bluuebunny/crossref_metadata_2025 huggingface dataset:
AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL
Authors: Xu C. , Sui H. G. , Li D. R. , Sun K. M. , Liu J. Y.
Published: June 2016

Abstract. Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.
INTERGRATION OF LiDAR DATA WITH AERIAL IMAGERY FOR ESTIMATING ROOFTOP SOLAR PHOTOVOLTAIC POTENTIALS IN CITY OF CAPE TOWN
Authors: Adeleke A. K. , Smit J. L.
Published: June 2016

Abstract. Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1) automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its’ outline and areal coverage; and (2) estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.
HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM
Authors: Hoang Nguyen Tien , Koike Katsuaki
Published: June 2016

Abstract. Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.
DIFFERENTIAL SEARCH ALGORITHM BASED EDGE DETECTION
Authors: Gunen M. A. , Civicioglu P. , Beşdok E.
Published: June 2016

Abstract. In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.
COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS
Authors: Landmann J. M. , Rutzinger M. , Bremer M. , chmidtner K.
Published: June 2016

Abstract. Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (Larix decidua MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as “bias correction” for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.
RESEARCH ON THE CONSTRUCTION METHOD OF COMPREHENSIVE EVALUATION INDEX OF GEOGRAPHIC CONDITIONS
Authors: Lv S. H. , Zhang J. X. , Zhang Z. H. , Jiao J. C.
Published: June 2016

Abstract. This article proposes a construction method for a comprehensive geographic conditions evaluation index based on geographic conditions survey data combined with thematic data on society, economy, ecology and population. This article constructs a three-level evaluation framework composed of index level, object level and factor level from the perspectives of ecological coordination, urban development, regional economic potential and basic public services, studies a method of acquiring all-level factor data on geographic conditions and discusses the comprehensive evaluation factor system of geographic conditions. The components of the all-level index are selected through principal component analysis, and iterative analysis is performed by innovatively setting conditions to ensure the independence of the factors and establish an evaluation factor set for geographic conditions. The weighting for the all-level index is obtained through the analytic hierarchy process resulting in the index of geographic conditions. From the perspective of geographic conditions, this article makes a dynamic and quantitative evaluation of national conditions and strengths to provide a reference basis for regional sustainable development and governmental management decisions. By using the method, this article first obtains the index of geographic conditions of Q city with comprehensive evaluation and analysis to verify the objectivity and scientific nature of the method and expand and deepen the application of survey data on geographic conditions.
ENHANCEMENT OF SPATIAL RESOLUTION OF THE LROC WIDE ANGLE CAMERA IMAGES
Authors: Mahanti P. , Robinson M. S. , Sato H. , Awumah A. , Henriksen M.
Published: June 2016

Abstract. Image fusion, a popular method for resolution enhancement in Earth-based remote sensing studies involves the integration of geometric (sharpness) detail of a high-resolution panchromatic (Pan) image and the spectral information of a lower resolution multi-spectral (MS) image. Image fusion with planetary images is not as widespread as with terrestrial studies, although successful application of image fusion can lead to the generation of higher resolution MS image data. A comprehensive comparison of six image fusion algorithms in the context of lunar images is presented in this work. Performance of these algorithms is compared by visual inspection of the high-resolution multi-spectral products, derived products such as band-to-band ratio and composite images, and performance metrics with an emphasis on spectral content preservation. Enhanced MS images of the lunar surface can enable new science and maximize the science return for current and future missions.
CLASSIFICATION OF CROPLANDS THROUGH FUSION OF OPTICAL AND SAR TIME SERIES DATA
Authors: Park S. , Im J.
Published: June 2016

Abstract. Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of single data (e.g., such as cloud contamination) through multi-sensor data fusion and the use of time series data. This study investigated two areas with different environment and climate conditions: one in South Korea and the other in US. Cropland classification was conducted by using multi-temporal Landsat 5, Radarsat-1 and digital elevation models (DEM) based on two machine learning approaches (i.e., random forest and support vector machines). Seven classification scenarios were examined and evaluated through accuracy assessment. Results show that SVM produced the best performance (overall accuracy of 93.87%) when using all temporal and spectral data as input variables. Normalized Difference Water Index (NDWI), SAR backscattering, and Normalized Difference Vegetation Index (NDVI) were identified as more contributing variables than the others for cropland classification.
A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY
Authors: Roncat A. , Briese C. , Pfeifer N.
Published: June 2016

