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wildfire</title> <meta name="description" content="Search results for: wildfire"> <meta name="keywords" content="wildfire"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="wildfire"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 41</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: wildfire</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">41</span> Evaluating the Social Learning Processes Involved in Developing Community-Informed Wildfire Risk Reduction Strategies in the Prince Albert Forest Management Area </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carly%20Madge">Carly Madge</a>, <a href="https://publications.waset.org/abstracts/search?q=Melanie%20Zurba"> Melanie Zurba</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryan%20%20Bullock"> Ryan Bullock</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Boreal Forest has experienced some of the most drastic climate change-induced temperature rises in Canada, with average winter temperatures increasing by 3°C since 1948. One of the main concerns of the province of Saskatchewan, and particularly wildfire managers, is the increased risk of wildfires due to climate change. With these concerns in mind Sakaw Askiy Management Inc., a forestry corporation located in Prince Albert, Saskatchewan with operations in the Boreal Forest biome, is developing wildfire risk reduction strategies that are supported by the shareholders of the corporation as well as the stakeholders of the Prince Albert Forest Management Area (which includes citizens, hunters, trappers, cottage owners, and outfitters). In the past, wildfire management strategies implemented through harvesting have been received with skepticism by some community members of Prince Albert. Engagement of the stakeholders of the Prince Albert Management Area through the development of the wildfire risk reduction strategies aims to reduce this skepticism and rebuild some of the trust that has been lost between industry and community. This research project works with the framework of social learning, which is defined as the learning that occurs when individuals come together to form a group with the purpose of understanding environmental challenges and determining appropriate responses to them. The project evaluates the social learning processes that occur through the development of the risk reduction strategies and how the learning has allowed Sakaw to work towards implementing the strategies into their forest harvesting plans. The incorporation of wildfire risk reduction strategies works to increase the adaptive capacity of Sakaw, which in this case refers to the ability to adjust to climate change, moderate potential damages, take advantage of opportunities, and cope with consequences. Using semi-structured interviews and wildfire workshop meetings shareholders and stakeholders shared their knowledge of wildfire, their main wildfire concerns, and changes they would like to see made in the Prince Albert Forest Management Area. Interviews and topics discussed in the workshops were inductively coded for themes related to learning, adaptive capacity, areas of concern, and preferred methods of wildfire risk reduction strategies. Analysis determined that some of the learning that has occurred has resulted through social interactions and the development of networks oriented towards wildfire and wildfire risk reduction strategies. Participants have learned new knowledge and skills regarding wildfire risk reduction. The formation of wildfire networks increases access to information on wildfire and the social capital (trust and strengthened relations) of wildfire personnel. Both factors can be attributed to increases in adaptive capacity. Interview results were shared with the General Manager of Sakaw, where the areas of concern and preferred strategies of wildfire risk reduction will be considered and accounted for in the implementation of new harvesting plans. This research also augments the growing conceptual and empirical evidence of the important role of learning and networks in regional wildfire risk management efforts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20capacity" title="adaptive capacity">adaptive capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=community-engagement" title=" community-engagement"> community-engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20learning" title=" social learning"> social learning</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20risk%20reduction" title=" wildfire risk reduction "> wildfire risk reduction </a> </p> <a href="https://publications.waset.org/abstracts/132044/evaluating-the-social-learning-processes-involved-in-developing-community-informed-wildfire-risk-reduction-strategies-in-the-prince-albert-forest-management-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132044.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">40</span> Exploring the Influence of Wind on Wildfire Behavior in China: A Data-Driven Study Using Machine Learning and Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rida%20Kanwal">Rida Kanwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Yuhui"> Wang Yuhui</a>, <a href="https://publications.waset.org/abstracts/search?q=Song%20Weiguo"> Song Weiguo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wildfires are one of the most prominent threats to ecosystems, human health, and economic activities, with wind acting as a critical driving factor. This study combines machine learning (ML) and remote sensing (RS) to assess the effects of wind on wildfires in Chongqing Province from August 16-23, 2022. Landsat 8 satellite images were used to estimate the difference normalized burn ratio (dNBR), representing prefire and postfire vegetation conditions. Wind data was analyzed through geographic information system (GIS) mapping. Correlation analysis between wind speed and fire radiative power (FRP) revealed a significant relationship. An autoregressive integrated moving average (ARIMA) model was developed for wind forecasting, and linear regression was applied to determine the effect of wind speed on FRP. The results identified high wind speed as a key factor contributing to the surge in FRP. Wind-rose plots showed winds blowing to the northwest (NW), aligning with the wildfire spread. This model was further validated with data from other provinces across China. This study integrated ML, RS, and GIS to analyze wildfire behavior, providing effective strategies for prediction and management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfires" title="wildfires">wildfires</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20speed" title=" wind speed"> wind speed</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20behavior" title=" wildfire behavior"> wildfire behavior</a> </p> <a href="https://publications.waset.org/abstracts/192554/exploring-the-influence-of-wind-on-wildfire-behavior-in-china-a-data-driven-study-using-machine-learning-and-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192554.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">39</span> A Systematic Map of the Research Trends in Wildfire Management in Mediterranean-Climate Regions </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Renata%20Martins%20Pacheco">Renata Martins Pacheco</a>, <a href="https://publications.waset.org/abstracts/search?q=Jo%C3%A3o%20Claro"> João Claro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wildfires are becoming an increasing concern worldwide, causing substantial social, economic, and environmental disruptions. This situation is especially relevant in Mediterranean-climate regions, present in all the five continents of the world, in which fire is not only a natural component of the environment but also perhaps one of the most important evolutionary forces. The rise in wildfire occurrences and their associated impacts suggests the need for identifying knowledge gaps and enhancing the basis of scientific evidence on how managers and policymakers may act effectively to address them. Considering that the main goal of a systematic map is to collate and catalog a body of evidence to describe the state of knowledge for a specific topic, it is a suitable approach to be used for this purpose. In this context, the aim of this study is to systematically map the research trends in wildfire management practices in Mediterranean-climate regions. A total of 201 wildfire management studies were analyzed and systematically mapped in terms of their: Year of publication; Place of study; Scientific outlet; Research area (Web of Science) or Research field (Scopus); Wildfire phase; Central research topic; Main objective of the study; Research methods; and Main conclusions or contributions. The results indicate that there is an increasing number of studies being developed on the topic (most from the last 10 years), but more than half of them are conducted in few Mediterranean countries (60% of the analyzed studies were conducted in Spain, Portugal, Greece, Italy or France), and more than 50% are focused on pre-fire issues, such as prevention and fuel management. In contrast, only 12% of the studies focused on “Economic modeling” or “Human factors and issues,” which suggests that the triple bottom line of the sustainability argument (social, environmental, and economic) is not being fully addressed by fire management research. More than one-fourth of the studies had their objective related to testing new approaches in fire or forest management, suggesting that new knowledge is being produced on the field. Nevertheless, the results indicate that most studies (about 84%) employed quantitative research methods, and only 3% of the studies used research methods that tackled social issues or addressed expert and practitioner’s knowledge. Perhaps this lack of multidisciplinary studies is one of the factors hindering more progress from being made in terms of reducing wildfire occurrences and their impacts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire" title="wildfire">wildfire</a>, <a href="https://publications.waset.org/abstracts/search?q=Mediterranean-climate%20regions" title=" Mediterranean-climate regions"> Mediterranean-climate regions</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=policy" title=" policy"> policy</a> </p> <a href="https://publications.waset.org/abstracts/122431/a-systematic-map-of-the-research-trends-in-wildfire-management-in-mediterranean-climate-regions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122431.