다양한 규모에서의 기후변화에 따른 산사태 영향 및 취약성 평가

학위논문 (박사)-- 서울대학교 대학원 : 조경학, 2017. 2. 이동근.Republic of Korea has increasingly experienced extreme weather events such as typhoons or heavy rainfall because of climate change. Extreme weather events result in damage in different forms, among which damage caused by landslide occurs every year. Landslide not only damages property but also causes human loss and calls for imperative adaptive measures to prevent or reduce damage. Currently, studies that seek to identify landslide-prone regions and high-priority areas that need adaptive measures are becoming important field. Impact of climate change is found on a variety of scales and in many regions, and yet, adaptive measures have been taken in different forms at the levels of nation, province, and county. Data and evaluation of the impact of climate change can differ to the scales of study target. Thus, proper method also differ to scale when assessing the impact of climate change. The purpose of this study is, in this sense, to develop and apply a methodology for different scales—central government, province, and counties—to evaluate the impact of and vulnerability to climate change on the occurrence of landslides. The research developed a methodology and framework for finding vulnerable and hazard areas considering different scales and available data. At the national level, the study evaluated many items including landslide and found key vulnerable regions, which the central government needs to priority for support. At the provincial level, the study analyzed areas prone to landslide in Gangwon-do Province from the present to the future and found out specific regions that need adaptive measures. At the county level, Inje-gun of Gangwon-do Province was assessed by 10 models to investigate high-risk areas with uncertainties of models, and the uncertainties were quantified to assist establishing more effective adaptive measures. This study seeks to propose a method that can support decision-makers in establishing adaptation measures against climate change, by developing and applying a viable methodology and framework that can tailor to each users objective and available data for evaluating the impact of climate change. The results would be help for establishing adaptive measures to reduce damage from landslide and evaluating the impact of and vulnerability to climate change by governments of different scales.1.INTRODUCTION 1 2.CHAPTER 1 : Climate Change Vulnerability and Impact Assessment for Republic of Korea 3 2.1.Introduction 3 2.2.Methods 4 2.3.Results and discussion 12 2.4.Conclusion 27 3.CHAPTER 2 : Evaluating Landslide Impact Assessment Considering Climate Change for Gangwon-do 30 3.1.Introduction 30 3.2.Methods 33 3.3.Results 38 3.4.Discussion 51 3.5.Conclusion 52 4.CHAPTER 3 : Estimating Landslide Susceptibility Areas in Consideration of the Uncertainty Inherent in Modeling Results 54 4.1.Introduction 54 4.2.Methods 56 4.3.Results and discussion 66 4.4.Conclusion 76 5.DISCUSSION AND CONCLUSION 79 6.REFERENCES 81Docto

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products

An intelligent classification system for land use and land cover mapping using spaceborne remote sensing and GIS

The objectives of this study were to experiment with and extend current methods of Synthetic Aperture Rader (SAR) image classification, and to design and implement a prototype intelligent remote sensing image processing and classification system for land use and land cover mapping in wet season conditions in Bangladesh, which incorporates SAR images and other geodata. To meet these objectives, the problem of classifying the spaceborne SAR images, and integrating Geographic Information System (GIS) data and ground truth data was studied first. In this phase of the study, an extension to traditional techniques was made by applying a Self-Organizing feature Map (SOM) to include GIS data with the remote sensing data during image segmentation. The experimental results were compared with those of traditional statistical classifiers, such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance classifiers. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification (with respect to the period of inundation by regular flooding) was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers. It also achieved higher accuracies for more classes in comparison to the other classifiers. However, it was also observed that different classifiers produced better accuracy for different classes. Therefore, the investigation was extended to consider Multiple Classifier Combination (MCC) techniques, which is a recently emerging research area in pattern recognition. The study has tested some of these techniques to improve the classification accuracy by harnessing the goodness of the constituent classifiers. A Rule-based Contention Resolution method of combination was developed, which exhibited an improvement in the overall accuracy of about 2% in comparison to its best constituent (SOM) classifier. The next phase of the study involved the design of an architecture for an intelligent image processing and classification system (named ISRIPaC) that could integrate the extended methodologies mentioned above. Finally, the architecture was implemented in a prototype and its viability was evaluated using a set of real data. The originality of the ISRIPaC architecture lies in the realisation of the concept of a complete system that can intelligently cover all the steps of image processing classification and utilise standardised metadata in addition to a knowledge base in determining the appropriate methods and course of action for the given task. The implemented prototype of the ISRIPaC architecture is a federated system that integrates the CLIPS expert system shell, the IDRISI Kilimanjaro image processing and GIS software, and the domain experts' knowledge via a control agent written in Visual C++. It starts with data assessment and pre-processing and ends up with image classification and accuracy assessment. The system is designed to run automatically, where the user merely provides the initial information regarding the intended task and the source of available data. The system itself acquires necessary information about the data from metadata files in order to make decisions and perform tasks. The test and evaluation of the prototype demonstrates the viability of the proposed architecture and the possibility of extending the system to perform other image processing tasks and to use different sources of data. The system design presented in this study thus suggests some directions for the development of the next generation of remote sensing image processing and classification systems

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

Modelling of Floods in Urban Areas

This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU.info:eu-repo/semantics/publishedVersio

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced