This PDF file contains the front matter associated with SPIE Proceedings Volume 9808, including the Title Page, Copyright information, Table of Contents, Authors, Introduction, and Conference Committee listing.

The tasseled cap transformation of remote sensing data has been widely used in environment, agriculture, forest and ecology. Tasseled cap transformation coefficients matrix of HJ multi-spectrum data has been established through Givens rotation matrix to rotate principal component transform vector to whiteness, greenness and blueness direction of ground object basing on 24 scenes year-round HJ multispectral remote sensing data. The whiteness component enhances the brightness difference of ground object, and the greenness component preserves more detailed information of vegetation change while enhances the vegetation characteristic, and the blueness component significantly enhances factory with blue plastic house roof around the town and also can enhance brightness of water. Tasseled cap transformation coefficients matrix of HJ will enhance the application effect of HJ multispectral remote sensing data in their application fields.

Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.

With the broad attention of countries in the areas of sea transportation and trade safety, the requirements of efficiency and accuracy of moving ship tracking are becoming higher. Therefore, a systematic design of moving ship tracking onboard based on FPGA is proposed, which uses the Adaptive Inter Frame Difference (AIFD) method to track a ship with different speed. For the Frame Difference method (FD) is simple but the amount of computation is very large, it is suitable for the use of FPGA to implement in parallel. But Frame Intervals (FIs) of the traditional FD method are fixed, and in remote sensing images, a ship looks very small (depicted by only dozens of pixels) and moves slowly. By applying invariant FIs, the accuracy of FD for moving ship tracking is not satisfactory and the calculation is highly redundant. So we use the adaptation of FD based on adaptive extraction of key frames for moving ship tracking. A FPGA development board of Xilinx Kintex-7 series is used for simulation. The experiments show that compared with the traditional FD method, the proposed one can achieve higher accuracy of moving ship tracking, and can meet the requirement of real-time tracking in high image resolution.

In recent years, the effect of urban heat island (UHI) is increasingly obvious with moving forward in further urbanization process, which has become one of the prominent issues of environment. The image data of Nanchang city supplied by Landsat 5 Thematic Mapper (TM) in September 2006 is used in this paper, and the land surface temperature (LST) over the same period has been retrieved by using a mono-window algorithm based on remote sensing technology. The classification of LST is subsequently fulfilled by the method of proper density cutting. Characteristics of intensity and spatial distribution of UHI effect in Nanchang, as well as its relationships with land use type and vegetation coverage degree (VCD) are discussed in detail. The result shows that the phenomena of UHI are significantly presented in urban area with an inhomogeneous distribution, and the degree of influence of UHI depends on types of land uses. The intensity of UHI effect has a significant negative linear correlation with normalized difference vegetation index (NDVI). It is deduced that suitably optimizing land use types and raising VCR are obvious and effective ways to reduce UHI.

Vegetation Supply Water Index (VSWI) and Temperature Vegetation dryness Index (TVDI) are two most commonly used methods for surface soil moisture (SSM) retrieval using electromagnetic spectrum of visible, near infrared and thermal infrared band. Both of them take into account the effect of vegetation index (VI) and surface temperature (Ts) on SSM. A comparative analysis of the ability and effect of the two methods for SSM retrieval in karst areas was carried out, using the remote sensing data of Landsat 8 OLI_TIRS. The study area is located in Guilin, which is a typical karst area. The experimental results show that TVDI is more suitable for SSM retrieval in karst areas.

With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road; Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.

High-speed railway construction will produce a large amount of abandoned dregs, so it is necessary to build enough dreg deposition fields along the railway. The task of the department of soil and water conservation is to monitor the construction and usage of abandoned dreg fields according to the design in the whole process of railway construction. As long linear construction projects, many high-speed railways go through regions of complex terrain, which poses great difficulties to monitoring current status of abandoned dreg fields. With the advantages of low cost, flexible launch and landing, safety, under-cloud-flying, hyperspatial image resolution, Unmanned Aerial Vehicles (UAVs) are very suitable for obtaining remote sensing imagery along the railway. One segment of the high-speed railway from Chongqing to Wanzhou and its neighborhood was chosen as the study area to demonstrate key technologies and specific procedures of monitoring abandoned dreg fields using the UAV system. The UAV system and its components are introduced along with the flight trajectories, acquired UAV imagery, and attitude data. Image preprocessing and generation of DEM and DOM are described in detail followed by image-based measurement accuracy assessment and abandoned dreg field status investigation on the resulting DOM and DEM. Results prove the feasibility and effectiveness of applying the fixed wing UAV system to rapidly monitoring the construction and usage of abandoned dreg fields

The ocean surface albedo (OSA) is a deciding factor on ocean net surface shortwave radiation (ONSSR) estimation. Several OSA schemes have been proposed successively, but there is not a conclusion for the best OSA scheme of estimating the ONSSR. On the base of analyzing currently existing OSA parameterization, including Briegleb et al.(B), Taylor et al.(T), Hansen et al.(H), Jin et al.(J), Preisendorfer and Mobley(PM86), Feng’s scheme(F), this study discusses the difference of OSA’s impact on ONSSR estimation in condition of actual downward shortwave radiation(DSR). Then we discussed the necessity and applicability for the climate models to integrate the more complicated OSA scheme. It is concluded that the SZA and the wind speed are the two most significant effect factor to broadband OSA, thus the different OSA parameterizations varies violently in the regions of both high latitudes and strong winds. The OSA schemes can lead the ONSSR results difference of the order of 20 w m-2. The Taylor’s scheme shows the best estimate, and Feng’s result just following Taylor’s. However, the accuracy of the estimated instantaneous OSA changes at different local time. Jin’s scheme has the best performance generally at noon and in the afternoon, and PM86’s is the best of all in the morning, which indicate that the more complicated OSA schemes reflect the temporal variation of OWA better than the simple ones.

We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

The terrestrial water storage changes (TWSC) for global scale as well as area scale can be estimated by GRACE (Gravity Recovery And Climate Experiment) mission, while hydrologic and climate model named GLDAS (Global Land Data Assimilation System) provide variations of soil moisture and snows which can also derive the TWSC. These new technologies provide new methods to monitor global terrestrial water storage variations and climate change. The global monthly TWSC have been derived from GRACE observations and GLDAS model estimates respectively, it shows there have high There have high correlations between TWSC derived from GRACE observations and GLDAS model estimates, while due to lack of information from GLDAS models, TWSC derived from GRACE observations is more reliable. The monthly mean TWSC from April 2002 to August 2011 have been derived from GRACE observations, and there has a steady condition of the global TWSC with a slowly growing rapid of 0.142±0.0028cm/year in this period. while the change range of Northern Hemisphere of TWSC is more obvious than those of Southern Hemisphere. Due to the unbalance of regional rainfall, the TWSC in the middle latitude regions are changing more significantly than those in high-and-low latitude regions. It shows great correlations between global TWSC and global land surface temperature with correlation coefficient of 0.91, while the change of land surface temperature always occurred about two months later than the TWSC, and both of them have obvious change with annual period.

Multi-feature classification and image segmentation are two cores in object-oriented classification method of high resolution remote sensing images. Multi-feature object identification is an important part of multi-feature classification, which is identification for the image regions or the segmentation objects segmented by image segmentation under the guidance of a corresponding relationship between objects and features or combination. A method of multi-feature object identification was proposed based on vague soft sets. Firstly, the vague soft sets were formed by building the parameter sets according to spectral characteristics and object-oriented features of the segmentation objects. Secondly, according to general TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution), a TOPSIS based on Vague soft sets for multi-feature object identification was proposed, which obtained a object identification result of the segmentation objects by using similarity measure of vague soft sets to sort attribution of the cover types for the segmentation objects. The experimental results show that the proposed method obtains a correct result of object identification and is feasible and effective.

Accurate data on the spatial distribution and potential growth estimation of human population are playing pivotal role in addressing and mitigating heavy lose caused by earthquake. Traditional demographic data is limited in its spatial resolution and is extremely hard to update. With the accessibility of massive DMSP/OLS night time imagery, it is possible to model population distribution at the county level across China. In order to compare and improve the continuity and consistency of time-series DMSP night-time satellite imagery obtained by different satellites in same year or different years by the same satellite from 2002-2010, normalized method was deployed for the inter-correction among imageries. And we referred to the reference F162007 Jixi city, whose social-economic has been relatively stable. Through binomial model, with average R² 0.90, then derived the correction factor of each year. The normalization obviously improved consistency comparing to previous data, which enhanced the correspondent accuracy of model. Then conducted the model of population density between average night-time light intensity in eight-economic districts. According to the two parameters variation law of consecutive years, established the prediction model of next following years with R²of slope and constant typically 0.85 to 0.95 in different regions. To validate the model, taking the year of 2005 as example, retrieved quantitatively population distribution in per square kilometer based on the model, then compared the results to the statistical data based on census, the difference of the result is acceptable. In summary, the estimation model facilitates the quick estimation and prediction in relieving the damage to people, which is significant in decision-making.

Coastal bathymetry data are great important environmental resources and disaster management. Today, there are many technologies used to explore the bathymetry, depending on the purpose of surveying. But almost technologies can be time consuming, complicated, and quite expensive. Remote sensing by satellite imagery is one of technology used to estimate the coastal bathymetry in term of accuracy, quality and up to datedness, timely availability and cost effectiveness. Especially the Coastal Blue Band of WorldView-2 for bathymetric measurements will improve both in depth and accuracy. From investigated the relation between surface reflectance and coastal bathymetry of the eight bands of WorldView-2 that band 1,2,3 and 4 are good reflection and when the depth are increase, the digital number (DN) will decrease. The objective of this research is to developing the classification of the coastal bathymetry estimation from satellite imagery. By utilized WorldView-2 satellite images along with regressive function and improving the accuracy result by comparing with in-situ truth depth to assess a coastal bathymetry.

Extracting façade texture is the importance part in the produce of constructing virtual city. This paper proposes a novel method to make sure extract the texture image can be maximized. We calculate the azimuth angle and distances between the points belongs to buildings and camera center. And we choose the max and min azimuth angle to determine the boundary of texture image. This paper first employs the DBM (digital building model) and aerial image to search the building’s location in image. After that we also have proposed a method to choose the optimal texture image by calculating the displacement of target building’s corner point. The experimental results demonstrate that the method proposed in this paper can effectively extract texture image and make sure the texture image can be maximized.

