Статті в журналах з теми "Fire prevention Remote sensing"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Fire prevention Remote sensing.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Fire prevention Remote sensing".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
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.
Повний текст джерелаАнотація:
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.
2
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.
Повний текст джерелаАнотація:
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.
3
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.
Повний текст джерелаАнотація:
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.
4
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.
Повний текст джерелаАнотація:
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.
5
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.
Повний текст джерелаАнотація:
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.
6
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.
Повний текст джерелаАнотація:
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).
7
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.
Повний текст джерелаАнотація:
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.
8
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.
Повний текст джерелаАнотація:
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.
9
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.
Повний текст джерелаАнотація:
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.
10
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.
Повний текст джерелаАнотація:
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.
11
Gizatullin, A. T., N. A. Alekseenko, and V. S. Moiseeva. "Development of the preventive natural fire danger assessment algorithm using remote sensing data." Geodesy and Cartography 943, no. 1 (February 20, 2019): 102–9. http://dx.doi.org/10.22389/0016-7126-2019-943-1-102-109.
Повний текст джерелаАнотація:
This article is devoted to the development of an algorithm for the preventive assessment of the fire danger of natural areas using remote sensing data (the preventive natural fire danger assessment algorithm). The problems of the current state of the remote sensing materials use for fires researches as a justification for the need of the algorithm are considered. A review of existing methods and algorithms of natural fire danger assessment is done. The algorithm development includes description of the general structure and the content filling process of different algorithm components. The algorithm is a stages sequence of remote sensing data processing and analysis in terms of fire danger. As a result of algorithm, the fire danger assessment of the observed territory is formed. A special feature of the algorithm is its preventiveness, universality (applicability for any territory), practical automatability (the ability to represent in the form of a program code for the processing of RSD) and flexibility (the ability to add and branch the sequence). In the end, general conclusions and recommendations on the use of the algorithm are given.
12
Krsnik, Goran, Eduard Busquets Olivé, Míriam Piqué Nicolau, Asier Larrañaga, Adrián Cardil, Jordi García-Gonzalo, and José Ramón González Olabarría. "Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning." Remote Sensing 12, no. 24 (December 17, 2020): 4124. http://dx.doi.org/10.3390/rs12244124.
Повний текст джерелаАнотація:
Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia.
13
Majdalani, Georgia, Nikos Koutsias, Ghaleb Faour, Jocelyne Adjizian-Gerard, and Florent Mouillot. "Fire Regime Analysis in Lebanon (2001–2020): Combining Remote Sensing Data in a Scarcely Documented Area." Fire 5, no. 5 (September 21, 2022): 141. http://dx.doi.org/10.3390/fire5050141.
Повний текст джерелаАнотація:
Fire is a recurrent disturbance in Mediterranean ecosystems. Data assemblage from forest fire services can provide useful information for understanding climate controls on daily fire hazard or long term trends. Located at the driest range of the Mediterranean bioclimate, and with contrasting socio-political systems compared to the European area, the southern Mediterranean ecosystems are subjected to more extreme climate and social events. This could potentially lead to unique fire regimes and trends worth being characterized for prevention plans and ecosystem management. However, the region is far less documented, due to missing or inhomogeneous fire records, leaving local authorities with no management strategies when large fires happen. We filled this knowledge gap for Lebanon by combining high spatial resolution Landsat data with high temporal resolution VIIRS (S-NPP and NOAA-20) and MODIS (MCD14ML) hotspots to characterize the seasonal and interannual fire regime over the 2001–2020 period. Numerous small fires were hardly detected by global remote sensing. We estimated that 2044 ha burn annually, representing 0.58% of the wildland cover, with no significant trend over the period, but with non climate-related fires detected during the year experiencing socio-political troubles. The spatial and temporal resolution of this dataset identified a particular prolonged fire season up to November, and an unusual bimodal fire season peaking in July and November. We related these features to the prolonged autumnal soil drought and high August air humidity in the region. This updated fire regime in Lebanon illustrates the benefits of this combined approach for data-scarce regions and provides new insights on the variability of fire weather types in the Mediterranean basin.
