bluuebunny/crossref_metadata_2025 huggingface dataset:
EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA
Authors: Homolová L. , Janoutová R. , Malenovský Z.
Published: June 2016

Abstract. In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm−2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.
IDENTIFICATION OF A ROBUST LICHEN INDEX FOR THE DECONVOLUTION OF LICHEN AND ROCK MIXTURES USING PATTERN SEARCH ALGORITHM (CASE STUDY: GREENLAND)
Authors: Salehi S. , Karami M. , Fensholt R.
Published: June 2016

Abstract. Lichens are the dominant autotrophs of polar and subpolar ecosystems commonly encrust the rock outcrops. Spectral mixing of lichens and bare rock can shift diagnostic spectral features of materials of interest thus leading to misinterpretation and false positives if mapping is done based on perfect spectral matching methodologies. Therefore, the ability to distinguish the lichen coverage from rock and decomposing a mixed pixel into a collection of pure reflectance spectra, can improve the applicability of hyperspectral methods for mineral exploration. The objective of this study is to propose a robust lichen index that can be used to estimate lichen coverage, regardless of the mineral composition of the underlying rocks. The performance of three index structures of ratio, normalized ratio and subtraction have been investigated using synthetic linear mixtures of pure rock and lichen spectra with prescribed mixing ratios. Laboratory spectroscopic data are obtained from lichen covered samples collected from Karrat, Liverpool Land, and Sisimiut regions in Greenland. The spectra are then resampled to Hyperspectral Mapper (HyMAP) resolution, in order to further investigate the functionality of the indices for the airborne platform. In both resolutions, a Pattern Search (PS) algorithm is used to identify the optimal band wavelengths and bandwidths for the lichen index. The results of our band optimization procedure revealed that the ratio between R894-1246 and R1110 explains most of the variability in the hyperspectral data at the original laboratory resolution (R2=0.769). However, the normalized index incorporating R1106-1121 and R904-1251 yields the best results for the HyMAP resolution (R2=0.765).
ACADEME-LOCAL GOVERNMENT PARTNERSHIP TOWARDS EFFECTIVE APPLICATION OF GEOSPATIAL TECHNOLOGIES FOR SMARTER FLOOD DISASTER MANAGEMENT AT THE LOCAL LEVEL: AN EXAMPLE FROM MINDANAO, PHILIPPINES
Authors: Makinano-Santillan M. , Santillan J. R. , Morales E. M. O. , Asube L. C. S. , Amora A. M. , Cutamora L. C. , Makinano R. M.
Published: June 2016

Abstract. In this paper, we discuss how an academe-local government partnership can lead the way for the effective use of geospatial technologies for smarter and geospatially-informed decision making before, during, and after a flood disaster. In Jabonga municipality, in the province of Agusan del Norte, in Mindanao, Philippines, two significant flooding events occurred in the year 2014 which were caused by overflowing water bodies due to continuous heavy rains. These flood events inundated populated areas, caused massive evacuation, made roads un-passable, and greatly damaged sources of incomes such as croplands and other agricultural areas. The partnership between Caraga State University and the local government of Jabonga attempts to improve localized flood disaster management through the development of web-based Near-real Time Flood Event Visualization and Damage Estimations (Flood EViDEns) application. Flood EViDENs utilizes LiDAR-derived elevation and information products as well as other elevation datasets, water level records by monitoring stations, flood simulation models, flood hazard maps, and socio-economic datasets (population, household information, etc.), in order to visualize in near-real time the current and future extent of flooding, to disseminate early warnings, and to provide maps and statistics of areas and communities affected and to be affected by flooding. The development of Flood EViDEns as the main product of the partnership is an important application of geospatial technologies that will allow smarter and geospatially-informed decision making before, during, and after a flood disaster in Jabonga.
NEAR REAL-TIME DETERMINATION OF EARTHQUAKE SOURCE PARAMETERS FOR TSUNAMI EARLY WARNING FROM GEODETIC OBSERVATIONS
Authors: Manneela Sunanda , Srinivasa Kumar T. , Nayak Shailesh R.
Published: June 2016

