bluuebunny/crossref_metadata_2025 huggingface dataset:
POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS
Authors: Shi Shuo , Gong Wei , Du Lin , Sun Jia , Yang Jian
Published: August 2016

Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.
CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS
Authors: Verma Amit Kumar , Garg P. K. , Hari Prasad K. S. , Dadhwal V. K.
Published: August 2016

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.
COMPREHENSIVE EVALUATION OF URBAN SPRAWL ON ECOLOGICAL ENVIRONMENT USING MULTI-SOURCE DATA: A CASE STUDY OF BEIJING
Authors: Wang Hao , Ning Xiaogang , Zhu Weiwei , Li Fei
Published: August 2016

With urban population growing and urban sprawling, urban ecological environment problems appear. Study on spatiotemporal characteristics of urban sprawl and its impact on ecological environment is useful for ecological civilization construction. Although a lot of work has been conducted on urban sprawl and its impact on ecological environment, resolution of images to extract urban boundary was relatively coarse and most studies only focused on certain indicators of ecological environment, rather than comprehensive evaluation of urban ecological environmental impact. In this study, high-resolution remote sensing images of Beijing from aerial photography in 2002 and 2013 respectively are employed to extract urban boundary with manual interpretation. Fractional Vegetation Coverage (FVC), Water Density (WD), Impervious Surfaces Coverage (ISC), Net Primary Production (NPP), and Land Surface Temperature (LST) are adopted to represent ecological environment. The ecological environment indicators are measured with some general algorithms by combining Landsat images, GIS data and metrological data of 243 day, 2001 and 244 day, 2013. In order to evaluate the impact of urban sprawl on ecological environment, pseudo changes due to metrological variation and other noise in this time period are removed after images calibration. The impact of urban sprawl on ecological environment is evaluated at different scales of urban extent, Beijing ring road and watershed. Results show that Beijing had been undergoing a rapid urbanization from 2002 to 2013, with urban area increase from 600 square kilometres to 987 square kilometres. All ecological environment indicators except LST became terrible in urban sprawl region, with carbon reduction of approximate 40508 tons. The Beiyun River watershed of Beijing degraded seriously since ISC increased to 0.59. Gratifyingly, ecological environment indicators including NDVI, NPP, and LST inside of 4th Ring Road became well.
RELATIONSHIP BETWEEN LANDCOVER PATTERN AND SURFACE NET RADIATION IN AN COASTAL CITY
Authors: Zhao X. , Liu L. , Liu X. , Zhao Y.
Published: August 2016

Taking Xiamen city as the study area this research first retrieved surface net radiation using meteorological data and Landsat 5 TM images of the four seasons in the year 2009. Meanwhile the 65 different landscape metrics of each analysis unit were acquired using landscape analysis method. Then the most effective landscape metrics affecting surface net radiation were determined by correlation analysis, partial correlation analysis, stepwise regression method, etc. At both class and landscape levels, this paper comprehensively analyzed the temporal and spatial variations of the surface net radiation as well as the effects of land cover pattern on it in Xiamen from a multi-seasonal perspective. The results showed that the spatial composition of land cover pattern shows significant influence on surface net radiation while the spatial allocation of land cover pattern does not. The proportions of bare land and forest land are effective and important factors which affect the changes of surface net radiation all the year round. Moreover, the proportion of forest land is more capable for explaining surface net radiation than the proportion of bare land. So the proportion of forest land is the most important and continuously effective factor which affects and explains the cross-seasonal differences of surface net radiation. This study is helpful in exploring the formation and evolution mechanism of urban heat island. It also gave theoretical hints and realistic guidance for urban planning and sustainable development.
MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING
Authors: Ben-Romdhane H. , Marpu P. R. , Ghedira H. , Ouarda T. B. M. J.
Published: August 2016

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.
OIL SPILL DETECTION AND MONITORING OF ABU DHABI COASTAL ZONE USING KOMPSAT-5 SAR IMAGERY
Authors: Harahsheh H. A.
Published: August 2016

Abu Dhabi Government endorsed vision for its Maritime Strategy ‘A safe, secure and sustainable maritime domain for Abu Dhabi'. This research study share this vision using the concept of monitoring as tool for marine protection against any possible oil pollution. The best technology to detect and monitor oil pollution and in particularly oil spill is SAR imagery In this case study we chose KOMPSAT-5 SAR.

