Space-Air-Ground integrated earth observation network (SAGIEON) is not only the most promising high-tech area, but also a fundamental infrastructure closely related to national security and economic/social development. Firstly, the scientific concept, key technologies, current situation and tendency of SAGIEON are comprehensively represented. Secondly, an integrated data processing system for native remote sensing satellites is introduced, including its objectives and key technologies. Thirdly, the connotation of generalized spatial information grid are proposed on the basis of the above mentioned discussions. Finally, some conclusions are drawn. For the propose of providing fast, precise and real-time spatial information service to everyone, it is very important to carry out research on the theories and technologies of SAGIEON.
There are two methods for GIS similarity measurement problems, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. Both of these two methods can not measure subzone similarity of GIS subzone based on universal background. The rough measurement based on membership function solved this problem well. In this paper we used rough sets to calculate the GIS subzone discrete data similarity measurement, and used neighborhood rough sets to calculate continuous data's upper and lower approximation. We used neighborhood particle to calculate continuous attribute's rough membership function, then to calculate continuous attribute's subzone similarity measurement problem. This paper used rough membership to measure similarity problem for different subzones. Because Moran's I can only measure universe or each unit's spatial autocorrelation, it can not measure subzone, so our method in this paper can compute GIS subzone similarity based on universe. And for continuous value, we used distance function and neighborhood rough sets to divide continuous value's upper and lower approximation and classification problem, then we put forward a rough membership function based on neighborhood information granulation. Then, we used rough similarity measurement formula to measure GIS subzone similarity problem. This method can provide a new direction for GIS point group or others' object group similarity measurement. At last, using an example that includes discrete and continuous value, we can find that spatial autocorrelation can not measure discrete value, cross-coefficient can not measure discrete value too. If the subzone in map is not equal length for continuous value, cross-coefficient can not measure similarity, but the rough measurement based on membership function solved this problem well.
The Huaihe River watershed has been suffering from industrial point pollution and other non-point pollution since 1970s. The media has reported the emergence of ‘cancer village'. Spatial distribution pattern on upper digestive tract cancer and environmental factors was studied in 14 pilot counties including 5810 villages in Huaihe River watershed. From the multiple perspectives such as watershed and jurisdictional areas, Ordinary Least Squares (OLS) and Robust regression model were used for environmental factors selection. Robust regression model can detect abnormal value and put the weight for each of them. Surface water, phreatic water, river density, soil PAHs, fertilizer, population density and economical density (GDP) were imported into the model. Geographically Weighted Regression (GWR) model was used to locally detect the impact of different environmental factors on Empirical Bayes smoothed upper digestive tract cancer mortality. Through the correlative analysis of them, the risk assessment model of upper digestive tract cancer mortality was developed. Based on the regression coefficient of each environmental factor and statistical test in each pilot, the local main environmental factors were extracted. Even six factors were imported into the global regression model, the impact of each factor was different in distinct areas. In general, all the selected environmental factors show the positive correlation with the upper digestive tract cancer mortality except the surface water quality level and GDP. However the pollution type was diverse in different area based on the regression coefficient of each factor. The main findings were listed as follow: fertilizer amount, soil PAHs, GDP and river density are the main factors in Jiangsu segment in Huaihe River watershed; main factors in Anhui segment included soil PAHs and fertilizer amount; main factors in Henan segment included phreatic water, river density and fertilizer amount, With Shenqiu County, as one of the pilots in Henan, showed the strong positive correlation between surface water and cancer mortality; some kind of environmental risk factors were also detected in Shandong segment, but the result showed no correlation between these risk factors and cancer mortality, which needed to be studied any further.