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">38</span> Wildfire-Related Debris-Flow and Flooding Using 2-D Hydrologic Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheong%20Hyeon%20Oh">Cheong Hyeon Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongho%20Nam"> Dongho Nam</a>, <a href="https://publications.waset.org/abstracts/search?q=Byungsik%20Kim"> Byungsik Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the recent climate change, flood damage caused by local floods and typhoons has frequently occurred, the incidence rate and intensity of wildfires are greatly increased due to increased temperatures and changes in precipitation patterns. Wildfires cause primary damage, such as loss of forest resources, as well as secondary disasters, such as landslides, floods, and debris flow. In many countries around the world, damage and economic losses from secondary damage are occurring as well as the direct effects of forest fires. Therefore, in this study, the Rainfall-Runoff model(S-RAT) was used for the wildfire affected areas in Gangneung and Goseong, which occurred on April 2019, when the stability of vegetation and soil were destroyed by wildfires. Rainfall data from Typhoon Rusa were used in the S-RAT model, and flood discharge was calculated according to changes in land cover before and after wildfire damage. The results of the calculation showed that flood discharge increased significantly due to changes in land cover, as the increase in flood discharge increases the possibility of the occurrence of the debris flow and the extent of the damage, the debris flow height and range were calculated before and after forest fire using RAMMS. The analysis results showed that the height and extent of damage increased after wildfire, but the result value was underestimated due to the characteristics that using DEM and maximum flood discharge of the RAMMS model. This research was supported by a grant(2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS). This paper work (or document) was financially supported by Ministry of the Interior and Safety as 'Human resoure development Project in Disaster management'. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire" title="wildfire">wildfire</a>, <a href="https://publications.waset.org/abstracts/search?q=debris%20flow" title=" debris flow"> debris flow</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title=" land cover"> land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall-runoff%20meodel%20S-RAT" title=" rainfall-runoff meodel S-RAT"> rainfall-runoff meodel S-RAT</a>, <a href="https://publications.waset.org/abstracts/search?q=RAMMS" title=" RAMMS"> RAMMS</a>, <a href="https://publications.waset.org/abstracts/search?q=height" title=" height"> height</a> </p> <a href="https://publications.waset.org/abstracts/113919/wildfire-related-debris-flow-and-flooding-using-2-d-hydrologic-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">37</span> Preliminary WRF SFIRE Simulations over Croatia during the Split Wildfire in July 2017</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ivana%20%20%C4%8Cavlina%20Toma%C5%A1evi%C4%87">Ivana Čavlina Tomašević</a>, <a href="https://publications.waset.org/abstracts/search?q=Vi%C5%A1njica%20Vu%C4%8Deti%C4%87"> Višnjica Vučetić</a>, <a href="https://publications.waset.org/abstracts/search?q=Maja%20Teli%C5%A1man%20Prtenjak"> Maja Telišman Prtenjak</a>, <a href="https://publications.waset.org/abstracts/search?q=Barbara%20Male%C4%8Di%C4%87"> Barbara Malečić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Split wildfire on the mid-Adriatic Coast in July 2017 is one of the most severe wildfires in Croatian history, given the size and unexpected fire behavior, and it is used in this research as a case study to run the Weather Research and Forecasting Spread Fire (WRF SFIRE) model. This coupled fire-atmosphere model was successfully run for the first time ever for one Croatian wildfire case. Verification of coupled simulations was possible by using the detailed reconstruction of the Split wildfire. Specifically, precise information on ignition time and location, together with mapped fire progressions and spotting within the first 30 hours of the wildfire, was used for both – to initialize simulations and to evaluate the model’s ability to simulate fire’s propagation and final fire scar. The preliminary simulations were obtained using high-resolution vegetation and topography data for the fire area, additionally interpolated to fire grid spacing at 33.3 m. The results demonstrated that the WRF SFIRE model has the ability to work with real data from Croatia and produce adequate results for forecasting fire spread. As the model in its setup has the ability to include and exclude the energy fluxes between the fire and the atmosphere, this was used to investigate possible fire-atmosphere interactions during the Split wildfire. Finally, successfully coupled simulations provided the first numerical evidence that a wildfire from the Adriatic coast region can modify the dynamical structure of the surrounding atmosphere, which agrees with observations from fire grounds. This study has demonstrated that the WRF SFIRE model has the potential for operational application in Croatia with more accurate fire predictions in the future, which could be accomplished by inserting the higher-resolution input data into the model without interpolation. Possible uses for fire management in Croatia include prediction of fire spread and intensity that may vary under changing weather conditions, available fuels and topography, planning effective and safe deployment of ground and aerial firefighting forces, preventing wildland-urban interface fires, effective planning of evacuation routes etc. In addition, the WRF SFIRE model results from this research demonstrated that the model is important for fire weather research and education purposes in order to better understand this hazardous phenomenon that occurs in Croatia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=meteorology" title="meteorology">meteorology</a>, <a href="https://publications.waset.org/abstracts/search?q=agrometeorology" title=" agrometeorology"> agrometeorology</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20weather" title=" fire weather"> fire weather</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfires" title=" wildfires"> wildfires</a>, <a href="https://publications.waset.org/abstracts/search?q=couple%20fire-atmosphere%20model" title=" couple fire-atmosphere model"> couple fire-atmosphere model</a> </p> <a href="https://publications.waset.org/abstracts/162421/preliminary-wrf-sfire-simulations-over-croatia-during-the-split-wildfire-in-july-2017" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36</span> Assessing the Legacy Effects of Wildfire on Eucalypt Canopy Structure of South Eastern Australia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yogendra%20K.%20Karna">Yogendra K. Karna</a>, <a href="https://publications.waset.org/abstracts/search?q=Lauren%20T.%20Bennett"> Lauren T. Bennett </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fire-tolerant eucalypt forests are one of the major forest ecosystems of south-eastern Australia and thought to be highly resistant to frequent high severity wildfires. However, the impact of different severity wildfires on the canopy structure of fire-tolerant forest type is under-studied, and there are significant knowledge gaps in relation to the assessment of tree and stand level canopy structural dynamics and recovery after fire. Assessment of canopy structure is a complex task involving accurate measurements of the horizontal and vertical arrangement of the canopy in space and time. This study examined the utility of multitemporal, small-footprint lidar data to describe the changes in the horizontal and vertical canopy structure of fire-tolerant eucalypt forests seven years after wildfire of different severities from the tree to stand level. Extensive ground measurements were carried out in four severity classes to describe and validate canopy cover and height metrics as they change after wildfire. Several metrics such as crown height and width, crown base height and clumpiness of crown were assessed at tree and stand level using several individual tree top detection and measurement algorithm. Persistent effects of high severity fire 8 years after both on tree crowns and stand canopy were observed. High severity fire increased the crown depth but decreased the crown projective cover leading to more open canopy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=canopy%20gaps" title="canopy gaps">canopy gaps</a>, <a href="https://publications.waset.org/abstracts/search?q=canopy%20structure" title=" canopy structure"> canopy structure</a>, <a href="https://publications.waset.org/abstracts/search?q=crown%20architecture" title=" crown architecture"> crown architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=crown%20projective%20cover" title=" crown projective cover"> crown projective cover</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal%20lidar" title=" multi-temporal lidar"> multi-temporal lidar</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20severity" title=" wildfire severity"> wildfire severity</a> </p> <a href="https://publications.waset.org/abstracts/94022/assessing-the-legacy-effects-of-wildfire-on-eucalypt-canopy-structure-of-south-eastern-australia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">35</span> Impacts on Atmospheric Mercury from Changes in Climate, Land Use, Land Cover, and