14
Elhag, Mohamed, Nese Yimaz, Jarbou Bahrawi, and Silvena Boteva. "Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece." Earth Systems and Environment 4, no. 4 (December 2020): 813–26. http://dx.doi.org/10.1007/s41748-020-00195-1.
Повний текст джерелаАнотація:
AbstractForest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.
15
Putra, Raden, Tastaptyani K. Nufutomo, Yuni Lisafitri, Novi K. Sari, Alfian Zurfi, Deni O. Lestari, and Muhammad U. Nuha. "Rapid Land Cover Change in The South Sumatera Peat Area Associated With 2015 Peat Fires." Journal of Geoscience, Engineering, Environment, and Technology 7, no. 1 (March 30, 2022): 34–38. http://dx.doi.org/10.25299/jgeet.2022.7.1.6395.
Повний текст джерелаАнотація:
The peat fire events in Indonesia, particularly the South Sumatra area, changed the appearance of surface vegetation. The fires usually occur during the dry season from July to October. This study aims to evaluate land cover changes due to 2015’s peat fire in the South Sumatra peatlands. Remote sensing techniques using a Normalized Difference Vegetation Index (NDVI) method were used to identify the change of vegetation density in the study area. The results showed that 69% of the total South Sumatra peatland was burned due to the 2015 peat fire event. The level of vegetation density was considerably decreased by fire events. The degradation in the burned area was dominated by land cover class of ferns/shrub. The Peat fires during the observation period have a negative impact on the peat ecosystem, so improvements are needed in peatland management practices. Improvements need to be made in fire prevention and management practices, as well as restoration of burnt land.
16
Vu, Minh Thanh, and Hien Thi Thu Le. "Utilisation of GIS and remote sensing for forest fire risk zone mapping at Tram Chim National Park." Science and Technology Development Journal 18, no. 4 (December 30, 2015): 221–35. http://dx.doi.org/10.32508/stdj.v18i4.952.
Повний текст джерелаАнотація:
Identification of areas of high fire risk is extremely important task in fire prevention and fire fighting. This study focuses on utilizing GIS and remote sensing to predict highest forest fire risk zones at Tram Chim National Park. Forest fire risk index was calculated based on forest-fire causing factors. The factors consist of landcover density and types, distance to water and settlements, surface temperature and leaf wetness index. And then, two forest fire risk maps were completed, one of them represented the fire risk in the rainy season in 2013, the other performed the fire risk in the dry season 2014. High fire risk zones locate mostly at the edge of the park where the bionass is rich and are near settlements. According to this fire risk computing, in the rainy season, area of high fire risk zone was 1,014.65 ha, about 14 % natural areas of Tram Chim National Park. In additional, in the dry season, high forest fire risk zones was 3,344.65 ha, and there is no safety zone. Results of the research contribute to the forest protecting at Tram Chim National Park and over the country.
17
Oliveras, Imma, Marc Gracia, Gerard Moré, and Javier Retana. "Factors influencing the pattern of fire severities in a large wildfire under extreme meteorological conditions in the Mediterranean basin." International Journal of Wildland Fire 18, no. 7 (2009): 755. http://dx.doi.org/10.1071/wf08070.
Повний текст джерелаАнотація:
In Mediterranean ecosystems, large fires frequently burn under extreme meteorological conditions, but they are usually characterized by a spatial heterogeneity of burn severities. The way in which such mixed-severity fires are a result of fuels, topography and weather remains poorly understood. We computed fire severity of a large wildfire that occurred in Catalonia, Spain, as the difference between the post- and pre-fire Normalized Difference Vegetation Index (NDVI) values obtained through Landsat images. Fuel and topographic variables were derived from remote sensing, and fire behavior variables were obtained from an exhaustive reconstruction of the fire. Results showed that fire severity had a negative relationship with percentage of canopy cover, i.e. green surviving plots were mainly those with more forested conditions. Of the topographic variables, only aspect had a significant effect on fire severity, with higher values in southern than in northern slopes. Fire severity was higher in head than in flank and back fires. The interaction of these two variables was significant, with differences between southern and northern aspects being small for head fires, but increasing in flank and back fires. The role of these variables in determining the pattern of fire severities is of primary importance for interpreting the current landscapes and for establishing effective fire prevention and extinction policies.
