Relevant bibliographies by topics / Fire prevention Remote sensing

Academic literature on the topic 'Fire prevention Remote sensing'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Fire prevention Remote sensing"

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Bisquert, Mar, Eduardo Caselles, Juan Manuel Sánchez, and Vicente Caselles. "Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data." International Journal of Wildland Fire 21, no. 8 (2012): 1025. http://dx.doi.org/10.1071/wf11105.

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Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.
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Tian, Yuping, Zechuan Wu, Mingze Li, Bin Wang, and Xiaodi Zhang. "Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images." Remote Sensing 14, no. 18 (September 6, 2022): 4431. http://dx.doi.org/10.3390/rs14184431.

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With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.
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Gizatullin, Almaz T. "DEVELOPMENT OF REMOTE SENSING METHODS FOR NATURAL FIRE PREVENTION." Географический вестник = Geographical bulletin, no. 1 (56) (2021): 149–61. http://dx.doi.org/10.17072/2079-7877-2021-1-149-161.

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The study deals with remote sensing methods for natural fire prevention, provides analysis and systematization on the subject. It traces the historical development and demonstrates the diversity of the methods. The main development stages and their characteristics were identified taking into account the increasing number of the sources and types of remote sensing and deepening knowledge of the subject. Fire interpretation includes fundamentally different processes of ignition and fire spread. The concepts of fire danger and its factors were introduced, the ways for their selection and application in the methods were analyzed. The source data for the methods were defined: satellite imagery of various resolutions (Landsat, Sentinel, MODIS/Terra-Aqua, AVHRR/NOAA, etc.), UAV images, lidar data, as well as technologies to process those. The study demonstrates that the most commonly used are traditional methods of geoinformation analysis, simulation modelling and neural networks. The methods were described, features of their implementation were identified. The description includes specific examples of fire danger assessment methods based on GIS, simulation models of fire spread, fire prevention methods based on neural networks and their application for territories of different spatial levels – global, regional and local.
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Mycke-Dominko, Małgorzata. "The Remote Sensing Method of Forest Fire Danger Rating Categorization." Miscellanea Geographica 11, no. 1 (December 1, 2004): 359–62. http://dx.doi.org/10.2478/mgrsd-2004-0038.

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Abstract The article presents the use of satellite images in the determination of forest fire danger rating categories. The assessment was carried out based on images from the LANDSAT TM, IKONOS and NOAA satellites, with the finding that the LANDSAT TM images are the most useful. A new solution proposed is to make forest fire danger rating categories refer to forest ranger sub-districts, what gives the forest service greater control over forest fire prevention activities. Forest fire danger assessment was done taking into account remote sensing indices such as the NDVI, TNDVI, and IHT, as well by the analysis of the spatial distribution and the number of fires in the previous six years. In accordance with the Polish State Forest Classification System, three classes were specified: 1 – high fire danger, 2 – moderate fire danger, 3 – low fire danger.
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Gizatullin, Almaz. "Development of natural fire prevention method based on remote sensing data: case study of Krasnoyarsk region forests." InterCarto. InterGIS 27, no. 2 (2021): 340–54. http://dx.doi.org/10.35595/2414-9179-2021-2-27-340-354.

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The stages of development of natural fire prevention method based on remote sensing data were considered. The case study is focused on Krasnoyarsk region forests. There was a rationale for selecting a study area on the basis of statistical fire data (FIRMS thermal hot spots 2016–2018) and a variety of fire conditions. The fire assessment was founded on the most informative fire factors—surface temperature, vegetation cover inhomogenuity and man-made load, which are derived by the natural-fire characteristics of the territory. These factors were evaluated by measuring parameters closed to them, respectively—radiobrightness temperature based on thermal emission, vegetation index NDVI and integral indicator of distance to settlements and roads. Materials from the Terra/Aqua, Sentinel-3, Landsat-8, Sentinel-2 satellites and Open Street Maps vector map layers were used as data sources. With use of statistical data, the relationship between above parameters and the present fire danger of Krasnoyarsk region was analyzed. Based on the results, we obtained different by forest rayon and fire season month correlation coefficients that described the contribution of individual factors to a fire danger, and threshold values of parameters for preventing fires. Then a sequence of stages of analytical and synthetic fire danger assessment as a study method was built. Validation of the method was performed in the most fire dangerous and representative in terms of fire conditions area in the south-west of the Krasnoyarsk Territory from April 1 to May 10, 2019. It showed sufficient accuracy (65 %) and reliability (58 %) of fire forecast.
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Rybakov, A. V., E. V. Ivanov, A. V. Dmitriev, and A. E. Borisov. "Assessment of the influence of the normalized vegetation index on the fire situation in the fire-hazardous period." Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta 7, no. 4 (December 25, 2021): 432–37. http://dx.doi.org/10.22281/2413-9920-2021-07-04-432-437.