Satellite monitoring is a mean of verifying the suspicious activities by interpreting the state and analyzing the targets in satellite imagery combining other information,management of targets of satellite monitoring usually has three characteristics, the process of monitoring targets takes a long time, is a repetitious one, and needs multi-users' editing. In practical study the targets are usually stored in a relational database in the form of spatial features, and a "lock→change→release" strategy is usually applied to relational database. However this approach is not suitable for concurrent accessing database. To resolve the problem, so as to restore and manage much status of monitoring targets in the lifecycle of remote sense interpretation, and to support the mission of satellite monitoring. firstly, a discussion was made out about the mechanism of ArcSDE version, and feasibility was researched on that the version was applied to managing satellite monitoring targets, by the version technology the users were allowed to edit targets without locking features, and the other user's edited work was not affected. Therefore, Version technology can resolve the problem caused by multi-user concurrent operation and long transaction processing; Secondly, the feature and mask of satellite monitoring targets was deeply analyzed, it was concluded that the management of targets of satellite monitoring was closely relate to the version management technology. and then the framework of target managing system of Satellite monitoring based on ArcSDE version is designed basing the aforementioned study, the system includes the main functions of version contrasting, historical version tracing, and conflict-resolving strategy among versions; using the system the result of targets interpretation and reappearance of historical status of targets could be accessed and viewed. At last the system is built by the ArcGIS Engine component. The result of system indicated that this system showed good performance in viewing various status of targets and contrasting the same target among various results of interpreting targets, as well as recalling of the historical status, by which the targets managing task of satellite monitoring can efficiently accomplished. and which can be well applied in the field of city planning, management etc.
With the rapid development of space survey technology, GIS is facing a challenge of fast growing size on spatial data and complexity of spatial analysis algorithm. Traditional serial spatial analysis method isn't able to deal with this condition well. High performance computer and new computing methods provide an innovative way for spatial data processing and analysing problem. Remote sensing data processing is data-intensive and an ideal domain to use parallel computing, but vector data operation is computing-intensive which needs more computing ability. In this paper, a distributed spatial analysis framework based on MySQL spatial and MPI is described. Parallel spatial vector data mean is explored in kind of cluster way. This framework uses MySQL spatial cluster to store and manage GIS data which can resolve the problem about fault-tolerant and concurrent access for the same data block. MPI is good at passing messages in distributed network nodes, so it's not necessary to control telecom between nodes manually. Task management and distribution use prior queue to achieve load balance and fault-tolerant through monitoring the status of cluster. Finally, a parallel polygon overlay operation is experimented on this distributed system to test the performance of the cluster. The strategy of parallel Overlay operation is in a pipeline way, each node gets a part set of the polygons in the overlaid layers. And this method got relative better speedup than the serial overlay operation.
The coal mine safety management is a complicated system engineering. In recent years, our coal mine safety accidents occur frequently, and the safety problem is very outstanding. Reasonable evaluation of the production condition in the production safety administration, and the personnel escape and rescue in emergency cases are the research focus of mine safety and production. The overall design of the coal mine safety production system is given based on GIS, the system structure and data organization is analysised. The application functions of the coal mine safety production are designed in detail, such as the underground transportation, the coal mine ventilation, the underground water supply and drainage, the underground power supply, the safety emergency, etc. The spatial database and property database of the coal mine safety production system are designed. Combining with the business model, the system is developed in support of the inquiry, analysis, and other modules which provided by SuperMap. The analysis of escape routes, the network analysis of transportation, ventilation, power supply, supply and drainage and so on are implemented on SuperMap platform. The visualization technology of the coal mine safety production is realized, the business problems in coal mine ventilation and safety emergency and so on are solved. A strong technical support is provided for the prevention and treatment of disaster. The application of escape routes is analysed in detail combined with the underground transportation network and the ventilation network, in order to guide the underground staff evacuate to safety area the most quickly when the mine disaster happens. The application of computing the production conditions is analysed in detail to ensure the security of the production conditions. It provids scientific supports for the coal mine safety production management and has wide application prospects.
The remote sensing of microphysical and optical properties of fine mode aerosol has gain most attention in both environmental and climatological fields. Aerosol retrieval over land is confronted with two difficulties: selecting the aerosol type and taking out of the surface contribute. As the polarized light reflected by ground targets is small and stable enough to allow for correction in TOA measurements, polarization remote sensing has unique predominance and potential. Based on the directional polarization method, the fine mode aerosol optical depths and size distributions are retrieved using PARASOL Level 1B data through vector radiative transfer model and semi empirical surface polarized reflectance model. Aerosol model selecting is a key factor in the work of aerosol retrieval, so importing local aerosol model is essential in our work. Compared with the products of the ground-based AERONET, the precision of fine mode aerosol optical depth retrieving is bigger than 81% and size distribution retrieving needs to be improved. The results show that the directional polarization method is more accurate for the retrieval of fine mode aerosol and more stable in different seasons and different types of land surfaces. As aerosols over land are mostly composed of fine modes and coarse modes, we need farther study on the aerosol retrieval, such as importing scalar signal to vector method considering the different instrument characteristics and retrieval strategies of scalar and vector methods.