The process of vehicle examination by using satellite images is complicated and cumbersome process. At the present, the high definition satellite images are being used, however, the images of the vehicles can be seen as just a small point which is difficult to separate it out from the background that the image details are not sufficient to identify small objects. In this research, the techniques for the process of vehicle examination by using satellite images were applied by using image data from Pléiades which is the satellite image with high resolution of 0.40 m. The objective of this research is to study and develop the device for data extracting from satellite images, and the received data would be organized and created as Geospatial information by the concept of the picture matching with a pattern matching or Template Matching developed with Matlab program and Sum of Absolute Difference method collaborated with Neural Network technique in order to help evaluating pattern matching between template images of cars and cars’ images which were used to examine from satellite images. The result obtained from the comparison with template data shows that data extraction accuracy is greater than 90%, and the extracted data can be imported into Geospatial information database. Moreover, the data can be displayed in Geospatial information Software, and it also can be searched by quantity condition and satellite image position.

High-precision surface elevation information in large scale can be obtained efficiently by airborne Interferomatric Synthetic Aperture Radar (InSAR) system, which is recently becoming an important tool to acquire remote sensing data and perform mapping applications in the area where surveying and mapping is difficult to be accomplished by spaceborne satellite or field working. . Based on the study of the three-dimensional (3D) positioning model using interferogram phase and Position and Orientation System (POS) data and block adjustment error model, a block adjustment method to produce seamless wide-area mosaic product generated from airborne InSAR data is proposed in this paper. The effect of 6 parameters, including trajectory and attitude of the aircraft, baseline length and incline angle, slant range, and interferometric phase, on the 3D positioning accuracy is quantitatively analyzed. Using the data acquired in the field campaign conducted in Mianyang county Sichuan province, China in June 2011, a mosaic seamless Digital Elevation Model (DEM) product was generated from 76 images in 4 flight strips by the proposed block adjustment model. The residuals of ground control points (GCPs), the absolute positioning accuracy of check points (CPs) and the relative positioning accuracy of tie points (TPs) both in same and adjacent strips were assessed. The experimental results suggest that the DEM and Digital Orthophoto Map (DOM) product generated by the airborne InSAR data with sparse GCPs can meet mapping accuracy requirement at scale of 1:10 000.

This paper propose a new definition of a region with a hole based on existing researches, and then we also proposed a new approach which can describe the special topological relations between a simple region and a region with a hole. Through the new method, we obtained 23 kinds of topological relations between a simple region and a region with a hole. In the end, through experiments, demonstrate the new method is correctly, simply calculation, stronger expressive ability for description of spatial topological relations.

Oasis is an important component of desert ecosystem. This paper employs Landsat Thematic Mapper (TM) multi-spectral data to extract fractional vegetation cover of oasis in Tarim Basin with four methods. The mixture pixel decomposition model based on normalized difference vegetation index(NDVI) is firstly used to estimate fractional vegetation cover(FVC). The results indicated that the method is mainly underestimating the FVC at the low FVC area and overestimating the FVC at high FVC area. Thereafter, a stepwise regression model between 15 Vegetation Indices (VIs) and measured FVC data and a log-linear model have been established through the relation analysis of FVC and NDVI. Trials of these two models showed that they are mainly overestimating the FVC. Finally, a dimidiate fractional cover model was proposed, which is composed of two linear functions. When the NDVI is less than 0.3, the linear function is formed by stress related vegetation index (STVI1) and normal differential water index (NDWI) (R², 0.764) while the NDVI is greater than 0.3, the linear function is composed of NDVI and perpendicular vegetation index (PVI) (R², 0.801). The validation of the dimidiate fractional cover model has been tested with the measured data. In the optimal case, the mean error is 0.002 and the RMSE is 0.051, demonstrating that the model can be used in estimating fractional vegetation cover of oasis in Tarim Basin.

This paper combines virtual technology with visualization visual simulation theory, construct the framework of visual simulation platform, apply open source software FlightGear simulator combined with GoogleEarth design a small UAV flight visual simulation platform. Using software AC3D to build 3D models of aircraft and complete the model loading based on XML configuration, the design and simulation of visualization modeling visual platform is presented. By using model-driven and data transforming in FlightGear , the design of data transmission module is realized based on Visual Studio 2010 development platform. Finally combined with GoogleEarth it can achieve the tracking and display.

Since the early 2010s, governments in most countries have adopted and promoted open data policy and open data platform. Korea are in the same situation, and government and public organizations have operated the public-accessible open data portal systems since 2011. The number of open data and data type have been increasing every year. These trends are more expandable or extensible on mobile environments. The purpose of this study is to design and implement a mobile application service to visualize various typed or formatted public open data with geo-based images on the mobile web. Open data cover downloadable data sets or open-accessible data application programming interface API. Geo-based images mean multi-sensor satellite imageries which are referred in geo-coordinates and matched with digital map sets. System components for mobile service are fully based on open sources and open development environments without any commercialized tools: PostgreSQL for database management system, OTB for remote sensing image processing, GDAL for data conversion, GeoServer for application server, OpenLayers for mobile web mapping, R for data analysis and D3.js for web-based data graphic processing. Mobile application in client side was implemented by using HTML5 for cross browser and cross platform. The result shows many advantageous points such as linking open data and geo-based data, integrating open data and open source, and demonstrating mobile applications with open data. It is expected that this approach is cost effective and process efficient implementation strategy for intelligent earth observing data.

Digital elevation model (DEM) co-registration is one of the hottest research problems, and it is the critical technology for multi-temporal DEM analysis, which has wide potential application in many fields, such as geological hazards. Currently, the least-squares principle is used in most DEM co-registration methods, in which the matching parameters are obtained by iteration; the surface co-registration is then accomplished. To improve the iterative convergence rate, a Gauss-Newton method for DEM co-registration (G-N) is proposed in this paper. A gradient formula based on a gridded discrete surface is derived in theory, and then the difficulty of applying the Gauss-Newton method to DEM matching is solved. With the G-N algorithm, the surfaces approach each other along the maximal gradient direction, and therefore the iterative convergence and the performance efficiency of the new method can be enhanced greatly. According to experimental results based on the simulated datasets, the average convergence rates of rotation and translation parameters of the G-N algorithm are increased by 40 and 15% compared to those of the ICP algorithm, respectively. The performance efficiency of the G-N algorithm is 74.9% better.

MOD17A3 NPP datasets from 2000-2010 and meteorological data were used to quantitatively analyze the spatial and temporal variations of vegetation NPP and its correlation with meteorological factors by using linear regression and correlation analysis methods in Guangxi. Results show: (1) the annual NPP ranged from 626 to 715 g C·m^-2·a^-1, with an average of 663 g C·m^-2·a^-1. The average annual vegetation NPP was higher in the west and east while lower in the middle and north part of the study area. The highest NPP (> 900 g C·m^-2·a^-1) mainly distributed in Baise, Chongzuo, Fangchenggang and Wuzhou. While the lowest NPP (<450 g C·m^-2·a^-1) mainly located in cities of Guigang and Hezhou. (2) In recent 11 years, the linear changing trend of NPP in Guangxi was decreased slowly. The regions of NPP with no significant difference (P>0.05), increased significantly (P<0.05) and decreased significantly (P<0.05) were respectively about 93.15%, 0.80% and 6.05% of the total study area. The area with an annual NPP decreased rate lower than -10% occupied 26.96% of the total study area, and the area with more than 10% NPP increment was 8.37%, and an unchanged area (NPP variation from -10% to 10%) accounted for 64.68%. (3) At the annual time scale, the correlations between vegetation NPP and temperature/precipitation were spatially various in Guangxi. The correlation of vegetation NPP with temperature was positive in 58.41% of the total area, and negative in the rest 41.59%, while its correlation with precipitation was positive in 38.16% of the study area, and negative for the other 61.84%. The above analysis indicated that temperature affecting vegetation growth more remarkable than precipitation.

Through quick, accurate and non-contact scan, Terrestrial 3D Laser Scanner can get high density point cloud data. But limited by the scanning range, Terrestrial 3D Laser Scanner could not finish scanning data at one time. Therefore, in order to reconstruct a three-dimensional model of the object, we must get the point cloud data from different perspectives; Next, finish the registration of the multi-view point data; Finally, realize the 3D visualization. The point cloud data registration is the key to the 3D visualization of complete object. Traverse survey is proposed as a new point cloud data registration method in this paper, this method is based on traversing principle, according to the conventional control survey, synchronization of scanning and control measure can be realized, and the efficiency of the Terrestrial 3D Laser Scanner will be improved. By comparing the result of different methods, the mean square error of a point got by the traverse survey data registration method is equally distributed and not affected by the time of registration. So, the point cloud data registration of traverse survey is with certain operability.

Unmanned aerial vehicle (UAV) can be a flexible, cost-effective, and accurate method to monitor landslides with high resolution aerial images. Images acquired on 05 May 2013 and 13 December 2014 of the Xishan landslide, China, have been used to produce a high-resolution ortho-mosaic of the entire landslide and digital elevation model (DEM). The UAV capability for imaging detection and displacements on the landslide surface has been evaluated, and the subsequent image processing approaches for suitably georectifying the data have been assessed. Objects derived from the segmentation of a multispectral image were used as classifying units for landslide object-oriented analysis. Spectral information together with various morphometric characteristics was applied for recognizing landslides from false positives. Digital image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two temporal UAV images corresponded to a visual interpretation of landslide change. Therefore, the UAV can demonstrate its capability for producing valuable landslide mapping data and deformation information.

The objective of this study is to investigate the use of DEMs derived from ASTER and SRTM remote sensing images and topographic maps to detect and quantify natural sinkholes in a karst area in Zhijin county, southwest China. Two methodologies were implemented. The first is a semi-automatic approach which stepwise identifies the depression using DEMs: 1) DEM acquisition; 2) sink fill; 3) sink depth calculation using the difference between the original and sinkfree DEMs; and 4) elimination of the spurious sinkholes by the threshold values of morphometric parameters including TPI (topographic position index), geology, and land use. The second is the traditional visual interpretation of depressions based on the integrated analysis of the high-resolution aerial photographs and topographic maps. The threshold values of the depression area, shape, depth and TPI appropriate for distinguishing true depressions were abstained from the maximum overall accuracy generated by the comparison between the depression maps produced by the semi-automatic model or visual interpretation. The result shows that the best performance of the semi-automatic model for meso-scale karst depression delineation was using the DEM from the topographic maps with the thresholds area ⪆ 60 m², ellipticity ⪆ 0.2 and TPI ≤ 0. With these realistic thresholds, the accuracy of the semi-automatic model ranges from 0.78 to 0.95 for DEM resolutions from 3 to 75 m.