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The paper presents an analysis of the parameter obtained by remote sensing of the planet Earth, the normalized vegetation index (NDVI index). The results of assessing the impact of the index on the occurrence of fires in a certain area are presented. Using the example of statistical data for the Krasnoyarsk Territory, the index values for two periods of spring and summer were obtained, and the influence of NDVI values on the change in the probability of a forest fire was estimated. Static data on the index were selected from the «data lake» of the Ministry of Emergency Situations of Russia, data is collected from 2014 to the present, and data on thermal points from 2012. The consequences after wildfires will cause significant damage to forestry in Russia, and to the environment as a whole. Therefore, the allocation of previously known areas with a high probability of a natural fire will not only improve preventive measures for prevention, but will also make it possible to prevent most of the consequences. This article considers one of the parameters obtained by remote sensing of the Earth NDVI of its change before and after the event in question (natural fire).
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Barmpoutis, Panagiotis, Periklis Papaioannou, Kosmas Dimitropoulos, and Nikos Grammalidis. "A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing." Sensors 20, no. 22 (November 11, 2020): 6442. http://dx.doi.org/10.3390/s20226442.

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The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.
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Akhloufi, Moulay A., Andy Couturier, and Nicolás A. Castro. "Unmanned Aerial Vehicles for Wildland Fires: Sensing, Perception, Cooperation and Assistance." Drones 5, no. 1 (February 22, 2021): 15. http://dx.doi.org/10.3390/drones5010015.

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Wildfires represent a significant natural risk causing economic losses, human death and environmental damage. In recent years, the world has seen an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small-scale environments. However, wildland fires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS) were proposed. UAVs have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper, previous works related to the use of UAV in wildland fires are reviewed. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, some of the recent frameworks proposing the use of both aerial vehicles and unmanned ground vehicles (UGV) for a more efficient wildland firefighting strategy at a larger scale are presented.
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Zhang, Wen, and Juan Wu. "To Explore the UAV Application in Disaster Prevention and Reduction." Applied Mechanics and Materials 590 (June 2014): 609–12. http://dx.doi.org/10.4028/www.scientific.net/amm.590.609.

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The UAV remote sensing is an important way of aerial remote sensing, and increasingly become an important means for spatial data acquisition, which has the advantages of long life time, image real-time transmission, high-risk area detection, low cost, flexibility, is a powerful supplement satellite and aerial remote sensing."Tianyi" series of UAV has a number of intellectual property rights, and repeatedly used in major natural disaster emergency in the country. Has the advantages of small volume, light weight, small target characteristics, the use of fast, flexible, convenient operation and repair, and can be widely used in the field of disaster prevention and mitigation, search rescue, traffic control, resource exploration, land resources monitoring, border patrol, forest fire prevention, crop monitoring, and provides an example of the application.
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Benguerai, Abdelkader, Khéloufi Benabdeli, and Abdelkader Harizia. "Forest Fire Risk Assessment Model Using Remote Sensing and GIS Techniques in Northwest Algeria." Acta Silvatica et Lignaria Hungarica 15, no. 1 (June 1, 2019): 9–21. http://dx.doi.org/10.2478/aslh-2019-0001.

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Abstract Algeria loses more than 20,000 hectares of forest to fire every year. The losses are costly both in terms of life and property damage, which weighs heavily on the environment and the local economy. Geomatics can complement the conventional methods used in fire hazard prevention and management. The objective of our study is to use the geographic information system (GIS) and the Remote Sensing (RS) technology to develop the fire risk assessment map of the forest massif of Zelamta located in Southeast Mascara province (Northwest Algeria). The methodology employed was an empirical model involving three parameters that can control fire behaviour: geomorphology, vegetal cover combustibility, and human activity. The obtained results can help in the decision-making process as well as provide cartographic support for forest fire prevention and management.
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Dissertations / Theses on the topic "Fire prevention Remote sensing"

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Wang, Zhen. "Modeling wildland fire radiance in synthetic remote sensing scenes /." Online version of thesis, 2007. http://hdl.handle.net/1850/5787.

