas occupy 55.5%. This indicates that a majority of the region owns moderate and light vulnerability. The vulnerability in this area is mainly caused by nature factors, and human activities are the critical external reasons to cause the variable of vulnerability degree. The natural factors affecting the eco-environmental vulnerability of the North Shanxi Province are aridity, normalized difference vegetation index (NDVI), water and soil erosion rate, etc., whereas the human factors are land use, proportion of the second industry accounting for GDP, index of environmental protection investment and water source amounts, etc. The main characteristics of the extremely and heavily vulnerable regions are aridity, low NDVI, serious water and soil erosion, unreasonable land use, high proportion of the second industry accounting for GDP, low environment protection investment index and few water source amounts. Degree of eco-environmental vulnerability in North Shanxi Province will drop with increasingly rational human activities and more and more environmental protection investments. Results of this research can provide a basis for rational regulating and controlling of human activities and eco-environment protection and improvement in North Shanxi Province.
We selected 380 state owned commercial bank outlets and 211 nation-wide joint-stock commercial bank outlets in eight districts (located in south of the Yangtze River) of Nanjing as the study samples. Using buffer analysis and Ripley's K (d) function and spatial hot spot detection method, we analyzed the distribution characteristics of these outlets. Firstly, we studied the density characteristics of these outlets in different buffer zones by the spatial analysis module of ArcGIS 9.3. Secondly, Ripley's K (d) function is applied to measure whether the commercial bank outlets are clustered distribution and their cluster range. Thirdly, we used hot spot detection method to detect the commercial bank outlets clustered hot spots in the eight districts. The results showed that, generally speaking, both of the commercial banks tend to cluster in the center of Nanjing, while the tendency towards the CBD differs. Joint-stock commercial bank outlets show a decreasing tendency from inside to outside, which accord to the circle structure with Xinjiekou as the center with 2 km, 4 km, 6km and 8 km as the radius buffer zones, while state owned commercial bank outlets show a homogeneous distribution in the first three buffer zones. Outlets of both the banks show the way of increasing first and then decreasing as an inverted "U" -shaped structure, but the spatial scale differs, i.e., state owned commercial bank outlets concentrated in a larger scale compared to joint-stock commercial bank outlets. Joint-stock commercial bank outlets show a "small area, large cluster" mode in Nanjing, while state owned commercial bank outlets present a "large area, low cluster" mode. By analyzing the overlaying zones of the hot spots, the location choosing of outlets of the two commercial banks can be divided into 3 types: business center oriented, labor-governmental agency-business center oriented and high-tech-business center oriented.
LUCC is a hot and difficult topic in the research of global change. Through the Mongolia Plateau LUCC research,we aimed to provide scientific and technological support for the upgrading of the regional land use structure and sustainable development. Based on EOA 300m land cover datasets,with reference to the remote sensing images in 1970s and 2005 of Mongolia Plateau,we established 1970s and 2005 land use dynamic database and used land use amount models(such as land use changes degree,land use change dynamic degree, land use change consumption reduction degree and land use change development degree)to analyze the process of land use type conversation between the Republic of Mongolia and Inner Mongolia,reveal the regional land use change characteristics and regularity,predict various land use types change,and analysis the land change driving force. The results are as follows:the grassland area decreased year by year,grassland degradation trend is evident;bare land area increases ceaselessly,the desertification phenomenon is serious;farmland and urban construction land area continue to grow;water area presenting an attenuation trend;unused land is the primary source of a variety of other types of land use increasing;forest land area of the Republic Mongolia slightly reduced, while Inner Mongolia forest land area increased significantly. Considering the special geographical environment and the influence of national policy of the Mongolia Plateau,especially the environmental protection policy and the rapid economical development in recent years,land use changes would become very complex. The dynamic changes of land use of Mongolia Plateau were mainly caused by climate drying and warming,policy change,economic development,population growth and so on. Natural factors determine the regional characteristics of land use pattern that main land use types are grassland and bare land in the Mongolia Plateau. Human factors determined the difference in land use dynamic change between Republic of Mongolia and Inner Mongolia over the past 30 years.