The reasonable remote sensing sale transformation method is very important for RS data utilization. In order to find a suitable scaling model for ascending scale and downscaling transformation with spectral data, given the randomness and structural of soil moisture data as regional variation, point Kriging method accounting for spatial heterogeneity is used in this study to transfer from ASTER soil moisture data with 15m resolution to 7.5m resolution. The similarity degree of fractal dimension is shown that the transformation result is reasonable. Block Kriging method is engaged in the upscaling transformation for the field measured point soil content to 7.5m spatial resolution. The 7.5m resolution upscaling scale conversion results and average of measured sample point soil moisture with downscaling 7.5 m resolution remote sensing data were compared respectively. The result shows that 7.5m resolution soil moisture mean error of the measured sample points is in the range of 1.5782 to 5.019, the error between 7.5m resolution upscaling scale conversion results and downscaling 7.5 m resolution remote sensing data is from 1.2825 to 5.0481, which indicates that soil moisture value acquired by Kriging is much better than the soil moisture average without considering spatial heterogeneity under the same scale because of Kriging method taking account of the relationship between the points and the surrounding.
The change of land surface temperature(LST)can reveal the heat environment of city,which leads to urban heat island (UHI). As a metropolis, Beijing has a serious urban heat island effect, so, analyzing and studying spatial and temporal changes of Beijing's urban heat island has a great significance. In this paper, we referenced image-based method to retrieve the land surface temperature using Environment Satellite image (HJ-1B) CCD/IRS as the main data source from 2008 to 2011, made preliminary validation on retrieval result using MODIS temperature products, then analyzed the annual and inter annual changes of urban heat island effect in Beijing. Further, we used thermal field variability index to make quantitative analyze of UHI. Finally, we analyzed the relationship among NDVI, NDBI and land use types. The results show that: (1) between 2008 and 2011, there is a significant heat island effect in Beijing. The heat island expanded from 2008 to 2010 but eased in 2011, the UHI were 5.2℃, 5.2℃, 9.2℃ and 8.2℃, respectively; (2) There is a significant heat island effect in four seasons of Beijing in 2010, the heat island in summer is the strongest, followed by spring and autumn, winter is the weakest, and the UHI were 8.2℃, 9.4℃, 9.2℃ and 4.3℃, respectively; (3) The quantitative distribution of UHI in Beijing from 2008 to 2011 showed that the UHI is mainly in urban, the heat island effect in the Southern of Fangshan District and Daxing District reduced year by year, the heat island effect of Changping District in 2011 is higher than in the previous three years, and vegetation and water formed urban cool island; (4) LST and NDVI are negatively correlated. LST and NDBI are positively correlated obviously, NDVI and NDBI have a significant impact on urban heat island, and urban heat island has a clear correction with land use types. So, increasing the city green area and water area can effectively slow down the heat island effect.
Remote sensing technology was used to estimate the interrelationship between urban green land development and land surface temperature in Beijing. The processes involved green land identification, greenness evaluation, land surface temperature retrieval, and analysis of relationship between land surface temperature and greenness. The decision tree for hierarchical classification was built up to identify the green vegetation area according to reflectance value in different bands for land covers in urban area: water, built-ups, bare soil, high-density vegetation and low density vegetation. NDVI value was used to specify the vegetation area, for high-density vegetation area it was between 0.27 and 1.0, and for low-density area it was between 0.1and 0.26. With the help of Rapid Eye remote sensing image and on site investigation, the green land area was evaluated and it was 197.3 km2 accounting for 29.6% of total urban area.50 investigated quadrates were distributed in 10 parks, such as Summer Palace, Olympic Park, and so on. The correlation coefficient for greenness and NDVI is 0.73 based on Rapid Eye imagines, while it is 0.60 based on Landsat 5 TM imagines. The estimated greenness is 2450.7 km2 within 5th ring road of Beijing. The land surface temperature was retrieved from the Landsat 5 TM 6 remote sensing image, and it was between 31℃ to 46.2℃. The land surface temperature for water body and vegetation area was 4-6℃ lower than nearby built-ups. The proportion for low temperature region, middle temperature region, sub heat island region, and heat island region were 12.3%,34.7%,40.4%, and 12.6%, respectively. It shown a negative correlation between land surface temperature and greenness as y=-1278.7x+60650 (x represented land surface temperature(℃); y represented greenness(m2). The green land declined the land surface temperature by 2.6℃ in Beijing.