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Contezac, Jonathan M. "Micrometeorological Observations of Fire-Atmosphere Interactions and Fire Behavior on a Simple Slope." Thesis, San Jose State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10937563.

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An experiment was designed to capture micrometeorological observations during a fire spread on a simple slope. Three towers equipped with a variety of instrumentation, an array of fire-sensing packages, and a Doppler lidar was deployed to measure various aspects of the fire. Pressure and temperature perturbations were analyzed for each of the grid packages to determine if the fire intensity could be observed in the covariance of the two variables. While two of the packages measured a covariance less than –15 °C hPa, there was no clear trend across the grid. The fire front passage at each of the three towers on the slope yielded extreme swings in observed turbulent kinetic energy and sensible heat flux. Vertical velocity turbulence spectra showed that the high-intensity fire front passage at the bottom tower was 2 to 3 orders of magnitude larger than the low-intensity fire front passages at the top two towers. Opposing wind regimes on the slope caused a unique L-shaped pattern to form in the fire front. A vorticity estimation from the sonic anemometers showed that vorticity reached a maximum just as a fire whirl formed in the bend of the L-shaped fire front, leading to a rapid increase in fire spread.

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Medler, Michael Johns 1962. "Integrating remote sensing and terrain data in forest fire modeling." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/282480.

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Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.
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Henry, Mary Catherine. "Characterizing fire-related spatial patterns in fire-prone ecosystems using optical and microwave remote sensing." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/280006.

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The use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.
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Oliveira, Sofia Luísa de Jesus. "Frequency, patchiness and intensity of tropical savanna fires: analysis using field data and remote sensing." Doctoral thesis, ISA/UL, 2014. http://hdl.handle.net/10400.5/7322.

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Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia
In tropical savannas, one of the most fire-prone biomes on Earth, fire management is a continuous and iterative process that can only be effectively achieved with thorough consideration of fire regimes. Based on remotely sensed imagery and in-situ field data, key fire regime components were assessed for tropical savannas of northern Australia (frequency, patchiness, intensity, and severity) and Brazil (frequency). The discrete lognormal model was found to be the best method for modelling fire frequency in tropical savannas, and demonstrated that fire frequency is very high in both countries. In northern Australia, fire patchiness was lower in the late dry season, characterized by shorter and fewer unburned patches, than in the early dry season. Fire intensity and severity were highest in the late dry season. The observed temporal differences are consistent with the hypothesis that climate is the main driver of fire regime seasonality. Fuel load and fuel continuity explained fire regime differences between vegetation types. Fire season was bimodal, with peaks in May and October, related to periods of anthropogenic fire and optimal fire weather conditions. Prescribed burning in the early dry season can increase the patchiness and reduce the intensity of late dry season fires, with substantial benefits for biodiversity and a reduction in greenhouse gas emissions
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Hadlow, Ann Marie. "Changes in fire season precipitation in Idaho and Montana from 1982-2006." Diss., [Missoula, Mont.] : The University of Montana, 2009. http://etd.lib.umt.edu/theses/available/etd-05292009-105116/.

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Thesis (M.S.) -- University of Montana, 2009.
Title from author supplied metadata. Description based on contents viewed on October 14, 2009. Author supplied keywords: Fire, precipitation, season ending event, Remote Automated Weather Stations, North American Regional Reanalysis, Northern Rockies, climate change. Includes bibliographical references.
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Duncan, Brean. "NATIVE FIRE REGIME AS A REFERENCE FOR ESTABLISHING MANAGEMENT PRACTICES." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4058.