Soil moisture is an important parameter in the study of agriculture, ecology and carbon cycle. However, it has great difficulties to retrieve soil moisture content using remote sensing techniques. Even, field measurements can hardly reflect the spatial variation of soil moisture, due to the tremendous heterogeneity in its spatial distribution. Wireless Sensor Network (WSN), as a new technology for ground data collection, has been gradually applied to various fields. This novel technique has great advantages in monitoring soil moisture content, obtaining the soil moisture data in real time from multiple sites and different depths. Taking Huailai remote sensing comprehensive experimental station of Chinese Academy of Sciences for example, this paper introduces the calibration and data validation of soil moisture wireless sensor network. Oven drying method is used to calibrate the soil moisture sensor EC-5. The analysis indicates that the data measured by EC-5 had fairly well accuracy, so that the further calibration is not necessary. Data validation experiments had been taken from three aspects: data validity verification, temporal and spatial validation. It is clear to see that WSN data reveals the changes of soil moisture both in spatial domain and in different depths. Although the soil moisture data measured by WSN still do not have enough absolute accuracy, its continuous real-time data can clearly reflect the temporal and spatial relative variation, and the wide installation of sensors enables the data be obtained by the large amount, which was practically unavailable before.

This paper discussed the impact of the distribution of parks including water bodies on the relief of urban heat. An image of QuickBird on Aug. 30, 2013 was employed to perform the detailed land cover classification. One swath of Landsat 8 THIR image was collected to derive the land surface temperature. After some necessary preprocessing procedures, object-based classification method was used to classify the land cover as residential region, square and road, water body, as well as park. The results showed that water bodies and parks play an important role in reducing the land surface temperature. Grass, shrub and trees were extracted out respectively by manual from parks that were adopted to test the influence of proportions among trees, shrubs and grass on the fluctuation of land surface temperature in urban area. The results achieved in this paper could be helpful for the local governments to make a decision in urban plan and management.

In order to avoid rework and improve the efficiency of GPS static observation, it is very important to control the quality of GPS data in real time. On the basis of analysis of observation data decoding and observation data quality indices, a new program was compiled for decoding the original observation data and calculating and analyzing the quality indices such as the multipath effect and cycle slips etc. and the quality indices can be displayed intuitively by graphics in realtime. Moreover, the experimental verification is carried out with the engineering practice, and the results proved the correctness and reliability of the designed software RTQC, which can provide guidance and help for the practical work.

Attitude fluctuation of satellite platform is a crucial error source that influences the performance of the satellite surveying and mapping and extensively existed in all spacecrafts. Verification and detection of fluctuation frequency of the high spatial resolution remote sensing satellite plays a key role in farther optimizing the geo-location accuracy when the geometric accuracy of satellite reaches a fairly high precision. In this paper, an approximate sampling theorem is designed to verify and detect the attitude fluctuation frequency of satellite, and this novel method is based on Nyquist- Shannon sampling theorem, which applies ingeniously attitude acquisition time interval. The sampling interval of this approach will be appropriately altered by proposed sampling model while the satellite attitude acquisition frequency is not proportional to the fluctuation frequency. A simulative triangular wave and a strip attitude data of ZY-3 obtained while satellite was early on-orbit are experimented, and these results demonstrate the following findings: 1)the method proposed is effective and can filter irrelevant wave partially; 2)the fluctuation components obtained contain a distinct frequency of around 0.67Hz meets the inherent frequency of ZY-3 detected by other methods.

Range is the key parameter for synthetic aperture radar imaging. The range parameter estimation using the relationship between the radar internal calibration signals and system signal is studied in this paper. The delay of the system signal is computed based on the delays of the three internal calibration signals recorded. Using the approach proposed, the standard deviation of the range delay of the radar system signal is about 0.1mm.

LiDAR is a technology to acquire object surface measurements which intergrates GPS, IMU, laser scanning and ranging system and imaging devices together. LIDAR technology has the characteristics of highly automation, short data production cycle, the little effect of external environment and high precision and accuracy to acquire measurement information. But the number of liDAR point cloud is huge. When using large amounts of point cloud data to construct DEM, instead of improving the accuracy of DEM no significant effect, it will lead to the rapid decline in data processing speed. So it is necessary to simplify the LiDAR point cloud. When simplifying the point cloud, the criterions of point cloud simplification directly influence the distribution and quality of retention points. Usually, the point simplification criterions are based on topographic feature. Hence,this paper will proposal a new approach based on terrain complexity metrics to simplify LiDAR point cloud. Terrain complexity index present a comprehensive description of topographic features. First the index is calculated based on the existing rough precision DEM data;next,find out the point cloud simplification threshold according to the index;then set simplify rules to retain the feature points and simplify the useless points;finally, using geostatistical method,high accuracy DEM is constructed by the retention points and the precision and accuracy of LiDAR point cloud simplification is evaluated. The method will be expected to improve the precision and accuracy of LiDAR point cloud simplification.

In stereo vision technology, image matching is one of the most important parts, and the coefficient of correlation matching is recognized to be more mature and stable matching algorithm. Correlation coefficient method has high sensitivity to image rotation, but do not have rotation invariance, and require a large computational complexity. Because of this it cannot be widely applied in the field of real-time image matching. This paper is aimed at this drawback to make its computational complexity greatly reduced, posses the scale and rotation invariance, so as to meet the requirements of real-time image matching system, this paper proposes a image registration algorithm of accurate registration combined with Fourier-Mellin transform and Radon transform of image. After the introduction of Fourier transform and correlation coefficient method to detect the correct rotation factor and scale factor, it is provided a reliable basis for correlation coefficient method of image registration to achieve both rotation and scaling invariance, image using this method is verified by the experiments on the feasibility of the registration, the registration accuracy is improved.

Ruoergai Conservation area belongs to the Aba Tibetan and Qiang Autonomous Prefecture, Sichuan Province. In recent decades, the wetland and grassland have degraded very seriously. In this paper, based on GIS spatial analysis techniques, we utilized the Landsat 7 ETM + images in 2007, 2009, and 2011, to extract the land use/land cover change in Ruoergai. Firstly, the images were enhanced, mosaicked, and subset. Secondly, the supervised and unsupervised classification method were used to derive the land use/land cover change information in Ruoergai. Finally, the land use / land cover change in Ruoergai was analyzed between 2007 and 2011 . Our results were listed below: (1)The area of water body and swamp in Ruoergai reduced by 52.346% between 2007 and 2011. (2)The area of grass land in Ruoergai decreased by 12.754% between 2007 and 2011. (3) The area of woodland in Ruoergai reduced by 3.224% between 2007 and 2011. (4) The area of bare land, cultivated land and construction land increased by 6.647% between 2007 and 2011.

In the realistic 3D model reconstruction, the requirement of the texture is very high. Texture is one of the key factors that affecting realistic of the model and using texture mapping technology to realize. In this paper we present a practical approach of texture mapping based on photogrammetry theory from multiple aerial imageries in urban areas. By collinearity equation to matching the model and imageries, and in order to improving the quality of texture, we describe an automatic approach for select the optimal texture to realized 3D building from the aerial imageries of many strip. The texture of buildings can be automatically matching by the algorithm. The experimental results show that the platform of texture mapping process has a high degree of automation and improve the efficiency of the 3D modeling reconstruction.

The aerial survey as one of the branches of the Space Information Technology system, has an important application in data acquisition of the earth's surface. In recent years, the trend of UVA (unmanned aerial vehicle) to replace traditional survey aircraft has become increasingly obvious with the progress of science and technology. At present, the price of the commercial UAV Flight Control System is higher, limiting the application of UVA. This paper mainly discusses the possibility that the open-source’s flight-control-system take the place of the commercial one. Result is that the costs of UVA are reduced, and make the application more widely.

Image matching is one of the key technologies in the image processing. In order to increase its efficiency and precision, a new method for image matching which based on the improved SURF and Delaunay-TIN is proposed in this paper. Based on the original SURF algorithm, three constraint conditions, color invariant model, Delaunay-TIN, triangle similarity function and photography invariant are added into the original SURF model. With the proposed algorithm, the image color information is effectively retained and the erroneous matching rate of features is largely reduced. The experimental results shows that this proposed method has the characteristics of higher matching speed, uniform distribution of feature points to be matched, and higher correct matching rate than the original algorithm does.

An exceptional drought struck Henan province during the summer of 2014. It caused directly the financial loss reaching to hundreds of billion Yuan (RMB), and brought the adverse influence for people’s life, agricultural production as well as the ecosystem. The study in this paper characterized the Henan 2014 summer drought event through analyzing the spatial distribution of drought severity using precipitation data from Tropical Rainfall Measuring Mission (TRMM) sensor and Normalized difference vegetation index (NDVI) and land surface temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The trend analysis of the annual precipitation from 2003 to 2014 showed that the region over Henan province is becoming dry. Especially in the east of Henan province, the decrease of precipitation is more obvious with the maximum change rate of ~48 mm/year. The rainfall in summer (from June to August) of 2014 was the largest negtive anomaly in contrast with the same period of historical years, which was 43% lower than the average of the past ten years. Drought severity derived from Standardized Precipitation Index (SPI) indicated that all areas of Henan province experienced drought in summer of 2014 with different severity levels. The extreme drought, accounting for about 22.7 % of Henan total area, mainly occurred in Luohe, Xuchang, and Pingdingshan regions, and partly in Nanyang, Zhengzhou, and Jiaozuo. This is consistent with the statistics from local municipalities. The Normalized Drought Index Anomaly (NDAI), calculated from MODIS NDVI and LST products, can capture the evolution of the Henan 2014 summer drought effectively. Drought severity classified by NDAI also agreed well with the result from the SPI.

A novel registration strategy for aerial image sequence is put forward to adapt to the long spatio-temporal span of the aerial remote sensing imaging. By setting keyframe, this strategy aligns all images in sequence to a unified datum with high registration sustainability and precision. The contrast experiment on different registration strategies is carried out based on SIFT feature matching of mid-infrared aerial sequences. The experiment results show that the proposed strategy performs well on long spatio-temporal sequences with different imaging resolutions and scenes.