 KOMPSAT-5 carries X-band SAR for earth observation, and is capable of day-and-night imaging under all weather condition. It provides three operation modes: High Resolution Mode to provide 1 m resolution, Standard Mode to provide 3 m resolution and Wide Swath Mode to provide 20 m resolution with 100 km swath at 550 km altitude, with four modes of polarization. KOMPSAT-5 provides products for various applications; security and defense, mapping, and natural resource management, environmental monitoring, disaster monitoring and more. For our case study we chose to work with Wide Swath mode (WS) with Vertical polarization (VV) to cover a wide area of interest located to the north west of Abu Dhabi including some important islands like ”Zirku Island”, and areas with oil production activities.

 The results of data acquired on 4th May 2015 show some spot of oil spill with length estimated about 3 KM, and the daily satellite data acquisition over the period July 24 through July 31 shows serious and many oil spill events some are small, but many others are considered to be big with area size around 20 km2.

 In the context of oil spill pollution in the seas, we have to consider the development and increase of overseas transportation, which is an important factor for both social and economic sectors. The harmful effects of marine pollution are numerous, from the damage of marine life to the damage of the aquatic ecosystem as whole. As such, the need for oil slick detection is crucial, for the location of polluted areas and to evaluate slick drift to protect the coastline. Satellite-based oil spill monitoring system now can be used to take precautions and even to determine the possible polluter; it has a vital importance on the detection and protection of national and international waters from the possible damages of petroleum hazard. Finally, and as we suggested in previous studies, we recommend to the national authorities to establish a national near-real time oil spill monitoring system based on SAR satellite imagery, with the support of other tools like AIS and navigation radars
SPATIAL CHANGES AND POPULATION MOVEMENTS ON THE ALBANIAN COASTLINE
Authors: Kanjir U. , Gregorič Bon N.
Published: August 2016

The last decade has seen a large increase in construction along the southern Albanian coastline, mainly in the rise of large tourist complexes comprising hotels, apartment houses, touristic villages, and so on. These constructions rarely follow urban planning and not only change its landscape but also often threaten the ecological value of the coastal zone. The uncontrolled and devastating construction along the coast has been accompanied by coastal erosion caused by the sea with the intensity up to 50 cm/year. This paper investigates the environmental change monitoring on the Albanian Riviera by analysing optical remote sensing data (Landsat 5 and 8) in the period between 1984 and 2015. The image analysis results grounded on the change vector analysis indicate coastal morphology changes and land cover changes in the coastal environment, which appear mostly due to erosion in river delta and urban growth. Apart from identifying both phenomena through time, the objective of this study is to show that these landscape changes in fact correlate with the population migration as well as to explain why and the extent to which Albania is one of the most migratory countries in Europe. Based on the multidisciplinary research, which combines anthropological method with spatial analysis, this presentation anticipates future changes in this area. It argues that movements of both people and in landscape formations strongly influence each other, constituting a closely corresponding relationship.
BEACH VOLUME CHANGE USING UAV PHOTOGRAMMETRY SONGJUNG BEACH, KOREA
Authors: Yoo C. I. , Oh T. S.
Published: August 2016