18
Du, Xiaomin, Dongqi Sun, Feng Li, and Jing Tong. "A Study on the Propagation Trend of Underground Coal Fires Based on Night-Time Thermal Infrared Remote Sensing Technology." Sustainability 14, no. 22 (November 9, 2022): 14741. http://dx.doi.org/10.3390/su142214741.
Повний текст джерелаАнотація:
Underground coal fires in coal fields endanger the mine surface ecological environment, endanger coal resources, threaten mine safety and workers’ health, and cause geological disasters. The study of methods by which to monitor the laws that determine the way underground coal fires spread is helpful in the safe production of coal and the smooth execution of fire extinguishing projects. Based on night-time ASTER thermal infrared images of 2002, 2003, 2005 and 2007 in Huangbaici and Wuhushan mining areas in the Wuda coalfield, an adaptive-edge-threshold algorithm was used to extract time-series for underground coal fire areas. A method of time-series dynamic analysis for geometric centers of underground coal fire areas was proposed to study the propagation law and development trend of underground coal fires. The results indicate that, due to the effective prevention of the external influences of solar irradiance, topographic relief and land cover, the identification accuracy of coal fires via the use of a night-time ASTER thermal infrared image was higher by 7.70%, 13.19% and 14.51% than that of the daytime Landsat thermal infrared image in terms of producer accuracy, user accuracy and overall accuracy, respectively. The propagation direction of the geometric center of the time-series coal fire areas can be used to represent the propagation direction of underground coal fires. There exists a linear regression relationship between the migration distance of the geometric center of coal fire areas and the variable-area of coal fires in adjacent years, with the correlation coefficient reaching 0.705, which indicates that the migration distance of the geometric center of a coal fire area can be used to represent the intensity variation of underground coal fires. This method can be applied to the analysis of the trends of underground coal fires under both natural conditions and human intervention. The experimental results show that the Wuda underground coal fires spread to the southeast and that the area of the coal fires increased by 0.71 km2 during the period of 2002–2003. From 2003 to 2005, Wuda’s underground coal fires spread to the northwest under natural conditions, and the area of coal fires decreased by 0.30 km2 due to the closure of some small coal mines. From 2005 to 2007, due to increased mining activities, underground coal fires in Wuda spread to the east, south, west and north, and the area of coal fires increased dramatically by 1.76 km2.
19
Zatserkovnyi, V., P. Savkov, I. Pampukha, and К. Vasetska. "APPLICATION OF GIS AND REMOTE SENSING OF THE EARTH FOR THE FOREST FIRE MONITORING." Visnyk Taras Shevchenko National University of Kyiv. Military-Special Sciences, no. 2 (44) (2020): 54–58. http://dx.doi.org/10.17721/1728-2217.2020.44.54-58.
Повний текст джерелаАнотація:
The paper considers the problems of the forest industry, namely forest fires. Emphasis is placed on the suffering of theforests of Ukraine from large-scale fires. The main factors in reducing forest areas are forest fires. Despite the constantimplementation of preventive and precautionary fire-fighting measures, fires affect large areas of forests, which places a heavyburden on the country's budget. In addition to direct detection of fires, assessment of their power and development forecast, theurgent task is to monitor the parameters of fires: area, perimeter of the edge and radiation power of the fire, damage,quantification of vegetation changes and more. The ability to determine the areas burned during large forest fires, allows you tomake an inventory of the post-fire condition of forests. An important task of both economic and strategic nature is the study ofdynamic changes and the state of forests. Highly informative observations from artificial satellites of the Earth make it possible to quickly and objectively assess the reserves of forest resources and investigate changes in them: fires, damage assessments,reforestation in fires and deforestation, clarification of estimates of forest damage by diseases and pests, fires, identification ofcutting activities for the purpose of further control of their legality, solution of inventory problems, assessment of forest cover ofterritories, mapping of forest cover of areas and breed structure of forests. This allows to take timely measures for the rat ionaluse of forest resources and prevent damage.