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Our understanding of natural fire regimes in human-dominated landscapes is limited. Fire regimes operating in the pyrogenic ecosystems of Florida have been altered by fire suppression and fuel fragmentation. This is especially true of North Merritt Island, Florida, where human impacts have led to an incomplete knowledge of current fire regimes. We know that growing season fires frequently occurred within general return intervals and that many native terrestrial species require fire to remain viable. A 20-year plus period of fire suppression caused structural and compositional changes to vegetation/fuels that led to catastrophic fires and the decline of native species populations such as the Florida Scrub-Jay. Fire has been reintroduced as a means to reduce fuels and maintain habitat requirements for native species. Scientific studies have documented the effects and benefits of prescribed burning on KSC/MINWR habitat/fuels structure. The necessity for fire to maintain vegetation/fuels structure and composition on the landscape is clear so fire is being applied to the landscape despite our imperfect knowledge of the native fire regime. It is imperative for the survival of many native species that fire managers be able to mimic the results of the native fire regime. Fire regime research is difficult in shrublands, and using dendrochronologic techniques are often not possible in flatwoods communities. I therefore used a process of remote sensing, GIS mapping, and spatial modeling to quantify lightning fire ignition properties, the current managed fire regime, and the natural fire regime. Chapter one develops a new remote sensing technique to accurately map burned areas in Florida scrub and pine flatwoods dominated communities on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. At the center of this technique is a newly developed separation index (SI) that was used to evaluate each individual satellite image band for its power to discriminate unburned and burned areas. Burned areas were classified and found to be highly accurate in relation to empirical fire records. This chapter addressed a number of issues relevant to the classification of burned areas including: the effect of geographic extent of remote sensing data on classification, determining the best bands for classification, and cleaning classification results by using GIS masking. It also serves as the first published effort to map fire scars in the Florida scrub and flatwoods vegetative communities of the southeastern U.S. using image processing techniques. Chapter two quantified a managed fire regime on John F. Kennedy Space Center, Florida and surrounding federal properties by mapping all fires between 1983 to 2005 using the image processing technique developed in chapter one, time series satellite imagery, and GIS techniques. The goals were to: (1) determine if an image processing technique designed for individual fire scar mapping could be applied to an image time series for mapping a managed fire regime in a rapid re-growth pyrogenic system; (2) develop a method for labeling mapped fire scar confidence knowing that a formal accuracy analysis was not possible; and (3) compare results of the managed fire regime with regional information on natural fire regimes to look for similarities/differences that might help optimize management for persistence of native fire-dependent species. The area burned by managed fire peaked when the drought index was low and was reduced when the drought index was high. This contrasts with the expectations regarding the natural fire regime of this region. Chapter three quantified the natural lightning ignition regime on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. Lightning is the natural ignition source in Florida, substantiating the need for understanding lightning fire incidence. Sixteen years of lightning data (1986-2003, excluding 1987 and 2002 due to missing data) from the NASA Cloud to Ground Lightning Surveillance System and fire ignition records were used to quantify the relationship between lightning incidence and fire ignition. Precipitation influenced the efficiency of lightning ignitions, particularly July precipitation. Negative polarity strikes caused the majority of ignitions. Pine flatwoods was ignited more frequently than expected given equal chance of ignition among landcover types. About half (51%) of detected fires were instantaneous ignitions and the other 49% were delayed an average of two days. Summer lightning ignitions were dominant, especially during July, with only one winter lightning ignition. Chapter four used an existing fire regime model (HFire) to simulate the natural fire regime (prior to European settlement) on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. A sensitivity analysis was performed to establish which parameters were most important and the range of variation surrounding empirically derived model information from the same model. A mosaic pattern of small fires dominated this fire regime with extremely large fires occurring during dry La Nina periods. Dead fuel moisture and wind speed had the largest influence on model outcome. The majority of variability was found to be in the largest fires. The research presenter here provides a comprehensive perspective on current and historic fire regimes that may be useful for optimizing land management on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida and throughout the southeastern United States. Native fire dependent species are suffering from many changes imposed from human alteration. The success of conservation efforts protecting native fire dependent species hinge on my factors. Two of the largest factors are first protecting native habitat and then secondly managing that protected habitat to mimic natural maintenance processes for suitable structure and composition which may favor their demography. This study focuses on developing techniques necessary for producing information that can aid the optimization of fire management on these properties and within the southeastern United States, but may be useful in other fire maintained ecosystems globally.
Ph.D.
Department of Biology
Sciences
Conservation Biology PhD
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Sparks, Aaron, Crystal Kolden, Alan Talhelm, Alistair Smith, Kent Apostol, Daniel Johnson, and Luigi Boschetti. "Spectral Indices Accurately Quantify Changes in Seedling Physiology Following Fire: Towards Mechanistic Assessments of Post-Fire Carbon Cycling." MDPI AG, 2016. http://hdl.handle.net/10150/621493.