The diurnal variation and inter-annual variation of urban heat island in Beibu Gulf Economic Zone were quantitatively analyzed, based on 2005-2014 MOD11A2 land surface temperature dataset, by the method of land surface temperature departure. Furthermore, the relationship between vegetation NDVI and urban heat island intensity were studied. Results showed: (1) Spatial distribution and extent of urban heat island in Beibu Gulf Economic Zone had a certain difference between daytime and nighttime in summer, the temperature departure decrease with distance from the city center increasing, and the decreasing trend of daytime was significant compared with nighttime. The temperature difference was about 4 to °C between the city and suburb in the daytime, and was about 2-6°C at night (2) The inter-annual variation intensity of urban heat island was not obvious in summer daytime during 2005-2014, while the difference of spatial distribution was significant. The urban heat island intensity increased in Nanning city during 2005-2011, and slowed in 2014. The heat island variation intensity was not obvious in Beihai city before 2011, but increased significantly in 2014. (3) The correlation coefficient between NDVI and land surface temperature in summer is -0.29, which showed high vegetation coverage plays an important role in reducing the urban heat island.

The water resources crisis is intensifying in Southwest China (SWC), which includes the world’s largest continuous coverage of karst landforms, due to recent severe drought events. However, because of the special properties of karstic water system, such as strong heterogeneity, monitoring the variation of karstic water resources at large scales remains still difficult. Satellite gravimetry has emerged as an effective tool for investigating the global and regional water cycles. In this study, we used GRACE (Gravity Recovery and Climate Experiment) data from January 2003 to January 2013 to investigate karstic water storage variability over the karst region of SWC. We assessed the impacts of the recent severe droughts on karst water resources, including two heavy droughts in September 2010 to May 2010 and August 2011 to January 2012. Results show a slightly water increase tend during the studied period, but these two severe droughts have resulted in significant water depletion in the studied karst region. The latter drought during 2011 and 2012 caused more water deficits than that of the drought in 2010. Strong correlation between the variations of GRACE-based total water storage and precipitation suggests that climate change is the main driving force for the significant water absent over the studied karst region. As the world’s largest continuous coverage karst aquifer, the karst region of SWC offers an example of GRACE applications to a karst system incisively and will benefit for water management from a long-term perspective in karst systems throughout the world.

Land surface temperature (LST) is widely used in a variety of applications, such as meteorology, climatology, and ecology. Up to now, there are no all-weather LST products at high spatial resolution. In this study, we propose a method to generate an all-weather LST product by merging MODIS and AMSR-E data. Two main processes are performed in this method, including retrieving AMSR-E LST and downscaling AMSR-E LST to MODIS pixel resolution. After the implement of these two processes, MODIS LSTs under clear-sky conditions and AMSR-E LSTs under cloudy conditions are merged to generate an all-weather LST product. Results indicate that the merged LSTs filled up the missing data in the original MODIS LSTs due to the effects of cloud when compared with the original MODIS LSTs.

In this study, field data such as secchi depth of th Sishili Bay and its adjacent areas in Yantai’s coastal waters were observed, meanwhile, hyperspectral remote snesing data were measured with Ocean Optics USB4000 spectrometer during two cruises carried out on 22nd and 23rd June 2009. The coastal water-leaving refectance of HICO was simulate from in situ hyperspectral remote sensing spectrum with 0.19 nm spectral resolution. According to the spectral simulation, characteristics bands of HICO were identified for water clarity estimation model. On the basis of correlation analysis between water transparency and simulation spectra, a single band model for estimating Secchi depth was established. Moreover, the relationship between Secchi depth band ratios of HICO were analyzed, and then a linear regression model of water clarity using simulated HICO was developed, with a mean relative error of 5.6% and relative mean square error of 0.29 m. The result indicates that the band ratio model of Rrs(507.18)/Rrs(518.64) of HICO could be used to retrieve Secchi depth in coastal waters.

To make full use of the data resources and realize a sharing for the different types of data in different industries, a method of format conversion between CAD data and GIS data based on ArcGIS was proposed. To keep the integrity of the converted data, some key steps to process CAD data before conversion were made in AutoCAD. For examples, deleting unnecessary elements such as title, border and legend avoided the appearance of unnecessary elements after conversion, as layering data again by a national standard avoided the different types of elements to appear in a same layer after conversion. In ArcGIS, converting CAD data to GIS data was executed by the correspondence of graphic element classification between AutoCAD and ArcGIS. In addition, an empty geographic database and feature set was required to create in ArcGIS for storing the text data of CAD data. The experimental results show that the proposed method avoids a large amount of editing work in data conversion and maintains the integrity of spatial data and attribute data between before and after conversion.

The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

The space constellation of BeiDou Navigation Satellite System(BDS)has three main components, Geostationary Earth Orbits (GEOs), Medium Earth Orbits (MEOs) and Inclined Geosynchronous Satellite Orbits(IGSOs).This paper selected 6 satellite respectively in three types to simulate their exiting service, and used the statistical methods to assess receiver autonomous integrity monitoring（RAIM） availability and fault detection （FD） capability of BeiDou14（Phase Ⅱ with 14 satellites）under the circumstances. This paper assessed RAIM availability performance from satellites and constellation geometry configuration by the number of visible satellites (NVS, NVS⪆5) and position dilution of precision (PDOP, PDOP≤6) together. The FD capability of RAIM is assessed by the maximum minimal detectable bias (MDB) and the maximum minimal detectable effect (MDE). The analyses of simulation results testify that the exiting of single MEO or IGSO satellite have no obvious effect on RAIM availability and error detection ability. However GEO satellite’s exiting can make the number of points in where the constellation geometry is not available and maximum minimum detectable deviation and maximum minimum detectable influence significantly increase. Relative to other two satellites, GEO satellites’ health have a significant impact on the RAIM performance of BDS.

Land surface emissivity (LSE) is a critical parameter for retrieving land surface temperature (LST) from remotely sensed data. Due to its non-uniformity and a change through vegetation and physical parameters such as texture, composition, surface moisture, roughness, and view angle, the measurement of LSE in laboratory cannot reflect the real world conditions that material interacts with its background and the environment. The filed measurement currently observed by a thermal sensor is radiance, which is a function of many contributing parameters. To accurately obtain the LSE, this paper devotes to develop a scheme for deriving the spectral emissivity from field measured radiance observed by a hand portable FT-IR spectroradiometer Model 102F. A piecewise linear spectral emissivity constraint method is used to decouple the LST and LSE. The results show that the trends of the derived emissivity spectra for different natural surfaces of sand, bare soil and grass are reasonable. Comparisons of several field and laboratory collected LSE spectra for different natural surfaces show that the root mean square errors (RMSEs) are below 0.02, which indicates that the proposed method is accurately to derive LSE spectrum from the measurement of field natural surface with 102F field spectroradiometer.

Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.

In order to analyze the effect of physical geographic factors on vegetation change in arid and semi-arid ecosystems, assess the relative role of individual physical geographic factors and the interaction between factors on vegetation changes quantitatively, this study takes the Xinjiang area as an example, uses the GIS spatial analysis technology and Geographical Detector model based on the analysis of variance to analysis the influence of physical geographic factors on the vegetation quantitatively. First of all, the spatial-temporal variations of vegetation in Xinjiang area over the last 30 years were analyzed using 1982-2011 GIMMS NDVI3g data as the indicator of vegetation activity. Secondly, the effects of mean annual precipitation, mean annual temperature, sunshine duration, mean annual wind velocity, DEM, slope and aspect, soil type and vegetation type were selected as potential physical geographic factors. Finally, the influence of physical geographic factors on vegetation change in Xinjiang area was analyzed using the Geographical Detector model. The results show that: (1) the annual coverage of vegetation in Xinjiang area was gradually increasing in 1982-2011 years (linear rate 0.0017/a, P=0.000). (2) the area of vegetation improvement was greater than the area of vegetation degradation. The area of vegetation improvement was mainly distributed in the northern part of the Tianshan Mountains and the Tarim Watershed, the vegetation degradation region was mainly distributed in the southern and Northeast part of Xinjiang. (3) precipitation, soil and vegetation types had the greatest influence on NDVI, followed by temperature, sunshine duration and DEM, and the other factors had little effect. (4) DEM enhanced the effect of soil type on NDVI, and sunshine duration and DEM enhanced all the effect of temperature on NDVI. So, sunshine duration and DEM can be used as the auxiliary indicator in the vegetation growth monitoring. Our results brought new insights on the monitoring of vegetation dynamic and could provide a basic reference for the local inhabitants and policy-makers to restore degraded arid and semi-arid ecosystems.

Combines the wavelet analysis and neural network, this paper will be processed the data and the traditional BP neural network and kalman filter are analyzed and compared. First of all to obtain data of dam deformation wavelet denoising, excluding the contaminated data, obtain the optimal data set. Threshold denoising is generally adopted. Then based on the BP neural network, wavelet analysis to improve the traditional neural network model. Improve the underlying layer upon layer number and the number of nodes. Combined with the optimized dam deformation data, using the improved network model, the results to the regression model, ordinary kalman filter, this paper compares and analyzes the prediction effect evaluation.Comparison result is more ideal, which indicates that the combination of wavelet neural network model for deformation data processing has a good precision.

The Wireless Sensor Networks of Coarse-resolution Pixel Parameters (CPP-WSN) was established to monitor the heterogeneity of coarse spatial resolution pixel, with consideration of different categories of land surface parameters in Huailai, Hebei province, China (40.349°N, 115.785°E). The observation network of radiation parameters (RadNet) in CPP-WSN was developed for multi-band radiation measurement and consisted of 6 nodes covering 2km*2km area to capture its heterogeneity. Each node employed four sensors to observe the five radiation parameters. The number and location of nodes in RadNet were determined through the representativeness-based sampling method. Thus, the RadNet is a distributed observation system with nodes work synchronously and measurements used together.

The intercomparison experiment for RadNet is necessary and was conducted in Huailai Remote Sensing Station from 5th Aug to 10th Aug in 2012. Time series observations from various sensors were collected and analyzed. The maximum relative differences among sensors of UVR, SWR, LWR, PAR, and LST are 4.83%, 5.3%, 3.71%, 11%, and 0.54%, respectively. Sensor/parameter differences indeed exist and are considerable large for PAR, SWR, UVR, and LWR, which cannot be ignored. The linear normalization and quadratic polynomial normalization perform similar for CUV5/UVR, PQS1/PAR, CNR4/SWR, and SI-111/LST. As for CNR4/LWR, quadratic polynomial normalization show higher accuracy than linear normalization, especially in node2, node4, and node5. Thus, the LWR measured by CNR4 is proved to be nonlinear, and should be normalized with quadratic polynomial coefficients for higher precision.