Wildfires</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiliang%20Wu">Shiliang Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Huanxin%20Zhang"> Huanxin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Kumar"> Aditya Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There have been increasing concerns on atmospheric mercury as a toxic and bioaccumulative pollutant in the global environment. Global change, including changes in climate change, land use, land cover and wildfires activities can all have significant impacts on atmospheric mercury. In this study, we use a global chemical transport model (GEOS-Chem) to examine the potential impacts from global change on atmospheric mercury. All of these factors in the context of global change are found to have significant impacts on the long-term evolution of atmospheric mercury and can substantially alter the global source-receptor relationships for mercury. We also estimate the global Hg emissions from wildfires for present-day and the potential impacts from the 2000-2050 changes in climate, land use and land cover and Hg anthropogenic emissions by combining statistical analysis with global data on vegetation type and coverage as well as fire activities. Present global Hg wildfire emissions are estimated to be 612 Mg year-1. Africa is the dominant source region (43.8% of global emissions), followed by Eurasia (31%) and South America (16.6%). We find significant perturbations to wildfire emissions of Hg in the context of global change, driven by the projected changes in climate, land use and land cover and Hg anthropogenic emissions. 2000-2050 climate change could increase Hg emissions by 14% globally. Projected changes in land use by 2050 could decrease the global Hg emissions from wildfires by 13% mainly driven by a decline in African emissions due to significant agricultural land expansion. Future land cover changes could lead to significant increases in Hg emissions over some regions (+32% North America, +14% Africa, +13% Eurasia). Potential enrichment of terrestrial ecosystems in 2050 in response to changes in Hg anthropogenic emissions could increase Hg wildfire emissions both globally (+28%) and regionally. Our results indicate that the future evolution of climate, land use and land cover and Hg anthropogenic emissions are all important factors affecting Hg wildfire emissions in the coming decades. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title=" land cover"> land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfires" title=" wildfires"> wildfires</a> </p> <a href="https://publications.waset.org/abstracts/81118/impacts-on-atmospheric-mercury-from-changes-in-climate-land-use-land-cover-and-wildfires" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81118.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">34</span> Wildfire Risk and Biodiversity Management: Understanding Perceptions and Preparedness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emily%20Moskwa">Emily Moskwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Delene%20Weber"> Delene Weber</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacob%20Arnold"> Jacob Arnold</a>, <a href="https://publications.waset.org/abstracts/search?q=Guy%20M.%20Robinson"> Guy M. Robinson</a>, <a href="https://publications.waset.org/abstracts/search?q=Douglas%20K.%20Bardsley"> Douglas K. Bardsley</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Management strategies to reduce the risks to human life and property from wildfire are key contemporary concerns, with a growing literature exploring these issues from a social research perspective. Efforts range from narrowly focused examinations, such as comparing the level of community support for vegetation clearance with that of controlled burning, to broader considerations of what constitutes effective fire management policy and education campaigns. However, little analysis is available that integrates the social component of risk mitigation and the influence of educational materials with the biodiversity conservation strategies so often needed in fire-prone ecosystems found on the periphery of urban areas. Indeed many communities living on the fringe of Australian cities face major issues relating to an increased risk of wildfire events and a decline in local biodiversity. Inadequate policy and planning, and a lack of awareness or information, exacerbate this risk. This has brought forward an emerging governance challenge that requires the mitigation of wildfire risk while simultaneously supporting improved conservation practices in these urban-fringe areas. Focusing on the perceptions and experiences of residents of the Lower Eyre Peninsula in South Australia, this study analyses data collected from a series of semi-structured interviews with landholders (n=20) living in rural and urban-fringe areas surrounding the city of Port Lincoln, a city with a growing population and one that has faced a number of very large fires in recent years. In South Australia, new policies have assigned increased responsibility on individual landholders to manage their land and prepare themselves for a wildfire event, potentially to the detriment of the surrounding native vegetation. Our findings indicate the value of gaining a more nuanced understanding of the perceptions and behaviours of landholders living in areas of high fire risk, who often choose to live there in order to be close to the natural environment. Many interviewees demonstrated a high awareness of wildfire risk as a result of their past experience with fire, and the majority considered themselves to be well-prepared in the event of a future fire. Community interactions and educational programs were found to be effective in raising awareness of risk; however, negative trust relationships with government authorities and low exposure to information concerning biodiversity resulted in an overall misunderstanding of the relationship between risk mitigation and biodiversity protection. The study offers insights into how catastrophic fires are reframing perceptions of what constitutes effective vegetation management. It provides recommendations to assist with the development of education strategies that concurrently address wildfire management and biodiversity conservation, and contribute towards environmentally-informed and risk conscious governance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biodiversity%20conservation" title="biodiversity conservation">biodiversity conservation</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=peri-urban%20planning" title=" peri-urban planning"> peri-urban planning</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20management" title=" wildfire management"> wildfire management</a> </p> <a href="https://publications.waset.org/abstracts/42317/wildfire-risk-and-biodiversity-management-understanding-perceptions-and-preparedness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">33</span> Application of Machine Learning on Google Earth Engine for Forest Fire Severity, Burned Area Mapping and Land Surface Temperature Analysis: Rajasthan, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisha%20Sinha">Alisha Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Laxmi%20Kant%20Sharma"> Laxmi Kant Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire%20susceptibility%20mapping" title="wildfire susceptibility mapping">wildfire susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=LST" title=" LST"> LST</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=GEE" title=" GEE"> GEE</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS" title=" MODIS"> MODIS</a>, <a href="https://publications.waset.org/abstracts/search?q=climatic%20parameters" title=" climatic parameters"> climatic parameters</a> </p> <a href="https://publications.waset.org/abstracts/191316/application-of-machine-learning-on-google-earth-engine-for-forest-fire-severity-burned-area-mapping-and-land-surface-temperature-analysis-rajasthan-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191316.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Generating Individualized Wildfire Risk Assessments Utilizing Multispectral Imagery and Geospatial Artificial Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gus%20Calderon">Gus Calderon</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20McCreight"> Richard McCreight</a>, <a href="https://publications.waset.org/abstracts/search?q=Tammy%20Schwartz"> Tammy Schwartz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forensic analysis of community wildfire destruction in California has shown that reducing or removing flammable vegetation in proximity to buildings and structures is one of the most important wildfire defenses available to homeowners. State laws specify the requirements for homeowners to create and maintain defensible space around all structures. Unfortunately, this decades-long effort had limited success due to noncompliance and minimal enforcement. As a result, vulnerable communities continue to experience escalating human and economic costs along the wildland-urban interface (WUI). Quantifying vegetative fuels at both the community and parcel scale requires detailed imaging from an aircraft with remote sensing technology to reduce uncertainty. FireWatch has been delivering high spatial resolution (5” ground sample distance) wildfire hazard maps annually to the community of Rancho Santa Fe, CA, since 2019. FireWatch uses a multispectral imaging system mounted onboard an aircraft to create georeferenced orthomosaics and spectral vegetation index maps. Using proprietary algorithms, the vegetation type, condition, and proximity to structures are determined for 1,851 properties in the community. Secondary data processing combines object-based classification of vegetative fuels, assisted by machine learning, to prioritize mitigation strategies within the community. The remote sensing data for the 10 sq. mi. community is divided into parcels and sent to all homeowners in the form of defensible space maps and reports. Follow-up aerial surveys are