The difference in land surface features and land cover conditions have a great impact on anisotropy surface albedo distribution. Remote sensing is an effective means to study the land surface features by obtaining spatial and temporal characteristics of surface albedo. The northern slope of the Tianshan Mountains is well-known with its typical mountain-basin geomorphology pattern system and mountains-oasis-desert landscape. The unique and complicated background forms the special vertical distribution of the surface albedo. This paper examines the spatial distribution of surface albedo on the northern slope of the Tianshan Mountains using Landsat TM images. Topographic correction was implemented on the images using topographic normalization model based on DEM and atmospheric correction was completed using 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model. The results indicate: (1) The methodology for surface albedo inversed from the moderate spatial resolution remotely sensed data is reliable to be used for estimation of the surface albedo over the northern slope of the Tianshan Mountains with significant heterogeneity in elevation. (2) The distribution of the surface albedo on the northern slope of the Tianshan Mountains is affected by the land surface features, land cover conditions and surface soil moisture. Therefore, the spatial distribution of the surface albedo on the northern slope of the Tianshan Mountains presents a distinctly vertical zonal feature. As to the mountainous forest and the dry grassland in low mountain area, the surface albedo presents regular fluctuation under the effects of the undulating terrain and elevation changes. (3) Influenced by the cover types, the crop structure and the crop phenology of the cropland, the variation of the surface albedo of the oasis in the Sangong River Basin shows instability. In the oasis area, the surface albedo varies obviously with growth seasons of the crops as well.
Due to its simple structure and less input data, CA model of logistic regression is widely applied in urban simulation. However, data dependency has some impact on the accuracy. Therefore, an in-depth research should be conducted to modify the traditional model. This paper established an improved CA model of logistic regression in two major aspects. First, the urbanization factors were divided into forbidden constraint and general constraint. The input data were sampled only in general constraint, while the urbanization probability in forbidden constraint was set to be 0. Second, we reduced the data dependency of general constraint using principal component analysis in SPSS. In the case study of Guangzhou, the improved CA model was applied to simulate the urban growth from 2000 to 2008. Compared to the traditional CA model, the improved CA model made a 4% improvement both on model fitness and simulation accuracy, in which constraints division contributed a 3% improvement on overall simulation accuracy and a 6% improvement on non-urban simulation accuracy, while data dependency reduction gave a more reasonable explanation for urbanization mechanism. The study aimed to establish an improved CA model, which can mine a more reasonable urbanization mechanism, and provide more scientific support for urban planning and land management.
The spatial gradation of slope position has a great effect on the soil, hydrological, geomorphic phenomena and processes in small watershed or on slope. The fuzzy slope positions extracted with various methods can quantify the spatial gradation of slope positions and are considered as a kind of promising information to geographic modeling, such as digital soil mapping at finer scale. However, few studies have actually applied the fuzzy slope positions in geographic modeling. This paper attempts to examine the possibility of application of fuzzy slope positions in predicting spatial distribution of soil property at finer scale. In this case, two fundamental assumptions are made as follows: 1) terrain condition which can be comprehensively reflected by slope positions shows most important effect on spatial distribution of soil property in small catchment, and 2) soil property on typical slope position generally represents a typical value when soil property co-varies spatially with slope position. Based on these two assumptions, a weighted average model in which typical values of soil property on typical slope positions are weighted with fuzzy slope positions is developed to predict the spatial distribution of soil property (A-horizon soil organic matter in this study). The fuzzy information of a system of five slope positions (i.e., summit, shoulder slope, back slope, foot slope, and valley) was derived by a method based on typical locations of slope positions. The weighted average model was evaluated in a low-relief catchment (about 60 km2) of Nenjiang watershed in Heilongjiang Province, Northeast China. The multiple linear regression model based on topographic attributes was also applied to comparison of model performance. Three indices, i.e. correlation coefficient between predicted and observed values, mean absolute error (MAE) and root mean square error (RMSE) based on a validation set of 70 soil samples were calculated for quantitative assessment of the model performance. Results show that the weighted average method with very few modeling points can better predict the spatial distribution of A-horizon soil organic matter than the multiple linear regression model does.