Snow is solid state of water resources on earth, and plays an important role in human life. Satellite remote sensing is significant in snow extraction with the advantages of cyclical, macro, comprehensiveness, objectivity, timeliness. With the continuous development of remote sensing technology, remote sensing data access to the trend of multiple platforms, multiple sensors and multiple perspectives. At the same time, in view of the remote sensing data of compute-intensive applications demand increase gradually. However, current the producing system of remote sensing products is in a serial mode, and this kind of production system is used for professional remote sensing researchers mostly, and production systems achieving automatic or semi-automatic production are relatively less. Facing massive remote sensing data, the traditional serial mode producing system with its low efficiency has been difficult to meet the requirements of mass data timely and efficient processing.

In order to effectively improve the production efficiency of NDSI products, meet the demand of large-scale remote sensing data processed timely and efficiently, this paper build NDSI products production system based on Hadoop platform, and the system mainly includes the remote sensing image management module, NDSI production module, and system service module. Main research contents and results including:

(1)The remote sensing image management module: includes image import and image metadata management two parts. Import mass basis IRS images and NDSI product images (the system performing the production task output) into HDFS file system; At the same time, read the corresponding orbit ranks number, maximum/minimum longitude and latitude, product date, HDFS storage path, Hadoop task ID (NDSI products), and other metadata information, and then create thumbnails, and unique ID number for each record distribution, import it into base/product image metadata database.

(2)NDSI production module: includes the index calculation, production tasks submission and monitoring two parts. Read HDF images related to production task in the form of a byte stream, and use Beam library to parse image byte stream to the form of Product; Use MapReduce distributed framework to perform production tasks, at the same time monitoring task status; When the production task complete, calls remote sensing image management module to store NDSI products.

(3)System service module: includes both image search and DNSI products download. To image metadata attributes described in JSON format, return to the image sequence ID existing in the HDFS file system; For the given MapReduce task ID, package several task output NDSI products into ZIP format file, and return to the download link

(4)System evaluation: download massive remote sensing data and use the system to process it to get the NDSI products testing the performance, and the result shows that the system has high extendibility, strong fault tolerance, fast production speed, and the image processing results with high accuracy.

As one of the most important indexes for describing spatial complexity of urban road networks, radius fractal dimension has been proved to be useful in single-central cities. The method needs to choose a traffic hub as the center of measurement, but if the city has more than one traffic center, it will be difficult to choose a proper center and portray spatial complexity of the whole road network. The modified method proposed in this paper regards all the nodes of a network as centers of measurement and considers the whole effect of traffic centers in a polycentric city, so the modified radius fractal dimension describes the spatial complexity of a road network from an overall perspective and overcomes the problem that the traditional method relies on only one center. The experimental results show the modified radius fractal dimension is reliable, which can describe urban road networks in a new perspective.

Many problems of land environment in urban development, with the support of 3S technology, the research of land environment evolved into the stage of spatial-temporal scales. This paper combining space, time and attribute features in land environmental change, with elements of "air-earth-life" framework for the study of pattern, researching the analysis method of land environment big data storage due to the limitations of traditional processing method in land environment spatial-temporal data, to reflect the organic couping relationship among the multi-dimensional elements in land environment and provide the theory basis of data storage for implementing big data analysis application platform in land environment.

Stereo adjustment is the key to stereo mapping of domestic optical satellite image. In recent years, the most common satellite data used for stereo mapping was the along-track stereo pairs from the tri-linear CCD mapping camera in domestic. With the improvement of satellite mobility and the measurement accuracy of orbit and attitude, using the single line array CCD camera, by side sway, to obtain across-track stereo pairs becomes possible. In this paper, the YG- 24 satellite panchromatic very high resolution (HR) images were used to obtain the across-track stereo pairs. Based on undistorted RFM, the stereo pairs were processed with affine transformation model under the condition of different number of ground control points (GCPs). The result shows that, the stereo positioning accuracy of bundle adjustment can reach 35m without GCPs; while the root mean square errors (RMSEs) of plane is 0.7m and the RMSEs of altitude is 0.9m with GCPs, which can meet the secondary accuracy requirements of 1:25000 mapping.

The strong demands for satellite images are increasing not only in professional fields, but also in the non-professionals. But the online map services with up-to-date satellite images can serve few demands. One challenge is how to provide online processing service, which need to handle real-time online data-intensive geospatial computation and visualization. Under the background of the development of cloud computing technology, the problem can be figured out partly. The other challenge is how to implement user-customized online processing without professional background and knowledge. An online self-service processing system of ZY-3 Satellite images is designed to implement an on-demand service mode in this paper. It will work with only some simple parameters being set up for the non-professionals without having to care about the specific processing steps. And the professionals can assemble the basic processing services to a service chain, which can work out a more complex processing and a better result. This intelligent self-service online system for satellite images processing, which is called the prototype of satellite image cloud service in this paper, is accelerated under the development of cloud computing technology and researches on data-intensive computing. To realize the goal, the service mode and framework of the online self-service processing system of ZY-3 Satellite images are figured out in this paper. The details of key technologies are also discussed, including user space virtualization management, algorithm-level parallel image processing, image service chain construction, etc. And the experimental system is built up as a prospective study of image cloud services.

A general function model of spatial straight line is established according to its linear parametric equation. And a structured total least squares algorithm for spatial straight line fitting is investigated in this paper, not only taking into account the errors caused by X, Y, Z three directions and the results impacted by constant columns in the coefficient matrix, but also considering the repeating elements of the coefficient matrix get the same corrections in different locations. The example given in this contribution illustrates that the efficiency and feasibility of this algorithm.

In order to study the applicability of the elevation model with considering terrain fluctuation factor in the calculation of the atmospheric water vapor conversion coefficient, this article selects different elevation data for five years from Xinjiang region sounding stations, using elevation model and Emardson model without considering the terrain fluctuation to calculate water vapor conversion coefficient K, and analyzing the applicability of the elevation model in Xinjiang region where is a large area of terrain, then comparing the accuracy of the conversion coefficient between the same latitude and different elevations as well as between the same elevation and different latitudes by the elevation model, researching the influence on elevation model from station’s latitude and altitude. The research shows that: (1) Adding terrain fluctuation factor of elevation model and Emardson model without considering the effects of elevation will appear the phenomenon of increasing accuracy, and precision of elevation model is slightly better than that of Emardson model with station’s altitude increasing. (2) When latitude acts as influence factor, the lower latitude the measuring station is, the higher accuracy of the elevation model will be. When elevation acts as influence factor, the bigger elevation the measuring station is, the higher accuracy of the elevation model will be. (3) The applicability of elevation model is better in these regions which located in low latitude and high altitude.

The construction of urban shelters is one of the most important work in urban planning and disaster prevention. The spatial allocation assessment is a fundamental pre-step for spatial location-allocation of urban shelters. This paper introduces a new method which makes use of agent-based technology to implement evacuation simulation so as to conduct dynamic spatial allocation assessment of urban shelters. The method can not only accomplish traditional geospatial evaluation for urban shelters, but also simulate the evacuation process of the residents to shelters. The advantage of utilizing this method lies into three aspects: (1) the evacuation time of each citizen from a residential building to the shelter can be estimated more reasonably; (2) the total evacuation time of all the residents in a region is able to be obtained; (3) the road congestions in evacuation in sheltering can be detected so as to take precautionary measures to prevent potential risks. In this study, three types of agents are designed: shelter agents, government agents and resident agents. Shelter agents select specified land uses as shelter candidates for different disasters. Government agents delimitate the service area of each shelter, in other words, regulate which shelter a person should take, in accordance with the administrative boundaries and road distance between the person's position and the location of the shelter. Resident agents have a series of attributes, such as ages, positions, walking speeds, and so on. They also have several behaviors, such as reducing speed when walking in the crowd, helping old people and children, and so on. Integrating these three types of agents which are correlated with each other, evacuation procedures can be simulated and dynamic allocation assessment of shelters will be achieved. A case study in Jing'an District, Shanghai, China, was conducted to demonstrate the feasibility of the method. A scenario of earthquake disaster which occurs in nighttime was set to simulate the evacuation process of the residents to the earthquake shelter candidates in the study area. The simulation results convinced that the proposed method can better evaluate the spatial configuration of urban shelter than traditional GIS methods. The method can help local decision-makers preferably handle shelter planning and emergency evacuation management problems. It can also be extended to conduct similar assessment work in other urban regions for different kinds of shelters.

In many interpolation methods, with its simple interpolation principle, Inverse distance weighted (IDW) interpolation is one of the most common interpolation method. There are anisotropic spatial structures with actual geographical spatial phenomenon. When the IDW interpolation is used, anisotropic spatial structures should be considered. Geostatistical theory has a characteristics of exploring anisotropic spatial structures. In this paper, spatial interpolation approach based on IDW with anisotropic spatial structures is proposed. The DEM data is tested in this paper to prove reliability of the IDW interpolation considering anisotropic spatial structures. Experimental results show that IDW interpolation considering anisotropic spatial structures can improve interpolation precision when sampling data has anisotropic spatial structures feature.

The Kingdom of Thailand has been facing with natural disasters every year: landslide, drought, wind storm, landslide etc. especially, the last decade the natural disaster was most frequency and devastated vast areas. Furthermore, landslide occurrences have become more and more recurrence and human impacts have been increasing on seriously natural disasters problem during the past couple of decades. The study has been designed to analyze the risk landslide areas for landslide management in Phetchabun province, Thailand. This study aim to apply the geo-informatics technology, create landslide risk map, and develop landslide monitoring and warning systems used for formulating preparedness and recovery plans. This analyzed the concerned physical and environmental factors though statistical techniques and spatial analysis. The analyzed factors included with river, elevation, street, land use, sub-basin area, slope, drainage and rainfall. Potential Surface Analysis (PSA) technique has been used for analysis included with overlaying and Weighting-Rating Model for landslide risk area. The validation model compared with historical data. The result could show risk areas of landslide in Phetchabun province that high risk areas are covering north-eastern and central of province. In addition, we divided risk area as three levels; high risky, moderate and less. Furthermore, the consequences can be protect or relieved by using appropriate measures; including both publicizing risk information and be prepared for the happening of such disasters. However, some of spatial data have to up to date and improve to high accuracy.