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Fire activity, in terms of intensity, frequency, and total area burned, is expected to increase with a changing climate. A challenge for landscape-level assessment of fire effects, often termed burn severity, is that current remote sensing assessments provide very little information regarding tree/vegetation physiological performance and recovery, limiting our understanding of fire effects on ecosystem services such as carbon storage/cycling. In this paper, we evaluated whether spectral indices common in vegetation stress and burn severity assessments could accurately quantify post-fire physiological performance (indicated by net photosynthesis and crown scorch) of two seedling species, Larix occidentalis and Pinus contorta. Seedlings were subjected to increasing fire radiative energy density (FRED) doses through a series of controlled laboratory surface fires. Mortality, physiology, and spectral reflectance were assessed for a month following the fires, and then again at one year post-fire. The differenced Normalized Difference Vegetation Index (dNDVI) spectral index outperformed other spectral indices used for vegetation stress and burn severity characterization in regard to leaf net photosynthesis quantification, indicating that landscape-level quantification of tree physiology may be possible. Additionally, the survival of the majority of seedlings in the low and moderate FRED doses indicates that fire-induced mortality is more complex than the currently accepted binary scenario, where trees survive with no impacts below a certain temperature and duration threshold, and mortality occurs above the threshold.
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Mexicano, Vargas Maria de Lourdes. "Remote Sensing Methods To Classify a Desert Wetland." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/232457.

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The Cienega de Santa Clara is a 5600 ha, anthropogenic wetland in the delta of the Colorado River in Mexico. It is the inadvertent creation of the disposal of brackish agricultural waste water from the U.S. into the intertidal zone of the river delta in Mexico, but has become an internationally important wetland for resident and migratory water birds. The marsh is dominated by Typha domengensis with Phragmites australis as a sub-dominant species in shallower marsh areas. The most important factor controlling vegetation density was fire. The second significant (P<0.01) factor controlling NDVI was flow rate of agricultural drain water from the U.S. into the marsh. Reduced summer flows in 2001 due to canal repairs, and in 2010 during the YDP test run, produced the two lowest NDVI values of the time series from 2000 to 2011 (P<0.05). Salinity is a further determinant of vegetation dynamics as determined by greenhouse experiments, but was nearly constant over the period 2000 to 2011, so it was not a significant variable in regression analyses. Evapotranspiration (ET) and other water balance components were measured in Cienega de Santa Clara; we used a remote sensing algorithm to estimate ET from meteorological data and Enhanced Vegetation Index values from the Moderate Resolution Imaging Spectrometer (MODIS) sensors on the Terra satellite. We used Landsat NDVI imagery from 1978-2011 to determine the area and intensity of vegetation and to estimate evapotranspiration (ET) to construct a water balance. Remote sensing data was supplemented with hydrological data, site surveys and literature citations. The vegetated area increased from 1978 to 1995 and has been constant at about 4200 ha since then. The dominant vegetation type is Typha domingensis (southern cattail), and peak summer NDVI since 1995 has been stable at 0.379 (SD = 0.016), about half of NDVI(max). About 30% of the inflow water is consumed in ET, with the remainder exiting the Cienega as outflow water, mainly during winter months when T. domingensis is dormant.
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Gitas, Ioannis. "Geographical information systems and remote sensing in mapping and monitoring fire-altered forest landscapes." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621715.

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Books on the topic "Fire prevention Remote sensing"

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Zhang, Xiangmin. Coal fires in Northwest China: Detection, monitoring, and prediction using remote sensing data. [Hebei Province, China: X. Zhang, 1998.

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Thermal modelling of underground coal fires in northern China. Utrecht]: ITC, 2005.

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Gangopadhyay, Prasun Kumar. Coalfire related CO₂ emissions and remote sensing =: Aan steenkoolbranden gerelateerde CO₂ uitstoot en aardobservatie. Enschede: ITC, 2008.