Identification of landslide should first understand what it is. Broad sense of landslide means all the slope gravitational erosion phenomena including rock fall, landslide, clastic flow and debris flow and so on; narrow sense of landslide means the phenomenon that parts of slope move searing along one or several surfaces internal the slope under the function of the gravitation. Each type of landslide has own special morphological characteristics on the ground and basic geological environmental conditions and trigger factors for growing. According to the special characteristics the landslide can be identified. Digital Landslide Technique suggested based on the research and practice of RS+GIS for landslide at home and abroad identification of the landslide can be divided into 2 steps roughly. First, with RS+GIS technique, ground features at different time were showed on digital images with different solutions. Through registration and combination with geological structure and geographic information, the base for remote sensing interpretation is established. Second, under the guide of theory of landslide, by means of men-PC interactive mode, the remote sensing interpretation and time-space analysis are carried out and the key elements information of landslide for disaster prevention then are obtained. That methods has not been reached automatic identification of landslide, but the process of establishing the base for remote sensing interpretation has been accomplished with several soft program procedure by PC, so it is considered that the first two steps, digitalization and preprocess for landslide pattern recognition have already realized by PC. Now we should explore the methods of landslide recognition and analysis procedure with computer calculation instead of men-PC interactive mode. For the narrow landslide sense, the landslide topography recognition has already been conducted by computer based on DEM. If the relation between landslide scarp, deposit body on the ground and landslide surface, bed under the ground could be found, their spectral property could be determined and the calculating mode could be established, the landslide pattern recognition based on the remote sensing technique then could be realized.
Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.
Three classification methods: maximum likelihood at pixel level, maximum likelihood at object level and a hybrid model of pixel based and object-based classification method were used to classify these two sorts of images, i.e., 30m resolution HJ-1-A CCD imagery and 0.5 m resolution aerial images. Spectral separability in training samples between different classes directly affect the classification accuracy; on the contrary, spectral separability between different classes in the classification result can be used to evaluate the accuracy of classification. Based on this assumption, classification results of these three methods were evaluated by spectral separability between different classes. Result reveals that compared with the maximum likelihood at object level, classification at pixel level can make full use of the spectral information in remotely sensed image. Analysis also shows that hybrid model can improve the classification accuracy. Multi-resolution image analysis proves that the hybrid model is suitable for applications at different scales.
Agricultural soil surface roughness parameters including surface root mean square height (rms h)and surface correlation length (cl)are key for effectively explaining radar backscattering coefficient and passive microwave brightness temperature, and its measurement accuracy is affected by measured method, measured instruments and data preprocessing. For surface roughness field measurement, the urgent problem is how to evaluate its measurement accuracy. This paper uses two-dimension surface height data from laser scanner and one-dimensional surface height data from Monte Carlo simulation, and analyzes the factors which influence the accuracy of surface roughness including sampling times, sampling interval, profile length, correlation function and large-scale structure (data skew and row structure). Four regions with different surface rough condition are used in this paper for analyzing surface roughness measurement method and accuracy. The result shows that, the measurement accuracy of agriculture soil surface roughness is about 80% under the condition of more than 20 samples, less than 10mm sampling interval and more than 200 cl profile length; the agreement between fractal correlation function and experimental spatial correlation coefficient is higher than Gaussian and exponential correlation function; and the skew and row structure of surface height data seriously affect the result of surface roughness parameter which needs to be removed before surface roughness calculation.