As the development of Web 2.0, the social media like microblog, blogs and social network have supplied a bunch of information with locations (Volunteered Geographical Information, VGI).Recent years many cases have shown that, if disaster happened, the cyber citizens will get together very quickly and share the disaster information, this results a bunch of volunteered geographical information about disaster situation which is very valuable for disaster response if this VGIs are used efficiently and properly. This project will take typhoon disaster as case study. In this paper, we study the relations between weibo messages and the real typhoon situation, we proposed an analysis framework for mine the relations between weibo messages distribution and physical space. We found that the number of the weibo messages, key words frequency and spatial temporary distribution of the messages have strong relations with the disaster spread in the real world, and this research results can improve our disaster situation awareness in the future. The achievement of the study will give a method for typhoon disaster situation awareness based on VGI from the bottom up, and will locate the disaster spot and evolution quickly which is very important for disaster response and recover.

This paper presents retrieving the ocean wave parameters in shallow water area of the South China Sea from ERS-2 imagery. Based on the MPI (Max-Planck Institute) algorithm, the proposed method uses the TMA (Texel-Marsen-Arsloe) model, which used for description of the ocean wave spectra in shallow water, as the first-guess spectra to estimate the ocean wave parameters. The model is extended and developed through adding the factor of water depth. The ERS-2 images in South China Sea are selected as the experimental data. The data of ECMWF is used to verify the peak wavelength and SWH (significant wave height) retrieved by the proposed method. The results show the RMSE (Root Mean Square Error) of peak wavelength and SWH are 14.19 m and 0.13 m, respectively; the mean bias of peak wavelength and SWH are 11.30 m and 0.02 m, respectively. The analyzed results discover that the ocean wave spectrum parameters retrieved by the proposed method are consistent with the corresponding parameters from ECMWF in shallow water, and the developed model can effectively response the effects of coastal shoaling because of the varying bathymetry to the surface wave propagation.

Poyang Lake, the largest freshwater lake of China, is well known for its ecological and economic importance as a dynamic wetland system. But, influenced by the climate change and human activity, Poyang Lake wetland has changed a lot. The long time series of Terra/MODIS data between 2000 and 2014 were utilized to investigate the variation of Poyang Lake and to analyze Poyang lake response to variation of local precipitation with the meteorological data. The results showed: (1) Poyang Lake water body area showed a significant seasonal variation, minimum value was about 690 km² and maximum value reached 3500 km², and inter-annual fluctuation; (2)For the past 15 year , local precipitation directly affected the inundation changes. In particular, the impact of rainfall during the first half of the year is more significant (the relation coefficient with R2 of 0.61); (3) Taking into account humid activities, the impoundment of the Three Gorges dam (TGD) had a certain impact on Poyang Lake water body area, especially the persistent reduction of Poyang lake surface area in November was deteriorated by the impounding of TGD in October after 2006. Finally, the study provides a theoretical basis and data for changes in Poyang Lake wetland research and protection.

Underground water is different from surface water. Once contaminated, underground water is difficult to recover, so it is necessary to give priority to the prevention of the quality of underground water. High-density resistivity method is very important in the environmental engineering geophysical prospecting and it is widely used in mineral resources as well as monitoring the underground-water quality. In the experiment, multi-tools joint inversion is applied to build the model in order to increase the accuracy. In contrast with the pollution-free water model which is owned by the RES2DMOD, the inversion result of underground water quality with the high density resistivity method is useful to monitor the underground water quality, showing that different degree of water pollution depends on the position of abnormal and there is a more significant abnormal value in the vertical direction of the deep abnormal than that of the shallow abnormal, and high and low resistance pollution depends on the different value and forms of abnormal resistance. In conclusion, monitoring the underground water quality by the high density resistivity method is efficient. In the future research, it is necessary to accomplish more precise inversion models combining with field measurements to find out the optimal solution to monitor underwater quality.

With frequent occurrence of earthquake disaster, earthquake monitoring becomes increasingly concerned. Global observing by optical remote sensing is an emerging technology widely applied in monitoring temporal changes of topography in earthquake. It provides advantages of large width of observation, fast data acquisition and high time effectiveness. This technique takes advantages of accurate image registration of pre-seismic and post-seismic to spot surface rupture zones. Therefore, the spatial alignment accuracy of multi temporal images becomes a problem that hinder the earthquake monitoring. Considering the adverse impact of different imaging angle, camera lens distortion and other factors on image registration, a new approach of high accurate registration based on constraining positioning consistency in rational function model (RFM) is proposed. Ziyuan3 images of Yutian country in Xinjiang are used to perform the earthquake monitoring experiment. After applying the proposed method, registration accuracy of pre-seismic and postseismic images is better than 0.6 pixel; surface rupture zones caused by earthquake are acquired promptly.

Large Aperture Static Interference Imaging Spectrometer is a new type of spectrometer with light structure, high spectral linearity, high luminous flux and wide spectral range, etc ,which overcomes the contradiction between high flux and high stability so that enables important values in science studies and applications. However, there’re different error laws in imaging process of LASIS due to its different imaging style from traditional imaging spectrometers, correspondingly, its data processing is complicated. In order to improve accuracy of spectrum detection and serve for quantitative analysis and monitoring of topographical surface feature, the error law of LASIS imaging is supposed to be learned. In this paper, the LASIS errors are classified as interferogram error, radiometric correction error and spectral inversion error, and each type of error is analyzed and studied. Finally, a case study of Yaogan-14 is proposed, in which the interferogram error of LASIS by time and space combined modulation is mainly experimented and analyzed, as well as the errors from process of radiometric correction and spectral inversion.

With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision about 18% for AdaTree. WL decision tree, and 5% for SVM, the accuracy is over 90% for both classifiers.

An auto new segmentation approach based on graph theory which named self-adaptive mean-shift for high-resolution remote sensing images was proposed in this paper. This approach could overcome some defects that classic Mean-Shift must determine the fixed bandwidth through trial many times, and could effectively distinguish the difference between different features in the texture rich region. Segmentation experiments were processed with WorldView satellite image. The results show that the presented method is adaptive, and its speed and precision can satisfy application, so it is a robust automatic segmentation algorithm.

Surface subsidence is the main regional environmental geological disaster in plain area in China. The rapid growth of population, the over-exploitation of groundwater and the rapid development of urbanization impacts the occurrence and development of surface subsidence to some extent. The city of Beijing, located in the Beijing Plain, is one of international metropolis in China that experiences the severe surface subsidence. Because of conventional measurement methods with low spatial resolution, differential interferometric synthetic aperture radar(D-InSAR) is susceptible to signal decorrelation and atmospheric delay, persistent scatterer interferometric synthetic aperture radar(PS-InSAR) is based on a large number of SAR images, but small baseline subset interferometric synthetic aperture radar (SBASInSAR) only needs a small number of images and performs better than PS-InSAR for obtaining nonlinear deformation information, in this paper, SBAS-InSAR was used to obtain the high resolution surface subsidence information in Beijing region, China. A spatial-temporal analysis of the surface subsidence in Beijing region during the years of 2007- 2010 was performed utilizing eighteen C-band ENVISAT ASAR images (from August 1, 2007 to September 29, 2010). The results show that subsidence in Beijing region is severe uneven, subsidence funnels appear in Changping District, Shunyi District, Tongzhou District, Daxing District, etc., and many subsidence funnels are interconnected and have an eastward expansion trend; during the period of 2007 to 2010, the subsidence velocities are in the range of -158.5 mm/year to 12.4 mm/year and the maximum subsidence of subsidence center is over 400 mm; surface subsidence is influenced by groundwater exploitation and urbanization significantly.

Over the years, with the accelerating of city construction, urban heat-island effect has become increasingly significant.According to meteorological data of nearly ten years, some parts of the regional land surface temperature is higher, and then it influence people’s introduction and living directly. At the same time it also affect the ecological environment of the earth.This article bases on the Landsat8 remote sensing image of 2014, through the different resolution of image fusion to analyze the differences surface temperature of the study area and forecast the future development tendency. Research finding: in different resolution, due to details of the objects reflecting obviously differences, affected by it, the surface temperature also exists obvious difference. The lower resolution, the surface temperature difference is smaller; on the contrary,the higher resolution makes surface temperature difference more significant. This shows that with the expansion of cities and the change of vegetation, water, the regional differences of heat-island effect is more obvious. In future development, how to coordinate and plan buildings, factories, vegetation, water, etc will affect the distribution of urban heat-island effect.

Seamless contiguity is the key technology for parallel segmentation of remote sensing data with large quantities. It can be effectively integrate fragments of the parallel processing into reasonable results for subsequent processes. There are numerous methods reported in the literature for seamless contiguity, such as establishing buffer, area boundary merging and data sewing. et. We proposed a new method which was also based on building buffers. The seamless contiguity processes we adopt are based on the principle: ensuring the accuracy of the boundary, ensuring the correctness of topology. Firstly, block number is computed based on data processing ability, unlike establishing buffer on both sides of block line, buffer is established just on the right side and underside of the line. Each block of data is segmented respectively and then gets the segmentation objects and their label value. Secondly, choose one block(called master block) and do stitching on the adjacent blocks(called slave block), process the rest of the block in sequence. Through the above processing, topological relationship and boundaries of master block are guaranteed. Thirdly, if the master block polygons boundaries intersect with buffer boundary and the slave blocks polygons boundaries intersect with block line, we adopt certain rules to merge and trade-offs them. Fourthly, check the topology and boundary in the buffer area. Finally, a set of experiments were conducted and prove the feasibility of this method. This novel seamless contiguity algorithm provides an applicable and practical solution for efficient segmentation of massive remote sensing image.

In this paper we evaluated several typical matching costs including CENSUS, mutual information (MI) and the normalized cross correlation using the ISPRS Stereo Matching Benchmark datasets for DSM generation by stereo matching. Two kinds of global optimization algorithms including semi-global matching (SGM) and graph cuts (GC) were used as optimization method. We used a sub-pixel method to obtain more accurate MI lookup table and a sub-pixel method was also used when computing cost by MI lookup table. MI itself is sensitive to partial radiation differences. So we used a kind of cost combined MI and CENSUS. After DSM generation, the deviation data between the generated DSM and Lidar was statistics out to compute the mean deviation (Mean), the median deviation (Med), the standard deviation (Stdev), the normalized median absolute deviation (NMAD), the percentage of deviation in tolerance etc., which were used to evaluate the accuracy of DSM generated from different cost.

In this paper, a combination method, between the neural network and textures information, is proposed to remote sensing images classification. The methodology involves an extraction of texture features using the gray level co-occurrence matrix and image classification with BP artificial neural network. The combination of texture features and the digital elevation model (DEM) as classified bands to neural network were used to recognized different classes. This scheme shows high recognition accuracy in the classification of remote sensing images. In the experiments, the proposed method was successfully applied to remote sensing image classification and Land Use Change Detection, in the meanwhile, the effectiveness of the proposed method was verified.