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Gangopadhyay, Prasun Kumar. Coalfire related CO₂ emissions and remote sensing =: Aan steenkoolbranden gerelateerde CO₂ uitstoot en aardobservatie. Enschede: ITC, 2008.

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Chuvieco, Emilio. Earth Observation of Wildland Fires in Mediterranean Ecosystems. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Hao, Weimin. Remote sensing of fire: Science and application : 10 August 2008, San Diego, California, USA. Edited by SPIE (Society). Bellingham, Wash: SPIE, 2008.

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United States. Congress. House. Committee on Science, Space, and Technology (2011). Threats from space: A review of U.S. government efforts to track and mitigate asteroids and meteors (part I and part II) : hearing before the Committee on Science, Space, and Technology, House of Representatives, One Hundred Thirteenth Congress, first session, Tuesday, March 19, 2013 and Wednesday, April 10, 2013. Washington: U.S. Government Printing Office, 2013.

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Poellot, Michael R. Preliminary analysis of University of North Dakota aircraft data from the FIRE cirrus ITO-II: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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1954-, Thériault Jean-Marc, and Jensen James O, eds. Spectral sensing research for surface and air monitoring in chemical, biological and radiological defense and security applications. New Jersey: World Scientific, 2009.

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Paulter, Nicholas G. Guide to the technologies of concealed weapon and contratband imaging and detection. Washington, DC: U.S. Dept. of Justice, Office Justice Programs, National Institute of Justice, 2001.

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Book chapters on the topic "Fire prevention Remote sensing"

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Saharjo, Bambang Hero, and Alex Yungan. "Forest and Land Fires in Riau Province: A Case Study in Fire Prevention Policy Implementation with Local Concession Holders." In Springer Remote Sensing/Photogrammetry, 143–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67474-2_8.

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Chaparro, David, Mercè Vall-llossera, and Maria Piles. "A Review on European Remote Sensing Activities in Wildland Fires Prevention." In Remote Sensing of Hydrometeorological Hazards, 237–60. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315154947-11.

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Johnston, Joshua M., Ronan Paugam, Ellen Whitman, Tom Schiks, and Alan S. Cantin. "Remote Sensing of Fire Behavior." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_174-1.

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Johnston, Joshua M., Ronan Paugam, Ellen Whitman, Tom Schiks, and Alan S. Cantin. "Remote Sensing of Fire Behavior." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 879–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_174.

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Meng, Ran, and Feng Zhao. "Remote Sensing of Fire Effects." In Remote Sensing of Hydrometeorological Hazards, 261–83. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315154947-12.

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Martín, M. Pilar, Pietro Ceccato, Stéphane Flasse, and Ian Downey. "Fire detection and fire growth monitoring using satellite data." In Remote Sensing of Large Wildfires, 101–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60164-4_6.

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Chuvieco, Emilio, F. Javier Salas, Luis Carvacho, and Francisco Rodríguez-Silva. "Integrated fire risk mapping." In Remote Sensing of Large Wildfires, 61–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60164-4_5.

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Roy, David P., Luigi Boschetti, and Alistair M. S. Smith. "Satellite Remote Sensing of Fires." In Fire Phenomena and the Earth System, 77–93. Oxford: John Wiley & Sons, 2013. http://dx.doi.org/10.1002/9781118529539.ch5.

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Elvidge, Christopher D., Mikhail Zhizhin, Feng-Chi Hsu, and Kimberly E. Baugh. "Rapid Fire Detection, Characterization and Reporting from VIIRS Data." In Time-Sensitive Remote Sensing, 45–53. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2602-2_4.

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Camia, Andrea, Giovanni Bovio, Inmaculada Aguado, and Nicolas Stach. "Meteorological fire danger indices and remote sensing." In Remote Sensing of Large Wildfires, 39–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60164-4_4.

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Conference papers on the topic "Fire prevention Remote sensing"

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Xu, Aijun, Lihua Tang, Zhengchao Chen, and Xia Zhang. "Distribution of forest fire prevention resources based on GIS." In Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, edited by Qingxi Tong. SPIE, 2008. http://dx.doi.org/10.1117/12.816204.

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Haibiao Zhang and Haiwei Zhang. "Broadcasts TV engine room equipments safety prevention fire problem and solution." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5966085.

