bluuebunny/crossref_metadata_2025 huggingface dataset:
AUTOMATING THE PHOTOGRAMMETRIC WORKFLOW IN A NATIONAL MAPPING AGENCY
Authors: Holland D. , Gladstone C. , Sargent I. , Horgan J. , Gardiner A. , Freeman M.
Published: October 2012

Abstract. The goal of automating the process of identifying changes to topographic features in aerial photography, extracting the geometry of these features and recording the changes in a database, is yet to be fully realised. At Ordnance Survey, Britain's national mapping agency, research into the automation of these processes has been underway for several years, and is now beginning to be implemented in production systems. At the start of the processing chain is the identification of change – new buildings and roads being constructed, old structures demolished, alterations to field and vegetation boundaries and changes to inland water features. Using eCognition object-based image analysis techniques, a system has been developed to detect the changes to features. This uses four-band digital imagery (red, green, blue and near infra-red), together with a digital surface model derived by image matching, to identify all the topographic features of interest to a mapping agency. Once identified, these features are compared with those in the National Geographic Database and any potential changes are highlighted. These changes will be presented to photogrammetrists in the production area, who will rapidly assess whether or not the changes are real. If the change is accepted, they will manually capture the geometry and attributes of the feature concerned. The change detection process, although not fully automatic, cuts down the amount of time required to update the database, enabling a more efficient data collection workflow. Initial results, on the detection of changes to buildings only, showed a completeness value (proportion of the real changes that were found) of 92% and a correctness value (proportion of the changes found that were real changes) of 22%, with a time saving of around 50% when compared with the equivalent manual process. The completeness value is similar to those obtained by the manual process. Further work on the process has added vegetation, water and many other rural features to the list of features that can be detected, and the system is currently being evaluated in a production environment. In addition to this work, the research team at Ordnance Survey are working with the remote sensing (data collection) department to develop more efficient methods of DSM and DTM creation; more automated seamline-generation for the creation of orthoimage mosaics; and methods to automatically record simple building heights on buildings in the database. These are all methods that have been proven in a research environment – the challenge is to implement them within the working environment of the existing data collection process.
RETRIEVING FOREST STRUCTURE VARIABLES FROM VERY HIGH RESOLUTION SATELLITE IMAGES USING AN AUTOMATIC METHOD
Authors: B. Beguet  , N. Chehata  , S. Boukir  , D. Guyon
Published: October 2012

Abstract. The main goal of this study is to define a method to describe the forest structure of maritime pine stands from Very High Resolution satellite imagery. The emphasis is placed on the automatisation of the process to identify the most relevant image features, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick’s texture features (derived from Grey Level Co-occurrence Matrix). The main drawback of this well- known texture representation is the underlying parameters (window size, displacement length, orientation and quantification level) which are extremely difficult to set due to the spatial complexity of forest structure. To tackle this major issue, probably the main cause of poor texture analysis in practice, we propose an automatic feature selection process whose originality lies on the use of image test frames of adequate forest samples whose forest structure variables were measured at ground. This method, inspired by camera calibration protocols, selects the best image features via statistical modelling, exploring a wide range of parameter values. Hence, just a few samples are required to build up the test frames but allow a fast assessment of thousands of descriptors, given the large number of tested combinations of parameters values. This method was developed and tested on Quickbird panchromatic and multispectral images. It has been successfully applied to the modelling of 7 typical forest structure variables (age, tree height, crown diameter, diameter at breast height, basal area, density and tree spacing). The coefficient of correlation, R2, of the best single models for 6 of the forest variables of interest, estimated from the test frames, ranges from 0.89 to 0.97. Only the basal area was weakly correlated to the considered image features (0.64). To improve the results, combinations of panchromatic and or multi-spectral features were tested using multiple linear regressions. As collinearity is a very perturbing problem in multi-linear regression, this issue is carefully addressed. Different variables subset selection methods are tested. A new stepwise method, derived from LARS (Least Angular Regression), turned out the most convincing, significantly improving the quality of estimation for all the forest structure variables (R2 > 0:98). Validation is done through stand ages retrieval along the whole site. The best estimation results are obtained from subsets combining multi-spectral and panchromatic features, with various values of window size, highlighting the potential of a multi-scale approach for retrieving forest structure variables from VHR satellite images.
ANALYZING THE SHAPE CHARACTERISTICS OF LAND USE CLASSES IN REMOTE SENSING IMAGERY
Authors: Jiao L. , Liu Y.
Published: October 2012