Grassland degradation is one of the important ecological problems in China. During recent years, influenced by global warming and increasing human activities, different degrees of grassland degradation have occurred in China, which has become a research focus in the field of grassland ecology. Based on the systematic summary of studies in the grassland degradation and weed invasion in the latest decade, the limitations of traditional methods of monitoring grassland degradation and weed invasion by remote sensing are put forward, and some important study directions and priorities for future are reviewed. Grassland degradation is often shown as decline in species of high quality forage and increase in the number of weed species at the community scale. In the process of grassland degradation, weed species invasion leads to the increase of vegetation coverage. And thus traditional methods of monitoring grassland degradation based on the decrease of vegetation coverage, productivity and grass yield are limited in detecting the change characteristics of plant population, especially the process of weed species invasion. The results of our research show that there exists great limitations in using traditional methods to detect the change characteristics of plant population, but the weed invasion monitoring method using hyper-spectral data can detect the weed species in plant population and retrieve the area proportion, height and coverage, through making full use of the ample spectral information of hyper-spectral data, and integrate field measuring using spectrometer with quantitative analysis of spectral difference of plant population characteristics on grassland degradation. The studies on methods of monitoring grassland degradation and weed invasion using hyper-spectral data can provide important indicators of community succession process and trend for grassland degradation monitoring and treatment. It can also effectively solve the defects in traditional methods of monitoring grassland degradation based on remote sensing and provide new remote sensing methods for monitoring grassland degradation and weed invasion.
Gangue, as one of the industry-specific solid waste produced from coal mining and coal washing, effects on around environment significantly. Therefore, monitoring of coal yard is essential for the management and protection of ecological environment. Before gangue yard detailed investigation, obtaining preliminary data of the location and area of gangue yard by remote sensing image is needed and good for subsequent investigations. This article took the image of Landsat5 TM, received in September, 2011 as data source and did the radiometric and geometric correction to the images. According to the composition and formation characteristics of gangue, we extracted the gangue yard with following two steps: firstly, got land classification information that is confused with gangue through spectral analysis and unsupervised classification; secondly, combined spectral information and terrain, temperature and other ancillary information of the study region, and used four methods, i. e., unsupervised classification, supervised classification, spectrum-photometric method and hierarchical classification respectively to extract the gangue yard of the study area. By comparison of the above methods, we found that the unsupervised classification and supervised classification methods had a faster data extraction but with low extraction accuracy. The accuracy of spectrum-photometric method is a little higher than the former two methods. The hierarchical classification method has the highest accuracy in preliminary data extraction, and the identification accuracy of the gangue yard is up to 78% after post-processing. The result basically meets the requirement on dynamic supervision of gangue yard. Thus, these methods are also useful, as guidance, to continuing extract data of the area and location of the gangue yard under high resolution remote sensing images. Especially, the hierarchical classification method is more suitable for gangue yard information extraction.
Land use is the main form of human activities which has profoundly changed the natural geographical environment, and its direct result is the situation of land cover change. The degree of land use not only reflects the natural attributes of soil, and also the combined effect of human and natural factors. This paper takes Mary Oasis, Turkmenistan, as the study area to establish a land use degree index model. We used two remote sensing images which are in the same phase and SVM method for land use classification, and gave the corresponding properties of each type of land use. Sampling in the study area based on regular hexagonal grids, area land use degree index in each grid cell is calculated by GIS. We made the index interpolation and the land use degree forecast for the entire study area using Kriging Method, divided the whole area into five levels, and analyzed the land use degree of Mary Oasis. It concluded: (1) grassland, arable land and abandoned land are the main available land use types in the study area, and they account for about 50%, but have a decreasing trend totally; (2) the zonal distribution of land use degree of the study area is from the center to the periphery, and the contour 0.5 of land use degree is about the boundary of oasis and desert of the study area; (3) the area of middle and higher land use degree of the study area reduce at the rate of 0.079% per annum, the area ratio of main oasis and desert also reduced from 45.14:54.86 in 2001 to 42.06:57.94 in 2010, indicating that the ecological environment of the study area has a trend of deterioration; (4) the self-transfer rates of low, moderate and high land use degree are all greater than 80%, while the self-transfer rates of slightly low and middle land use degree are around 50%. We got the spatial distribution and inter-annual change of the land use degree of Mary Oasis. And this work has a guiding significance for social development and the protection of the ecological environment of the study area, and has a certain referential significance for the future relevant research on the technical processes and methods.