Virtual tour is designed based on the virtual reality technique platform, Internet or other vehicles. The travel landscape is dynamic and vividly presented in front of tourists. Tourists may choose tour route and view speed and viewpoints by themselves. Virtual tour will play an important role in propagandizing, protecting and reproducing scenes and decision-making support. Currently, virtual tour system on the development of research methods are more broadly grouped into four categories: 3D panorama, VRML/X3D language programming methods (such as DirextX, OpenGL), Web3D business software. These methods achieve results, ease of extent and scope of preferences for different applications, each with merits and demerits. Virtual tourist attractions are composed of virtual ground model and the virtual features model. The virtual ground model has geographical features, only located in the virtual scene to reality; virtual features model is needed to simulate the reality, allows users to carefully watch from outside to inside. At the same time, virtual tourist landscape is needed to balance fidelity and display speed, which are also the shortcomings and a major technical difficulty of virtual tour. Virtual tour is the tourism-oriented virtual reality platform on network using 3D-GIS, virtual reality and network technologies. Throughout the building of virtual tour system, the most basic and most important part is the realization of virtual tour landscape, which is related to display effect and display speed of the virtual landscape, as well as the realization of spatial analysis, simulation scenarios and the other functions. Based on the characteristics and realization difficulties of virtual tour landscape, the paper separately built virtual ground and virtual features using ArcGIS and 3DSMax, which have different configuration characteristics. All the models were transformed to VRML file format, and the VRML files were optimized through many methods. Which not only effectively fused virtual ground models and virtual features models, but also gave attention to the fidelity and display speed of models. The paper advanced a suit of design program and key technology of virtual tour landscape, and validated it through exploiting virtual tour landscape of Yuanmingyuan Ruins Park. The paper formed research and exploiting methods and technology of virtual tour, which could provide strong support of digital, virtual, high-tech information methods and means.
It is an important strategy to build Sichuan into the ecological shelter of the upper Yangtze River. As the forest and grass fire prevention is one of the important parts of the construction of ecological shelter and ecological security indemnity in Sichuan Province, the evaluation of the forest and grass fire risk is of great significance to the forest and grass fire prevention. Therefore, the forest and grass fire risk grade was evaluated by using remote sensing, GIS and Analytic Hierarchy Process (AHP) in Sichuan Province. The study shows: the fifth fire risk grade accounts for 0.8%, the fourth accounts for 20.2%, and the third grade, 47.2% in Sichuan Province. The highest fire risk is mainly distributed in Ganzi, Liangshan, Aba, Panzhihua and Ya'an, accounting for 49.2%, 24.7%, 15.7%, 3.5% and 3.4% respectively of the total area in the risk zone. The high fire risk is mainly distributed in the prefectures of Ganzi, Liangshan and Aba, which account for 72.1% of the total area in the risk region. We suggest that Ganzi, Liangshan, Aba, Panzhihua and Ya'an should be regarded as the key areas of the forest and grass fire prevention and should be given priority in getting labors, funds and goods for forest and grass fire prevention.
Horizontal resolution, which directly determines the degree of the closeness for DEM representing landform, is one of key variables for grid DEM. It also has distinct effects on the accuracy of terrain parameters and geosciences simulation based on grid DEM. So many researchers focus on the study of how to choose or decide a suitable resolution. This paper puts forward a method of suitable horizontal resolution based on geostatistics and nonparametric density estimation, which combines macroscopic variance and microcosmic variance. Firstly, supports of various scales are made by dividing the sampled data with different grids. Then regularization theory in geostatistics is used to carry out regularization variation of different supports based on the elevation sampled data. In order to ascertain the optimal support size to express macroscopic spatial variability structure of terrain, semivariance at a lag of one support interval plotted against different support sizes. The support size at which the peak occurs may help to identify the predominant scale of macroscopic spatial variation of the raw data, so it is named optimal support in this paper. After that, the theory estimation of the optimal bin size that can estimate the probability density function is referred to decide the appropriate resolution in the optimal scale support. The resolution is the suitable grid size to express the microcosmic terrain variance. Finally, the method was verified in practice by taking the sampled data sited in the Loess Plateau which is in the north of Shanxi province. Anisotropy, compute efficiency, RMSE statistics and contour-matching are used to analyze the results. The paper shows that the results resolution meets the exact accuracy limits for the given quality index. It is proved that the method may serve as a guide to decide the resolution from sampled elevation point data considering variance information contents of the raw data and topographic expression. The paper did the experiments only by taking the data from the Loess Plateau for examples. Future work needs to involve other topographic data and other different scales. Also, methods for verifying the result resolution should be further considered.
