bluuebunny/crossref_metadata_2025 huggingface dataset:
THE VEGETATION TRENDS AND DRIVERS IN BEIJING-TIANJING-HEIBEI REGION FROM 1982 TO 2013 BASED ON TIME SERIES GIMMS NDVI3g
Authors: Liu S. , Tian H. , Wang X. , Li H. , He Y.
Published: May 2018

Abstract. Vegetation plays a leading role in ecosystems. Plant communities are the main components of ecosystems. Green plants in ecosystems are the primary producers, and they provide the living organic matter for the survival of other organisms. The dynamics of most landscapes are driven by both natural processes and human activities. In this study, the growing season GIMMS NDVI3g and climatic data were used to analyse the vegetation trends and drivers in Beijing-Tianjin-Hebei region from 1982 to 2013. Result shows that, the vegetation in Beijing-Tianjin-Hebei region shows overall restoration and partial degradation trend. The significant restoration region accounts for 61.5 % of Beijing-Tianjin-Hebei region, while the significant degradation region accounts for 2.1 %. The dominant climatic factor for time series NDVI were analyzed using the multi-linear regression model. Vegetation growth in 17.9 % of Beijing-Tianjin-Hebei region is dominated by temperature, 35.5 % is dominated by precipitation, and 11.68 % is dominated by solar radiance. Human activities play important role for vegetation restoration in Beijing-Tianjin-Hebei Region, where the large scale forest restoration programs are the main human activities, such as the three-north shelterbelt construction project, Beijing-Tianjin-Hebei sandstorm source control project and grain for green projects.
APPLICATION OF DEEP LEARNING IN GLOBELAND30-2010 PRODUCT REFINEMENT
Authors: Liu T. , Chen X.
Published: May 2018

Abstract. GlobeLand30, as one of the best Global Land Cover (GLC) product at 30-m resolution, has been widely used in many research fields. Due to the significant spectral confusion among different land cover types and limited textual information of Landsat data, the overall accuracy of GlobeLand30 is about 80 %. Although such accuracy is much higher than most other global land cover products, it cannot satisfy various applications. There is still a great need of an effective method to improve the quality of GlobeLand30. The explosive high-resolution satellite images and remarkable performance of Deep Learning on image classification provide a new opportunity to refine GlobeLand30. However, the performance of deep leaning depends on quality and quantity of training samples as well as model training strategy. Therefore, this paper 1) proposed an automatic training sample generation method via Google earth to build a large training sample set; and 2) explore the best training strategy for land cover classification using GoogleNet (Inception V3), one of the most widely used deep learning network. The result shows that the fine-tuning from first layer of Inception V3 using rough large sample set is the best strategy. The retrained network was then applied in one selected area from Xi’an city as a case study of GlobeLand30 refinement. The experiment results indicate that the proposed approach with Deep Learning and google earth imagery is a promising solution for further improving accuracy of GlobeLand30.
STITCHING TYPE LARGE APERTURE DEPOLARIZER FOR GAS MONITORING IMAGING SPECTROMETER
Authors: Liu X. , Li M. , An N. , Zhang T. , Cao G. , Cheng S.
Published: May 2018

Abstract. To increase the accuracy of radiation measurement for gas monitoring imaging spectrometer, it is necessary to achieve high levels of depolarization of the incoming beam. The preferred method in space instrument is to introduce the depolarizer into the optical system. It is a combination device of birefringence crystal wedges. Limited to the actual diameter of the crystal, the traditional depolarizer cannot be used in the large aperture imaging spectrometer (greater than 100 mm). In this paper, a stitching type depolarizer is presented. The design theory and numerical calculation model for dual babinet depolarizer were built. As required radiometric accuracies of the imaging spectrometer with 250 mm × 46 mm aperture, a stitching type dual babinet depolarizer was design in detail. Based on designing the optimum structural parmeters，the tolerance of wedge angle，refractive index, and central thickness were given. The analysis results show that the maximum residual polarization degree of output light from depolarizer is less than 2 %. The design requirements of polarization sensitivity is satisfied.
MINERAL INFORMATION EXTRACTION BASED ON GAOFEN-5’S THERMAL INFRARED DATA
Authors: Liu L. , Shang K.
Published: May 2018