performed annually using repeat station imaging of fixed GPS locations to address changes in defensible space, vegetation fuel cover, and condition over time. These maps and reports have increased wildfire awareness and mitigation efforts from 40% to over 85% among homeowners in Rancho Santa Fe. To assist homeowners fighting increasing insurance premiums and non-renewals, FireWatch has partnered with Black Swan Analytics, LLC, to leverage the multispectral imagery and increase homeowners’ understanding of wildfire risk drivers. For this study, a subsample of 100 parcels was selected to gain a comprehensive understanding of wildfire risk and the elements which can be mitigated. Geospatial data from FireWatch’s defensible space maps was combined with Black Swan’s patented approach using 39 other risk characteristics into a 4score Report. The 4score Report helps property owners understand risk sources and potential mitigation opportunities by assessing four categories of risk: Fuel sources, ignition sources, susceptibility to loss, and hazards to fire protection efforts (FISH). This study has shown that susceptibility to loss is the category residents and property owners must focus their efforts. The 4score Report also provides a tool to measure the impact of homeowner actions on risk levels over time. Resiliency is the only solution to breaking the cycle of community wildfire destruction and it starts with high-quality data and education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defensible%20space" title="defensible space">defensible space</a>, <a href="https://publications.waset.org/abstracts/search?q=geospatial%20data" title=" geospatial data"> geospatial data</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20imaging" title=" multispectral imaging"> multispectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=Rancho%20Santa%20Fe" title=" Rancho Santa Fe"> Rancho Santa Fe</a>, <a href="https://publications.waset.org/abstracts/search?q=susceptibility%20to%20loss" title=" susceptibility to loss"> susceptibility to loss</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20risk." title=" wildfire risk."> wildfire risk.</a> </p> <a href="https://publications.waset.org/abstracts/155085/generating-individualized-wildfire-risk-assessments-utilizing-multispectral-imagery-and-geospatial-artificial-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155085.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">108</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31</span> Instance Segmentation of Wildfire Smoke Plumes using Mask-RCNN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamison%20Duckworth">Jamison Duckworth</a>, <a href="https://publications.waset.org/abstracts/search?q=Shankarachary%20Ragi"> Shankarachary Ragi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mask-RCNN" title=" mask-RCNN"> mask-RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=smoke%20plumes" title=" smoke plumes"> smoke plumes</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20bands" title=" spectral bands"> spectral bands</a> </p> <a href="https://publications.waset.org/abstracts/150196/instance-segmentation-of-wildfire-smoke-plumes-using-mask-rcnn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150196.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30</span> Volunteered Geographic Information Coupled with Wildfire Fire Progression Maps: A Spatial and Temporal Tool for Incident Storytelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cassandra%20Hansen">Cassandra Hansen</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Doherty"> Paul Doherty</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Ferner"> Chris Ferner</a>, <a href="https://publications.waset.org/abstracts/search?q=German%20Whitley"> German Whitley</a>, <a href="https://publications.waset.org/abstracts/search?q=Holly%20Torpey"> Holly Torpey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wildfire is a natural and inevitable occurrence, yet changing climatic conditions have increased the severity, frequency, and risk to human populations in the wildland/urban interface (WUI) of the Western United States. Rapid dissemination of accurate wildfire information is critical to both the Incident Management Team (IMT) and the affected community. With the advent of increasingly sophisticated information systems, GIS can now be used as a web platform for sharing geographic information in new and innovative ways, such as virtual story map applications. Crowdsourced information can be extraordinarily useful when coupled with authoritative information. Information abounds in the form of social media, emergency alerts, radio, and news outlets, yet many of these resources lack a spatial component when first distributed. In this study, we describe how twenty-eight volunteer GIS professionals across nine Geographic Area Coordination Centers (GACC) sourced, curated, and distributed Volunteered Geographic Information (VGI) from authoritative social media accounts focused on disseminating information about wildfires and public safety. The combination of fire progression maps with VGI incident information helps answer three critical questions about an incident, such as: where the first started. How and why the fire behaved in an extreme manner and how we can learn from the fire incident's story to respond and prepare for future fires in this area. By adding a spatial component to that shared information, this team has been able to visualize shared information about wildfire starts in an interactive map that answers three critical questions in a more intuitive way. Additionally, long-term social and technical impacts on communities are examined in relation to situational awareness of the disaster through map layers and agency links, the number of views in a particular region of a disaster, community involvement and sharing of this critical resource. Combined with a GIS platform and disaster VGI applications, this workflow and information become invaluable to communities within the WUI and bring spatial awareness for disaster preparedness, response, mitigation, and recovery. This study highlights progression maps as the ultimate storytelling mechanism through incident case studies and demonstrates the impact of VGI and sophisticated applied cartographic methodology make this an indispensable resource for authoritative information sharing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=storytelling" title="storytelling">storytelling</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20progression%20maps" title=" wildfire progression maps"> wildfire progression maps</a>, <a href="https://publications.waset.org/abstracts/search?q=volunteered%20geographic%20information" title=" volunteered geographic information"> volunteered geographic information</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20and%20temporal" title=" spatial and temporal"> spatial and temporal</a> </p> <a href="https://publications.waset.org/abstracts/140122/volunteered-geographic-information-coupled-with-wildfire-fire-progression-maps-a-spatial-and-temporal-tool-for-incident-storytelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29</span> Insect Outbreaks, Harvesting and Wildfire in Forests: Mathematical Models for Coupling Disturbances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20A.%20Leite">M. C. A. Leite</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Chen-Charpentier"> B. Chen-Charpentier</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Agusto"> F. Agusto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A long-term goal of sustainable forest management is a relatively stable source of wood and a stable forest age-class structure has become the goal of many forest management practices. In the absence of disturbances, this forest management goal could easily be achieved. However, in the face of recurring insect outbreaks and other disruptive processes forest planning becomes more difficult, requiring knowledge of the effects on the forest of a wide variety of environmental factors (e.g., habitat heterogeneity, fire size and frequency, harvesting, insect outbreaks, and age distributions). The association between distinct forest disturbances and the potential effect on forest dynamics is a complex matter, particularly when evaluated over time and at large scale, and is not well understood. However, gaining knowledge in this area is crucial for a sustainable forest management. Mathematical modeling is a tool that can be used to broader the understanding in this area. In this talk we will introduce mathematical models formulation incorporating the effect of insect outbreaks either as a single disturbance in the forest population dynamics or coupled with other disturbances: either wildfire or harvesting. The results and ecological insights will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=age-structured%20forest%20population" title="age-structured forest population">age-structured forest population</a>, <a href="https://publications.waset.org/abstracts/search?q=disturbances%20interaction" title=" disturbances interaction"> disturbances interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=harvesting%20insects%20outbreak%20dynamics" title=" harvesting insects outbreak dynamics"> harvesting insects outbreak dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%0D%0Amodeling" title=" mathematical modeling"> mathematical modeling</a> </p> <a href="https://publications.waset.org/abstracts/16948/insect-outbreaks-harvesting-and-wildfire-in-forests-mathematical-models-for-coupling-disturbances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16948.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">525</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28</span> Ecosystem Post-Wildfires Effects of Thasos Island</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=George%20D.%20Ranis">George D. Ranis</a>, <a href="https://publications.waset.org/abstracts/search?q=Valasia%20Iakovoglou"> Valasia Iakovoglou</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20N.