Rapid urbanization often results in intensive land use change especially in the urban area of China. As an important economic center in China, urban expansion in Shanghai is always at a high speed in recent years. Using land use datasets in 1990, 1995, 2000 and 2005, derived from interpretation based on Landsat TM/ETM images, this paper firstly analyzed the spatial-temporal characteristics of urban expansion during 1990-2005, then simulated this process using SLEUTH model and analyzed the simulation precision, and forecasted urban expansion and land use change process in the next 30 years. The results indicated that: (i) the urban expansion is very fast during 1990-2005, the urban land area increased by 81 278 hm2 (10.14%). The temporal process of urban expansion took on a first decrease and then increase trend. The most obvious expansion occurred in 2000-2005, the urban land area increased by 40 668 hm2 (5.08%), and the sea reclamation is the typical characteristic in this time period. (ii) The Lee-Sallee index were more than 0.75 in the calibration process of SLEUTH, and the amount and morphology of simulated urban expansion were more consistent with actual situation in 2005 than in the past. So SLEUTH model could simulate urban growth mode of Shanghai City and obtain high precision result of land use changes, and the Kappa coefficient could be up to 0.8476. (iii) In the next 30 years, Shanghai would expand at an annual average rate of 1.63%, and the city area would be reach 152 685 hm2 in 2040. The urbanization process would occupy 41 824 hm2 cultivated land, and the cultivated land area would decrease at a speed of annual average area of 1200 hm2, but it would not affect the aim of cultivated land protection in the future general land use planning. (iv) The main mode of urban expansion in Shanghai was the edge growth, the direction towards east along Wuzhou Avenue in the Pudong new area, the direction towards south along the Humin elevated road in Xuhui District, and the direction towards west along Beijing-Shanghai high-speed railway in Jiading and Qingpu districts, are the main city expansion direction in the future. And (v) from 2010 to 2040, urban expansion in Shanghai will occupy cultivated land 1200hm2 per year, and will occupy cultivated land 41 824 hm2 totally.
Global and regional land cover data is the foundational base for studies on land surface processes and modeling and land cover remote sensing mapping is important information for supporting studies on global change and regional sustainable development. Mapping accuracy assessment has a great significance to both data producers and data users, and to some extent, it is incomplete for the study of remote sensing mapping of land cover without an accuracy validation. From 2007 to 2009, two land cover datasets of China at the scale of 1:250 000 in the 1980s and circa 2005 were produced jointly by eight institutes of Chinese Academy of Sciences (CAS), respectively, including Institute of Remote Sensing Applications (IRSA) and Institute of Geographic Sciences and Natural Resources Research (IGSNRR). In order to evaluate the accuracy of this remote sensing mapping of land cover in circa 2005, a field survey was carried out in the Poyang Lake area in August, 2011, and 321 sampling sites of land cover categories including fixed-points validation, GPS points and interpretation keys were collected. Considering the conspicuous impact of time factor on the accuracy validation of remote sensing mapping, this paper removes 34 samples relating to time differences, and takes advantage of the hierarchical assessment method to compare the actual categories of land cover in field and those in remote sensing mapping at 287 survey sites at two scales, i.e. level-1 and level-2 land cover category, respectively, and uses the scoring method for assessing the mapping accuracy, that is, one point is scored if land cover category is classified correctly, and zero if land cover category is classified incorrectly. The results show that: (1) The overall mapping accuracy of land cover data of China in the Poyang Lake area based on 287 sampling sites at the level-1 scale is 61.67%, of which wetland/water body, farmland and forest with 19, 124 and 20 sampling sites reaches 100%, 98.4% and 80.0%, respectively. (2) The overall mapping accuracy of land cover dataset in 2005 at the level-2 category is 44.25%, of which the accuracy of two grass and three forest and one cropland level-2 categories is 0, and the very low accuracy of dry land, urban and rural settlement is 21.1%, 29.0% and 1.7%, respectively. The dataset of land cover at the scale of 1:250 000 in 2005 reflects the status of land cover in the Poyang Lake area well. However, there are still a lot of difficulties of different land cover categories, especially for atypical and transitional categories, such as forests and grasslands, that can be distinguished accurately only through the technology of remote sensing.