Abstract. Gaofen-5 carries six instruments aimed at various land and atmosphere applications, and it’s an important unit of China High-resolution Earth Observation System. As Gaofen-5’s thermal infrared payload is similar to that of ASTER, which is widely used in mineral exploration, application of Gaofen-5’s thermal infrared data is discussed regarding its capability in mineral classification and silica content estimation. First, spectra of silicate, carbonate, sulfate minerals from a spectral library are used to conduct spectral feature analysis on Gaofen-5’s thermal infrared emissivities. Spectral indices of band emissivities are proposed, and by setting thresholds of these spectral indices, it can classify three types of minerals mentioned above. This classification method is tested on a simulated Gaofen-5 emissivity image. With samples acquired from the study area, this method is proven to be feasible. Second, with band emissivities of silicate and their silica content from the same spectral library, correlation models have been tried to be built for silica content inversion. However, the highest correlation coefficient is merely 0.592, which is much lower than that of correlation model built on ASTER thermal infrared emissivity. It can be concluded that GF-5’s thermal infrared data can be utilized in mineral classification but not in silica content inversion.
THE RESEARCH OF SPECTRAL RECONSTRUCTION FOR LARGE APERTURE STATIC IMAGING SPECTROMETER
Authors: Lv H. , Lee Y. , Liu R. , Fan C. , Huang Y.
Published: May 2018

Abstract. Imaging spectrometer obtains or indirectly obtains the spectral information of the ground surface feature while obtaining the target image, which makes the imaging spectroscopy has a prominent advantage in fine characterization of terrain features, and is of great significance for the study of geoscience and other related disciplines. Since the interference data obtained by interferometric imaging spectrometer is intermediate data, which must be reconstructed to achieve the high quality spectral data and finally used by users. The difficulty to restrict the application of interferometric imaging spectroscopy is to reconstruct the spectrum accurately. Based on the original image acquired by Large Aperture Static Imaging Spectrometer as the input, this experiment selected the pixel that is identified as crop by artificial recognition, extract and preprocess the interferogram to recovery the corresponding spectrum of this pixel. The result shows that the restructured spectrum formed a small crest near the wavelength of 0.55 μm with obvious troughs on both sides. The relative reflection intensity of the restructured spectrum rises abruptly at the wavelength around 0.7 μm, forming a steep slope. All these characteristics are similar with the spectral reflection curve of healthy green plants. It can be concluded that the experimental result is consistent with the visual interpretation results, thus validating the effectiveness of the scheme for interferometric imaging spectrum reconstruction proposed in this paper.
REMOTE SENSING EXTRACTION OF STOPES AND TAILINGS PONDS IN AN ULTRA-LOW-GRADE IRON MINING AREA
Authors: Ma B. , Chen Y. , Li X. , Wu L.
Published: May 2018

Abstract. With the development of economy, global demand for steel has accelerated since 2000, and thus mining activities of iron ore have become intensive accordingly. An ultra-low-grade iron has been extracted by open-pit mining and processed massively since 2001 in Kuancheng County, Hebei Province. There are large-scale stopes and tailings ponds in this area. It is important to extract their spatial distribution information for environmental protection and disaster prevention. A remote sensing method of extracting stopes and tailings ponds is studied based on spectral characteristics by use of Landsat 8 OLI imagery and ground spectral data. The overall accuracy of extraction is 95.06 %. In addition, tailings ponds are distinguished from stopes based on thermal characteristics by use of temperature image. The results could provide decision support for environmental protection, disaster prevention, and ecological restoration in the ultra-low-grade iron ore mining area.
MULTI-FACTOR ANALYSIS FOR SELECTING LUNAR EXPLORATION SOFT LANDING AREA AND THE BEST CRUISE ROUTE
Authors: Mou N. , Li J. , Meng Z. , Zhang L. , Liu W.
Published: May 2018

Abstract. Selecting the right soft landing area and planning a reasonable cruise route are the basic tasks of lunar exploration. In this paper, the Von Karman crater in the Antarctic Aitken basin on the back of the moon is used as the study area, and multi-factor analysis is used to evaluate the landing area and cruise route of lunar exploration. The evaluation system mainly includes the factors such as the density of craters, the impact area of craters, the formation of the whole area and the formation of some areas, such as the vertical structure, rock properties and the content of (FeO + TiO2), which can reflect the significance of scientific exploration factor. And the evaluation of scientific exploration is carried out on the basis of safety and feasibility. On the basis of multi-factor superposition analysis, three landing zones A, B and C are selected, and the appropriate cruising route is analyzed through scientific research factors. This study provides a scientific basis for the lunar probe landing and cruise route planning, and it provides technical support for the subsequent lunar exploration.
OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD
Authors: Näsi R. , Viljanen N. , Oliveira R. , Kaivosoja J. , Niemeläinen O. , Hakala T. , Markelin L. , Nezami S. , Suomalainen J. , Honkavaara E.
Published: May 2018