20
KAGIYA, Koji, and Toshio OJIMA. "RESEARCH FOR EVALUATION OF VEGETATION ON FIRE PREVENTION IN SAFETY EVACUATION AREA USING REMOTE SENSING." Journal of Architecture and Planning (Transactions of AIJ) 62, no. 498 (1997): 89–94. http://dx.doi.org/10.3130/aija.62.89_3.
Повний текст джерела
21
Bisquert, M. M., J. M. Sánchez, and V. Caselles. "Fire danger estimation from MODIS Enhanced Vegetation Index data: application to Galicia region (north-west Spain)." International Journal of Wildland Fire 20, no. 3 (2011): 465. http://dx.doi.org/10.1071/wf10002.
Повний текст джерелаАнотація:
Galicia, in north-west Spain, is a region especially affected by devastating forest fires. The development of a fire danger prediction model adapted to this particular region is required. In this paper, we focus on changes in the condition of vegetation as an indicator of fire danger. The potential of the Enhanced Vegetation Index (EVI) together with period-of-year to monitor vegetation changes in Galicia is shown. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Terra satellite, was chosen for this study. A 6-year dataset of EVI images, from the product MOD13Q1 (16-day composites), together with fire data in a 10 × 10-km grid basis, were used. Logistic regression was used to assess the relationship between the percentage of fire activity and EVI variations together with period-of-year. The results show the ability of the model obtained to discriminate different levels of fire occurrence danger, with an estimation error of ~5%. This remote sensing technique may contribute to improving the efficiency of the currently used fire prevention systems.
22
Wu, H., and K. Fu. "A MANAGEMENT OF REMOTE SENSING BIG DATA BASE ON STANDARD METADATA FILE AND DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 653–57. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-653-2020.
Повний текст джерелаАнотація:
Abstract. As a kind of information carrier which is high capacity, remarkable reliability, easy to obtain and the other features，remote sensing image data is widely used in the fields of natural resources survey, monitoring, planning, disaster prevention and the others (Huang, Jie, et al, 2008). Considering about the daily application scenario for the remote sensing image in professional departments, the demand of usage and management of remote sensing big data is about to be analysed in this paper.In this paper, by combining professional department scenario, the application of remote sensing image analysis of remote sensing data in the use and management of professional department requirements, on the premise of respect the habits, is put forward to remote sensing image metadata standard for reference index, based on remote sensing image files and database management system, large data serialization of time management methods, the method to the realization of the design the metadata standard products, as well as to the standard of metadata content indexed storage of massive remote sensing image database management.
23
Vasilakos, Christos, Kostas Kalabokidis, John Hatzopoulos, George Kallos, and Yiannis Matsinos. "Integrating new methods and tools in fire danger rating." International Journal of Wildland Fire 16, no. 3 (2007): 306. http://dx.doi.org/10.1071/wf05091.
Повний текст джерелаАнотація:
Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.
24
Luz, Andréa Eliza O., Rogério G. Negri, Klécia G. Massi, Marilaine Colnago, Erivaldo A. Silva, and Wallace Casaca. "Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection." Remote Sensing 14, no. 10 (May 18, 2022): 2429. http://dx.doi.org/10.3390/rs14102429.
Повний текст джерелаАнотація:
The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.
25
Janiec, Piotr, and Sébastien Gadal. "A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia." Remote Sensing 12, no. 24 (December 18, 2020): 4157. http://dx.doi.org/10.3390/rs12244157.