Abstract. Exemplifying the tsunami source immediately after an earthquake is the most critical component of tsunami early warning, as not every earthquake generates a tsunami. After a major under sea earthquake, it is very important to determine whether or not it has actually triggered the deadly wave. The near real-time observations from near field networks such as strong motion and Global Positioning System (GPS) allows rapid determination of fault geometry. Here we present a complete processing chain of Indian Tsunami Early Warning System (ITEWS), starting from acquisition of geodetic raw data, processing, inversion and simulating the situation as it would be at warning center during any major earthquake. We determine the earthquake moment magnitude and generate the centroid moment tensor solution using a novel approach which are the key elements for tsunami early warning. Though the well established seismic monitoring network, numerical modeling and dissemination system are currently capable to provide tsunami warnings to most of the countries in and around the Indian Ocean, the study highlights the critical role of geodetic observations in determination of tsunami source for high-quality forecasting.
Evaluating the Human Damage of Tsunami at Each Time Frame in Aggregate Units Based on GPS data
Authors: Ogawa Y. , Akiyama Y. , Kanasugi H. , Shibasaki R. , Kaneda H.
Published: June 2016

Abstract. Assessments of the human damage caused by the tsunami are required in order to consider disaster prevention at such a regional level. Hence, there is an increasing need for the assessments of human damage caused by earthquakes. However, damage assessments in japan currently usually rely on static population distribution data, such as statistical night time population data obtained from national census surveys. Therefore, human damage estimation that take into consideration time frames have not been assessed yet. With these backgrounds, the objectives of this study are: to develop a method for estimating the population distribution of the for each time frame, based on location positioning data observed with mass GPS loggers of mobile phones, to use a evacuation and casualties models for evaluating human damage due to the tsunami, and evaluate each time frame by using the data developed in the first objective, and 3) to discuss the factors which cause the differences in human damage for each time frame. By visualizing the results, we clarified the differences in damage depending on time frame, day and area. As this study enables us to assess damage for any time frame in and high resolution, it will be useful to consider provision for various situations when an earthquake may hit, such as during commuting hours or working hours and week day or holiday.
ENVIRONMENTAL IMPACT ASSESSMENT OF ROSIA JIU OPENCAST AREA USING AN INTEGRATED SAR ANALYSIS
Authors: Poenaru V. D. , Dana Negula I. F. , Badea A. , Cuculici R.
Published: June 2016

Abstract. The satellite data provide a new perspective to analyse and interpret environmental impact assessment as function of topography and vegetation. The main goal of this paper is to investigate the new Staring Spotlight TerraSAR-X mode capabilities to monitor land degradation in Rosia Jiu opencast area taking into account the mining engineering standards and specifications. The second goal is to relate mining activities with spatio-temporal dynamics of land degradation by using differential Synthetic Aperture Radar interferometry (DInSAR). The experimental analysis was carried out on data acquired in the LAN_2277 scientific proposal framework during 2014-2015 period. A set of 25 very height resolution SAR data gathered in the VV polarisation mode with a resolution of 0.45 m x 0.16m and an incidence angle of 37° have been used in this study. Preliminary results showed that altered terrain topography with steep slopes and deep pits has led to the layover of radar signal. Initially, ambiguous results have been obtained due to the highly dynamic character of subsidence induced by activities which imply mass mining methods. By increasing the SAR data number, the land degradation assessment has been improved. Most of the interferometric pairs have low coherence therefore the product coherence threshold was set to 0.3. A coherent and non-coherent analysis is performed to delineate land cover changes and complement the deformation model. Thus, the environmental impact of mining activities is better studied. Moreover, the monitoring of changes in pit depths, heights of stock-piles and waste dumps and levels of tailing dumps provide additional information about production data.
ASSESSMENT OF FLOODED AREAS PROJECTIONS AND FLOODS POTENTIAL IMPACTS APPLYING REMOTE SENSING IMAGERY AND DEMOGRAPHIC DATA
Authors: Rodriguez D. A. , Carriello F. , Fernandes P. J. F. , Garofolo Lopes L. , Siqueira Júnior J. L.
Published: June 2016