Abstract. Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10 % better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40 % compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0 %) was obtained with multiview anisotropy corrected data set and the 3D features.
APPLICATION OF DEEP LEARNING OF MULTI-TEMPORAL SENTINEL-1 IMAGES FOR THE CLASSIFICATION OF COASTAL VEGETATION ZONE OF THE DANUBE DELTA
Authors: Niculescu S. , Ienco D. , Hanganu J.
Published: May 2018

Abstract. Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.
RESEARCH ON A DEM COREGISTRATION METHOD BASED ON THE SAR IMAGING GEOMETRY
Authors: Niu Y. , Zhao C. , Zhang J. , Wang L. , Li B. , Fan L.
Published: May 2018

Abstract. Due to the systematic error, especially the horizontal deviation that exists in the multi-source, multi-temporal DEMs (Digital Elevation Models), a method for high precision coregistration is needed. This paper presents a new fast DEM coregistration method based on a given SAR (Synthetic Aperture Radar) imaging geometry to overcome the divergence and time-consuming problem of the conventional DEM coregistration method. First, intensity images are simulated for two DEMs under the given SAR imaging geometry. 2D (Two-dimensional) offsets are estimated in the frequency domain using the intensity cross-correlation operation in the FFT (Fast Fourier Transform) tool, which can greatly accelerate the calculation process. Next, the transformation function between two DEMs is achieved via the robust least-square fitting of 2D polynomial operation. Accordingly, two DEMs can be precisely coregistered. Last, two DEMs, i.e., one high-resolution LiDAR (Light Detection and Ranging) DEM and one low-resolution SRTM (Shutter Radar Topography Mission) DEM, covering the Yangjiao landslide region of Chongqing are taken as an example to test the new method. The results indicate that, in most cases, this new method can achieve not only a result as much as 80 times faster than the minimum elevation difference (Least Z-difference, LZD) DEM registration method, but also more accurate and more reliable results.
QUANTIFYING THE CONTRIBUTIONS OF ENVIRONMENTAL PARAMETERS TO CERES SURFACE NET RADIATION ERROR IN CHINA
Authors: Pan X. , Yang Y. , Liu Y. , Fan X. , Shan L. , Zhang X.
Published: May 2018

Abstract. Error source analyses are critical for the satellite-retrieved surface net radiation (Rn) products. In this study, we evaluate the Rn error sources in the Clouds and the Earth’s Radiant Energy System (CERES) project at 43 sites from July in 2007 to December in 2007 in China. The results show that cloud fraction (CF), land surface temperature (LST), atmospheric temperature (AT) and algorithm error dominate the Rn error, with error contributions of ~-20, ~15, ~10 and ~10 W/m2 (net shortwave (NSW)/longwave (NLW) radiation), respectively. For NSW, the dominant error source is algorithm error (more than 10 W/m2), particularly in spring and summer with abundant cloud. For NLW, due to the high sensitivity of algorithm and large LST/CF error, LST and CF are the largest error sources, especially in northern China. The AT influences the NLW error large in southern China because of the large AT error in there. The total precipitable water has weak influence on Rn error even with the high sensitivity of algorithm. In order to improve Rn quality, CF and LST (AT) error in northern (southern) China should be decreased.
IMPROVEMENT OF FOREST FIRE DETECTION ALGORITHM USING BRIGHTNESS TEMPERATURE LAPSE RATE CORRECTION IN HIMAWARI-8 IR CHANNELS: APPLICATION TO THE 6 MAY 2017 SAMCHEOK CITY, KOREA
Authors: Park S. H. , Park W. , Jung H. S.
Published: May 2018

Abstract. Forest fires are a major natural disaster that destroys a forest area and a natural environment. In order to minimize the damage caused by the forest fire, it is necessary to know the location and the time of day and continuous monitoring is required until fire is fully put out. We have tried to improve the forest fire detection algorithm by using a method to reduce the variability of surrounding pixels. We focused that forest areas of East Asia, part of the Himawari-8 AHI coverage, are mostly located in mountainous areas. The proposed method was applied to the forest fire detection in Samcheok city, Korea on May 6 to 10, 2017.
COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES
Authors: Piragnolo M. , Lusiani G. , Pirotti F.
Published: May 2018

Abstract. Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.
THE RESEARCH ON LUCALIBRATION OF GF-4 SATELLITE
Authors: Qi W. , Tan W.
Published: May 2018