Geographical Cellular Automata (GeoCA) is an efficient model to simulate dynamic geographical process, which has been illustrated by a great number of researches. Surprisingly, there are few researches discussed in detail on the scale sensitivity of cell size during the geo-cellular automata modelling process. Among existing researches using GeoCA, cell size is always decided by the resolution of geographical data source. Whether cell size will affect the simulation results of GeoCA or not is a hotspot. If it does, what is the causality of scale sensitivity of GeoCA? How does cell size affect simulation result of GeoCA? And how can we choose a suitable cell size during geo-cellular automata modelling? Few researches have achieved answers for these questions. In order to figure out effects on simulation result of GeoCA caused by cell size and its mechanism, and to provide a principle to choose cell size for GeoCA, we take land use change simulation of Hangzhou City as a case, and choose cell sizes of 50m×50m, 100m×100m, 150m×150m and 200m×200m to analyze the scale sensitivity of GeoCA. And then, by analyzing transfer rule, causality of scale sensitivity is discussed in this paper. The research result shows that (1) simulation results of GeoCA are sensitive to cell size, and the more fine the cell sizes are, the higher the accuracy of the simulation results; (2) scale sensitivity of GeoCA is not uniform in all cellular sizes, it is sensitive in some range of cellular size while it is insensitive in other ranges; (3) scale sensitivity of GeoCA simulation results is by large caused by increasing isolated cells. These isolated cells and cells around them have lower neighborhood function value, and then their transfer probabilities are also lower. With the increase of cell sizes, the number of isolated cells in cellular automata space increases. Because of lower transfer probability of these isolated cells, these increasing isolated cells caused higher error rate of simulation results of GeoCA. So, the number of isolated cells is the main cause for scale sensitivity of cell sizes when we do the geo-cellular automata modelling.
The function of fundamental spatial database is to avoid redundant collection of spatial datasets, to coordinate spatial data application, and to strengthen information resources management effectively and economically. Based on the geographic information system technology, database technology and spatial database engine technology, this paper put forward the technological framework to construct urban industrial layout fundamental spatial database. In this database, spatial database and industrial layout information database are logical disjunction but storage in the same relational database system. The data structure used in spatial data organization is Geodatabase model developed by ESRI. In the Geodatabase data model, feature dataset together with raster dataset are stored in the relational database system through spatial database engine. On the other hand, metadata database is established to benefit system management. Beyond this work, we also achieved a set of distributed database prototype system for the effective organization, management and applications of massive spatial data. This system is developed by Microsoft Visual Studio 2005 and Arc Engine 9.2 based on client/server structure with four modules, which are system management module, data warehousing module, query and analysis module and data management module. System management module provides the functions for user management, log management, database backup and recovery. Data warehousing module provides the functions for importing industrial information and spatial data into database, spatial data quality check, etc. Query and analysis module provides the functions for spatial query and attribute query, statistical analysis, while data management module provides the functions for data updating and sharing. With the aid of this system, data administrators can manage the industrial layout fundamental spatial database through network. Different departments and users can share their data effectively. Decision-makers for urban industrial layout can also achieve supporting information from the system.
This paper describes the spatial distribution of cancer mortality and explores the spatial hotspot of death cases in the study area, based on the 4 kinds of digestive tract cancer death surveillance data and the population data. According to it, the environment and public surveillance will be held in the next step. With basic layer Voronoi technique, global Moran's Index method and spatial hotspot exploration, the spatial autocorrelation index graph was drawn using automatic multi-dimension exploration, which describes the relationship between the Moran's I and the distance. The accurate parameter was identified under the spatial analysis technique and the distribution character of cancer mortality, which was used to observe the spatial cluster in this county with GIS. It had a remarkable positive autocorrelation in the 4300 meters in space. At the same time, three hotspots were confirmed as high value cluster, including 58 villages and a population of about 30,000 in each cluster. The crude death rate in the hotspots is significantly higher than that in other areas and the average level of the county. The spatial hotspot exploration and analysis, which imported the spatial weight matrix, made up for the deficiency of traditional statistical method in spatial information and spatial correlation. It offered the evidence for making the risk factor of high cancer incidence much clearer. And it is the necessary makeup for the traditional statistics.
The acquisition grassland snow information has important significance for the determination of grassland snow disaster influence scope and disaster grade. On the basis of the advantages and disadvantages of optical remote sensing and microwave remote sensing applied to monitor grass snow, this paper presents the business process monitoring real-time grassland snow in China based on effective cooperation between optical MODIS data and passive microwave AMSR-E data, which describes the correlated algorithms and implementation steps. Then, according to the Ministry of Agriculture requirement, we carried out an all-weather real-time monitoring of the snowstorm situation of grassland in Inner Mongolia, one of the worst snowstorm-stricken regions in China, from December 1, 2007 to January 2008, and finally, we obtained the actual result, based on 51 days of continuous monitoring data, from which we can learn the space-time state during the monitoring period of the grassland in Inner Mongolia. This paper presents that the multi-source remote sensing data for the fusion application algorithm and technical route in monitoring grassland snow, and implements all-weather real-time monitoring for grassland snow. This can be applied in vocational operation of grassland snow monitoring to promote the snow disaster and disaster reduction emergency response associatecl with advanced remote sensing application level and monitoring accuracy. This can satisfy the emergency needs of the national snowstorm disaster reduction.