Natural beach is controlled by many factors related to wave and tidal forces, wind, sediment, and initial topography. For this reason, if numerous topographic data of beach is accurately collected, coastal erosion/acceleration is able to be assessed and clarified. Generally, however, many studies on coastal erosion have limitation to analyse the whole beach, carried out of partial area as like shoreline (horizontal 2D) and beach profile (vertical 2D) on account of limitation of numerical simulation. This is an important application for prevention of coastal erosion, and UAV photogrammetry is also used to 3D topographic data. This paper analyses the use of unmanned aerial vehicles (UAV) to 3D map and beach volume change. UAV (Quadcopter) equipped with a non-metric camera was used to acquire images in Songjung beach which is located south-east Korea peninsula. The dynamics of beach topography, its geometric properties and estimates of eroded and deposited sand volumes were determined by combining elevation data with quarterly RTK-VRS measurements. To explore the new possibilities for assessment of coastal change we have developed a methodology for 3D analysis of coastal topography evolution based on existing high resolution elevation data combined with low coast, UAV and on-ground RTK-VRS surveys. DSMs were obtained by stereo-matching using Agisoft Photoscan. Using GCPs the vertical accuracy of the DSMs was found to be 10 cm or better. The resulting datasets were integrated in a local coordinates and the method proved to be a very useful fool for the detection of areas where coastal erosion occurs and for the quantification of beach change. The value of such analysis is illustrated by applications to coastal of South Korea sites that face significant management challenges.
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: August 2016

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.
IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL
Authors: Chen Yi-Chen , Lin Chao-Hung
Published: August 2016

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.
USING MULTI-DIMENSIONAL MICROWAVE REMOTE SENSING INFORMATION FOR THE RETRIEVAL OF SOIL SURFACE ROUGHNESS
Authors: Marzahn P. , Ludwig R.
Published: August 2016

In this Paper the potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the Demmin testsite in the North-East Germany during AgriSAR 2006 campaign using fully polarimetric L-Band airborne SAR data. For ground truthing extensive soil surface roughness in addition to various other soil physical properties measurements were carried out using photogrammetric image matching techniques. The correlation between ground truth roughness indices and three well established polarimetric roughness estimators showed only good results for Re[ρRRLL] and the RMS Height s. Results in form of multitemporal roughness maps showed only satisfying results due to the fact that the presence and development of particular plants affected the derivation. However roughness derivation for bare soil surfaces showed promising results.
MAPPING SPATIAL MOISTURE CONTENT OF UNSATURATED AGRICULTURAL SOILS WITH GROUND-PENETRATING RADAR
Authors: Shamir O. , Goldshleger N. , Basson U. , Reshef M.
Published: August 2016

Soil subsurface moisture content, especially in the root zone, is important for evaluation the influence of soil moisture to agricultural crops. Conservative monitoring by point-measurement methods is time-consuming and expensive. In this paper we represent an active remote-sensing tool for subsurface spatial imaging and analysis of electromagnetic physical properties, mostly water content, by ground-penetrating radar (GPR) reflection. Combined with laboratory methods, this technique enables real-time and highly accurate evaluations of soils' physical qualities in the field. To calculate subsurface moisture content, a model based on the soil texture, porosity, saturation, organic matter and effective electrical conductivity is required. We developed an innovative method that make it possible measures spatial subsurface moisture content up to a depth of 1.5 m in agricultural soils and applied it to two different unsaturated soil types from agricultural fields in Israel: loess soil type (Calcic haploxeralf), common in rural areas of southern Israel with about 30% clay, 30% silt and 40% sand, and hamra soil type (Typic rhodoxeralf), common in rural areas of central Israel with about 10% clay, 5% silt and 85% sand. Combined field and laboratory measurements and model development gave efficient determinations of spatial moisture content in these fields. The environmentally friendly GPR system enabled non-destructive testing. The developed method for measuring moisture content in the laboratory enabled highly accurate interpretation and physical computing. Spatial soil moisture content to 1.5 m depth was determined with 1–5% accuracy, making our method useful for the design of irrigation plans for different interfaces.
BIODIVERSITY MAPPING VIA NATURA 2000 CONSERVATION STATUS AND EBV ASSESSMENT USING AIRBORNE LASER SCANNING IN ALKALI GRASSLANDS
Authors: Zlinszky A. , Deák B. , Kania A. , Schroiff A. , Pfeifer N.
Published: August 2016

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.
DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE
Authors: Miyazaki H. , Nagai M. , Shibasaki R.
Published: August 2016

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA
Authors: Rhee J. , Im J. , Park S.
Published: August 2016