Abstract. In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.
MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES
Authors: Sadeq H. , Drummond J. , Li Z.
Published: June 2016

Abstract. In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
ON THE CHALLENGES IN STEREOGRAMMETRIC FUSION OF SAR AND OPTICAL IMAGERY FOR URBAN AREAS
Authors: Schmitt M. , Zhu X. X.
Published: June 2016

Abstract. This paper discusses the challenges arising if SAR and optical imagery shall be fused for stereogrammetric 3D analysis of urban areas. In this context, a concept for SAR and optical data fusion is presented, which is meant to enable the reconstruction of urban topography independent of the type of the available data. This fusion is modelled in a voxelized object space, from which 3D hypotheses are projected into the available datasets. Among those hypotheses then the one showing the greatest SAR-optical similarity is chosen to be the reconstructed 3D point. Within first experiments, it is shown that the determination of similarity between high-resolution SAR and optical images is the major challenge within the framework of the proposed concept. After this challenge has been solved, the proposed method is expected to allow 3D reconstruction of urban areas from SAR-optical stereogrammetry for the first time. It is expected to be beneficial, e.g., for rapid mapping tasks in disaster situations where optical images may be available from geodata archives, but instantaneous data can only be provided by daylight- and weather-independent SAR sensors.
EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE
Authors: Demir N. , Kaynarca M. , Oy S.
Published: June 2016

Abstract. Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.
MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS
Authors: Zhang Yue , Fu Kun , Sun Xian , Xu Guangluan , Wang Hongqi
Published: June 2016

Abstract. The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.
EXPLOITING SENTINEL-1 AMPLITUDE DATA FOR GLACIER SURFACE VELOCITY FIELD MEASUREMENTS: FEASIBILITY DEMONSTRATION ON BALTORO GLACIER
Authors: Nascetti A. , Nocchi F. , Camplani A. , Di Rico C. , Crespi M.
Published: June 2016

Abstract. The leading idea of this work is to continuously retrieve glaciers surface velocity through SAR imagery, in particular using the amplitude data from the new ESA satellite sensor Sentinel-1 imagery. These imagery key aspects are the free access policy, the very short revisit time (down to 6 days with the launch of the Sentinel-1B satellite) and the high amplitude resolution (up to 5 m). In order to verify the reliability of the proposed approach, a first experiment has been performed using Sentinel-1 imagery acquired over the Karakoram mountain range (North Pakistan) and Baltoro and other three glaciers have been investigated. During this study, a stack of 11 images acquired in the period from October 2014 to September 2015 has been used in order to investigate the potentialities of the Sentinel-1 SAR sensor to retrieve the glacier surface velocity every month. The aim of this test was to measure the glacier surface velocity between each subsequent pair, in order to produce a time series of the surface velocity fields along the investigated period. The necessary coregistration procedure between the images has been performed and subsequently the glaciers areas have been sampled using a regular grid with a 250 × 250 meters posting. Finally the surface velocity field has been estimated, for each image pair, using a template matching procedure, and an outlier filtering procedure based on the signal to noise ratio values has been applied, in order to exclude from the analysis unreliable points. The achieved velocity values range from 10 to 25 meters/month and they are coherent to those obtained in previous studies carried out on the same glaciers and the results highlight that it is possible to have a continuous update of the glacier surface velocity field through free Sentinel-1 imagery, that could be very useful to investigate the seasonal effects on the glaciers fluid-dynamics.
COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES
Authors: Roychowdhury K.
Published: June 2016

Abstract. Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.
CENTIMETER COSMO-SKYMED RANGE MEASUREMENTS FOR MONITORING GROUND DISPLACEMENTS
Authors: Fratarcangeli F. , Nascetti A. , Capaldo P. , Mazzoni A. , Crespi M.
Published: June 2016