Abstract. Assessing vulnerability and potential impacts associated with extreme discharges requires an accurate topographic description in order to estimate the extension of flooded areas. However, in most populated regions, topographic data obtained by in-situ measurements is not available. In this case, digital elevation models derived from remote sensing date are usually applied. Moreover, this digital elevation models have intrinsic errors that introduce bigger uncertainty in results than the associated to hydrological projections. On the other hand, estimations of flooded areas through remote sensing images provide accurate information, which could be used for the construction of river level-flooded area relationships regarding vulnerability assessment. In this work, this approach is applied for the city of Porto Velho in the Brazilian Amazonia to assess potential vulnerability to floods associated with climate change projections. The approach is validated using census data, provided by the Brazilian Institute of Geography and Statistics, and information about socio-economical injuries associated to historical floods, provided by the Brazilian Civil Defence. Hydrological projections under climate change are carried out using several downscaling of climate projections as inputs in a hydrological model. Results show more accurate estimation of flood impacts than the obtained using digital elevation models derivate from remote sensing data. This reduces uncertainties in the assessment of vulnerability to floods associated with climate change in the region.
EXTRACTION OF FLOODED AREAS DUE THE 2015 KANTO-TOHOKU HEAVY RAINFALL IN JAPAN USING PALSAR-2 IMAGES
Authors: Yamazaki F. , Liu W.
Published: June 2016

Abstract. Triggered by two typhoons, heavy rainfall hit Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Increased river water by the continuous rainfall overflowed and destroyed several river banks and caused damaging floods in wide areas. PALSAR-2 onboard ALOS-2 satellite carried out emergency observation for the impacted areas during and after the heavy rainfall. In this study, two pre-event and four co- and post-event PALSAR-2 images were used to extract the inundation area in Joso city, Ibaraki prefecture. First, using the pre-event SAR intensity image and a detailed topographic map, the backscattering coefficient of river water was investigated. Then the flooded areas were extracted by a common threshold value of backscatter for water bodies in the six temporal images. The colour composite of the sigma naught values was also made to visualize pixels that had been converted from ground to water. Finally, the extracted results were compared with those from the visual interpretation of aerial photographs and field survey reports. This comparison revealed that the accuracy of the flood extraction was fairly good for agricultural lands and non-urban land uses. But for built-up urban areas, it was not easy to extract water body since radar illumination did to reach the ground (water) surface.
GEOGRAPHIC MEDICAL HISTORY: ADVANCES IN GEOSPATIAL TECHNOLOGY PRESENT NEW POTENTIALS IN MEDICAL PRACTICE
Authors: Faruque F. S. , Finley R. W.
Published: June 2016

Abstract. Genes, behaviour, and the environment are known to be the major risk factors for common diseases. When the patient visits a physician, typical questions include family history (genes) and lifestyle of the patient (behaviour), but questions concerning environmental risk factors often remain unasked. It is ironic that 25 centuries ago Hippocrates, known as the father of medicine, noted the importance of environmental exposure in medical investigation as documented in his classic work, “Airs, Waters, Places”, yet the practice of routinely incorporating environmental risk factors is still not in place. Modern epigenetic studies have found that unhealthy lifestyle and environmental factors can cause changes to our genes that can increase disease risk factors. Therefore, attempting to solve the puzzle of diseases using heredity and lifestyle alone will be incomplete without accounting for the environmental exposures. The primary reason why environmental exposure has not yet been a routine part of the patient’s medical history is mostly due to our inability to provide clinicians useful measures of environmental exposures suitable for their clinical practices. This presentation will discuss advances in geospatial technology that show the potential to catalyse a paradigm shift in medical practice and health research by allowing environmental risk factors to be documented as the patient’s “Geographic Medical History”. In order to accomplish this we need information on: a) relevant spatiotemporal environmental variables, and b) location of the individual in that person’s dynamic environment. Common environmental agents that are known to interact with genetic make-up include air pollutants, mold spores, pesticides, etc. Until recently, the other component, location of an individual was limited to a static representation such as residential or workplace location. Now, with the development of mobile technology, changes in an individual’s location can be tracked in real time if necessary. Technological advancements in the areas of estimating environmental agents and identifying locations of individuals at flexible spatio-temporal scales now present the potential of a paradigm shift in clinical practices by incorporating “Geographic Medical History” into determining disease risk factors.
INFLUENCE OF TOPOGRAPHIC AND HYDROGRAPHIC FACTORS ON THE SPATIAL DISTRIBUTION OF LEPTOSPIROSIS DISEASE IN SÃO PAULO COUNTY, BRAZIL: AN APPROACH USING GEOSPATIAL TECHNIQUES AND GIS ANALYSIS
Authors: Ferreira M. C. , Ferreira M. F. M.
Published: June 2016

