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NCSE's 13th National Conference on Disasters and Environment: Science, Preparedness and Resilience, Post Conference Follow-up Activities and Dissemination

The National Council for Science and the Environment (NCSE) received $15,000 from the US Department of Energy to support post-conference activities of the 13th National Conference on the theme of Disasters and the Environment: Science, Preparedness and Resilience, held on January 15-17, 2013 at the Ronald Reagan Building and International Trade Center in Washington, DC. Over 1,000 participants from the scientific, emergency response, policy, conservation, and business communities, as well as federal and local government officials, and international entities attended the event. The conference developed actionable outcomes that constructively advance the science behind decision-making on environmental disasters, with an intended result of more prepared and resilient communities in light of a changing climate. Disasters and Environment topic was addressed through six organizing themes: Cascading Disasters; Intersection of the Built and Natural Environments; Disasters as Mechanisms of Ecosystem Change; Rethinking Recovery and Expanding the Vision of Mitigation; Human Behavior and its Consequences; and "No Regrets" Resilience. The program featured eight plenary sessions, 24 symposia and 23 breakout workshops and addressed pivotal issues surrounding disasters and environment including lifeline services, the energy, climate, hazard nexus, grid collapse, community vulnerability, and natural resource management. Sessions, symposia and workshops were conducted by over 200 distinguished thought leaders, scientists, government officials, policy experts and international speakers throughout the three day event. Following the conference, NCSE prepared a set of recommendations and results from the workshops and disseminated the results to universities, organizations and agencies, the business community. NCSE’s national dissemination involved organized several targeted trips and meetings to disseminate significant findings to key stakeholder groups.

99 GENERAL AND MISCELLANEOUS↗

The role and future of space technology in disaster reduction

This paper describes the potential uses of satellite remote sensing in the mitigation of disasters and their effects by means of prevention, preparedness, and relief. A review is given of the capabilities of communications, geophysical, meteorological, and earth-resources satellites to point out potential applications in the three categories. A complete list of available spacecraft is presented in a table giving data on disaster-related applications. Key functions for disaster mitigation include their use as data resources and as experimental sources of information that can be of use in disaster prediction. Satellites with high temporal or spatial resolutions are useful for providing consistent prompt information required for disaster-management specialists. The use of SAR and TRMM scanning radar techniques to directly measure rainfall and other quantities of relevance to the prediction of disasters.

Walter, Louis S.↗

International disaster research

No existing telecommunications system can be expected to provide strategy and tactics appropriate to the complex, many faceted problem of disaster. Despite the exciting capabilities of space, communications, remote sensing, and the miracles of modern medicine, complete turnkey transfers to the disaster problem do not make the fit, and cannot be expected to do so. In 1980, a Presidential team assigned the mission of exploring disaster response within the U.S. Federal Government encountered an unanticipated obstacle: disaster was essentially undefined. In the absence of a scientifically based paradigm of disaster, there can be no measure of cost effectiveness, optimum design of manpower structure, or precise application of any technology. These problems spawned a 10-year, multidisciplinary study designed to define the origins, anatomy, and necessary management techniques for catastrophes. The design of the study necessarily reflects interests and expertise in disaster medicine, emergency medicine, telecommunications, computer communications, and forencsic sciences. This study is described.

Silverstein, Martin Elliot↗

The role and future of space technology in disaster reduction

Disaster mitigation consists of many activities, including vulnerability assessment, disaster warning and prediction and disaster relief. Various types of satellites can be applied to these endeavors: communications, geophysical, meteorological and Earth resources. The latter two are considered 'remote sensing' satellites. There are many limitations in the design and development of remote sensing satellites; limitations in cost and the acceptable data rate and limitations in our technology. Nevertheless, there are a large number of satellites, both currently in orbit and planned, with capabilities pertinent to disaster mitigation. Some of these are operational and can be relied upon to provide continued data sources. Others are experimental and provide the disaster management community and opportunity to assess the potential usefulness of the techniques and to impact the design of future operational systems. A table lists the operational parameters and potential application in disaster mitigation of 44 current and planned remote sensing satellites and instruments.