Abstract. The SAR (Synthetic Aperture Radar) imagery are widely used in order to monitor displacements impacting the Earth surface and infrastructures. The main remote sensing technique to extract sub-centimeter information from SAR imagery is the Differential SAR Interferometry (DInSAR), based on the phase information only. However, it is well known that DInSAR technique may suffer for lack of coherence among the considered stack of images. New Earth observation SAR satellite sensors, as COSMO-SkyMed, TerraSAR-X, and the coming PAZ, can acquire imagery with high amplitude resolutions too, up to few decimeters. Thanks to this feature, and to the on board dual frequency GPS receivers, allowing orbits determination with an accuracy at few centimetres level, the it was proven by different groups that TerraSAR-X imagery offer the capability to achieve, in a global reference frame, 3D positioning accuracies in the decimeter range and even better just exploiting the slant-range measurements coming from the amplitude information, provided proper corrections of all the involved geophysical phenomena are carefully applied. The core of this work is to test this methodology on COSMO-SkyMed data acquired over the Corvara area (Bolzano – Northern Italy), where, currently, a landslide with relevant yearly displacements, up to decimeters, is monitored, using GPS survey and DInSAR technique. The leading idea is to measure the distance between the satellite and a well identifiable natural or artificial Persistent Scatterer (PS), taking in account the signal propagation delays through the troposphere and ionosphere and filtering out the known geophysical effects that induce periodic and secular ground displacements. The preliminary results here presented and discussed indicate that COSMO-SkyMed Himage imagery appear able to guarantee a displacements monitoring with an accuracy of few centimetres using only the amplitude data, provided few (at least one) stable PS’s are available around the monitored area, in order to correct residual biases, likely due to orbit errors.
IMPROVED MODEL-BASED POLARIMETRIC DECOMPOSITION USING THE POlINSAR SIMILARITY PARAMETER
Authors: Latrache H. , Ouarzeddine M. , Souissi B.
Published: June 2016

Abstract. In this paper, we present a new approach to solve the problem of volume scattering ambiguity in urban area, for that we propose a volume model based on the polarimetric interferometric similarity parameter (PISP) . The new model is more adaptive and fits better with both forest and oriented built-up areas. Thereby, a new model-based polarimetric decomposition scheme is developed. To test the performance of the proposed method ESAR PolInSAR L bande data of Oberpfaffenhofen, Germany is used. Comparison experiments show that the proposed method gives good results, since all the oriented built-up areas are well discriminated as double or odd bounce structures.
A RAIL CENTRAL DISPLACEMENT METHOD ABOUT GB-SAR
Authors: Peng J. , Cai J. , Yang H.
Published: June 2016

Abstract. This paper presents a new method to correct rail errors of Ground Based Synthetic Aperture Radar (GB-SAR) in the discontinue mode. Generally, “light positioning” is performed to mark the GB-SAR position in the dis-continuous observation mode. Usually we assume there is no difference between the marked position and the real installation position. But in fact, it is hard to keep the GB-SAR positions of two campaigns the same, so repositioning errors can’t be neglected. In order to solve this problem, we propose an algorithm to correct the rail error after analyzing the GB-SAR rail error geometry. Results of the simulation experiment and the real experiment of a landslide in Lvliang, Shanxi, China, show the proposed method achieves an mm-level precision, enabling the D-GBSAR mode to be used in engineering projects.
SUPPORTING MANAGEMENT OF EUROPEAN REFUGEE STREAMS BY EARTH OBSERVATION AND GEOINFORMATION
Authors: Komp K.-U. , Müterthies A.
Published: June 2016

Abstract. The sharp increase in refugee numbers arriving in the European Union has recently caused major and manifold challenges for the member states and their administrative services. Location based situation reports and maps may support the refugee management from local to European level. The first support is mapping of the geographical distribution of migrating people which needs more or less real time data. The actual data sources are location related observations along the routes of refugees, actual satellite observations and data mining results. These tools and data are used to monitor spatial distributions as well as extrapolate the arrival of refugees for the subsequent weeks. The second support is the short term update of the location of initial registration facilities and first reception facilities, their capacities, and their occupancy. The third management level is the systematic inquiry for unoccupied housing facilities and for empty places within build-up areas. Geo-coded data sets of house numbers have to be cross-referenced with city maps and communal inhabitants address data. The legal aspects of data mining and secured access to personal data are strictly controlled by the administration allowing only limited access and distribution of data and results. However, the paper will not disclose scientific progress in Earth Observation and GIS, but will actually demonstrate an urgently needed new combination of existing methods to support actual needs. The societal benefits of EO/GIS are no longer just potential possibilities, but actual results in real political, administrative and humanitarian day to day reality.
THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA
Authors: Li W. Y. , Liu C. , Gao J.
Published: June 2016

Abstract. Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.
INNOVATIV AIRBORNE SENSORS FOR DISASTER MANAGEMENT
Authors: Altan M. O. , Kemper G.
Published: June 2016