The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.
3D GIS FOR FLOOD MODELLING IN RIVER VALLEYS
Authors: Tymkow P. , Karpina M. , Borkowski A.
Published: August 2016

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.
GEOGRAPHIC MEDICAL HISTORY: ADVANCES IN GEOSPATIAL TECHNOLOGY PRESENT NEW POTENTIALS IN MEDICAL PRACTICE
Authors: Faruque F. S. , Finley R. W.
Published: August 2016

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.
Sensitivity of land surface and Cumulus schemes for Thunderstorm prediction
Authors: Kumar Dinesh , Mohanty U. C. , Kumar Krishan
Published: August 2016

The cloud processes play an important role in all forms of precipitation. Its proper representation is one of the challenging tasks in mesoscale numerical simulation. Studies have revealed that mesoscale feature require proper initialization which may likely to improve the convective system rainfall forecasts. Understanding the precipitation process, model initial condition accuracy and resolved/sub grid-scale precipitation processes representation, are the important areas which needed to improve in order to represent the mesoscale features properly. Various attempts have been done in order to improve the model performance through grid resolution, physical parameterizations, etc. But it is the physical parameterizations which provide a convective atmosphere for the development and intensification of convective events. Further, physical parameterizations consist of cumulus convection, surface fluxes of heat, moisture, momentum, and vertical mixing in the planetary boundary layer (PBL). How PBL and Cumulus schemes capture the evolution of thunderstorm have been analysed by taking thunderstorm cases occurred over Kolkata, India in the year 2011. PBL and cumulus schemes were customized for WSM-6 microphysics because WSM series has been widely used in operational forecast. Results have shown that KF (PBL scheme) and WSM-6 (Cumulus Scheme) have reproduced the evolution of surface variable such as CAPE, temperature and rainfall very much like observation. Further, KF and WSM-6 scheme also provided the increased moisture availability in the lower atmosphere which was taken to higher level by strong vertical velocities providing a platform to initiate a thunderstorm much better. Overestimation of rain in WSM-6 occurs primarily because of occurrence of melting and freezing process within a deeper layer in WSM-6 scheme. These Schemes have reproduced the spatial pattern and peak rainfall coverage closer to TRMM observation. It is the the combination of WSM-6, and KF schemes which have preformed reasonably well to reproduce the right atmospheric condition for a thunderstorm leading to improved spatial and temporal rainfall over the study domain. Thus the parameterization schemes of WMS-6 and KF have shown significant improvement by capturing the location, intensity and surface meteorological parameters closer to observed details.
A NOVEL INDEX FOR ATMOSPHERIC AEROSOL TYPES CATEGORIZATION WITH SPECTRAL OPTICAL DEPTHS FROM SATELLITE RETRIEVAL
Authors: Lin Tang-Huang , Liu Gin-Rong , Liu Chian-Yi
Published: August 2016

In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as Ångström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global area which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested, a novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA providing. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized between mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well agreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips highly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD compositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol Subtype.
ESTIMATION OF PHOSPHORUS EMISSIONS IN THE UPPER IGUAZU BASIN (BRAZIL) USING GIS AND THE MORE MODEL
Authors: Acosta Porras E. A. , Kishi R. T. , Fuchs S. , Hilgert S.
Published: August 2016

Pollution emissions into the drainage basin have direct impact on surface water quality. These emissions result from human activities that turn into pollution loads when they reach the water bodies, as point or diffuse sources. Their pollution potential depends on the characteristics and quantity of the transported materials. The estimation of pollution loads can assist decision-making in basin management. Knowledge about the potential pollution sources allows for a prioritization of pollution control policies to achieve the desired water quality. Consequently, it helps avoiding problems such as eutrophication of water bodies. The focus of the research described in this study is related to phosphorus emissions into river basins. The study area is the upper Iguazu basin that lies in the northeast region of the State of Paraná, Brazil, covering about 2,965 km2 and around 4 million inhabitants live concentrated on just 16% of its area.