Abstract. Shape is an important aspect of spatial attributes of land use segments in remotely sensed imagery, but it is still rarely used as a component in land use classification or image-based land use analysis. This study aims to quantitatively characterize land use classes using shape metrics. The study is conducted in a case area located in south China, covering twelve scenes of SPOT-5 images. There were total ten metrics selected for the analysis, namely, Convexity (CONV), Solidity (SOLI), Elongation (ELONG), Roundness (ROUND), Rectangular Fitting (RECT), Compact (COMP), Form Factor (FORM), Square pixel metric (SqP), Fractal Dimension (FD), and Shape Index (SI). The last five metrics were used to measure the complexity of shape. Eight land use classes were investigated in the case area, namely, roads, cultivated lands, settlement places, rivers, ponds, forest and grass lands, reservoirs, and dams. The results show that all typical shape properties of land use segments can be well measured by shape metrics. We identified the land use classes whose values are significantly differentiated from most classes for each metric. Two of the five complexity metrics, FORM and SqP, were identified to be more effective in characterizing the complexity of land use classes. We finally selected six shape metrics and deduced the "Shape Metric Signatures" (SMS) of different land use classes. SMS can serve as accurate and predictive discriminators of land use classes within the study area. Our results show that SMS can clearly distinguish spectrally similar land use classes. The results will help to build a more accurate and intelligent object-oriented classification system for land use classes.
ORIENTATION BASED BUILDING OUTLINE EXTRACTION IN AERIAL IMAGES
Authors: Kovacs A. , Sziranyi T.
Published: October 2012

Abstract. The goal of this paper is to extract automatically the building contours regardless of shape. By extracting these contours, detection results will be more accurate, giving useful information about urban area, which is important for many tasks, like map updating and disaster management. First, we extract local feature points from the image, based on a modification of Harris detector's saliency function, which can represent urban area and building effectively. This point set is then used to define the main orientation of the buildings, which characterizes well an urban region and helps to define directions, where object contours have to be searched. Second, we applied shearlet approach to extract edges in the defined directions. This results in an edge map, which helps us to determine point subsets belonging to the same building. Convex hulls of the point subsets is used for contour initialization, then region based Chan-Vese Active Contour method is applied to extract the accurate building outlines.
APPLICATION OF MODIS DATA TO ASSESS THE LATEST FOREST COVER CHANGES OF SRI LANKA
Authors: Perera K. , Herath S. , Apan A. , Tateishi R.
Published: October 2012

Abstract. Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80 m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250 m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250 m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small isolated pockets of forest, or sub-pixel size forest patches when MODIS 250 m x 250 m data used in small regions.
ANALYSIS OF MULTITEMPORAL AND MULTISENSOR REMOTE SENSING DATA FOR CROP ROTATION MAPPING
Authors: Waldhoff G. , Curdt C. , Hoffmeister D. , Bareth G.
Published: October 2012

Abstract. For accurate regional modelling of (agro-)ecosystems, up-to-date land use information is essential to assess the impact of the permanent changing vegetation cover of agricultural land on matter fluxes in the soil-vegetation-atmosphere (SVA) system. In this regard, officially available land use datasets are mostly inadequate, since they only provide generalised information concerning agricultural land use. In this contribution, we present our work for the year 2008 on the generation of multi temporal, disaggregated land use data with the goal to derive a crop rotation map for the years 2008–2010 for the study area of the research project CRC/TR 32. For this purpose, the Multi-Data Approach (MDA) was used to integrate multitemporal remote sensing classifications with additional spatial information by the means of expert knowledge-based production rules. Our results show that the information content of a land use dataset is considerably enhanced by combining crop type information of multiple observations during each growing season. For a sufficient temporal coverage, the usage of multiple sensors is generally inevitable. Thus, datasets of ASTER, Landsat TM & ETM+ as well as IRS-P6 were incorporated. In terms of classification accuracy our analysis yielded similar results with support vector machines (SVM) and the classical maximum likelihood classifier (MLC) for all sensors, with SVM being mostly only slightly better. For the refinement of land parcel boundaries and the reduction of misclassification, the incorporation of the 'field block' (FB) vector information was very effective. 'Field blocks', provided by the chamber of agriculture, are coherent agricultural areas with (relatively) permanent boundaries. As a result, a much more accurate differentiation of agricultural land and non-agricultural land was achieved. With the enhanced annual MDA land use data of the three consecutive years containing crop type information sufficient information is available for the derivation of crop rotation. Again, adapted knowledge-based production rules are used for this purpose.
DEVELOPMENT OF A MODEL TO CORRECT MULTI-VIEW ANGLE ABOVE WATER MEASUREMENTS FOR THE ANALYSIS OF THE BIDIRECTIONAL REFLECTANCE OF CORAL AND OTHER REEF SUBSTRATES
Authors: Miller I. , Forster B. C. , Laffan S. W.
Published: October 2012