The combination of geographical knowledge and image calibration has long been the principal means of both the traditional visual interpretation and computer automatic classification in remote sensing mapping. Traditional visual interpretation could use the geographic knowledge well because of the artificial participation. However, it goes with the shortcomings that visual interpretation needs a lot of labor and is less efficient. In addition, the computer classification has not applied geographic knowledge in a proper way. Studies have shown that samples as the carrier of geographic knowledge can integrate geographic knowledge into the classification process to some extent. Meanwhile, unsupervised clustering can significantly improve the efficiency of sample selection and solve the problem of scarcity of samples in order to meet the requirement of distribution and purity. These studies provide a basic foundation for integration of geographic knowledge with computer classification. This paper presents an automatic sample selecting method which integrates image clustering with the aid of previous land cover data. The samples were selected automatically based on the TM images by the method mentioned above and used to classify the image later by the maximum likelihood classifier. We also classified the image using the manual samples by the maximum likelihood classifier in order to compare the classified results produced by these two kinds of samples. The test results indicated that the proposed method achieved an overall accuracy of 84.18% and a kappa coefficient of 0.8066 in seven categories, including water body, forest land, orchard and urban construction land. The method proposed in this paper is more efficient than the way of samples selected manually and provides better classification results.
Since vegetation is an important indicator of global climate change, then the way to extract vegetation changing data should be put as the top priority. Especially, the extraction of sub-category information of forest vegetation has always been a difficult point in remote sensing image classification. And it is more difficult to extract sub-category information of the forest vegetation type only by taking advantage of the spectral information. As a widely-used method, object-oriented classification has been rapidly developed from the beginning of this century. Object-oriented classification method is mainly used in high-resolution remote sensing imagines, and it is applicable to medium resolution remote sensing images. This paper took Mentougou District, Beijing, which is mainly covered with forest vegetation, as the object of this research, and took HJ-1 image as the main data source then different buildings can be extracted by using the object-oriented classification method. By the reason of complicated terrain in this district, a hierarchical segmentation method was proposed in this research. Then different segmentation parameters could be set according to different buildings. Based on the spectral characteristic of the vegetation, appropriate characteristic parameters could be chosen and subordination function is constructed. After then, land cover types in this district could be extracted step by step and at the same time could be compared with those by the traditional maximum likelihood method. The result indicates that extraction accuracy of the forest vegetation sub-category data in this Mentougou District is 83% by using the object-oriented classification method. Compared with the traditional method, the extraction accuracy has been boosted a lot.
The marine oil spill impacts on the marine environment with the features of high frequency, wide range and great hazards. Using remote sensing for marine oil spill detection has become an important means. In this paper, we started with the spectral character of the marine oil spill, and established the relationship with the common optical satellite sensors. Then we analyzed the main physical parameters of the different optical satellites, including the spatial resolution, the time resolution, width and band number. The MODIS sensor and HJ-1 satellite has a strong monitoring capabilities of marine oil spill. Based on this, we used MODIS, BJ-1, HJ-1 and FY3 optical satellites images to monitor the marine oil spill accident occurred in the Bohai Sea in March 2006 and June 2011. The MODIS image can reflect the information of marine oil spill clearly in 2006 and 2011. The HJ-1 image can reflect the information of marine oil spill clearly in 2011, but the BJ-1 and FY-3 images cannot reflect the information of marine oil spill clearly in the two oil spill accidents. HJ-1, BJ-1and FY3 satellites in the band set are similar, but the BJ-1 and FY-3 satellites cannot reflect the information of marine oil spill. So in this article we compared the contrast of oil-water spectrum and the variance of the sea water spectrum using the MODIS remote sensing data. Experimental results show that, when the variance of the sea water spectrum is less than the contrast of oil-water spectrum in the MODIS band, the oil spill information can be shown on this band clearly. When the contrast of oil-water spectrum is less than or close to the variance of the sea water spectrum, the oil spill information cannot be shown on this band clearly. So through the above analysis, the MODIS sensor has a strong monitoring capacity on the spectral response of oil spill, spatial resolution, temporal resolution and the instance. This article has certain practical value of the optical satellite selected in marine oil spill remote sensing monitoring.