Abstract. Leptospirosis is a zoonosis caused by Leptospira genus bacteria. Rodents, especially Rattus norvegicus, are the most frequent hosts of this microorganism in the cities. The human transmission occurs by contact with urine, blood or tissues of the rodent and contacting water or mud contaminated by rodent urine. Spatial patterns of concentration of leptospirosis are related to the multiple environmental and socioeconomic factors, like housing near flooding areas, domestic garbage disposal sites and high-density of peoples living in slums located near river channels. We used geospatial techniques and geographical information system (GIS) to analysing spatial relationship between the distribution of leptospirosis cases and distance from rivers, river density in the census sector and terrain slope factors, in Sao Paulo County, Brazil. To test this methodology we used a sample of 183 geocoded leptospirosis cases confirmed in 2007, ASTER GDEM2 data, hydrography and census sectors shapefiles. Our results showed that GIS and geospatial analysis techniques improved the mapping of the disease and permitted identify the spatial pattern of association between location of cases and spatial distribution of the environmental variables analyzed. This study showed also that leptospirosis cases might be more related to the census sectors located on higher river density areas and households situated at shorter distances from rivers. In the other hand, it was not possible to assert that slope terrain contributes significantly to the location of leptospirosis cases.
LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA
Authors: Wrable M. , Liss A. , Kulinkina A. , Koch M. , Biritwum N. K. , Ofosu A. , Kosinski K. C. , Gute D. M. , Naumova E. N.
Published: June 2016

Abstract. 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.
STOCHASTIC COLOURED PETRINET BASED HEALTHCARE INFRASTRUCTURE INTERDEPENDENCY MODEL
Authors: Nukavarapu Nivedita , Durbha Surya
Published: June 2016

Abstract. The Healthcare Critical Infrastructure (HCI) protects all sectors of the society from hazards such as terrorism, infectious disease outbreaks, and natural disasters. HCI plays a significant role in response and recovery across all other sectors in the event of a natural or manmade disaster. However, for its continuity of operations and service delivery HCI is dependent on other interdependent Critical Infrastructures (CI) such as Communications, Electric Supply, Emergency Services, Transportation Systems, and Water Supply System. During a mass casualty due to disasters such as floods, a major challenge that arises for the HCI is to respond to the crisis in a timely manner in an uncertain and variable environment. To address this issue the HCI should be disaster prepared, by fully understanding the complexities and interdependencies that exist in a hospital, emergency department or emergency response event. Modelling and simulation of a disaster scenario with these complexities would help in training and providing an opportunity for all the stakeholders to work together in a coordinated response to a disaster. The paper would present interdependencies related to HCI based on Stochastic Coloured Petri Nets (SCPN) modelling and simulation approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The entire model would be integrated with Geographic information based decision support system to visualize the dynamic behaviour of the interdependency of the Healthcare and related CI network in a geographically based environment.
RE-EMERGING MALARIA VECTORS IN RURAL SAHEL (NOUNA, BURKINA FASO): THE PALUCLIM PROJECT
Authors: Vignolles Cécile , Sauerborn Rainer , Dambach Peter , Viel Christian , Soubeyroux Jean-Michel , Sié Ali , Rogier Christophe , Tourre Yves M.
Published: June 2016

Abstract. The Paluclim project applied the tele-epidemiology approach, linking climate, environment and public health (CNES, 2008), to rural malaria in Nouna (Burkina Faso). It was to analyze the climate impact on vectorial risks, and its consequences on entomological risks forecast. The objectives were to: 1) produce entomological risks maps in the Nouna region, 2) produce dynamic maps on larvae sites and their productivity, 3) study the climate impact on malaria risks, and 4) evaluate the feasibility of strategic larviciding approach.
LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME
Authors: Cao W. , Tong X. H. , Liu S. C. , Wang D.
Published: June 2016

Abstract. Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should consider primarily the semantic aspects of the data. In this paper, we propose a scheme for recognizing landslides by using an object-oriented analysis technique and a semantic reasoning model on high spatial resolution optical imagery. Three case regions with different data sources are presented to evaluate its practicality. The procedure is designed as follows: first, the Gray Level Co-occurrence Matrix (GLCM) is used to extract texture features after the image explanation. Spectral features, shape features and thematic features are derived for semiautomatic landslide recognition. A semantic reasoning model is used afterwards to refine the classification results, by representing expert knowledge as first-order logic (FOL) rules. The experimental results are essentially consistent with the experts’ field interpretation, which demonstrate the feasibility and accuracy of the proposed approach. The results also show that the scheme has a good generality on diverse data sources.
IMPACT OF LEVEL OF DETAILS IN THE 3D RECONSTRUCTION OF TREES FOR MICROCLIMATE MODELING
Authors: Bournez E. , Landes T. , Saudreau M. , Kastendeuch P. , Najjar G.
Published: June 2016