Abstract. Spectral reflectance characteristics of substrates in a coral reef environment are often measured in the field by viewing a substrate at nadir. However, viewing a substrate from multiple angles would likely result in different spectral characteristics for most coral reef substrates and provide valuable information on structural properties. To understand the relationship between the morphology of a substrate and its spectral response it is necessary to correct the observed above-water radiance for the effects of atmosphere and water attenuation, at a number of view and azimuth angles. In this way the actual surface reflectance can be determined. This research examines the air-water surface interaction for two hypothetical atmospheric conditions (clear Rayleigh scattering and totally cloudcovered) and the global irradiance reaching the benthic surface. It accounts for both water scattering and absorption, with simplifications for shallow water conditions, as well as the additive effect of background reflectance being reflected at the water-air surface at angles greater than the critical refraction angle (~48°). A model was developed to correct measured above-water radiance along the refracted view angle for its decrease due to path attenuation and the "n squared law of radiance" and the additive surface reflectance. This allows bidirectional benthic surface reflectance and nadir-normalised reflectance to be determined. These theoretical models were adapted to incorporate above-water measures relative to a standard, diffuse, white reference panel. The derived spectral signatures of a number of coral and non-coral benthic surfaces compared well with other published results, and the signatures and nadir normalised reflectance of the corals and other benthic surface classes indicate good class separation.
FEASIBILITY TO DETECT SIGNS OF POTENTIAL CO2 LEAKAGE WITH MULTI-TEMPORAL SPOT SATELLITE VEGETATION IMAGERY IN OTWAY, VICTORIA
Authors: Cholathat R. , Ge L. , Li X. , Hu Z.
Published: October 2012

Abstract. This paper presents image processing results for the OtwayCO2storage site, a demonstration project of CO2 sequestration in south-western Victoria, Australia. These results were derived from SPOT-VGT S10 datasets of 2001 to mid 2011. Over 65,000 tonnes of CO2-rich gas stream was injected into a depleted gas reservoir at a depth of 2050 meters at the site since 2008. Over time, CO2 migration up-dip within the 31 m thick reservoir sandstone capped by the impervious thick seal rock has been recorded. But no top soil contamination has been discovered. This study has analysed the site vegetation growth using NDVI as a measure on a pixel by pixel basis. The multi-year time series result shows that NDVI values at the site regularly vary according to the seasons. Furthermore, precipitation levels were fluctuating in the past 10 years, especially in the years of 2002 and 2006, which correlated with low NDVI measuring results. But there are detected hot spots that cannot be linked with rainfall. Authors have found that some hot spots correspond with site well drilling and pipelines construction periods and locations. While others might be due to image data biased. Therefore, certain low NDVI spikes in the temporal evolution results cannot be attributed to only drought or pasture grazing. These subtle changes detected in the NDVI index prove the ability to use satellite image for providing valuable information to decision makers in relation to CO2 sequestration site environmental safety monitoring for searching CO2 leakage signals.
SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL
Authors: A. Gusso  , J. R. Ducati
Published: October 2012

Abstract. Evaluation of the MODIS Crop Detection Algorithm (MCDA) procedure for estimating historical planted soybean crop areas was done on fields in Mato Grosso State, Brazil. MCDA is based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite data of the MODIS (Moderate Resolution Imaging Spectroradiometer) imager, and was previously developed for soybean area estimation in Rio Grande do Sul State, Brazil. According to the MCDA approach, in Mato Grosso soybean area estimates can be provided in December (1st forecast), using images from the sowing period, and in February (2nd forecast), using images from sowing and maximum crop development period. The results obtained by the MCDA were compared with Brazilian Institute of Geography and Statistics (IBGE) official estimates of soybean area at municipal level. Coefficients of determination were between 0.93 and 0.98, indicating a good agreement, and also the suitability of MCDA to estimations performed in Mato Grosso State. On average, the MCDA results explained 96% of the variation of the data estimated by the IBGE. In this way, MCDA calibration was able to provide annual thematic soybean maps, forecasting the planted area in the State, with results which are comparable to the official agricultural statistics.
OBJECT-LEVEL CHANGE DETECTION BASED ON HIGH-RESOLUTION REMOTE-SENSING IMAGES AND ITS APPLICATION IN JAPANESE EARTHQUAKE ON MARCH 11, 2011
Authors: Sun K. , Li D. , Sui H. , Liu J. , Ma G.
Published: October 2012