%20Zaimes"> George N. Zaimes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fires are one of the main types of disturbances that shape ecosystems in the Mediterranean region. However nowadays, climate alterations towards higher temperature regimes results on the increased levels of the intensity, frequency and the spread of fires inducing obstacles for the natural regeneration. Thasos Island is one of the Greek islands that have experienced those problems. Since 1984, a series of wildfires led to the reduction of forest cover from 61.6% to almost 20%. The negative impacts were devastating in many different aspects for the island. The absence of plant cover, post-wildfire precipitation and steep slopes were the major factors that induced severe soil erosion and intense flooding events. That also resulted to serious economic problems to the local communities and the ability of the burnt areas to regenerate naturally. Despite the substantial amount of published work regarding Thasos wildfires, there is no information related to post-wildfire effects on the hydrology and soil erosion. More research related to post-fire effects should help to an overall assessment of the negative impacts of wildfires on land degradation through processes such as soil erosion and flooding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=erosion" title="erosion">erosion</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20degradation" title=" land degradation"> land degradation</a>, <a href="https://publications.waset.org/abstracts/search?q=Mediterranean%20islands" title=" Mediterranean islands"> Mediterranean islands</a>, <a href="https://publications.waset.org/abstracts/search?q=regeneration" title=" regeneration"> regeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=Thasos" title=" Thasos"> Thasos</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfires" title=" wildfires"> wildfires</a> </p> <a href="https://publications.waset.org/abstracts/39531/ecosystem-post-wildfires-effects-of-thasos-island" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27</span> Multi-Spectral Deep Learning Models for Forest Fire Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smitha%20Haridasan">Smitha Haridasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zelalem%20Demissie"> Zelalem Demissie</a>, <a href="https://publications.waset.org/abstracts/search?q=Atri%20Dutta"> Atri Dutta</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajita%20Rattani"> Ajita Rattani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-spectral%20learning" title=" multi-spectral learning"> multi-spectral learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20hazard%20detection" title=" natural hazard detection"> natural hazard detection</a> </p> <a href="https://publications.waset.org/abstracts/146865/multi-spectral-deep-learning-models-for-forest-fire-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">241</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26</span> Reduce the Impact of Wildfires by Identifying Them Early from Space and Sending Location Directly to Closest First Responders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Sullivan">Gregory Sullivan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=space%20detection" title="space detection">space detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20early%20warning" title=" wildfire early warning"> wildfire early warning</a>, <a href="https://publications.waset.org/abstracts/search?q=demonstration%20wildfire%20detection%20and%20action%20from%20space" title=" demonstration wildfire detection and action from space"> demonstration wildfire detection and action from space</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20detection%20to%20first%20responders" title=" space detection to first responders"> space detection to first responders</a> </p> <a href="https://publications.waset.org/abstracts/179337/reduce-the-impact-of-wildfires-by-identifying-them-early-from-space-and-sending-location-directly-to-closest-first-responders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25</span> The Response of Mammal Populations to Abrupt Changes in Fire Regimes in Montane Landscapes of South-Eastern Australia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Johnson">Jeremy Johnson</a>, <a href="https://publications.waset.org/abstracts/search?q=Craig%20Nitschke"> Craig Nitschke</a>, <a href="https://publications.waset.org/abstracts/search?q=Luke%20Kelly"> Luke Kelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fire regimes, climate and topographic gradients interact to influence ecosystem structure and function across fire-prone, montane landscapes worldwide. Biota have developed a range of adaptations to historic fire regime thresholds, which allow them to persist in these environments. In south-eastern Australia, a signal of fire regime changes is emerging across these landscapes, and anthropogenic climate change is likely to be one of the main drivers of an increase in burnt area and more frequent wildfire over the last 25 years. This shift has the potential to modify vegetation structure and composition at broad scales, which may lead to landscape patterns to which biota are not adapted, increasing the likelihood of local extirpation of some mammal species. This study aimed to address concerns related to the influence of abrupt changes in fire regimes on mammal populations in montane landscapes. It first examined the impact of climate, topography, and vegetation on fire patterns and then explored the consequences of these changes on mammal populations and their habitats. Field studies were undertaken across diverse vegetation, fire severity and fire frequency gradients, utilising camera trapping and passive acoustic monitoring methodologies and the collection of fine-scale vegetation data. Results show that drought is a primary contributor to fire regime shifts at the landscape scale, while topographic factors have a variable influence on wildfire occurrence at finer scales. Frequent, high severity wildfire influenced forest structure and composition at broad spatial scales, and at fine scales, it reduced occurrence of hollow-bearing trees and promoted coarse woody debris. Mammals responded differently to shifts in forest structure and composition depending on their habitat requirements. This study highlights the complex interplay between fire regimes, environmental gradients, and biotic adaptations across temporal and spatial scales. It emphasizes the importance of understanding complex interactions to effectively manage fire-prone ecosystems in the face of climate change. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fire" title="fire">fire</a>, <a href="https://publications.waset.org/abstracts/search?q=ecology" title=" ecology"> ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiversity" title=" biodiversity"> biodiversity</a>, <a href="https://publications.waset.org/abstracts/search?q=landscape%20ecology" title=" landscape ecology"> landscape ecology</a> </p> <a href="https://publications.waset.org/abstracts/172700/the-response-of-mammal-populations-to-abrupt-changes-in-fire-regimes-in-montane-landscapes-of-south-eastern-australia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24</span> A Comprehensive Study of Spread Models of Wildland Fires</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manavjit%20Singh%20Dhindsa">Manavjit Singh Dhindsa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ursula%20Das"> Ursula Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Kshirasagar%20Naik"> Kshirasagar Naik</a>, <a href="https://publications.waset.org/abstracts/search?q=Marzia%20Zaman"> Marzia Zaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Purcell"> Richard Purcell</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Sampalli"> Srinivas Sampalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Mutakabbir"> Abdul Mutakabbir</a>, <a href="https://publications.waset.org/abstracts/search?q=Chung-Horng%20Lung"> Chung-Horng Lung</a>, <a href="https://publications.waset.org/abstracts/search?q=Thambirajah%20Ravichandran"> Thambirajah Ravichandran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20management" title=" forest fire management"> forest fire management</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20risk%20assessment" title=" fire risk assessment"> fire risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20simulation" title=" fire simulation"> fire simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire%20modeling" title=" wildfire modeling"> wildfire modeling</a> </p> <a href="https://publications.waset.org/abstracts/176767/a-comprehensive-study-of-spread-models-of-wildland-fires" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176767.