Abstract. In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented.
SPATIOTEMPORAL EVALUATION OF NOCTURNAL COLD AIR DRAINAGE OVER A SIMPLE SLOPE USING THERMAL INFRARED IMAGERY
Authors: Ikani V. , Chokmani K. , Fathollahi L. , Granberg H. , Fournier R.
Published: June 2016

Abstract. Measurements of climatic processes such as cold air drainage flows are problematic over mountainous areas. Observation of cold air drainage is not available in the existing observation network and it requires a special methodology. The main objective of this study was to characterize the cold air drainage over regions with a slope. A high resolution infrared camera, a meteorological station and Digital Elevation Model (DEM) were used. The specific objective was to derive nocturnal cold air drainage velocity over the slope. To address these objectives, a number of infrared measurement campaigns were conducted during calm and clear sky conditions over an agricultural zone (blackcurrant farm) in Canada. Using thermal infrared images, the nocturnal surface temperature gradient were computed in hourly basis. The largest gradient magnitudes were found between 17h -20h. The cooling rates at basin area were two times higher in comparison to the magnitudes observed within slope area. The image analysis illustrated this considerable temperature gradient of the basin may be partly due to transport of cold air drainage into the basin from the slope. The results show that thermal imagery can be used to characterize and understand the microclimate related to the occurrence of radiation frost in the agricultural field. This study provided the opportunity to track the cold air drainage flow and pooling of cold air in low lying areas. The infrared analysis demonstrated that nocturnal drainage flow displayed continuous variation in terms of space and time in response to microscale slope heterogeneities. In addition, the analysis highlighted the periodic aspect for cold air drainage flow.
INFLUENCE OF DEM IN WATERSHED MANAGEMENT AS FLOOD ZONATION MAPPING
Authors: Alrajhi Muhamad , Khan Mudasir , Khan Mohammad Afroz , Alobeid Abdalla
Published: June 2016

Abstract. Despite of valuable efforts from working groups and research organizations towards flood hazard reduction through its program, still minimal diminution from these hazards has been realized. This is mainly due to the fact that with rapid increase in population and urbanization coupled with climate change, flood hazards are becoming increasingly catastrophic. Therefore there is a need to understand and access flood hazards and develop means to deal with it through proper preparations, and preventive measures. To achieve this aim, Geographical Information System (GIS), geospatial and hydrological models were used as tools to tackle with influence of flash floods in the Kingdom of Saudi Arabia due to existence of large valleys (Wadis) which is a matter of great concern. In this research paper, Digital Elevation Models (DEMs) of different resolution (30m, 20m,10m and 5m) have been used, which have proven to be valuable tool for the topographic parameterization of hydrological models which are the basis for any flood modelling process. The DEM was used as input for performing spatial analysis and obtaining derivative products and delineate watershed characteristics of the study area using ArcGIS desktop and its Arc Hydro extension tools to check comparability of different elevation models for flood Zonation mapping. The derived drainage patterns have been overlaid over aerial imagery of study area, to check influence of greater amount of precipitation which can turn into massive destructions. The flow accumulation maps derived provide zones of highest accumulation and possible flow directions. This approach provide simplified means of predicting extent of inundation during flood events for emergency action especially for large areas because of large coverage area of the remotely sensed data.
SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS
Authors: Chang K. T. , Dou J. , Chang Y. , Kuo C. P. , Xu K. M. , Liu J. K.
Published: June 2016

Abstract. The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.
SENTINEL-1/2 DATA FOR SHIP TRAFFIC MONITORING ON THE DANUBE RIVER
Authors: Dana Negula I. , Poenaru V. D. , Olteanu V. G. , Badea A.
Published: June 2016