Abstract. In accordance with the characteristics of change detection based on high-resolution remote-sensing images, this paper has put forward an object-level change detection method that is based on multi-feature integration and can take into account the properties of different types of object. This method classifies the most essential change information in applications into artificial objects related change information, water-related change information and vegetation-related change information. Direct association of object types and radiation, texture and geometric features is established by analyzing the characteristics of the three types of objects. During the application of object-level change detection method, first, feature vectors of objects are constructed by controlling the weight of radiation, texture and geometric features in different ways; then feature vectors of objects in multi-temporal images are analyzed with the method of object change vector analysis to obtain the change information of object types that are sensitive to a certain feature. In order to verify the validity of this method, this paper uses the high-resolution remote-sensing images from the Internet captured before and after the Japanese earthquake on March 11, 2011 to conduct some change detection experiments based on multifeature integration. Damage information is extracted and by controlling the weight of features, building damage, damage caused by submergence of seawater and vegetation damage are detected respectively. Experiments show that the method and processing put forward in this paper, flexible, practical and adaptable, are effective in such applications as the extraction of information about damage caused by earthquake and tsunami, and investigation of land use change.
THE MODEL OF WHEAT YIELD FORECAST BASED ON MODIS-NDVI – A CASE STUDY OF XINXIANG
Authors: Zhang H. , Chen H. , Zhou G.
Published: October 2012

Abstract. The yield estimation models on a regional scale are generally constrained by the lack of spatially distributed information on major environmental. The utilization of remote sensing data with various spatial and temporal resolutions can settle this problem. The NDVI, which retrieved from satellite remote sensing, was adopted to forecast winter wheat yields in this paper. There are two key steps in the process of calculating. The first is the establishment of the relationship between NDVI and Above Ground Biomass (AGB). The second is the Harvest Index(HI) calculating based on the change of NDVI from the period of re-greening to maturity. The validation results showed that forecast accuracy is satisfied and can be applied in practice of winter wheat yield forecasting. However, the error will be increased in abnormal weather condition, for higher or lower value of NDVI is made than normal situation, (such as re-greening too early, overgrowth, and late-maturing, etc.). So the model amendment is required according to the status of crop and weather condition in the year.
COMPARISON OF THE SPECTRAL PROPERTIES OF PANSHARPENED IMAGES GENERATED FROM AVNIR-2 AND PRISM ONBOARD ALOS
Authors: Matsuoka M.
Published: October 2012

Abstract. A considerable number of methods for pansharpening remote-sensing images have been developed to generate higher spatial resolution multispectral images by the fusion of lower resolution multispectral images and higher resolution panchromatic images. Because pansharpening alters the spectral properties of multispectral images, method selection is one of the key factors influencing the accuracy of subsequent analyses such as land-cover classification or change detection. In this study, seven pixel-based pansharpening methods (additive wavelet intensity, additive wavelet principal component, generalized Laplacian pyramid with spectral distortion minimization, generalized intensity-hue-saturation (GIHS) transform, GIHS adaptive, Gram-Schmidt spectral sharpening, and block-based synthetic variable ratio) were compared using AVNIR-2 and PRISM onboard ALOS from the viewpoint of the preservation of spectral properties of AVNIR-2. A visual comparison was made between pansharpened images generated from spatially degraded AVNIR-2 and original images over urban, agricultural, and forest areas. The similarity of the images was evaluated in terms of the image contrast, the color distinction, and the brightness of the ground objects. In the quantitative assessment, three kinds of statistical indices, correlation coefficient, ERGAS, and Q index, were calculated by band and land-cover type. These scores were relatively superior in bands 2 and 3 compared with the other two bands, especially over urban and agricultural areas. Band 4 showed a strong dependency on the land-cover type. This was attributable to the differences in the observing spectral wavelengths of the sensors and local scene variances.
AUTOMATIC REGISTRATION OF MULTI-SOURCE DATA USING MUTUAL INFORMATION
Authors: Parmehr E. G. , Zhang C. , Fraser C. S.
Published: October 2012

Abstract. Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric applications such as data fusion. The effectiveness of Mutual Information (MI) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications. In this paper, a new General Weighted MI (GWMI) approach that improves the robustness of MI to local maxima, particularly in the case of registering optical imagery and 3D point clouds, is presented. Two different methods including a Gaussian Mixture Model (GMM) and Kernel Density Estimation have been used to define the weight function of joint probability, regardless of the modality of the data being registered. The Expectation Maximizing method is then used to estimate parameters of GMM, and in order to reduce the cost of computation, a multi-resolution strategy has been used. The performance of the proposed GWMI method for the registration of aerial orthotoimagery and LiDAR range and intensity information has been experimentally evaluated and the results obtained are presented.
RADAR BACKSCATTER AND OPTICAL TEXTURAL INDICES FUSION FOR PINE PLANTATION STRUCTURE MAPPING
Authors: Shamsoddini A.
Published: October 2012