Abstract. Modern Disaster Management Systems are based on 3 columns, crisis preparedness, early warning and the final crisis management. In all parts, special data are needed in order to analyze existing structures, assist in the early warning system and in the updating after a disaster happens to assist the crises management organizations. How can new and innovative sensors assist in these tasks? Aerial images have been frequently used in the past for generating spatial data, however in urban structures not all information can be extracted easily. Modern Oblique camera systems already assist in the evaluation of building structures to define rescue paths, analyze building structures and give also information of the stability of the urban fabric. For this application there is no need of a high geometric accurate sensor, also SLC Camera based Oblique Camera system as the OI X5, which uses Nikon Cameras, do a proper job. Such a camera also delivers worth full information after a Disaster happens to validate the degree of deformation in order to estimate stability and usability for the population. Thermal data in combination with RGB give further information of the building structure, damages and potential water intrusion. Under development is an oblique thermal sensor with 9 heads which enables nadir and oblique thermal data acquisition. Beside the application for searching people, thermal anomalies can be created out of humidity in constructions (transpiration effects), damaged power lines, burning gas tubes and many other dangerous facts. A big task is in the data analysis which should be made automatically and fast. This requires a good initial orientation and a proper relative adjustment of the single sensors. Like that, many modern software tools enable a rapid data extraction. Automated analysis of the data before and after a disaster can highlight areas of significant changes. Detecting anomalies are the way to get the focus on the prior area. Also Lidar supports Disaster management by analyzing changes in the DSM before and after the “event”. Advantage of Lidar is that beside rain and clouds, no other weather conditions limit their use. As an active sensor, missions in the nighttime are possible. The new mid-format cameras that make use CMOS sensors (e.g. Phase One IXU1000) can capture data also under poor and difficult light conditions and might will be the first choice for remotely sensed data acquisition in aircrafts and UAVs. UAVs will surely be more and more part of the disaster management on the detailed level. Today equipped with video live cams using RGB and Thermal IR, they assist in looking inside buildings and behind. Thus, they can continue with the aerial survey where airborne anomalies have been detected.
A UAV BASED 3-D POSITIONING FRAMEWORK FOR DETECTING LOCATIONS OF BURIED PERSONS IN COLLAPSED DISASTER AREA
Authors: Moon H. , Kim C. , Lee W.
Published: June 2016

Abstract. Regarding spatial location positioning, indoor location positioning theories based on wireless communication techniques such as Wi-Fi, beacon, UWB and Bluetooth has widely been developing across the world. These techniques are mainly focusing on spatial location detection of customers using fixed wireless APs and unique Tags in the indoor environment. Besides, since existing detection equipment and techniques using ultrasound or sound etc. to detect buried persons and identify survival status for them cause 2nd damages on the collapsed debris for rescuers. In addition, it might take time to check the buried persons. However, the collapsed disaster sites should consider both outdoor and indoor environments because empty spaces under collapsed debris exists. In order to detect buried persons from the empty spaces, we should collect wireless signals with Wi-Fi from their mobile phone. Basically, the Wi-Fi signal measure 2-D location. However, since the buried persons have Z value with burial depth, we also should collect barometer sensor data from their mobile phones in order to measure Z values according to weather conditions. Specially, for quick accessibility to the disaster area, a drone (UAV; Unmanned Arial Vehicle) system, which is equipped with a wireless detection module, was introduced. Using these framework, this study aims to provide the rescuers with effective rescue information by calculating 3-D location for buried persons based on the wireless and barometer sensor fusion.
ANALYSIS OF DEBRIS FLOW DISASTER DUE TO HEAVY RAIN BY X-BAND MP RADAR DATA
Authors: Nishio M. , Mori M.
Published: June 2016

Abstract. On August 20 of 2014, Hiroshima City (Japan) was struck by local heavy rain from an autumnal rain front. The resultant debris flow disaster claimed 75 victims and destroyed many buildings. From 1:30 am to 4:30 am on August 20, the accumulated rainfall in Hiroshima City exceeded 200 mm. Serious damage occurred in the Asakita and Asaminami wards of Hiroshima City. As a disaster prevention measure, local heavy rain (localized torrential rains) is usually observed by the Automated Meteorological Data Acquisition System (AMeDAS) operated by the Japan Meteorological Agency (JMA) and by the C-band radar operated by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan, with spatial resolutions of 2.5 km and 1 km, respectively. The new X-band MP radar system enables more detailed rainfall observations than the C-band radar. In fact, this radar can observe local rainfall throughout Japan in near-real time over a minimum mesh size of 250 m. A fine-scale accumulated rainfall monitoring system is crucial for disaster prevention, and potential disasters can be alerted by the hazard levels of the accumulated rainfall.
3D GIS FOR FLOOD MODELLING IN RIVER VALLEYS
Authors: Tymkow P. , Karpina M. , Borkowski A.
Published: June 2016