Walter, Louis S.↗

Supporting Disaster Assessment and Response with the VIIRS Day-Night Band

When meteorological or man‐made disasters occur, first responders often focus on impacts to the affected population and other human activities. Often, these disasters result in significant impacts to local infrastructure and power, resulting in widespread power outages. For minor events, these power outages are often short-lived, but major disasters often include long‐term outages that have a significant impact on wellness, safety, and recovery efforts within the affected areas. Staff at NASA's Short‐term Prediction Research and Transition (SPoRT) Center have been investigating the use of the VIIRS day‐night band for monitoring power outages that result from significant disasters, and developing techniques to identify damaged areas in near real‐time following events. In addition to immediate assessment, the VIIRS DNB can be used to monitor and assess ongoing recovery efforts. In this presentation, we will highlight previous applications of the VIIRS DNB following Superstorm Sandy in 2012, and other applications of the VIIRS DNB to more recent disaster events, including detection of outages following the Moore, Oklahoma tornado of May 2013 and the Chilean earthquake of April 2014. Examples of current products will be shown, along with future work and other goals for supporting disaster assessment and response with VIIRS capabilities.

Schultz, Lori A.↗

The International Space Station: A Unique Platform for Remote Sensing of Natural Disasters

Assembly of the International Space Station (ISS) was completed in 2012, and the station is now fully operational as a platform for remote sensing instruments tasked with collecting scientific data about the Earth system. Remote sensing systems are mounted inside the ISS, primarily in the U.S. Destiny Module's Window Observational Research Facility (WORF), or are located on the outside of the ISS on any of several attachment points. While NASA and other space agencies have had remote sensing systems orbiting Earth and collecting publicly available data since the early 1970s, these sensors are carried onboard free-flying, unmanned satellites. These satellites are traditionally placed into Sun-synchronous polar orbits that allow imaging of the entire surface of the Earth to be repeated with approximately the same Sun illumination (typically local solar noon) over specific areas, with set revisit times that allow uniform data to be taken over long time periods and enable straightforward analysis of change over time. In contrast, the ISS has an inclined, Sun-asynchronous orbit (the solar illumination for data collections over any location changes as the orbit precesses) that carries it over locations on the Earth between approximately 52degnorth and 52deg south latitudes (figure 1). The ISS is also unique among NASA orbital platforms in that it has a human crew. The presence of a crew provides options not available to robotic sensors and platforms, such as the ability to collect unscheduled data of an unfolding event using handheld digital cameras as part of the Crew Earth Observations (CEO) facility and on-the-fly assessment of environmental conditions, such as cloud cover, to determine whether conditions are favorable for data collection. The crew can also swap out internal sensor systems installed in the WORF as needed. The ISS orbit covers more than 90 percent of the inhabited surface of the Earth, allowing the ISS to pass over the same ground locations at different times of the day and night. This is important for two reasons: 1) certain surface processes (i.e., development of coastal fog banks) occur at times other than local solar noon, making it difficult to collect relevant data from traditional satellite platforms, and 2) it provides opportunities for the ISS to collect data for short-duration events, such as natural disasters, that polar-orbiting satellites may miss due to their orbital dynamics - in essence, the ISS can be "in the right place at the right time" to collect data. An immediate application of ISS remote sensing data collection is that the data can be used to provide information for humanitarian aid after a natural disaster. This activity contributes directly to the station's Benefits to Humanity mission. The International Charter, Space and Major Disasters (also known as the International Disaster Charter, or IDC) is an agreement between agencies of several countries to provide - on a best-effort basis - remotely sensed data related to natural disasters to requesting countries in support of disaster response. In the United States, the lead agency for interaction with the IDC is the United States Geological Survey (USGS); when an IDC request, or activation, is received, the USGS notifies the science teams for NASA instruments with targeting information for data collection. In the case of the ISS, Earth scientists in the JSC ARES Directorate, in association with the ISS Program Science Office, coordinate targeting and data collection with the USGS. If data is collected, it is passed back to the USGS for posting on its Hazards Data Distribution System and made available for download. The ISS was added to the USGS's list of NASA remote sensing assets that could respond to IDC activations in May 2012. Initially, the NASA ISS sensor systems available to respond to IDC activations included the ISS Agricultural Camera (ISSAC), an internal multispectral visible-near infrared wavelength system mounted in the WORF; CEO, a project that collects imagery through the ISS windows using off-the-shelf handheld digital visible-wavelength cameras; and the Hyperspectral Imager for the Coastal Oceans (HICO), a visible to near-infrared system mounted externally on the Japanese Experiment Module - Exposed Facility. Since May 2012, there have been 37 IDC activations; ISS sensor systems have collected data for 10 of these events.