Oil spills can cause huge damage to the ecology of marine environment and its detection and clean-up plays a very important role in the reduction of economic and environmental losses. As an advanced technology, synthetic aperture radar (SAR) has the advantage of day-night all weather observation capability. Besides, SAR has relatively wide swath width and high resolution, which all helps a lot in the early warning and damage analysis of oil spills accidents. Due to the special imaging mechanism of SAR, oil spills can be found as dark spots in SAR images. However, there still remain a lot of difficulties in the related detection and classification algorithms. In this paper, a double-threshold oil spills detection based on characteristic possibility function was proposed for taking the best advantages of backscatter information contained in different grayscale levels. Both high and low levels of grayscale information were extracted from the backscatter image obtained from SAR signal. Then the density of pixels was evaluated by Gauss kernel to enhance the stability of the segmentation. By using high level segmentation result, look-alikes with large area are classified from oil spills by basic morphological analysis. By taking advantage of low threshold grayscale information, other look-alikes were distinguished from oil spills by means of probability likelihood function derived from morphological characters such as complexity, length to width ratio, Euler number, etc.. Finally, the detected spills were obtained by fusing classification result of different level and other auxiliary information. The proposed method was implemented on EVISAT ASAR images of coastal region around Hong Kong received by the satellite ground station, CUHK. Experimental results demonstrated that real oil spills and look-alikes generated by other natural phenomena such as low wind speed and internal water turbulence could be distinguished accurately and effectively. This method can be further developed and has potential use in the surveillance and early alarm of marine and coastal oil leak accidents.
The coastal engineering exerts a great impact on the economic development and ecological environment of the seacoast. Thus, coastal engineering monitoring is a focus in coast zone remote sensing and monitoring. Since the 1980s, satellite remote sensing has become an indispensable technique in detecting the dynamic changes of coastal engineering. The accuracy of changes in coastal engineering is determined by the applicability of data obtained from remote sensing system and the feasibility of the methods in detecting the changes. As a satellite developed by China, the BJ-1 small satellite has already obtained numerous achievements in environment and disaster monitoring, urban management and construction and national land resource surveying. However, little has been investigated concerning the utilization of BJ-1 small satellite in monitoring the coast engineering. We compared various typical detection methods, and summarized a highly accurate and stable method in monitering the costal engineering with BJ-1 small satellite remote sensing data. Different detection methods were applied to investigate the changes in 2 key areas-Tianjin Port and Caofeidian Port costal engineering from 2006 to 2010 based on the characteristics of BJ-1 small satellite data, and evaluated the detected results. Our findings showed that among the detecting methods with BJ-1 remote sensing data, the maximum precision was obtained when waveband substitution technique was combined with SVM classification to detect the changes in costal engineering. The second precisest and stablest method was object-oriented analysis. These results indicate that, BJ-1 remote sensing data meet the requirements for accuracy in different costal engineering monitoring. Meanwhile, combination of waveband substitution and SVM classification technique, as well as object-oriented analysis, has the highest accuracy and stability in different costal engineering monitoring.
High resolution remote sensing technology (including high spatial resolution and hyper-spectral resolution) is an important new technology in field of geological exploration since the new century. In order to present the application of high resolution remote sensing technology in geological field, the Quickbird high spatial resolution satellite remote sensing technology and CASI (Compact Airborne Spectrographic Imager)/SASI (Shortwave infrared Airborne Spectrographic Imager) airborne hyper-spectral remote sensing technology were used to mine the uranium exploration information in north fringe of Tarim Basin, Xingjiang, in this paper. In first part, the feature and processing method of Quickbird and CASI/SASI data were introduced. Especially, a new method called the reverse enhancement method of uranium mineralization bleached alteration was discussed. In second part, the application of Quickbird data in identifying and analyzing the sandstone-type uranium mineralization bleached alteration was illustrated. In this part, some bleached alteration information related with uranium mineralization in Baxhibulake uranium region in Tarim Basin were identified using Quickbird high resolution remote sensing datum, and their spatial distribution raw was also analyzed. In third part, the application to discover new uranium mineralization belt and evaluate the favorable target zone using Quickbird and CASI/SASI data comprehensively were presented. In this part, an uranium mineralization belt named Salamubulake in east of Keping uplift in the north fringe of Tarim Basin was discovered using Quickbird remote sensing technology, and some alteration adjacent in the belt were identified and analyzed. Furthermore, the mineral composition for those alterations were mapped using CASI/SASI high resolution remote sensing datum. Its uranium metallogenetic potential was evaluated, and the favorable areas for uranium exploration were predicted. At last, the field investigation was also introduced and some conclusion were made that high resolution remote sensing technology has been utilized in uranium exploration field fully, and good application effects was made. Therefore, the application potential of high resolution remote sensing technology to geological exploration is valuable in being mined in the future.