Abstract. One of the most important concerns of remote sensing research has been the quantification of forest structure variables. This issue has been investigated using different remotely sensed data including optical, radar and lidar data. Moreover, the utility of multisensor data has been examined for this task. Radar images are being considered as one of the available tools for forest studies; however, some constrains of this data such as speckle noise and the high sensitivity of this data to topography prevent it from being widely utilized for forest structure mapping. A possible solution is to integrate radar data with optical data to improve the accuracy of the biophysical parameter estimations; however, the results of the common fusion method does not show significant improvement if the efficacy of one of the datasets is much lower than the other one. In this study multi-date ALOS/PALSAR data with HH and HV polarizations were used along with SPOT-5 textural indices derived from the grey level co-occurrence matrix (GLCM), and the subtract and sum histogram (SADH), calculated in different orientations and window sizes for retrieval of a Pinus radiata plantation at plot-level in NSW, Australia. In order to overcome the deficiency of the common fusion method, a new fusion method called ratio fusion is examined for fusion of radar and optical data. The results showed that the estimation of biophysical parameters including mean height, mean DBH, stand volume, basal area and stocking using SPOT-5 textural indices was more accurate than that derived using the backscatter data derived from multi-date ALOS/PALSAR images. Also, the accuracy of estimation of these forest structure parameters increases when the ratio of the SPOT-5 textural indices to the radar backscatter is used for this task.
ADABOOST-BASED FEATURE RELEVANCE ASSESSMENT IN FUSING LIDAR AND IMAGE DATA FOR CLASSIFICATION OF TREES AND VEHICLES IN URBAN SCENES
Authors: Wei Y. , Yao W. , Wu J. , Schmitt M. , Stilla U.
Published: October 2012

Abstract. In this paper, we present an integrated strategy to comprehensively evaluate the feature relevance of point cloud and image data for classification of trees and vehicles in urban scenes. First of all, point cloud and image data are co-registered by backprojection with available orientation parameters if necessary. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per pixel/point. Then, various spatial-statistical and radiometric features can be extracted using a cylindrical volume neighborhood. Classification results as labeled pixels can be acquired from the classifier, and after appropriate refinements we obtain the objects of trees and vehicles. Compared to other methods which have assessed the classification and relevance simultaneously using a single classifier, we first introduce AdaBoost classifier combined with contribution ratio to provide both classification results and measures of feature relevance, and then utilize Random Forest classifier to evaluate and compare the feature relevance from a more independent viewpoint. In order to confirm the accuracy and reliability of classification and feature relevance results, we consider not only characteristics of the classifiers itself, but also errors of data co-registration and alterable parameters. We apply the procedure to two different datasets. In the dataset requiring co-registration a-priori, the AdaBoost classifier even achieves a great accuracy of 96.99% for trees and 83.45% for vehicles. The quantitative results of feature relevance assessment highlight the most important features for classification of tree covers and vehicles, such as NDVI, LiDAR intensity, planarity and entropy. By comparative analysis of the two independent approaches, the reliable and consistent feature selection for classification of trees and vehicles from LiDAR and image data could be validated and achieved, being unrelated to the classifiers.
A PARALLEL IMPLEMENTATION FRAMEWORK FOR REMOTELY SENSED IMAGE FUSION
Authors: Jinghui Y. , Jixian Z.
Published: October 2012

Abstract. The fusion algorithms are data-intensive and computation-intensive, whose processing performance is low if common implementation techniques are adopted. This paper presents a parallel implementation framework based on a generalized fusion model and parallel computing for remotely sensed image fusion. The steps in the implementation corresponding to each fusion algorithm are mainly the same, which discard the process steps, input and output （I/O）operations not impacting the last results. The parallel processing mechanism adopts the data decomposition strategies where different data blocks can be processed in different CPU cores. The developed software module shows that the framework can be applied to implement many algorithms which usually are divided into three general categories. The experiments show that the parallel implementation framework is high performance, which not only decreases the processing time in single CPU core environment but also efficiently utilizes the resources of multi-cores in computer.
EVALUATION OF FILTERS FOR ENVISAT ASAR SPECKLE SUPPRESSION IN PASTURE AREA
Authors: Wang X. , Ge L. , Li X.
Published: October 2012