Stefanov, William L.↗

NASA Disasters Program’s Response to the 2023 Türkiye/Syria Earthquakes

The February 2023 Türkiye/Syria Earthquakes are an example of how the NASA Disasters Program team supported a variety of stakeholders during a disaster response. In addition to the mass damage that occurred due to the ground movement itself, this event also caused cascading hazards, such as landslides, to occur. The NASA Disasters Program received requests for support from a variety of stakeholders, which led to the activation of the team. We were able to provide multiple relevant products to our stakeholders, supporting their response. This poster reviews the disaster life cycle through the lens of the NASA Disasters Program, highlighting key aspects of the coordination with stakeholders and how Earth observation data can aid in situational awareness during disaster response.

Rachel Soobitsky↗

Disasters can happen to anybody: The case of Korea

In Korea, there is a pervasive feeling of invincibility to the point that people and organizations do not believe that disasters can strike them. This has impact on the level of preparedness for disasters. This study aims to delve into how Korea has to change its governmental policies/practices with some private partners' efforts to mitigate disaster risks. A case study was utilized as the major methodology by comparing exclusive management with inclusive management. These two approaches have been comparatively analyzed via four variables, namely the central government, the local governments, the incident commander, and other stakeholders. The major finding is that Korea's practices and policies have to evolve from the current exclusive management into future-oriented inclusive management. Moreover, the importance of communication, cooperation, collaboration, and multi-discipline coordination is discussed. Additionally, the problem of reductionism and equal participation among all stakeholders, as well as the resistance from vested interests, are recognized and elaborated for Korea and the international community. - Highlights: • Only a few stakeholders in Korea believe that disaster can happen to anyone. This study aims to delve into how Korea has to change its current practices to mitigate disaster risks. • To compare exclusive management with inclusive management, we have examined four comparative variables, namely the central government's policy, local government's strategy, the incident commander's post, and other stakeholders' efforts. • The major finding is that Korea's practices and policies have to evolve from the current exclusive management into future-oriented inclusive management.

54 ENVIRONMENTAL SCIENCES↗

Socio-economic exposure to natural disasters

Even though the correct assessment of risks is a key aspect of the risk management analysis, we argue that limited effort has been devoted in the assessment of comprehensive measures of economic exposure at very low scale. For this reason, we aim at providing a series of suitable methodologies to provide a complete and detailed list of the exposure of economic activities to natural disasters. We use Input-Output models to provide information about several socio-economic variables, such as population density, employment density, firms' turnover and capital stock, that can be seen as direct and indirect socio-economic exposure to natural disasters. We then provide an application to the Italian context. These measures can be easily incorporated into risk assessment models to provide a clear picture of the disaster risk for local areas. - Highlights: • Ex ante assessment of economic exposure to disasters at very low geographical scale • Assessment of the cost of natural disasters in ex-post perspective • IO model and spatial autocorrelation to get information on socio-economic variables • Indicators supporting risk assessment and risk management models.

54 ENVIRONMENTAL SCIENCES↗

Vernacular design based on sustainable disaster’s mitigation communication and education strategy

Indonesia is located between three active tectonic plates, which are prone to natural disasters such as earthquake, volcanic eruption, and also giant tidal wave-tsunami. Adequate infrastructure plays an important role in disaster mitigation, yet without good public awareness, the mitigation process won’t be succeeded. The absence of awareness can lead to infrastructure mistreatment. Several reports on lack of understanding or misinterpretation of disaster mitigation especially from rural and coastal communities need to be solved, especially from communication aspects. This is an interdisciplinary study on disaster mitigation communication design and education strategy from visual communication design studies paradigm. This paper depicts research results which applying vernacular design base to elaborate sustainable mitigation communication and education strategy on various visual media and social campaigns. This paper also describes several design approaches which may becomes way to elaborate sustainable awareness and understanding on disaster mitigation among rural and coastal communities in Indonesia.