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> Relationship Between Wildfire and Plant Species in Arasbaran Forest, Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhila%20Hemati">Zhila Hemati</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Sajjad%20Hosseni"> Seyed Sajjad Hosseni</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohrab%20Zamzami"> Sohrab Zamzami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In nature, forests serve a multitude of functions. They stabilize and nourish soil, store carbon, clean the air and water, and support biodiverse ecosystems. A natural disaster that can affect forests and ecosystems locally or globally is wildfires. Iran experiences annual forest fires that affect roughly 6000 hectares, with the Arasbaran forest being the most affected. These fires may be generated unnaturally by human activity in the forests, or they could occur naturally as a result of climate change. These days, wildfires pose a major natural risk. Wildfires significantly reduce the amount of property and human life in ecosystems globally. Concerns regarding the immediate and longterm effects have been raised by the rise in fire activity in various Iranian regions in recent decades. Natural ecosystem abundance, quality, and health will all be impacted by pasture and forest fires. Monitoring is the first line of defense against and control for forest fires. To determine the spatial-temporal variations of these occurrences in the vegetation regions of Arasbaran, this study was carried out to estimate the areas affected by fires. The findings indicated that July through September, which spans over 130000 hectares, is when fires in Arasbaran's vegetation areas occur to their greatest extent. A significant portion of the nation's forests caught fire in 2024, particularly in the northwest of the Arasbaran vegetation area. On the other hand, January through March sees the least number of fire locations in the Arasbaran vegetation areas. The Arasbaran forest experiences its greatest number of forest fires during the hot, dry months of the year. As a result, the linear association between the burned and active fire regions in the Arasbaran forest indicates a substantial relationship between species abundance and plant species. This link demonstrates that some of the active forest fire centers are the burned regions in Arasbaran's vegetation areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire" title="wildfire">wildfire</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20species" title=" plant species"> plant species</a>, <a href="https://publications.waset.org/abstracts/search?q=forest" title=" forest"> forest</a> </p> <a href="https://publications.waset.org/abstracts/185188/relationship-between-wildfire-and-plant-species-in-arasbaran-forest-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">44</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> Characterization of Forest Fire Fuel in Shivalik Himalayas Using Hyperspectral Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Devi">Neha Devi</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20Joshi"> P. K. Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20fuel" title="forest fire fuel">forest fire fuel</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyperion" title=" Hyperion"> Hyperion</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20spectral%20unmixing" title=" linear spectral unmixing"> linear spectral unmixing</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20mixture%20analysis" title=" spectral mixture analysis"> spectral mixture analysis</a> </p> <a href="https://publications.waset.org/abstracts/102309/characterization-of-forest-fire-fuel-in-shivalik-himalayas-using-hyperspectral-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102309.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> An Overview of the SIAFIM Connected Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tiberiu%20Boros">Tiberiu Boros</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Ionita"> Angela Ionita</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Visan"> Maria Visan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wildfires are one of the frequent and uncontrollable phenomena that currently affect large areas of the world where the climate, geographic and social conditions make it impossible to prevent and control such events. In this paper we introduce the ground concepts that lie behind the SIAFIM (Satellite Image Analysis for Fire Monitoring) project in order to create a context and we introduce a set of newly created tools that are external to the project but inherently in interventions and complex decision making based on geospatial information and spatial data infrastructures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire" title="wildfire">wildfire</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire" title=" forest fire"> forest fire</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20applications" title=" mobile applications"> mobile applications</a>, <a href="https://publications.waset.org/abstracts/search?q=communication" title=" communication"> communication</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a> </p> <a href="https://publications.waset.org/abstracts/21764/an-overview-of-the-siafim-connected-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21764.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">581</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> Building Resilient Communities: The Traumatic Effect of Wildfire on Mati, Greece</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Vallianou">K. Vallianou</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Alexopoulos"> T. Alexopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Plaka"> V. Plaka</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Seleventi"> M. K. Seleventi</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Skanavis"> V. Skanavis</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Skanavis"> C. Skanavis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present research addresses the role of place attachment and emotions in community resiliency and recovery within the context of a disaster. Natural disasters represent a disruption in the normal functioning of a community, leading to a general feeling of disorientation. This study draws on the trauma caused by a natural hazard such as a forest fire. The changes of the sense of togetherness are being assessed. Finally this research determines how the place attachment of the inhabitants was affected during the reorientation process of the community. The case study area is Mati, a small coastal town in eastern Attica, Greece. The fire broke out on July 23<sup>rd</sup>, 2018. A quantitative research was conducted through questionnaires via phone interviews, one year after the disaster, to address community resiliency in the long-run. The sample was composed of 159 participants from the rural community of Mati plus 120 coming from Skyros Island that was used as a control group. Inhabitants were prompted to answer items gauging their emotions related to the event, group identification and emotional significance of their community, and place attachment before and a year after the fire took place. Importantly, the community recovery and reorientation were examined within the context of a relative absence of government backing and official support. Emotions related to the event were aggregated into 4 clusters related to: activation/vigilance, distress/disorientation, indignation, and helplessness. The findings revealed a decrease in the level of place attachment in the impacted area of Mati as compared to the control group of Skyros Island. Importantly, initial distress caused by the fire prompted the residents to identify more with their community and to report more positive feelings toward their community. Moreover, a mediation analysis indicated that the positive effect of community cohesion on place attachment one year after the disaster was mediated by the positive feelings toward the community. Finally, place attachment contributes to enhanced optimism and a more positive perspective concerning Mati’s future prospects. Despite an insufficient state support to this affected area, the findings suggest an important role of emotions and place attachment during the process of recovery. Implications concerning the role of emotions and social dynamics in meshing place attachment during the disaster recovery process as well as community resiliency are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20resilience" title="community resilience">community resilience</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disasters" title=" natural disasters"> natural disasters</a>, <a href="https://publications.waset.org/abstracts/search?q=place%20attachment" title=" place attachment"> place attachment</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire" title=" wildfire"> wildfire</a> </p> <a href="https://publications.waset.org/abstracts/119017/building-resilient-communities-the-traumatic-effect-of-wildfire-on-mati-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> Chemical Analysis of Particulate Matter (PM₂.₅) and Volatile Organic Compound Contaminants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ebadzadsahraei">S. Ebadzadsahraei</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Kazemian"> H. Kazemian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this research was to measure particulate matter (PM₂.₅) and Volatile Organic Compound (VOCs) as two classes of air pollutants, at Prince George (PG) neighborhood in warm and cold seasons. To fulfill this objective, analytical protocols were developed for accurate sampling and measurement of the targeted air pollutants. PM₂.₅ samples were analyzed for their chemical composition (i.e., toxic trace elements) in order to assess their potential source of emission. The City of Prince George, widely known as the capital of northern British Columbia (BC), Canada, has been dealing with air pollution challenges for a long time. The city has several local industries including pulp mills, a refinery, and a couple of asphalt plants that are the primary contributors of industrial VOCs. In this research project, which is the first study of this kind in this region it measures physical and chemical properties of particulate air pollutants (PM₂.₅) at the city neighborhood. Furthermore, this study quantifies the percentage of VOCs at the city air samples. One of the outcomes of this project is updated data about PM₂.₅ and VOCs inventory in the selected neighborhoods. For examining PM₂.₅ chemical composition, an elemental analysis methodology was developed to measure major trace elements including but not limited to mercury and lead. The toxicity of inhaled particulates depends on both their physical and chemical properties; thus, an understanding of aerosol properties is essential for the evaluation of such hazards, and the treatment of such respiratory and other related diseases. Mixed cellulose ester (MCE) filters were selected for this research as a suitable filter for PM₂.