 The MoRE (Modeling of Regionalized Emissions) model was used to estimate phosphorus emissions. MoRE is a model that uses empirical approaches to model processes in analytical units, capable of using spatially distributed parameters, covering both, emissions from point sources as well as non-point sources. In order to model the processes, the basin was divided into 152 analytical units with an average size of 20 km2. Available data was organized in a GIS environment. Using e.g. layers of precipitation, the Digital Terrain Model from a 1:10000 scale map as well as soils and land cover, which were derived from remote sensing imagery. Further data is used, such as point pollution discharges and statistical socio-economic data. The model shows that one of the main pollution sources in the upper Iguazu basin is the domestic sewage that enters the river as point source (effluents of treatment stations) and/or as diffuse pollution, caused by failures of sanitary sewer systems or clandestine sewer discharges, accounting for about 56% of the emissions. Second significant shares of emissions come from direct runoff or groundwater, being responsible for 32% of the total emissions. Finally, agricultural erosion and industry pathways represent 12% of emissions. This study shows that MoRE is capable of producing valid emission calculation on a relatively reduced input data basis.
UNDERSTANDING THE BEHAVIOUR OF CONTAMINATION SPREAD IN NAGARJUNA SAGAR RESERVOIR USING TEMPORAL LANDSAT DATA
Authors: Tarun Teja K. , Rajan K. S.
Published: August 2016

LANDSAT images are used to identify organic contaminants in water bodies, but, there is no enough evidence in present literature that LANDSAT is also good in identifying a mixture of organic and mineral contaminants such as agricultural waste. The focus of this paper is to evaluate the effectiveness of LANDSAT imagery to identify organic and mineral contamination (OMC) and to identify spread extent variations of pollution over the season/year in the Nagarjuna Sagar (NS) reservoir using only satellite images. A new band combination is proposed in order to detect OMC, because existing formulae based on band ratio proved to be inadequate in detecting the contamination in NS. Difference in reflectance values of Red and Green channel of an image helps clearly distinguish clear water from OMC water. This procedure was applied over LANDSAT data of the calendar years 2008, 2014 and 2015 to understand the contamination spread pattern through the reservoir. Results show that contamination is following a similar pattern over these calendar years. In January contamination starts at inlets and by May contamination spreads to almost 90% of the reservoir when the total area of water spread is also reduced by half. Contamination spread is low during the monsoonal period of June to September due to heavy inflow and heavy outflow of waters from NS reservoir. Post monsoon NS is contaminated again because of heavy inflow of runoffs from neighboring land use and limited water outflow. This contamination spread pattern matches the agricultural seasons and fertilizer application pattern in this region, indicating that agricultural use of fertilizers could be one of the primary causes of contamination for this waterbody.
SPATIOTEMPORAL DYNAMICS OF SURFACE WATER EXTENT FROM THREE DECADES OF SEASONALLY CONTINUOUS LANDSAT TIME SERIES AT SUBCONTINENTAL SCALE
Authors: Tulbure M. G. , Broich M. , Stehman Stephen V.
Published: August 2016

Surface water is a critical resource in semi-arid areas. The Murray-Darling Basin (MDB) of Australia, one of the largest semi-arid basins in the world is aiming to set a worldwide example of how to balance multiple interests (i.e. environment, agriculture and urban use), but has suffered significant water shrinkages during the Millennium Drought (1999-2009), followed by extensive flooding. Baseline information and systematic quantification of surface water (SW) extent and flooding dynamics in space and time are needed for managing SW resources across the basin but are currently lacking.

 To synoptically quantify changes in SW extent and flooding dynamics over MDB, we used seasonally continuous Landsat TM and ETM+ data (1986 – 2011) and generic machine learning algorithms. We further mapped flooded forest at a riparian forest site that experienced severe tree dieback due to changes in flooding regime. We used a stratified sampling design to assess the accuracy of the SW product across time.

 Accuracy assessment yielded an overall classification accuracy of 99.94%, with producer’s and user’s accuracy of SW of 85.4% and 97.3%, respectively. Overall accuracy was the same for Landsat 5 and 7 data but user’s and producer’s accuracy of water were higher for Landsat 7 than 5 data and stable over time.