Abstract. Starting from the lunar observation requirements of the GF-4 satellite, the main index such as the resolution, the imaging field, the reflect radiance and the imaging integration time are analyzed combined with the imaging features and parameters of this camera. The analysis results show that the lunar observation of GF-4 satellite has high resolution, wide field which can image the whole moon, the radiance of the pupil which is reflected by the moon is within the dynamic range of the camera, and the lunar image quality can be guaranteed better by setting up a reasonable integration time. At the same time, the radiation transmission model of the lunar radiation calibration is trace and the radiation degree is evaluated.
VALIDATION OF THE DAILY PASSIVE MICROWAVE SNOW DEPTH PRODUCTS OVER NORTHERN CHINA
Authors: Qiao D. , Li Z. , Wang N. , Zhou J. , Zhang P. , Gao S.
Published: May 2018

Abstract. Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002–2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20 cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20 cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs.
A COARSE-TO-FINE MODEL FOR AIRPLANE DETECTION FROM LARGE REMOTE SENSING IMAGES USING SALIENCY MODLE AND DEEP LEARNING
Authors: Song Z. N. , Sui H. G.
Published: May 2018

Abstract. High resolution remote sensing images are bearing the important strategic information, especially finding some time-sensitive-targets quickly, like airplanes, ships, and cars. Most of time the problem firstly we face is how to rapidly judge whether a particular target is included in a large random remote sensing image, instead of detecting them on a given image. The problem of time-sensitive-targets target finding in a huge image is a great challenge: 1) Complex background leads to high loss and false alarms in tiny object detection in a large-scale images. 2) Unlike traditional image retrieval, what we need to do is not just compare the similarity of image blocks, but quickly find specific targets in a huge image. In this paper, taking the target of airplane as an example, presents an effective method for searching aircraft targets in large scale optical remote sensing images. Firstly, we used an improved visual attention model utilizes salience detection and line segment detector to quickly locate suspected regions in a large and complicated remote sensing image. Then for each region, without region proposal method, a single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation is adopted to search small airplane objects. Unlike sliding window and region proposal-based techniques, we can do entire image (region) during training and test time so it implicitly encodes contextual information about classes as well as their appearance. Experimental results show the proposed method is quickly identify airplanes in large-scale images.
A NEW ALGORITHM FOR THE AOD INVERSION FROM NOAA/AVHRR DATA
Authors: Sun L. , Li R. , Yu H.
Published: May 2018

Abstract. The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.
ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY
Authors: Sun Y. S. , Zhang L. , Xu B. , Zhang Y.
Published: May 2018

Abstract. The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.
THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)
Authors: Talebi S. , Shi J. , Zhao T.
Published: May 2018

Abstract. This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.
CHANGE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES USING LEVENE-TEST AND FUZZY EVALUATION
Authors: Wang G. H. , Wang H. B. , Fan W. F. , Liu Y. , Liu H. J.
Published: May 2018

Abstract. High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.
THE RESEARCH AND COMPILATION OF CITY MAPS IN THE NATIONAL GEOMATICS ATLAS OF THE PEOPLE'S REPUBLIC OF CHINA
Authors: Wang G. , Wang D. , Zhou W. , Chen M. , Zhao T.
Published: May 2018

Abstract. The research and compilation of new century version of the National Huge Atlas of the People's Republic of China is the special basic work project by Ministry of Science and Technology of the People's Republic of China. Among them, the research and compilation of the National Geomatics Atlas of the People's Republic of China is its main content. The National Geomatics Atlas of China consists of 4 groups of maps and place name index. The 4 groups of maps are separately nationwide thematic map group, provincial fundamental geographical map group, landcover map group and city map group. The city map group is an important component part of the National Geomatics Atlas of China and mainly shows the process of urbanization in China. This paper, aim at design and compilation of 39 city-wide maps, briefly introduces mapping area research and scale design, mapping technical route, content selection and cartographic generalization, symbol design and visualization of map, etc.
INVERSION OF FARMLAND SOIL MOISTURE IN LARGE REGION BASED ON MODIFIED VEGETATION INDEX
Authors: Wang J. X. , Yu B. S. , Zhang G. Z. , Zhao G. C. , He S. D. , Luo W. R. , Zhang C. C.
Published: May 2018