Abstract. After a long period of drought, the water level of the Danube River has significantly dropped especially on the Romanian sector, in July-August 2015. Danube reached the lowest water level recorded in the last 12 years, causing the blockage of the ships in the sector located close to Zimnicea Harbour. The rising sand banks in the navigable channel congested the commercial traffic for a few days with more than 100 ships involved. The monitoring of the decreasing water level and the traffic jam was performed based on Sentinel-1 and Sentinel-2 free data provided by the European Space Agency and the European Commission within the Copernicus Programme. Specific processing methods (calibration, speckle filtering, geocoding, change detection, image classification, principal component analysis, etc.) were applied in order to generate useful products that the responsible authorities could benefit from. The Sentinel data yielded good results for water mask extraction and ships detection. The analysis continued after the closure of the crisis situation when the water reached the nominal level again. The results indicate that Sentinel data can be successfully used for ship traffic monitoring, building the foundation of future endeavours for a durable monitoring of the Danube River.
MULTI-TEMPORAL SAR INTERFEROMETRY FOR LANDSLIDE MONITORING
Authors: Dwivedi R. , Narayan A. B. , Tiwari A. , Dikshit O. , Singh A. K.
Published: June 2016

Abstract. In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoring related applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Multi-temporal InSAR methods are developed in recent times to retrieve the deformation signal from pixels with different scattering characteristics. Presently, two classes of multi-temporal InSAR algorithms are available- Persistent Scatterer (PS) and Small Baseline (SB) methods. This paper discusses the Stanford Method for Persistent Scatterer (StaMPS) based PS-InSAR and the Small Baselines Subset (SBAS) techniques to estimate the surface deformation in Tehri dam reservoir region in Uttarkhand, India. Both PS-InSAR and SBAS approaches used sixteen ENVISAT ASAR C-Band images for generating single master and multiple master interferograms stack respectively and their StaMPS processing resulted in time series 1D-Line of Sight (LOS) mean velocity maps which are indicative of deformation in terms of movement towards and away from the satellites. From 1D LOS velocity maps, localization of landslide is evident along the reservoir rim area which was also investigated in the previous studies. Both PS-InSAR and SBAS effectively extract measurement pixels in the study region, and the general results provided by both approaches show a similar deformation pattern along the Tehri reservoir region. Further, we conclude that StaMPS based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean Line of Sight (LOS) velocity as compared to SBAS method. It is also proposed to take up a few major landslides area in Uttarakhand for slope stability assessment.
A NEW METHOD TO DETECT REGIONS ENDANGERED BY HIGH WIND SPEEDS
Authors: Fischer P. , Ehrensperger S. , Krauß T.
Published: June 2016

Abstract. In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landform classification). Furthermore land cover data from the CORINE dataset is added. The response variable is maximum wind speed, measurements are provided by a network of weather stations. The area of interest is Switzerland, a country which suits perfectly for this study because of its highly dynamic orography and various landforms.
ASSESSMENT OF THE UTILITY OF THE ADVANCED HIMAWARI IMAGER TO DETECT ACTIVE FIRE OVER AUSTRALIA
Authors: Hally B. , Wallace L. , Reinke K. , Jones S.
Published: June 2016

Abstract. Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.
INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE
Authors: Lai J.-S. , Tsai F. , Chiang S.-H.
Published: June 2016

Abstract. This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.
3D GPS velocity field and its implications on the present-day postorogenic deformation of the Western Alps and Pyrenees
Authors: Nguyen Hai Ninh , Vernant Philippe , Mazzotti Stephane , Khazaradze Giorgi , Asensio Eva
Published: June 2016

Abstract. We present a new 3D GPS velocity solution for 182 sites for the region encompassing the Western Alps, Pyrenees, and southern France. The velocity field is based on a Precise Point Positioning (PPP) solution, to which we apply a common-mode filter, defined by the 26 longest time series, in order to correct for network-wide biases (reference frame, environmental noise, ...). We show that processing options, such as troposphere delay, can lead to systematic velocity variations of 0.1–0.5 mm yr−1 affecting both accuracy and precision, especially for short (
Long-term elevation of temperature affects organic N turnover and associated N2O emissions in a permanent grassland soil
Authors: Jansen-Willems Anne B. , Lanigan Gary J. , Clough Timothy J. , Andresen Louise C. , Müller Christoph
Published: June 2016