₅ air sampling. Chemical analyses were conducted using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for elemental analysis. VOCs measurement of the air samples was performed using a Gas Chromatography-Flame Ionization Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS) allowing for quantitative measurement of VOC molecules in sub-ppb levels. In this study, sorbent tube (Anasorb CSC, Coconut Charcoal), 6 x 70-mm size, 2 sections, 50/100 mg sorbent, 20/40 mesh was used for VOCs air sampling followed by using solvent extraction and solid-phase micro extraction (SPME) techniques to prepare samples for measuring by a GC-MS/FID instrument. Air sampling for both PM₂.₅ and VOC were conducted in summer and winter seasons for comparison. Average concentrations of PM₂.₅ are very different between wildfire and daily samples. At wildfire time average of concentration is 83.0 μg/m³ and daily samples are 23.7 μg/m³. Also, higher concentrations of iron, nickel and manganese found at all samples and mercury element is found in some samples. It is able to stay too high doses negative effects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollutants" title="air pollutants">air pollutants</a>, <a href="https://publications.waset.org/abstracts/search?q=chemical%20analysis" title=" chemical analysis"> chemical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=particulate%20matter%20%28PM%E2%82%82.%E2%82%85%29" title=" particulate matter (PM₂.₅)"> particulate matter (PM₂.₅)</a>, <a href="https://publications.waset.org/abstracts/search?q=volatile%20organic%20compound" title=" volatile organic compound"> volatile organic compound</a>, <a href="https://publications.waset.org/abstracts/search?q=VOCs" title=" VOCs"> VOCs</a> </p> <a href="https://publications.waset.org/abstracts/103168/chemical-analysis-of-particulate-matter-pm25-and-volatile-organic-compound-contaminants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103168.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> A Review of Intelligent Fire Management Systems to Reduce Wildfires</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nomfundo%20Ngombane">Nomfundo Ngombane</a>, <a href="https://publications.waset.org/abstracts/search?q=Topside%20E.%20Mathonsi"> Topside E. Mathonsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remote sensing and satellite imaging have been widely used to detect wildfires; nevertheless, the technologies present some limitations in terms of early wildfire detection as the technologies are greatly influenced by weather conditions and can miss small fires. The fires need to have spread a few kilometers for the technologies to provide accurate detection. The South African Advanced Fire Information System uses MODIS (Moderate Resolution Imaging Spectroradiometer) as satellite imaging. MODIS has limitations as it can exclude small fires and can fall short in validating fire vulnerability. Thus in the future, a Machine Learning algorithm will be designed and implemented for the early detection of wildfires. A simulator will be used to evaluate the effectiveness of the proposed solution, and the results of the simulation will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=moderate%20resolution%20imaging%20spectroradiometer" title="moderate resolution imaging spectroradiometer">moderate resolution imaging spectroradiometer</a>, <a href="https://publications.waset.org/abstracts/search?q=advanced%20fire%20information%20system" title=" advanced fire information system"> advanced fire information system</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithm" title=" machine learning algorithm"> machine learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=detection%20of%20wildfires" title=" detection of wildfires"> detection of wildfires</a> </p> <a href="https://publications.waset.org/abstracts/154851/a-review-of-intelligent-fire-management-systems-to-reduce-wildfires" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154851.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Sentinel-2 Based Burn Area Severity Assessment Tool in Google Earth Engine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Madhushanka">D. Madhushanka</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Liu"> Y. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20C.%20Fernando"> H. C. Fernando</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=burnt%20area" title="burnt area">burnt area</a>, <a href="https://publications.waset.org/abstracts/search?q=burnt%20severity" title=" burnt severity"> burnt severity</a>, <a href="https://publications.waset.org/abstracts/search?q=fires" title=" fires"> fires</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20earth%20engine%20%28GEE%29" title=" google earth engine (GEE)"> google earth engine (GEE)</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel-2" title=" sentinel-2"> sentinel-2</a> </p> <a href="https://publications.waset.org/abstracts/141421/sentinel-2-based-burn-area-severity-assessment-tool-in-google-earth-engine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> Assessment of Causes of Building Collapse in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olufemi%20Oyedele">Olufemi Oyedele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Building collapse (BC) in Nigeria is becoming a regular occurrence, each recording great casualties in the number of lives and materials lost. Building collapse is a situation where building which has been completed and occupied, completed but not occupied or under construction, collapses on its own due to action or inaction of man or due to natural event like earthquake, storm, flooding, tsunami or wildfire. It is different from building demolition. There are various causes of building collapse and each case requires expert judgment to decide the cause of its collapse. Rate of building collapse is a reflection of the level of organization and control of building activities and degree of sophistication of the construction professionals in a country. This study explored the use of case study by examining the causes of six (6) collapsed buildings (CB) across Nigeria. Samples of materials from the sites of the collapsed buildings were taken for testing and analysis, while critical observations were made at the sites to note the conditions of the ground (building base). The study found out that majority of the building collapses in Nigeria were due to poor workmanship, sub-standard building materials, followed by bad building base and poor design. The National Building Code 2006 is not effective due to lack of enforcement and the Physical Development Departments of states and Federal Capital Territory are just mere agents of corruption allowing all types of construction without building approvals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20collapse" title="building collapse">building collapse</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20tests" title=" concrete tests"> concrete tests</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20settlement" title=" differential settlement"> differential settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=integrity%20test" title=" integrity test"> integrity test</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a> </p> <a href="https://publications.waset.org/abstracts/57378/assessment-of-causes-of-building-collapse-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57378.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> A Case Study of Physical and Psychological Forces in the Nigerian Criminal and Military Interrogations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Onimisi%20Ekuh%20Abdullahi">Onimisi Ekuh Abdullahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lasbat%20Omoshalewa%20Akinsemoyin"> Lasbat Omoshalewa Akinsemoyin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Nigeria, over two decades now, there has been a steady increase in the insecurity of human lives and physical properties. In the South-South Nigeria, there is an acute insecurity of militants destroying oil pipe-lines and kidnapping cases; in the Middle-Belt zone, insecurity centers on kidnapping and in a few states crises between Herdsmen and Farmers range like wildfire; in the South-Western zone, kidnapping is vile, in the North-East zone the issue of Boko Haram has become World-wide concern, and in North-west zone, cattle rustlers and religious crisis are of great concern. At the initial stage, the Nigerian Police Force was called upon to quell the crisis. It soon became obvious that the dimension of the crisis was beyond police force. The Nigerian Armed Forces were called to maintain peace and order because the magnitude of the crisis was threatening the national unity and cohesion. The main objective of this paper, was to examine the investigative techniques of criminal by the military in Nigeria. Specifically to examine the physical and psychological force; the abusive techniques and tactics; and suggest modern psychological techniques of interrogating criminals accepted to Human Right Activists and the rule of law. The process is to create room behaviour and practices that carefully monitored the trust and reliability of admissions produced by Psychological manipulative process in Nigeria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=military" title="military">military</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigerian%20criminal" title=" Nigerian criminal"> Nigerian criminal</a>, <a href="https://publications.waset.org/abstracts/search?q=physical" title=" physical"> physical</a>, <a href="https://publications.waset.org/abstracts/search?q=psychological%20force" title=" psychological force"> psychological force</a> </p> <a href="https://publications.waset.org/abstracts/91051/a-case-study-of-physical-and-psychological-forces-in-the-nigerian-criminal-and-military-interrogations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91051.