Land surface emissivity (LSE) is a key parameter for characterizing the land surface, and is vital for a wide variety of surface-atmosphere studies. This paper retrieved LSEs of land surfaces over the city of Madrid, Spain from airborne hyperspectral scanner (AHS) thermal infrared data using temperature emissivity separation (TES) method. Six different kinds of urban surfaces: asphalt, bare soil, granite, pavement, shrub and grass pavement, were selected to evaluate the performance of the TES method in urban areas. The results demonstrate that the TES method can be successfully applied to retrieve LSEs in urban area. The six urban surfaces have similar curve shape of emissivity spectra, with the lowest emissivity in band 73, and highest in band 78; the LSE for bare soil varies significantly with spectra, approximately from 0.90 in band 72 to 0.98 in band 78, whereas the LSE for grass has the smallest spectral variation, approximately from 0.965 in band 72 to 0.974 in band 78, and the shrub presents higher LSE than other surfaces in bands 72, 73, 75-77, but a little lower in bands 78 and 79. Furthermore, it is worth noting that band 73 is suitable for discriminating different urban surfaces because large LSE differences exist in this channel for different urban surfaces.

Fuxian Lake located in the middle of Yunnan Province is second deepest lake in china. The water level of Fuxian Lake descends and its water area reduces in recent years owing to the climate changing. Therefore, it is crucial for rational utilization of lake resources to study the change trend of Fuxian Lake’s area. Landsat images from 1974 to 2014 were used to monitor Fuxian Lake’s area change. Monitoring results show that there are four apparent features of Fuxian Lake’s area: (1) Years in which Fuxian Lake’s area are larger are concentrated in 2006 to 2009. (2) From 1974 to 1990, Fuxian Lake’s area change has a trend of decrease. (3) From 1990 to 2005, Fuxian Lake’s area change shows a rise trend on the whole. (4) From 2005 to 2014, there is an obvious decrease trend of Fuxian Lake’s area change.

Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.

Sedimentary manganese deposits widely distribute in North Guangxi with the characteristic existing Celosia argentea. Celosia argentea is a kind of plant which has a strong ability to enrich manganese. In order to study the relationship between the hyperspectral characteristics of Celosia argentea and the concentration effect of manganese in the soil, we used soil of B layer in mining area, background soil and the soil adding reagent of MnCl⁴ to make up experimental sample soil with 10 levels Manganese content for the same batch Celosia argentea. The levels are 0mg/kg, 4500mg/kg, 9000mg/kg, 13500mg/kg, 18000mg/kg, 18020mg/kg, 18040mg/kg, 18080mg/kg, 18160mg/kg. ASD FieldSpec-4 has been used to measure the abnormal spectrums of these Celosia argentea through a whole growth cycle. After pretreating the spectral data, we used Successive Projections Algorithm (SPA) to extract the characteristic variables for extracting 1603 bands into 8 bands. Finally, the relationship between the spectral variables and the concentration of manganese was predicted by the Model of Partial Least Squares Regression (PLSR). The results show that the correlation coefficient-r² are 0.8714 and 0.9141 in two sets of data. The prediction results are satisfactory, but the front 5 groups are closer to the regression line than the last 5 groups.

Based on the geometric characteristic of ZY3 surveying and mapping satellite, this paper analyses the main error sources of the geometric accuracy of ZY3 satellite image product, and proposes a key technique to improve the accuracy of geometric positioning of ZY-3 satellite image products without the Ground Control Points. Firstly, 556 ZY-3 satellite images distributed in the central western China, with an area of 350 million km², were used for the planar positioning accuracy verification. The results show that the planar accuracy of ZY-3 image without the GCPs is about 10.8 meters (1σ), and more than 96.9% of experimental image without the GCPs have the planar accuracy higher than 25 meters. Subsequently, the Digital Surface Model (DSM) produced by the ZY-3 three linear array image in Shanxi without the GCPs and the high-precise Lidar-DEM were compared. The comparison shows that overall vertical accuracy of DSM is higher than 6 meters (1σ), and higher than 5.5 and 6.4 meters (1σ) in plane and mountainous area respectively. So the validation confirmed the overall accuracy of ZY-3 satellite images, indicating that ZY-3 satellite can achieve a higher geometric accuracy.

The paper first studied the geometric correction of historical CORONA satellite imagery in the 1960s and used the historical imagery to extract KRD (karst rocky desertification). The study area is located in the karst region of Guangxi Province, China. Finally, we used the Landsat-5 imagery to extract rocky desertification in 2005, then we could find the changes of the karst rocky desertification in Guangxi from 1960s to 2005 about nearly 40 years. And comparison analysis was conducted and the results showed that, over the 40 years, Guangxi karst rocky desertification area has significantly changed. Guangxi has typical karst environment and is one of the most serious areas of rocky desertification in southwest China provinces, thus our research on this has great practical significance.

This paper mainly introduces the five ocean chlorophyll concentration inversion algorithm and 3 main models for computing ocean primary production based on ocean chlorophyll concentration. Through the comparison of five ocean chlorophyll inversion algorithm, sums up the advantages and disadvantages of these algorithm,and briefly analyzes the trend of ocean primary production model.

Hierarchical structure of road network has received intensive attention either in urban planning or multi-scale representation. On the one hand, high-efficiency traffic flow counts on a reasonable hierarchical structure. On the other hand, it is a guide-line for cartographic generalization of road network. The paper attempts to investigate the hierarchical structure of a road network from two perspectives, a) the ht-index in terms of the degree connectivity, which was proposed to quantify the scaling and hierarchical structure of the network, b) the renormalization process, originated from complex network analysis, which is able to uncover the self-similarity of a network and reveal its hierarchical structure. We argue that the first point exhibits a big picture of the whole network, revealing the depth of the hierarchy, while the second point further illustrates how the nodes are organized to form a hierarchical structure at different scales. The hierarchical structures of 6 road networks in reality are examined accordingly. Results show that both indices are able to reveal the complexity of the hierarchy of a network. These conclusions can be beneficial to the road network generalization.

In this paper, according to the characteristics of the grey forecast method and the neural network, constructed the parallel grey neural network model(PGNN) and apply to forecast a tunnel monitoring point’s settlement displacement data based on Nanjing metro. The results showed that the prediction accuracy of PGNN is significantly higher than that of unitary grey and neural forecast method. proves that the effectiveness of PGNN in the tunnel settlement prediction. Keywords: Tunnel settlement, grey model, neural network model, prediction

Aiming at the diversity of emergency aerial photogrammetric mission requirements, complex ground and air environmental constraints make the planning mission time-consuming. This paper presents a fast adaptation for the UAV aerial photogrammetric mission planning. First, Building emergency aerial UAVs mission the unified expression of UAVs model and mechanical model of performance parameters in the semantic space make the integrated expression of mission requirements and low altitude environment. Proposed match assessment method which based on resource and mission efficiency. Made the Adaptive match of UAV aerial resources and mission. According to the emergency aerial resource properties, considering complex air-ground environment and mission requirements constraints. Made accurate design of UAV route. Experimental results show, the method scientific and efficient, greatly enhanced the emergency response rate.

Use CityEngine as the 3D modeling platform, research on urban rapid 3D modeling technology based on the CGA(Computer Generated Architectur) rule , solved the problem of the rapid creation of urban 3D model in large scenes , and research on building texture processing and 3D model optimization techniques based on CGA rule , using component modeling method , solved the problem of texture distortion and model redundancy in the traditional fast modeling 3D model , and development of a three-dimensional view and analysis system based on ArcGIS Engine , realization of 3D model query , distance measurement , specific path flight , 3D marking , Scene export,etc.

This thesis presents a study on spatial-temporal analysis of building surface temperatures in Hung Hom. Observations were collected from Aug 2013 to Oct 2013 at a 30-min interval, using iButton sensors (N=20) covering twelve locations in Hung Hom. And thermal images were captured in PolyU from 05 Aug 2013 to 06 Aug 2013. A linear regression model of iButton and thermal records is established to calibrate temperature data. A 3D modeling system is developed based on Visual Studio 2010 development platform, using ArcEngine10.0 component, Microsoft Access 2010 database and C# programming language. The system realizes processing data, spatial analysis, compound query and 3D face temperature rendering and so on. After statistical analyses, building face azimuths are found to have a statistically significant relationship with sun azimuths at peak time. And seasonal building temperature changing also corresponds to the sun angle and sun azimuth variations. Building materials are found to have a significant effect on building surface temperatures. Buildings with lower albedo materials tend to have higher temperatures and larger thermal conductivity material have significant diurnal variations. For the geographical locations, the peripheral faces of campus have higher temperatures than the inner faces during day time and buildings located at the southeast are cooler than the western. Furthermore, human activity is found to have a strong relationship with building surface temperatures through weekday and weekend comparison.

With the deepening of the smart city construction, the model "smart+" is rapidly developing. Guilin, the international tourism metropolis fast constructing need smart tourism technology support. This paper studied the smart scenic spot service object and its requirements. And then constructed the smart service platform of the scenic spot application of 3S technology (Geographic Information System (GIS), Remote Sensing (RS) and Global Navigation Satellite System (GNSS)) and the Internet of things, cloud computing. Based on Guilin Seven-star Park scenic area as an object, this paper designed the Seven-star smart scenic spot service platform framework. The application of this platform will improve the tourists’ visiting experience, make the tourism management more scientifically and standardly, increase tourism enterprises operating earnings.

In the project of upgrading the Guangxi CORS(GXCORS) Beidou Ground-Based Augmentation System, Guangxi Bureau of Surveying, Mapping and Geoinformation, had completed the examination for the instrument of multiple producers about the Compass ground-based augmentation system. The contents of the tests contain the network RTK positioning accuracy, the static processing accuracy, the time availability, the space availability, the environmental availability, etc.. through analyzing the test data, in this paper, drawing some conclusions that reflect the current situation about the Compass Ground-based Augmentation System objectively, it is benefit for the construction and development of the Compass Ground-based Augmentation System.