Abstract. The objective of this study is implementation of system architecture for collecting and analysing data as well as visualizing results for hydrodynamic modelling of flood flows in river valleys using remote sensing methods, tree-dimensional geometry of spatial objects and GPU multithread processing. The proposed solution includes: spatial data acquisition segment, data processing and transformation, mathematical modelling of flow phenomena and results visualization. Data acquisition segment was based on aerial laser scanning supplemented by images in visible range. Vector data creation was based on automatic and semiautomatic algorithms of DTM and 3D spatial features modelling. Algorithms for buildings and vegetation geometry modelling were proposed or adopted from literature. The implementation of the framework was designed as modular software using open specifications and partially reusing open source projects. The database structure for gathering and sharing vector data, including flood modelling results, was created using PostgreSQL. For the internal structure of feature classes of spatial objects in a database, the CityGML standard was used. For the hydrodynamic modelling the solutions of Navier-Stokes equations in two-dimensional version was implemented. Visualization of geospatial data and flow model results was transferred to the client side application. This gave the independence from server hardware platform. A real-world case in Poland, which is a part of Widawa River valley near Wroclaw city, was selected to demonstrate the applicability of proposed system.
3D VISUALIZATION OF VOLCANIC ASH DISPERSION PREDICTION WITH SPATIAL INFORMATION OPEN PLATFORM IN KOREA
Authors: Youn J. , Kim T.
Published: June 2016

Abstract. Visualization of disaster dispersion prediction enables decision makers and civilian to prepare disaster and to reduce the damage by showing the realistic simulation results. With advances of GIS technology and the theory of volcanic disaster prediction algorithm, the predicted disaster dispersions are displayed in spatial information. However, most of volcanic ash dispersion predictions are displayed in 2D. 2D visualization has a limitation to understand the realistic dispersion prediction since its height could be presented only by colour. Especially for volcanic ash, 3D visualization of dispersion prediction is essential since it could bring out big aircraft accident. In this paper, we deals with 3D visualization techniques of volcanic ash dispersion prediction with spatial information open platform in Korea. First, time-series volcanic ash 3D position and concentrations are calculated with WRF (Weather Research and Forecasting) model and Modified Fall3D algorithm. For 3D visualization, we propose three techniques; those are 'Cube in the air', 'Cube in the cube', and 'Semi-transparent plane in the air' methods. In the 'Cube in the Air', which locates the semitransparent cubes having different color depends on its particle concentration. Big cube is not realistic when it is zoomed. Therefore, cube is divided into small cube with Octree algorithm. That is 'Cube in the Cube' algorithm. For more realistic visualization, we apply 'Semi-transparent Volcanic Ash Plane' which shows the ash as fog. The results are displayed in the 'V-world' which is a spatial information open platform implemented by Korean government. Proposed techniques were adopted in Volcanic Disaster Response System implemented by Korean Ministry of Public Safety and Security.
COMBINING REMOTELY SENSED ENVIRONMENTAL CHARACTERISTICS WITH SOCIAL AND BEHAVIORAL CONDITIONS THAT AFFECT SURFACE WATER USE IN SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA
Authors: Kulinkina A. V. , Walz Y. , Liss A. , Kosinski K. C. , Biritwum N. K. , Naumova E. N.
Published: June 2016

Abstract. Schistosoma haematobium transmission is influenced by environmental conditions that determine the suitability of the parasite and intermediate host snail habitats, as well as by socioeconomic conditions, access to water and sanitation infrastructure, and human behaviors. Remote sensing is a demonstrated valuable tool to characterize environmental conditions that support schistosomiasis transmission. Socioeconomic and behavioral conditions that propagate repeated domestic and recreational surface water contact are more difficult to quantify at large spatial scales. We present a mixed-methods approach that builds on the remotely sensed ecological variables by exploring water and sanitation related community characteristics as independent risk factors of schistosomiasis transmission.
EXTENDING LKN CLIMATE REGIONALIZATION WITH SPATIAL REGULARIZATION: AN APPLICATION TO EPIDEMIOLOGICAL RESEARCH
Authors: Liss Alexander , Gel Yulia R. , Kulinkina Alexandra , Naumova Elena N.
Published: June 2016