Abstract. Soil moisture is an important parameter for agricultural production. Efficient and accurate monitoring of soil moisture is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural moisture monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for moisture monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the soil moisture in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of soil moisture on NDVI and soil moisture on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of soil moisture because of the better correlation between soil moisture and modified NDVI and the higher prediction accuracy of the regression model of soil moisture on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.
ESTIMATION OF MONTHLY NEAR SURFACE AIR TEMPERATURE USING GEOGRAPHICALLY WEIGHTED REGRESSION IN CHINA
Authors: Wang M. M. , He G. J. , Zhang Z. M. , Zhang Z. J. , Liu X. G.
Published: May 2018

Abstract. Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.
RESEARCH ON COORDINATE TRANSFORMATION METHOD OF GB-SAR IMAGE SUPPORTED BY 3D LASER SCANNING TECHNOLOGY
Authors: Wang P. , Xing C.
Published: May 2018

Abstract. In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D) plane coordinate system with the common three-dimensional (3D) terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.
SNOW COVER MAPPING AND ICE AVALANCHE MONITORING FROM THE SATELLITE DATA OF THE SENTINELS
Authors: Wang S. , Yang B. , Zhou Y. , Wang F. , Zhang R. , Zhao Q.
Published: May 2018

Abstract. In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2 h, which meet the requirements of disaster emergency monitoring.
NEAR REAL-TIME GEOREFERENCE OF UMANNED AERIAL VEHICLE IMAGES FOR POST-EARTHQUAKE RESPONSE
Authors: Wang S. , Wang X. , Dou A. , Yuan X. , Ding L. , Ding X.
Published: May 2018

Abstract. The rapid collection of Unmanned Aerial Vehicle (UAV) remote sensing images plays an important role in the fast submitting disaster information and the monitored serious damaged objects after the earthquake. However, for hundreds of UAV images collected in one flight sortie, the traditional data processing methods are image stitching and three-dimensional reconstruction, which take one to several hours, and affect the speed of disaster response. If the manual searching method is employed, we will spend much more time to select the images and the find images do not have spatial reference. Therefore, a near-real-time rapid georeference method for UAV remote sensing disaster data is proposed in this paper. The UAV images are achieved georeference combined with the position and attitude data collected by UAV flight control system, and the georeferenced data is organized by means of world file which is developed by ESRI. The C # language is adopted to compile the UAV images rapid georeference software, combined with Geospatial Data Abstraction Library (GDAL). The result shows that it can realize rapid georeference of remote sensing disaster images for up to one thousand UAV images within one minute, and meets the demand of rapid disaster response, which is of great value in disaster emergency application.
MITIGATION ATMOSPHERIC EFFECTS IN INTERFEROGRAM WITH USING INTEGRATED MERIS/MODIS DATA AND A CASE STUDY OVER SOUTHERN CALIFORNIA
Authors: Wang X. , Zhang P. , Sun Z.
Published: May 2018

Abstract. Interferometric synthetic aperture radar(InSAR), as a space geodetictechnology, had been testified a high potential means of earth observation providing a method fordigital elevation model (DEM) and surface deformation monitoring of high precision. However, the accuracy of the interferometric synthetic aperture radar is mainly limited by the effects of atmospheric water vapor. In order to effectively measure topography or surface deformations by synthetic aperture radar interferometry (InSAR), it is necessary to mitigate the effects of atmospheric water vapor on the interferometric signals. This paper analyzed the atmospheric effects on the interferogram quantitatively, and described a result of estimating Precipitable Water Vapor (PWV) from the the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground-based GPS, compared the MERIS/MODIS PWV with the GPS PWV. Finally, a case study for mitigating atmospheric effects in interferogramusing with using the integration of MERIS and MODIS PWV overSouthern California is given. The result showed that such integration approach benefits removing or reducing the atmospheric phase contribution from the corresponding interferogram, the integrated Zenith Path Delay Difference Maps (ZPDDM) of MERIS and MODIS helps reduce the water vapor effects efficiently, the standard deviation (STD) of interferogram is improved by 23 % after the water vapor correction than the original interferogram.
CONSTRUCTION AND APPLICATION OF ENHANCED REMOTE SENSING ECOLOGICAL INDEX
Authors: Wang X. , Liu C. , Fu Q. , Yin B.
Published: May 2018