Abstract. In order to quantify real time pasture biomass from SAR image, regression model between ground measurements of biomass and ENVISAT ASAR backscattering coefficient should be built up. An important prerequisite of valid and accurate regression model is accurate grass backscattering coefficient which, however, cannot be obtained when there is speckle. Speckle noise is the best known problem of SAR images because of the coherent nature of radar illumination imaging system. This study aims to choose better adaptive filter from NEST software to reduce speckle noise in homogeneous pasture area, with little regard to linear feature (e.g. edge between pasture and forest) or point feature (e.g. pond, tree) preservation. This paper presents the speckle suppression result of ENVISAT ASAR VV/VH images in pasture of Western Australia (WA) using four built-in adaptive filters of the NEST software: Frost, Gamma Map, Lee, and Refined Lee filter. Two indices are usually used for evaluation of speckle suppression ability: ENL (Equivalent Number of Looks) and SSI (Speckle Suppression Index). These two, however, are not reliable because sometimes they overestimate mean value. Therefore, apart from ENL and SSI, the authors also used a new index SMPI (Speckle Suppression and Mean Preservation Index). It was found that, Lee filter with window size 7×7 and Frost filter (damping factor = 2) with window size 5×5 gave the best performance for VV and VH polarization, respectively. The filtering, together with radiometric calibration and terrain correction, paves the way to extraction of accurate backscattering coefficient of grass in homogeneous pasture area in WA.
TERRASAR-X INSAR PROCESSING IN NORTHERN BOHEMIAN COAL BASIN USING CORNER REFLECTORS (PRELIMINARY RESULTS)
Authors: Hlaváčová I. , Halounová L. , Svobodová K.
Published: October 2012

Abstract. The area of Northern Bohemian coal basin is rich in brown coal. Part of it is undermined, but large areas were mined using open-pit mines. There are numerous reclaimed waste dumps here, with a horse racetrack, roads and in some cases also houses. However, on most of the waste dumps, there are forests, meadows and fields. Above the coal basin, there are the Ore mountains which are suspected to be sliding down to the open mines below them. We installed 11 corner reflectors in the area and monitor them using the TerraSAR-X satellite. One of the reflectors is situated in the area of radar layover, therefore it cannot be processed. We present preliminary results of monitoring the remaining corner reflectors, with the use of 7 TerraSAR-X scenes acquired between June and December 2011. We process whole scene crops, as well as the artificial reflector information alone. Our scene set contains interferometric pairs with perpendicular baselines reaching from 0 to 150 m. Such a configuration allows us to distinguish deformations from DEM errors, which are usual when the SRTM (Shuttle Radar Topography Mission) DEM (X-band) is used for Stripmap data. Unfortunately, most of the area of interest is decorrelated due to vegetation that covers both the Ore mountains and the reclaimed waste dumps. We had to enlarge the scene crop in order to be able to distinguish deformations from the atmospheric delay. We are still not certain about the stability of some regions. For the installed artificial reflectors, the expected deformations are in the order of mm/year. Generally, deformations in the area of interest may reach up to about 5 cm/year for the Ervěnice corridor (a road and railway built on a waste dump). When processing artificial corner reflector information alone, we check triangular sums and perform the processing for all possible point combinations – and that allows us to correct for some unwrapping errors. However, the problem is highly ambiguous.
HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM
Authors: Honkavaara E. , Kaivosoja J. , Mäkynen J. , Pellikka I. , Pesonen L. , Saari H. , Salo H. , Hakala T. , Marklelin L. , Rosnell T.
Published: October 2012

Abstract. The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, which is based on support vector regression machine. It was concluded that the central factors influencing on the accuracy of the estimation process were the quality of the image data, the quality of the image processing and digital surface model generation, and the performance of the regressor. In the wider perspective, our investigation showed that very low-weight, low-cost, hyperspectral, stereoscopic and spectrodirectional 3D UAV-remote sensing is now possible. This cutting edge technology is powerful and cost efficient in time-critical, repetitive and locally operated remote sensing applications.
VALIDATION OF DEMs DERIVED FROM HIGH RESOLUTION SAR DATA: A CASE STUDY ON BARCELONA
Authors: Sefercik U. G. , Schunert A. , Soergel U. , Watanabe K.
Published: October 2012