With advantages of large capacity, long distance, low energy consumption, low cost, less land occupation and light pollution, inland waterway transportation becomes one of the most important constituents of the comprehensive transportation system and comprehensive water resources utilization in China. As one of “three elements” of navigation, waterway is the important basis for the development of water transportation and plays a key supporting role in shipping economic. The paper discuss how to realize the informatization and digitization of waterway management based on constructing an integrated system of standard inland electronic navigation chart production, waterway maintenance, navigation mark remote sensing and control, ship dynamic management, and water level remote sensing and report, which can also be the foundation of the intelligent waterway construction. Digital waterway construction is an information project and also has a practical meaning for waterway. It can not only meet the growing high assurance and security requirements for waterway, but also play a significant advantage in improving transport efficiency, reducing costs, promoting energy conservation and so on. This study lays a solid foundation on realizing intelligent waterway and building a smooth, efficient, safe, green modern inland waterway system, and must be considered as an unavoidable problem for the coordinated development between “low carbon” transportation and social economic.

The characterization of ionosphere delay estimated with precise point positioning is analyzed in this paper. The estimation, interpolation and application of the ionosphere delay are studied based on the processing of 24-h data from 5 observation stations. The results show that the estimated ionosphere delay is affected by the hardware delay bias from receiver so that there is a difference between the estimated and interpolated results. The results also show that the RMSs (root mean squares) are bigger, while the STDs (standard deviations) are better than 0.11 m. When the satellite difference is used, the hardware delay bias can be canceled. The interpolated satellite-differenced ionosphere delay is better than 0.11 m. Although there is a difference between the between the estimated and interpolated ionosphere delay results，it cannot affect its application in single-frequency positioning and the positioning accuracy can reach cm level.

Sensor technology applied in dynamic measurement in recent years become the mainly focus subject of attention. In this paper, the high-frequency data collected by two kinds of sensors from vibration sensor and GPS receiver are comparatively analyzed. The integrated scheme of dynamic measurement is proposed based on the high-frequency data for vibration sensor and GPS receiver. The result of experiment is shown that the above sensors with the some same characteristics based on the advantages of the sensors applied in the scheme. Finally, the feasibility and effectiveness of the integrated scheme for the vibration sensor and GPS receiver is verified in the dynamic measurement experiment.

In this paper, the history of the airborne lidar and the development stages of the technology are reviewed. The basic principle of airborne lidar and the method of processing point-cloud data were discussed. At present, single point laser scanning method is widely used in bathymetric survey. Although the method has high ranging accuracy, the data processing and hardware system is too much complicated and expensive. For this reason, this paper present a kind of improved dual-frequency method for bathymetric and sea surface survey, in this method 176 units of 1064nm wavelength laser has been used by push-broom scanning and due to the airborne power limits still use 532nm wavelength single point for bathymetric survey by zigzag scanning. We establish a spatial coordinates for obtaining the WGS-84 of point cloud by using airborne POS system.

In this study, an improved linear emissivity constraint temperature and emissivity separation (I-LECTES) method was first proposed to overcome the discontinuities problem of the retrieved land surface emissivities (LSEs) in the former linear emissivity constraint temperature and emissivity separation (LECTES) method. Consequently, the hyperspectral thermal infrared data were carefully simulated according to the configuration of Designs & Prototypes microFTIR Model 102, and were used to evaluate the performance of the I-LECTES method. Meanwhile, the I-LECTES method was also compared with the LECTES method. Different the atmosphere and surface circumstances were considered, as well as the different levels of noise equivalent temperature difference (NEΔT). The results showed that the proposed I-LECTES method is of a better accuracy compared with the LECTES method and has the characteristic of keeping the retrieved LSEs continuous, which sounds more reasonable. Because the noises in the ground measured radiance may have more effects on the accuracies of land surface temperature (LST) and LSEs than those in the atmospheric downwelling radiance, the noise in the ground measured radiance should be removed as much as possible to improve the accuracies of retrieved LST and LSEs. Furthermore, taken into account the lower retrieval accuracies for the cold and dry atmosphere, both the I-LECTES method and the LECTES method should be taken a full consideration. The proposed method is regarded to be promising because of its holding continuity and noise-immune.

This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies – which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.

Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082N (December 9, 2015); doi:10.1117/12.2220616 From Conference Volume 9808 International Conference on Intelligent Earth Observing and Applications 2015 Guoqing Zhou; Chuanli Kang Guilin, China | October 23, 2015 This paper was published on December 9, 2015, and retracted from the SPIE Digital Library on April 14, 2016, by the publisher upon verification that substantial portions of the paper were copied from the following work without attribution or permission: Kazushi Yamashina, Takeshi Ohkawa, Kanemitsu Ootsu, and Takashi Yokota, “Proposal of ROS-compliant FPGA Component for Low-Power Robotic Systems－case study on image processing application,” 2nd International Workshop on FPGAs for Software Programmers (FSP 2015), London, United Kingdom, September 1, 2015. http://arxiv.org/abs/1508.07123

High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods’. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.

Large-scale bridges are among the most important infrastructures whose safe conditions concern people’s daily activities and life safety. Monitoring of large-scale bridges is crucial since deformation might have occurred. How to obtain the deformation information and then judge the safe conditions are the key and difficult problems in bridge deformation monitoring field. Deflection is the important index for evaluation of bridge safety. This paper proposes a forecasting modeling of stepwise regression analysis. Based on the deflection monitoring data of Yangtze River Bridge, the main factors influenced deflection deformation is chiefly studied. Authors use the monitoring data to forecast the deformation value of a bridge deflection at different time from the perspective of non-bridge structure, and compared to the forecasting of gray relational analysis based on linear regression. The result show that the accuracy and reliability of stepwise regression analysis is high, which provides the scientific basis to the bridge operation management. And above all, the ideas of this research provide and effective method for bridge deformation analysis.

With the rise of the wisdom of urban construction, the intelligent city requirements expression to fine the geometric structure of spatial entity, so as to realize the refined analysis of urban geographic information, to achieve the purpose of intelligent management. But the traditional geographic entity geometric data organization method ignores the express geographic entity's internal geometry information,, and can not meet the requirements of the fine analysis. Therefore, it is a problem to be solved that how to make a thorough physical and geometrical data of its spatial geometry. In this paper, we propose a method of organizing the Tetrahedral Network in the geographic entity and combining with hierarchical representation for B-rep model to solve the problem.

In view of monitoring the changes of buildings on Earth's surface ,by analyzing the distribution characteristics of building in remote sensing image, combined with multi-scale in image segmentation and the advantages of mathematical morphology, this paper proposes a multi-scale combined with mathematical morphology of high resolution remote sensing image segmentation method, and uses the multiple fuzzy classification method and the shadow of auxiliary method to extract information building, With the comparison of k-means classification, and the traditional maximum likelihood classification method, the results of experiment object based on multi-scale combined with mathematical morphology of image segmentation and extraction method, can accurately extract the structure of the information is more clear classification data, provide the basis for the intelligent monitoring of earth data and theoretical support.

Based on the rapid development of the wisdom tourism, it is conform to the trend of the development of the wisdom tourism through the scientific method to realize the prediction of the scenic information. The article,using the super nonlinear fitting ability of RBF neural network[1-2],builds a prediction and inference method of comprehensive information for the complex geographic time, space and attribute of scenic through the hyper-surface data organization of the scenic geographic entity information[3]. And it uses Guilin scenic area as an example to deduce the process of the forecasting of the whole information.

Aiming at the characteristic of nonlinear and non-stationary in ionospheric total electron content(TEC), this article bring Wavelet Analysis into the autoregressive integrated moving average model to forecast the next four days’ TEC values by using six days’ ionospheric grid observation data of Chinese area in 2010 provided by IGS station. Taking IGS station’s observation data as true value, compare the forecast value with it then count the forecast accuracies which are to prove that it has a quite good result by using WARIMA model to forecast Chinese area’s Ionospheric grid data. But near the geomagnetic latitude of about ±20°grid, the model’s forecast results are a little worse than others’ because Geomagnetic activity is irregular which lead to the TEC values there change greatly.

Klobuchar model can reflect the spatial and temporal variations of ionospheric feature, but model fixed initial phase and night-time delay will introduce a large number of errors. Aiming at the shortcomings of the models, take least-squares surface fitting model as the background, using CORS network in Nanning region to measure the data correctly, the Klobuchar model's initial phase, amplitude, and night-time delay values are steadily corrected, so as to establish regional ionospheric model in Nanning, the results show that the accuracy of Klobuchar model is improved significantly.

Shadows commonly exist in high resolution image, particularly in urban areas .The presence of shadows can represent a serious obstacle for their full exploitation. Especially, because of the complexity of the urban circumstance, there are some factors effecting the shadow detection. For example, the high reflectivity ground, or the glass wall of the building, makes shadow somewhere brighter so that it may cause their intensities close to the non-shadow area. In order to solve these problems, we combine the zero-crossing detection method with the DBM-based geometric method of computing the zenith angle of the sun and the altitude angle of the sun .Firstly, the proposed method uses the zero-crossing detection method to detect the edge of the whole image .Then it selects the edges of both the buildings and their shadows by matching the coordinates of DBM. Because the building and its shadow are adjacent, we match these shadows with the ideal shadow gotten by the DBM-based geometric method to get the only shadow of the building. Compared with other methods, this method may detect the shadow areas of the buildings accurately.

Based on analyzing the multi-scale segmentation technique of watershed transform, by combining the multiple features of textured images, spectra and shape, an integrated image segmentation method of a combination of watershed and multi-scale can be presented. This article is combined with object-oriented technology, and applied it to extracting the information of arable land in Guangxi. The paper aims to establish classification system to solve the problem of extracting feature information by extracting the feature or feature combination of the class. The experimental result showed that the object-oriented extraction method of arable land information based on a combination of watershed and multi-scale could be better to realize extract and apply the information of high resolution remote sensing image.

Using the eleven Radiosonde Stations’ data in southwestern of China from 2010 to 2013 to calculate the conversion coefficient K which is a reference value of Precipitable Water Vapor (PWV). Then build the EMARDSON model and the EMARDSON K model which introduced with elevation parameter and altitude. And to analysis the accuracy of the two models in the southwest China by radiosonde data in 2014. The results show: 1) The K value calculated by EMARDSON model has good adaptability in southwest region. 2) The method of spatial interpolation prediction by choosing 7 Radiosonde Stations’ K value uniformity is more adaptable than using 11 Radiosonde Stations’ K value to build basic model in the case of predicting 11 Radiosonde Stations’ K value, and it has a certain accuracy when predicting by using spatial interpolation in some areas where lacking data. 3) The accuracy by using the A-EMARDSON model to predict K value was improved obviously. at the same time, when predicting K value by the method of spatial interpolation, both the precision of inner and the precision of outer are better than EMARDSON model. So it can be concluded that the altitude factor is an important factor to influence the K value prediction.