58 GEOSCIENCES↗

Co-developing a SAR for Disasters Reference Guide for the International Applications Community

Synthetic Aperture Radar (SAR) data are widely applicable for disaster monitoring, especially in Central and South America where atmospheric effects are a frequent obstacle for disaster monitoring. There is a growing interest in the application of SAR data to diverse disasters, ranging from landslides to volcanic eruptions and floods. With this interest comes a growing need for user-friendly guides that facilitate the integration of SAR into existing workflows. A joint effort between AmeriGEO, university networks in the Americas, NASA SERVIR’s Science Coordination Office (SCO), and the NASA Earth Science Disasters Program, culminated in the “SAR for Disasters” Guide. The guide is modeled after the one-page informational sheets created for SERVIR’s SAR Handbook, with the aim to succinctly communicate the key considerations needed to apply relevant analyses using SAR. Developed with contributors from diverse backgrounds, this guide is designed to be a useful reference for experienced technical users and beginners alike.

SAR↗

Overview of JAXA and NASA Collaboration on Manned and Unmanned Aircraft Operations Integration in Disaster Response

Natural disasters have increased in frequency and intensity. With that increase has come an increased focus on improving disaster response efforts. As part of this focus, the Japan Aerospace Exploration Agency (JAXA) and the National Aeronautics and Space Administration (NASA) have been in collaboration since 2016 to explore improved airspace management for disaster response through the integration of manned aircraft and small Unmanned Aircraft Systems (UAS). The approach to this integration has been through leveraging JAXA’s Disaster Relief Aircraft Information Sharing Network (D-NET) system for the coordinated management of manned aircraft assets combined with NASA’S UAS Traffic Management (UTM) system for the management of UAS assets. To drive the innovation in improved response, JAXA and NASA have successfully connected respective systems and conducted a series of live flight tests first as part of a large-scale disaster drill and later in dedicated research flights. In each case, the D-NET and UTM systems were connected and exchanged data in real-time, which supported the planning and tracking of manned and unmanned operations. Through these test events, a number of key findings were derived through data analysis and stakeholder interviews with respect to the improvements offered by the integrated systems as well as key considerations for situation awareness of pilots regarding other operations in the airspace and flight path management.

disaster response↗

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.

97 MATHEMATICS AND COMPUTING↗

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

AI↗

International Space Station Instmments Collect Imagery of Natural Disasters

A new focus for utilization of the International Space Station (ISS) is conducting basic and applied research that directly benefits Earth's citizenry. In the Earth Sciences, one such activity is collecting remotely sensed imagery of disaster areas and making those data immediately available through the USGS Hazards Data Distribution System, especially in response to activations of the International Charter for Space and Major Disasters (known informally as the "International Disaster Charter", or IDC). The ISS, together with other NASA orbital sensor assets, responds to IDC activations following notification by the USGS. Most of the activations are due to natural hazard events, including large floods, impacts of tropical systems, major fires, and volcanic eruptions and earthquakes. Through the ISS Program Science Office, we coordinate with ISS instrument teams for image acquisition using several imaging systems. As of 1 August 2013, we have successfully contributed imagery data in support of 14 Disaster Charter Activations, including regions in both Haiti and the east coast of the US impacted by Hurricane Sandy; flooding events in Russia, Mozambique, India, Germany and western Africa; and forest fires in Algeria and Ecuador. ISS‐based sensors contributing data include the Hyperspectral Imager for the Coastal Ocean (HICO), the ISERV (ISS SERVIR Environmental Research and Visualization System) Pathfinder camera mounted in the US Window Observational Research Facility (WORF), the ISS Agricultural Camera (ISSAC), formerly operating from the WORF, and high resolution handheld camera photography collected by crew members (Crew Earth Observations). When orbital parameters and operations support data collection, ISS‐based imagery adds to the resources available to disaster response teams and contributes to the publicdomain record of these events for later analyses.

Evans, C. A.↗

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions "on the ground" more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages.These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e.,millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

emergency response↗

Using NASA LANCE Near Real-Time Products for Disaster Risk Reduction

The NASA Earth Science Disasters Program handles requests from stakeholders and provides rapid response for Disaster Risk Reduction using Near Real-Time (NRT) products from NASA’s Land, Atmosphere NRT Capability for Earth Observing System (EOS) (LANCE). The combination of all available LANCE NRT satellite products provides global coverage at multiple times per day, which makes it possible to help users in different phases of the disaster’s life cycle. LANCE NRT fire and atmosphere products have been used to locate fires and high-temperature heat sources, and to assess the extent of air pollution. LANCE NRT global flood products and NASA’s Black Marble night-time light products have been used to monitor land cover and land use change over time in disaster impacted areas. Generated products and related information have been archived in NASA Disasters Mapping Portal for the use of stakeholders and end-users.

Tian Yao↗

Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