Abstract. In recent years, Synthetic Aperture Radar (SAR) data have been widely used for scientific applications and several SAR missions were realized. The active sensor principle and the signal wavelength in the order of centimeters provide all-day and all-weather capabilities, respectively. The modern German TerraSAR-X (TSX) satellite provides high spatial resolution down to one meter. Based on such data SAR Interferometry may yield high quality digital surface models (DSMs), which includes points located on 3d objects such as vegetation, forest, and elevated man-made structures. By removing these points, digital elevation model (DEM) representing the bare ground of Earth is obtained. The primary objective of this paper is the validation of DEMs obtained from TSX SAR data covering Barcelona area, Spain, in the framework of a scientific project conducted by ISPRS Working Group VII/2 "SAR Interferometry" that aims the evaluation of DEM derived from data of modern SAR satellite sensors. Towards this purpose, a DSM was generated with 10 m grid spacing using TSX StripMap mode SAR data and converted to a DEM by filtering. The accuracy results have been presented referring the comparison with a more accurate (10 cm–1 m) digital terrain model (DTM) derived from large scale photogrammetry. The results showed that the TSX DEM is quite coherent with the topography and the accuracy is in between ±8–10 m. As another application, the persistent scatterer interferometry (PSI) was conducted using TSX data and the outcomes were compared with a 3d city model available in Google Earth, which is known to be very precise because it is based on LIDAR data. The results showed that PSI outcomes are quite coherent with reference data and the RMSZ of differences is around 2.5 m.
CAPABILITIES OF REMOTE SENSING HYPERSPECTRAL IMAGES FOR THE DETECTION OF LEAD CONTAMINATION: A REVIEW
Authors: Maliki A. A. , Owens G. , Bruce D.
Published: October 2012

Abstract. Advances in remote sensing technologies are increasingly becoming more useful for resource, ecosystem and agricultural management applications to the extent that these techniques can now also be applied for monitoring of soil contamination and human health risk assessment. While, extensive previous studies have shown that Visible and Near Infrared Spectroscopy (VNIRS) in the spectral range 400–2500 nm can be used to quantify various soil constituents simultaneously, the direct determination of metal concentrations by remote sensing and reflectance spectroscopy is not as well examined as other soil parameters. The application of VNIRS, including laboratory hyperpectral measurements, field spectrometer measurements or image spectroscopy, generally achieves a good prediction of metal concentrations when compared to traditional wet chemical methods and has the advantage of being relatively less expensive and faster, allowing chemical assessment of contamination in close to real time. Furthermore, imaging spectroscopy can potentially provide significantly more samples over a larger spatial extent than traditional ground sampling methods. Thus the development of remote sensing techniques (field based and either airborne or satellite hyperspectral imaging) can support the monitoring and efficient mapping of metal contamination (in dust and soil) for environmental and health impact assessment. This review is concerned with the application of remote sensing and reflectance spectroscopy to the detection of heavy metals and discusses how current methods could be applied for the quantification of Pb contaminated soil surrounding mines and smelters.
HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL
Authors: Zhang H.
Published: October 2012

Abstract. Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images. This paper proposes a cubic total variation (CTV) model by combining the 2-D total variation model for spatial domain with the 1-D total variation model for spectral domain, and then applies the termed CTV model to hyperspectral image denoising. The augmented Lagrangian method is utilized to improve the speed of solution of the desired hyperspectral image. The experimental results suggest that the proposed method can achieve competitive image quality.
P-wave velocity changes in freezing hard low-porosity rocks: a laboratory-based time-average model
Authors: Draebing D. , Krautblatter M.
Published: October 2012

Abstract. P-wave refraction seismics is a key method in permafrost research but its applicability to low-porosity rocks, which constitute alpine rock walls, has been denied in prior studies. These studies explain p-wave velocity changes in freezing rocks exclusively due to changing velocities of pore infill, i.e. water, air and ice. In existing models, no significant velocity increase is expected for low-porosity bedrock. We postulate, that mixing laws apply for high-porosity rocks, but freezing in confined space in low-porosity bedrock also alters physical rock matrix properties. In the laboratory, we measured p-wave velocities of 22 decimetre-large low-porosity (< 10%) metamorphic, magmatic and sedimentary rock samples from permafrost sites with a natural texture (> 100 micro-fissures) from 25 °C to −15 °C in 0.3 °C increments close to the freezing point. When freezing, p-wave velocity increases by 11–166% perpendicular to cleavage/bedding and equivalent to a matrix velocity increase from 11–200% coincident to an anisotropy decrease in most samples. The expansion of rigid bedrock upon freezing is restricted and ice pressure will increase matrix velocity and decrease anisotropy while changing velocities of the pore infill are insignificant. Here, we present a modified Timur's two-phase-equation implementing changes in matrix velocity dependent on lithology and demonstrate the general applicability of refraction seismics to differentiate frozen and unfrozen low-porosity bedrock.
Keeping Up with the Times
Authors: Burambayeva Valentina
Published: October 2012