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Sanjaya, Hartanto, G. Fajar Suryono, Azalea Eugenie, Mega Novetrishka Putri, Marina C. G. Frederik, Agustan Agustan, Oni Bibin Bintoro, and Heri Sadmono. "Indonesia Fire Danger Rating System (Ina-FDRS), a New Algorithm for the Fire Prevention in Indonesia." In 2019 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). IEEE, 2019. http://dx.doi.org/10.1109/agers48446.2019.9034326.

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Laneve, Giovanni, Guido Bernini, Lorenzo Fusilli, and Pablo Marzialetti. "Satellite-based products for forest fire prevention and recovery: the PREFER experience." In Fourth International Conference on Remote Sensing and Geoinformation of the Environment, edited by Kyriacos Themistocleous, Diofantos G. Hadjimitsis, Silas Michaelides, and Giorgos Papadavid. SPIE, 2016. http://dx.doi.org/10.1117/12.2240397.

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Laneve, G., L. Fusilli, and G. Bernini. "Achievements of the PREFER project in the prevention phase of the forest fire management." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730506.

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Chengkun Sun, Jian Yao, and Dongmei Tian. "Experiment study on preventing and extinguishing fire using stopping agent." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5965205.

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Liu Yajing and Jiang Nan. "Design and realization of drought-resistant and flood-prevention for the five provinces in southwest of China." In 2010 Second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iita-grs.2010.5603075.

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Wu, Shengli, and Cheng Liu. "A new model for fire forecast." In Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2010. http://dx.doi.org/10.1117/12.865533.

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Utkin, Andrei B., Alexander Lavrov, and Rui Vilar. "Application of rangefinder for small forest fire detection." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Christopher M. U. Neale, and Ben T. Gouweleeuw. SPIE, 2006. http://dx.doi.org/10.1117/12.687261.

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Lobo, Agustin, Nicolau Pineda, Rafael Navarro-Cedillo, Pilar Fernandez-Rebollo, Francisco J. Salas, Jose-Luis Fernandez-Turiel, and Arturo Fernandez-Palacios. "Mapping forest fire impact from Landsat-TM imagery." In Remote Sensing, edited by Edwin T. Engman. SPIE, 1998. http://dx.doi.org/10.1117/12.332768.

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Reports on the topic "Fire prevention Remote sensing"

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Douglas, Thomas, M. Jorgenson, Hélène Genet, Bruce Marcot, and Patricia Nelsen. Interior Alaska DoD training land wildlife habitat vulnerability to permafrost thaw, an altered fire regime, and hydrologic changes. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43146.

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Climate change and intensification of disturbance regimes are increasing the vulnerability of interior Alaska Department of Defense (DoD) training ranges to widespread land cover and hydrologic changes. This is expected to have profound impacts on wildlife habitats, conservation objectives, permitting requirements, and military training activities. The objective of this three-year research effort was to provide United States Army Alaska Garrison Fort Wainwright, Alaska (USAG-FWA) training land managers a scientific-based geospatial framework to assess wildlife habitat distribution and trajectories of change and to identify vulnerable wildlife species whose habitats and resources are likely to decline in response to permafrost degradation, changing wildfire regimes, and hydrologic reorganization projected to 2100. We linked field measurements, data synthesis, repeat imagery analyses, remote sensing measurements, and model simulations focused on land cover dynamics and wildlife habitat characteristics to identify suites of wildlife species most vulnerable to climate change. From this, we created a robust database linking vegetation, soil, and environmental characteristics across interior Alaska training ranges. The framework used is designed to support decision making for conservation management and habitat monitoring, land use, infrastructure development, and adaptive management across the interior Alaska DoD cantonment and training land domain.
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Lasko, Kristofer. Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42122.

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Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and postburn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics.
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Douglas, Thomas A., Christopher A. Hiemstra, Stephanie P. Saari, Kevin L. Bjella, Seth W. Campbell, M. Torre Jorgenson, Dana R. N. Brown, and Anna K. Liljedahl. Degrading Permafrost Mapped with Electrical Resistivity Tomography, Airborne Imagery and LiDAR, and Seasonal Thaw Measurements. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41185.

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Accurate identification of the relationships between permafrost extent and landscape patterns helps develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools are particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT was a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. The transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies, suggesting attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. The results from our study indicated limitations in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.
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