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> Global Emission Inventories of Air Pollutants from Combustion Sources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shu%20Tao">Shu Tao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on a global fuel consumption data product (PKU-FUEL-2007) compiled recently and a series of databases for emission factors of various sources, global emission inventories of a number of greenhouse gases and air pollutants, including CO2, CO, SO2, NOx, primary particulate matter (total, PM 10, and PM 2.5), black carbon, organic carbon, mercury, volatile organic carbons, and polycyclic aromatic hydrocarbons, from combustion sources have been developed. The inventories feather high spatial and sectorial resolutions. The spatial resolution of the inventories are 0.1 by 0.1 degree, based on a sub-national disaggregation approach to reduce spatial bias due to uneven distribution of per person fuel consumption within countries. The finely resolved inventories provide critical information for chemical transport modeling and exposure modeling. Emissions from more than 60 sources in energy, industry, agriculture, residential, transportation, and wildfire sectors were quantified in this study. With the detailed sectorial information, the inventories become an important tool for policy makers. For residential sector, a set of models were developed to simulate temporal variation of fuel consumption, consequently pollutant emissions. The models can be used to characterize seasonal as well as inter-annual variations in the emissions in history and to predict future changes. The models can even be used to quantify net change of fuel consumption and pollutant emissions due to climate change. The inventories has been used for model ambient air quality, population exposure, and even health effects. A few examples of the applications are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollutants" title="air pollutants">air pollutants</a>, <a href="https://publications.waset.org/abstracts/search?q=combustion" title=" combustion"> combustion</a>, <a href="https://publications.waset.org/abstracts/search?q=emission%20inventory" title=" emission inventory"> emission inventory</a>, <a href="https://publications.waset.org/abstracts/search?q=sectorial%20information" title=" sectorial information"> sectorial information</a> </p> <a href="https://publications.waset.org/abstracts/25284/global-emission-inventories-of-air-pollutants-from-combustion-sources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25284.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">369</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Enhancing Disaster Resilience: Advanced Natural Hazard Assessment and Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mariza%20Kaskara">Mariza Kaskara</a>, <a href="https://publications.waset.org/abstracts/search?q=Stella%20Girtsou"> Stella Girtsou</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Prodromou"> Maria Prodromou</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexia%20Tsouni"> Alexia Tsouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Christodoulos%20Mettas"> Christodoulos Mettas</a>, <a href="https://publications.waset.org/abstracts/search?q=Stavroula%20Alatza"> Stavroula Alatza</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyriaki%20Fotiou"> Kyriaki Fotiou</a>, <a href="https://publications.waset.org/abstracts/search?q=Marios%20Tzouvaras"> Marios Tzouvaras</a>, <a href="https://publications.waset.org/abstracts/search?q=Charalampos%20Kontoes"> Charalampos Kontoes</a>, <a href="https://publications.waset.org/abstracts/search?q=Diofantos%20Hadjimitsis"> Diofantos Hadjimitsis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural hazard assessment and monitoring are crucial in managing the risks associated with fires, floods, and geohazards, particularly in regions prone to these natural disasters, such as Greece and Cyprus. Recent advancements in technology, developed by the BEYOND Center of Excellence of the National Observatory of Athens, have been successfully applied in Greece and are now set to be transferred to Cyprus. The implementation of these advanced technologies in Greece has significantly improved the country's ability to respond to these natural hazards. For wildfire risk assessment, a scalar wildfire occurrence risk index is created based on the predictions of machine learning models. Predicting fire danger is crucial for the sustainable management of forest fires as it provides essential information for designing effective prevention measures and facilitating response planning for potential fire incidents. A reliable forecast of fire danger is a key component of integrated forest fire management and is heavily influenced by various factors that affect fire ignition and spread. The fire risk model is validated by the sensitivity and specificity metric. For flood risk assessment, a multi-faceted approach is employed, including the application of remote sensing techniques, the collection and processing of data from the most recent population and building census, technical studies and field visits, as well as hydrological and hydraulic simulations. All input data are used to create precise flood hazard maps according to various flooding scenarios, detailed flood vulnerability and flood exposure maps, which will finally produce the flood risk map. Critical points are identified, and mitigation measures are proposed for the worst-case scenario, namely, refuge areas are defined, and escape routes are designed. Flood risk maps can assist in raising awareness and save lives. Validation is carried out through historical flood events using remote sensing data and records from the civil protection authorities. For geohazards monitoring (e.g., landslides, subsidence), Synthetic Aperture Radar (SAR) and optical satellite imagery are combined with geomorphological and meteorological data and other landslide/ground deformation contributing factors. To monitor critical infrastructures, including dams, advanced InSAR methodologies are used for identifying surface movements through time. Monitoring these hazards provides valuable information for understanding processes and could lead to early warning systems to protect people and infrastructure. Validation is carried out through both geotechnical expert evaluations and visual inspections. The success of these systems in Greece has paved the way for their transfer to Cyprus to enhance Cyprus's capabilities in natural hazard assessment and monitoring. This transfer is being made through capacity building activities, fostering continuous collaboration between Greek and Cypriot experts. Apart from the knowledge transfer, small demonstration actions are implemented to showcase the effectiveness of these technologies in real-world scenarios. In conclusion, the transfer of advanced natural hazard assessment technologies from Greece to Cyprus represents a significant step forward in enhancing the region's resilience to disasters. EXCELSIOR project funds knowledge exchange, demonstration actions and capacity-building activities and is committed to empower Cyprus with the tools and expertise to effectively manage and mitigate the risks associated with these natural hazards. Acknowledgement:Authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earth%20observation" title="earth observation">earth observation</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20hazards" title=" natural hazards"> natural hazards</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/186482/enhancing-disaster-resilience-advanced-natural-hazard-assessment-and-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186482.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">38</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Mapping Social and Natural Hazards: A Survey of Potential for Managed Retreat in the United States</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Ahmed">Karim Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study was to investigate how factoring the impact of natural disasters beyond flooding would affect managed retreat policy eligibility in the United States. For the study design, a correlation analysis method compared weighted measures of flooding and other natural disasters (e.g., wildfires, tornadoes, heatwaves, etc.) to CBSA Populated areas, the prevalence of cropland, and relative poverty on a county level. The study found that the vast majority of CBSAs eligible for managed retreat programs under a policy inclusive of non-flooding events would have already been covered by flood-only managed retreat policies. However, it is noteworthy that a majority of those counties that are not covered by a flood-only managed retreat policy have high rates of poverty and are either heavily populated and/or agriculturally active. The correlation is particularly strong between counties that are subject to multiple natural hazards and those that have both high rates of relative poverty and cropland prevalence. There is currently no managed retreat policy for agricultural land in the United States despite the environmental implications and food supply chain vulnerabilities related to at-risk cropland. The findings of this study suggest both that such a policy should be created and, when it is, that special attention should be paid to non-flood natural disasters affecting agricultural areas. These findings also reveal that, while current flood-based policies in the United States serve many areas that do need access to managed retreat funding and implementation, other vulnerable areas are overlooked by this approach. These areas are often deeply impoverished and are therefore particularly vulnerable to natural disaster; if and when those disasters do occur, these areas are often less financially prepared to recover or retreat from the disaster’s advance and, due to the limitations of the current policies discussed above, are less able to take the precautionary measures necessary to mitigate their risk. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard" title=" hazard"> hazard</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=managed%20retreat" title=" managed retreat"> managed retreat</a>, <a href="https://publications.waset.org/abstracts/search?q=wildfire" title=" wildfire"> wildfire</a> </p> <a 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