This paper takes three main wetland distributing counties of Dongtai, Dafeng and Sheyang of Yancheng as study areas. With the processing of four-period (every six years from 1988 to 2006) remote sensing (RS) images, a dynamic change analysis of the Yancheng wetland types was illuminated at first. Then, according to the results of the RS images interpretation, the change prediction of the wetland types was analyzed by using cellular automata (CA) model based on extension matter-element model and Markov model. The results are shown as follows: (1) It is a feasible method in wetland types prediction according to the comparability (70%) after the comparison between the results calculated by CA model based on extension matter-element model and the remote sensing classification. (2) The results of CA model based on extension matter-element model is greatly consistent with the results of the results of Markov model, that is, the aquaculture farm is the main wetland type covering areas of 750.06 km2 and 740.20 km2 in 2006, respectively. Generally, the area of cropland, residential land, aquaculture farm, Spartina patens and seepweed has an increasing tendency, while the area of mudflat, reed and brine pan tends to decrease sharply. Spartina patens will become the dominant species gradually due to its evolution trend. The most important reason for these changes is the current policy of large-scale coastal exploitation.
Poyang Lake wetland, as an important wetland in the world, is known for its periodic variation of water level in a year affected by the Yangtze River and its tributaries of Ganjiang, Fujiang, Xinjiang, Raojiang and Xiushui rivers. It is very significant to understand the variation of water level for rational exploitation and conservancy of the wetland. This paper, taking Poyang Lake Nature Reserve as a study area, developed a model to simulate the water level and the status of submersion or emersion of wetland using DEM, multi-temporal images and multi-year water level data. First, a new water index FDWI was created to identify the water pixels based on spectrum feature difference between bands 2 and 7 of Landsat TM. The accuracy of classification for water and non-water reaches about 98%. Spatial overlay analysis was performed to obtain the elevations of both water and non-water pixels. Two normal distribution curves were then presented according to elevation statistics. The elevation in the crossed point of two curves was set as the value of water level. With the water level correlation analysis between wetland and Poyang Lake, the regression equations in different lake buffer areas were developed. Furthermore, the model for water level and status of submersion or emersion of wetland was constructed, concerning the topography, buffer areas and water level of Poyang Lake. Finally, the model was validated in estimating the temporal changes of water level of wetland for flood, normal and dry years respectively, as well as in simulating the spatial distribution of both water and non-water pixels of wetland on October 9, 2000. The result shows that the model has satisfactory simulated effect with accuracy above 85%.
Currently, Geo-data-mining and knowledge discovering, a new kernel of GIS spatial analysis study, which help to break theoretic limitation of Geo-expert system and to reveal an innovative research roadmap for new era Geo-information sciences, represent latest trend in researching GIS. Various research communities have tried to apply or revise mathematic tools as probability theory, spatial statistic, fuzzy set and rule based induction method to studies concerning specific geo-scientific problems. According to the latest decade development in this study area, data mining method has absorbed, borrowed and revised latest mathematic tools and theories rising in AI study area; and focused both on theoretic research and its application in mining rules lying in spatial dataset. Development of Geo-data-mining couples tightly with AI and application mathematics by widely crossing and deeply fusing. CBR (Case Based Reasoning), a new AI method that expands knowledge capturing channels, encapsulating problems by case, solving new problem by referencing historical similar ones, storing and re-using successful cases, has advantages such as simplicity, flexibility, scalability, high efficiency, knowledge learning and accumulation, which enable CBR to analyse and reason complex geo-problems. This paper mainly discusses Geo-CBR from a spatial data mining view and deems it as a kind of problem oriented spatial data mining method. Firstly, a detailed Geo-CBR definition and its encapsulating method are given as well as discrimination between spatial data mining and problem oriented Geo-CBR. Then, considering physical geography zonal and regional variation effect, inter-dependent and mutually condition relationships between geo-cases are examined in depth. And a quantitative data-mining method to explore intrinsic spatial relationships from geo-cases is presented based on rough set theory. In addition, due to variation of spatial feature types and their spatial relationships in geo-case representative model, 3 categories of spatial similarity calculating models are derived. Finally, a pilot study for LU is provided with purposes of landuse problems quantitative analysis and deduction and demonstration of Geo-CBR's characteristics and advantages in solving and analysis spatial related problems.