Abstract. In order to monitor the change of regional ecological environment quality, this paper use MODIS and DMSP / OLS remote sensing data, from the production capacity, external disturbance changes and human socio-economic development of the three main factors affecting the quality of ecosystems, select the net primary productivity, vegetation index and light index, using the principal component analysis method to automatically determine the weight coefficient, construction of the formation of enhanced remote sensing ecological index, and the ecological environment quality of Hainan Island from 2001 to 2013 was monitored and analyzed. The enhanced remote sensing ecological index combines the effects of the natural environment and human activities on ecosystems, and according to the contribution of each principal component automatically determine the weight coefficient, avoid the design of the weight of the parameters caused by the calculation of the human error, which provides a new method for the operational operation of regional macro ecological environment quality monitoring. During the period from 2001 to 2013, the ecological environment quality of Hainan Island showed the characteristics of decend first and then rise, the ecological environment in 2005 was affected by severe natural disasters, and the quality of ecological environment dropped sharply. Compared with 2001, in 2013 about 20000 square kilometers regional ecological environmental quality has improved, about 8760 square kilometers regional ecological environment quality is relatively stable, about 5272 square kilometers regional ecological environment quality has decreased. On the whole, the quality of ecological environment in the study area is good, the frequent occurrence of natural disasters, on the quality of the ecological environment to a certain extent.
THE ONE TO MULTIPLE AUTOMATIC HIGH ACCURACY REGISTRATION OF TERRESTRIAL LIDAR AND OPTICAL IMAGES
Authors: Wang Y. , Hu C. , Xia G. , Xue H.
Published: May 2018

Abstract. The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two – dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.
BUILDING FACADE MODELING UNDER LINE FEATURE CONSTRAINT BASED ON CLOSE-RANGE IMAGES
Authors: Liang Y. , Sheng Y. H.
Published: May 2018

Abstract. To solve existing problems in modeling facade of building merely with point feature based on close-range images , a new method for modeling building facade under line feature constraint is proposed in this paper. Firstly, Camera parameters and sparse spatial point clouds data were restored using the SFM , and 3D dense point clouds were generated with MVS; Secondly, the line features were detected based on the gradient direction , those detected line features were fit considering directions and lengths , then line features were matched under multiple types of constraints and extracted from multi-image sequence. At last, final facade mesh of a building was triangulated with point cloud and line features. The experiment shows that this method can effectively reconstruct the geometric facade of buildings using the advantages of combining point and line features of the close - range image sequence，especially in restoring the contour information of the facade of buildings.
Black Hole or Brave New World?
Authors: Glassford Sarah
Published: May 2018

Since the late twentieth-century, the popularity and prevalence of digital formats have presented challenges for two groups closely associated with archives: archivists and historians. If archivists cannot fulfill their mandate to preserve our growing digital documentary heritage in the face of rapid technological change, the historians who are one of the core user groups of these records will have very little material with which to reconstruct and analyze the late-twentieth and early-twenty-first centuries. This article explores the benefits and challenges that digitization and digital preservation have brought to archivists, as well as the repercussions of these developments for historians. Although the challenges are significant, there is reason to be optimistic: new archival approaches and technologies are being developed every year, and historians have a tradition of making the most of whatever archivists can preserve.
Access to Information in the Age of Trump
Authors: Schoenberger Nicole
Published: May 2018

As a primary supplier of information and research of importance and value to the public, the government’s activity in doing so must be subject to scrutiny. This paper examines access to information under the government’s control within the context of the current United States presidential administration. After providing an overview of access to information, the paper moves to a discussion of current issues, highlighted by actions taken by the Trump administration. Of particular interest are the removal of information from government websites and gag orders or other restrictions imposed on government agencies. These have led to a lack of transparency as well as concerns regarding the authority and reliability of government data. In these ways, the Trump administration has limited and significantly harmed access to information. The paper also makes connections to larger information policy concerns, ending with a discussion of ways to promote access to government information.
Deception strategies and threats for online discussions
Authors: Varol Onur , Uluturk Ismail
Published: May 2018

Communication plays a major role in social systems. Effective communications, which requires the transmission of messages between individuals without disruptions or noise, can be a powerful tool to deliver intended impact. Language and style of content can be leveraged to deceive and manipulate recipients. These deception and persuasion strategies can be applied to exert power and amass capital in politics and business. In this work, we provide a modest review of how such deception and persuasion strategies were applied to different communication channels over time. We provide examples of campaigns that occurred over the last century, together with their corresponding dissemination media. In the Internet age, we enjoy access to a vast amount of information and an ability to communicate without borders. However, malicious actors work toward abusing online systems to disseminate disinformation, disrupt communication, and manipulate individuals, with automated tools such as social bots. It is important to study traditional practices of persuasion in order to investigate modern procedures and tools. We provide a discussion of current threats against society while drawing parallels with historical practices and recent research on systems of detection and prevention.