Abstract. Regional climate is a critical factor in public health research, adaptation studies, climate change burden analysis, and decision support frameworks. Existing climate regionalization schemes are not well suited for these tasks as they rarely take population density into account. In this work, we are extending our recently developed method for automated climate regionalization (LKN-method) to incorporate the spatial features of target population. The LKN method consists of the data limiting step (L-step) to reduce dimensionality by applying principal component analysis, a classification step (K-step) to produce hierarchical candidate regions using k-means unsupervised classification algorithm, and a nomination step (N-step) to determine the number of candidate climate regions using cluster validity indexes. LKN method uses a comprehensive set of multiple satellite data streams, arranged as time series, and allows us to define homogeneous climate regions. The proposed approach extends the LKN method to include regularization terms reflecting the spatial distribution of target population. Such tailoring allows us to determine the optimal number and spatial distribution of climate regions and thus, to ensure more uniform population coverage across selected climate categories. We demonstrate how the extended LKN method produces climate regionalization can be better tailored to epidemiological research in the context of decision support framework.
SPATIAL DISTRUBITON OF CHILDREN TREATED BY CANCER IN ZONGULDAK, TURKEY
Authors: Topan Aysel , Bayram Dilek , Özendi Mustafa , Cam Ali , Öztürk Cam , Kuzlu Ayyıldız Tülay , Kulakçı Hülya , Veren Funda
Published: June 2016

Abstract. This research is focused on the examination of child cancer cases in Zonguldak (Turkey) descriptively in epidemiological aspect thanks to GIS. Universe of the study is composed of 60 children between 0-19 years old, treated in Children Oncology Clinic of Health Application and Research Center in BEU. Whole universe was reached without selecting a sample in the study. Data were collected by using a form prepared by obtaining expert advice and they were applied to children and their parents at study dates. Results were expressed as percentages. Chi-Square test was used in intergroup comparisons, results were assessed within 95% confidence interval and p<0.05 was considered as statistically significant. Variables that were used in the study were assessed, recorded in prepared data collection form and distribution maps were produced. When disease diagnosis of the children participated in the study were evaluated, it was observed that 33.3% (n=20) were being treated for ALL, 13.3% (n=8) for Medullablastoma and 11.7% (n=7) for Hodgkin-nonHodgkin Lymphoma. It was detected that 31.7% (n=19) were in Ereğli, 31.7% (n=19) were in Central district and 18.3% (n=11) were in Çaycuma, when the places where children were living were evaluated. Statistically significant difference was found (p=0.016) comparing disease diagnosis with living place, and overall distribution map of the number of cancer cases was produced in this context. This is the first research subjecting the distribution of cancer cases for Zonguldak province.
DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI
Authors: Bektas Balcik F. , Ergene E. M.
Published: June 2016

Abstract. Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.
REFINEMENT METHOD FOR RESIDENTIAL AREA REVISION USING REMOTE SENSING IMAGE AND GIS DATA IN EARTHQUAKE RISK ASSESSMENT
Authors: Dou A. X. , Yuan X. X. , Wang X. Q. , Li Z. M.
Published: June 2016

Abstract. This paper proposes an automatic approach for residential areas revision by means of analysing the correlation between the residential area and the topographic and geographical factors. The approach consists of four major steps: the extracting of missing residential area from the remote sensing images with high resolution; the statistic analysing on the size changes of missing residential area in each grade of the elevation, slope, distance from the road and other impact factors; modelling of residential area modification in the urban and rural region; testing the methods using 100 counties data which are located in the middle part of China North-South Seismic Belt and comparing the result to the Land Use in map scale 1:100000. The experimental results present the accuracy of urban residents by 70% increased to 89.4%, rural residents by 47% up to 81.9%, rural residents from 8% increased to 78.5%. Therefore, there is available risk exposure information in a sparsely populated area because the spatial grid distributions of population and buildings are based on the residential areas. The proposed approach in this paper will improve the accuracy of the seismic risk assessment if it is applied to the national or the whole world.