Abstract. Over the last century an increase in mean soil surface temperature has been observed and it is predicted to increase further in the future. To evaluate the legacy effects of increased temperature on both nitrogen (N) transformation rates in the soil and nitrous oxide (N2O) emissions, an incubation experiment was conducted with soils taken from a long term in situ warming experiment on temperate permanent grassland. In this experiment the soil temperature was elevated by 0 (control), 1, 2 or 3 °C (4 replicates per treatment) using IR-lamps over a period of 6 years. The soil was subsequently incubated under common conditions (20 °C and 50 % humidity) and labelled with NO315NH4 Gly, 15NO3NH4 Gly or NO3NH4 15N-Gly. Both inorganic N (NO3−NH4+) and NO32− contents were higher in soil subjected to the +2 and +3 °C temperature elevations. Analyses of N transformations using a 15N tracing model, showed that, following incubation, gross organic (and not inorganic) N transformation rates decreased in response to the prior soil warming treatment. This was also reflected in reduced N2O emissions associated with organic N oxidation and denitrification. A newly developed source partitioning model showed the importance of oxidation of organic N as a source of N2O. Concluding, long term soil warming can cause a legacy effect which diminishes organic N turn over and the release of N2O from organic N and denitrification.
Property Rights and Sustainable Natural Resource Management
Authors: Poudel Krishna Lal , Johnson Thomas G. , Tewari Rachna
Published: June 2016

This paper reviews recent research articles in the field of property rights and natural resources management, with the goal of identifying the most effective policy measures to achieve sustainable resource management through well designed property rights. Scarcity of economic resources is a major cause of conflict in human society. Institutions impact the resilience of the environment, and the institutions which guide humans as they employ resources from the environment are therefore essential to sustainable resource management. Institutions which create and enforce property rights can control resource degradation and improve both economic and ecological efficiency. Property rights which lead to an equitable allocation of natural resources and delegation of management authority among stakeholders is the most likely pathway to sustainable ecosystem management. However, there is a lack of consensus regarding the best system of property rights to achieve sustainability. The ‘best system’ is contextual and spatiotemporal dynamic. 
Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS
Authors: Mitchell Jessica , Glenn Nancy , Anderson Matthew , Hruska Ryan
Published: June 2016

Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (Poa secunda) from invasives such as burr buttercup (Ranunculus testiculatus) in a shrubland environment. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup. 
Factors Influencing Crowdfunding Plausibility in Post Hyperinflationary Zimbabwe
Authors: Munyanyi Watson , Mapfumo Alexander
Published: June 2016

In many developing econonmies access to traditional entrepreneurial financing like banks and venture capitalists has remained suppressed. This has forced many entrepreneurs to resort to non traditiona lmethods of venture financing among them, financial bootstrapping and micro loans. One alternative that these ventures could employ is crowdfunding. Although crowdfunding has remained predominantly a developed world phenomenon, there has been strides in the emerging economies to employ it as an entrepreneurial financing alternative. This paper spught to investigate the factors that are driving the possibility of Zimbabwean entrepreneurs employing crowdfunding as a financing technique. Through survey data collection techniques and statistical data analysis this research mananged to highlight the factors in the crowdfunding ecosystem tht makes the Zimbabwean economy ready for crowdfunding activities. In line with existing literature five factors were uncovered as the major drivers that make crowdfunding possible in Zimbabwe and the results are used to infer to other emerging economies with similar characteristics.
Comparison of Phacoemulsification Cataract Surgery with Low versus Standard Fluidic Settings and the Impact on Postoperative Parameters
Authors: Chang Anthony , Fridberg Alexander , Kugelberg Maria
Published: June 2016

Purpose

To compare complications after cataract surgery using 2 different phacoemulsification fluidic settings.

Methods

One cataract surgeon (M.K.) performed phacoemulsification cataract surgery in one eye of 43 patients in this randomized prospective clinical study conducted at St. Erik Eye Hospital, Stockholm, Sweden. The patients were randomized to phacoemulsification with low fluidic settings in one group and standard fluidic settings in the other group. Corrected distance visual acuity (CDVA), central corneal thickness (CCT), endothelial cell density (ECD), anterior chamber flare, intraocular pressure (IOP), and macular thickness were measured preoperatively and postoperatively, with a final evaluation at 3 months. Surgical time, ultrasound energy, and amount of balanced saline solution used intraoperatively were recorded.

Results

Twenty-one patients were included in the group with standard settings and 22 patients were in the group with low settings. There were no significant differences between the groups in CDVA, CCT, flare, IOP, macular thickness, or ECD at 1 day, 3 weeks, or 3 months postoperatively. The surgical time was significantly (p = 0.009) longer and cumulative dissipated energy was significantly (p<0.001) higher in the group with low fluidic settings.

Conclusions

Although the surgical time and ECD were significantly higher in the group with low fluidic settings, there were no differences in the CCT, ECD, macular thickness, or inflammation postoperatively between the low and standard fluidic settings during phacoemulsification.