 Our validated results document a rapid loss in SW bodies. The number, size, and total area of SW showed high seasonal variability with highest numbers in winter and lowest numbers in summer. SW extent per season per year showed high interannual and seasonal variability, with low seasonal variability during the Millennium Drought.

 Examples of current uses of the new dataset will be presented and include (1) assessing ecosystem response to flooding with implications for environmental water releases, one of the largest investment in environment in Australia; (2) quantifying drivers of SW dynamics (e.g. climate, human activity); (3) quantifying changes in SW dynamics and connectivity for water dependent organisms; (4) assessing the impact of flooding on riparian vegetation health. The approach developed here is globally applicable, relevant to areas with competing water demands (e.g. Okavango River delta, Mekong River Basin). Future work should incorporate Landsat 8 and Sentinel-2 data for continued quantification of SW dynamics.
RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA
Authors: Huang J. , Zhou Q.
Published: August 2016

Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.
DETECTION AND DISCRIMINATION OF THE THICK OIL PATCHES ON THE SEA SURFACE
Authors: Dubucq Dominique , Sicot Guillaume , Lennon Marc , Miegebielle Véronique
Published: August 2016

Detection of natural or accidental oil slick at sea surface is important both for exploration purposes and for environment protection. Radar imagery, either satellite or airborne is the prime tool to detect those slicks. Radar is widely used by the national agencies to monitor their maritime areas for accidental pollutions or boat discharges. Radar images can detect oil slick even in the presence of clouds. However the sea surface back scattered energy is rather insensitive to oil thickness. On the contrary several studies tend to prove that optical data may be used to estimate the oil thickness. These data may be in the form of hyperspectral data or thermal infrared data. The objective of this study is to show that SWIR satellite data which are more widely available than hyperspectral data, better resolved than thermal data and available at a very limited cost, can be used to detect and qualitatively assess the thickness of oil slicks. This is important to assess volumes of naturally release oil in the oceans and in case of a crisis to send intervention teams where oil is thickest.
GIS-BASED WIND FARM SITE SELECTION MODEL OFFSHORE ABU DHABI EMIRATE, UAE
Authors: Saleous N. , Issa S. , Al Mazrouei J.
Published: August 2016

The United Arab Emirates (UAE) government has declared the increased use of alternative energy a strategic goal and has invested in identifying and developing various sources of such energy. This study aimed at assessing the viability of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favourable sites for such farms using Geographic Information Systems (GIS) procedures and algorithms. Based on previous studies and on local requirements, a set of suitability criteria was developed including ocean currents, reserved areas, seabed topography, and wind speed. GIS layers were created and a weighted overlay GIS model based on the above mentioned criteria was built to identify suitable sites for hosting a new offshore wind energy farm. Results showed that most of Abu Dhabi offshore areas were unsuitable, largely due to the presence of restricted zones (marine protected areas, oil extraction platforms and oil pipelines in particular). However, some suitable sites could be identified, especially around Delma Island and North of Jabal Barakah in the Western Region. The environmental impact of potential wind farm locations and associated cables on the marine ecology was examined to ensure minimal disturbance to marine life. Further research is needed to specify wind mills characteristics that suit the study area especially with the presence of heavy traffic due to many oil production and shipping activities in the Arabian Gulf most of the year.
THIN ICE AREA EXTRACTION IN THE SEA OF OKHOTSK FROM GCOM-W1/AMSR2 DATA
Authors: Cho K. , Sato Y. , Naoki K.
Published: August 2016

Passive microwave radiometers on-board satellites can penetrate clouds and can monitor the global sea ice distribution on daily basis. The authors have developed an algorithm to extract thin ice area in the Sea of Okhotsk from the passive microwave sensor AMSR2 on-board GCOM-W1 satellite. The algorithm uses the brightness temperature scatter plots of AMSR2 19 GHz polarization difference(V–H) vs. 19 GHz V polarization. The results were verified using simultaneously collected MODIS images in the Sea of Okhotsk. The most of the thin ice areas visually identified in the MODIS images were automatically extracted from AMSR2 data using the algorithm.