N/A
Analysis of Entropy Generation Minimization in Circular Porous Fins
Authors: Saedodin Seyfolah , Sadeghi Siamak , Shahbabaei Majid
Published: October 2012

This work introduces a simple method of exergy analysis in a typical circular porous fin. The entropy generation of any thermodynamic system provides a useful measure of the extent of irreversibility. The irreversibility causes the loss of useful work (exergy) in the system and hence the loss of exergy has to be minimized. Entropy generation is a parameter that quantifies the loss of exergy. Circular fins are relatively good heat transfer augmentation features with superior aerodynamic performance and as a result find application in some solar air heaters. In this paper, the entropy generation in a circular porous fin is calculated and its performance is compared with respect to entropy generation. Also shown in porous fins, with increase of porosity. The entropy generation number will increase; also states with porosity have higher entropy generation number than states with nonporosity (). Also at higher Reynolds number the effect of the on is negligible, but at lower Reynolds number the variation of the is negligible. Also we can see that with increased porosity (), the entropy generation () will decrease. The entropy generation is calculated for circular porous fins with mass constraint.
A Two-Years' Survey on the Prevalence of Tuberculosis Caused by Mycobacterium caprae in Red Deer (Cervus elaphus) in the Tyrol, Austria
Authors: Schoepf Karl , Prodinger Wolfgang M. , Glawischnig Walter , Hofer Erwin , Revilla-Fernandez Sandra , Hofrichter Johannes , Fritz Johannes , Köfer Josef , Schmoll Friedrich
Published: October 2012

A survey of 143 hunter-harvested red deer for tuberculosis was conducted in an Alpine area in Western Austria over two subsequent years. There, single tuberculosis cases caused by Mycobacterium caprae had been detected in cattle and red deer over the preceding decade. The area under investigation covered approximately 500 km2, divided into five different hunting plots. Lymph nodes of red deer were examined grossly and microscopically for typical tuberculosis-like lesions and additionally by microbiological culturing. Executing a detailed hunting plan, nine M. caprae isolates were obtained. Six out of nine originated from one single hunting plot with the highest estimated prevalence of tuberculosis, that is, 23.1%. All isolates were genotyped by mycobacterial interspersed repetitive unit—variable number of tandem repeat (MIRU-VNTR) typing of 24 standard loci plus VNTR 1982. All nine isolates belonged to a single cluster termed “Lechtal” which had been found in cattle and red deer in the region, demonstrating a remarkable dominance and stability over ten years. This is the first report on a systematic prospective study investigating the prevalence and strain variability of M. caprae infection in red deer in Austria and in the Alpine countries.
A Logistic -Moment-Based Analog for the Tukey -, , , and - System of Distributions
Authors: Headrick Todd C. , Pant Mohan D.
Published: October 2012

This paper introduces a standard logisticL-moment-based system of distributions. The proposed system is an analog to the standard normal conventional moment-based Tukeyg-h,g,h, andh-hsystem of distributions. The system also consists of four classes of distributions and is referred to as (i) asymmetric -, (ii) log-logistic , (iii) symmetric , and (iv) asymmetric -. The system can be used in a variety of settings such as simulation or modeling events—most notably when heavy-tailed distributions are of interest. A procedure is also described for simulating -, , , and - distributions with specifiedL-moments andL-correlations. The Monte Carlo results presented in this study indicate that estimates ofL-skew,L-kurtosis, andL-correlation associated with the -, , , and - distributions are substantially superior to their corresponding conventional product-moment estimators in terms of relative bias and relative standard error.
Living with a Chemically Sensitive Wife: A “We” Situation
Authors: Hutton Carlsen K. , Topp A. M. , Skovbjerg S.
Published: October 2012

Objectives. Multiple chemical sensitivity (MCS) is a medically unexplained and socially disabling disorder characterized by negative health effects attributed to exposure to common airborne chemicals. How spouses of chemically sensitive women experience and cope with their partners illness has not been described previously. Methods. This study presents data from three semistructured focus group interviews with a selective sample of 13 men whose spouses had MCS. Data was analyzed using systematic text condensation with a special focus on coping. Results. The informants expressed a great concern for their wives and described their role in the relationship as that of a “watchdog”, always alert and ready to protect. A considerable amount of time and effort was thus given to avoid symptom-eliciting chemicals to prevent illness. Informing guests of restrictions and observing neighbours’ daily routines were common occurrences. Masks, installation of additional air filters in cars and houses, and other protective measures had necessitated several participants taking on extra work to alleviate the economic burden. The wives' illness had thus become a “we” situation. Discussion. In conclusion, MCS was perceived as a “we” situation and may thus impose considerable strain on the relationship and the family as a whole.