Повний текст джерелаАнотація:
The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling. The random forest method showed better results in the macroscale, however, the maximum entropy model was better in the microscale. The exclusion of variables that do not show a high correlation, does not always improve the modeling results. The random forest presence prediction model is a more accurate method and significantly reduces the risk territory. The reverse is the method of maximum entropy, which is not as accurate and classifies very large areas as endangered. Further study of this topic requires a clearer and conceptually developed approach to the application of remote sensing data. Therefore, this work makes sense to lay the foundations of the future, which is a completely automated fire risk assessment application in the Republic of Sakha. The results can be used in fire prophylactics and planning fire prevention. In the future, to determine the risk well, it is necessary to combine the obtained maps with the seasonal risk determined using indices (for example, the Nesterov index 1949) and the periodic dynamics of forest fires, which Isaev and Utkin studied in 1963. Such actions can help to build an application, with which it will be possible to determine the risk of wildfire and the spread of fire during extreme events.
26
Heisig, Johannes, Edward Olson, and Edzer Pebesma. "Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing." Fire 5, no. 1 (February 20, 2022): 29. http://dx.doi.org/10.3390/fire5010029.
Повний текст джерелаАнотація:
Climate change causes more extreme droughts and heat waves in Central Europe, affecting vegetative fuels and altering the local fire regime. Wildfire is projected to expand into the temperate zone, a region traditionally not concerned by fire. To mitigate this new threat, local forest management will require spatial fire hazard information. We present a holistic and comprehensible workflow for quantifying fuels and wildfire hazard through fire spread simulations. Surface and canopy fuels characteristics were sampled in a small managed temperate forest in Northern Germany. Custom fuel models were created for each dominant species (Pinus sylvestris, Fagus sylvatica, and Quercus rubra). Canopy cover, canopy height, and crown base height were directly derived from airborne LiDAR point clouds. Surface fuel types and crown bulk density (CBD) were predicted using random forest and ridge regression, respectively. Modeling was supported by 119 predictors extracted from LiDAR, Sentinel-1, and Sentinel-2 data. We simulated fire spread from random ignitions, considering eight environmental scenarios to calculate fire behavior and hazard. Fuel type classification scored an overall accuracy of 0.971 (Kappa = 0.967), whereas CBD regression performed notably weaker (RMSE = 0.069; R2 = 0.73). Higher fire hazard was identified for strong winds, low fuel moisture, and on slopes. Fires burned fastest and most frequently on slopes in large homogeneous pine stands. These should be the focus of preventive management actions.
27
Devadevan V. and Suresh Sankaranarayanan. "Forest Fire Information System Using Wireless Sensor Network." International Journal of Agricultural and Environmental Information Systems 8, no. 3 (July 2017): 52–67. http://dx.doi.org/10.4018/ijaeis.2017070104.
Повний текст джерелаАнотація:
Forest fire is the most common hazard which is a great threat to the ecosystem. Remote Sensing and GIS are widely used for forest fire detection. Wireless Sensor Network (WSN) is an emerging technology which is used to monitor environmental parameters towards alerting forest department officers for prevention or control. In this research, the authors developed Forest Fire Information System (FFIS) that provides interface to monitor, assess and analyze the forest fire data emanating from WSN which is a part of Intelligent Forest Fire Detection System. The information system also maintains necessary details of forest fire incidents that can be used for analysis and report generation. It also has a Decision Support System (DSS) integrated into it that can be used by forest officials for strategic planning. This has been developed using PHP and MySQL. This paper is an extension of research work carried about Intelligent Forest Fire Detection System using WSN.
28
Singh, Priya P., Chandra S. Sabnani, and Vijay S. Kapse. "Hotspot Analysis of Structure Fires in Urban Agglomeration: A Case of Nagpur City, India." Fire 4, no. 3 (July 21, 2021): 38. http://dx.doi.org/10.3390/fire4030038.
Повний текст джерелаАнотація:
Fire Service is the fundamental civic service to protect citizens from irrecoverable, heavy losses of lives and property. Hotspot analysis of structure fires is essential to estimate people and property at risk. Hotspot analysis for the peak period of last decade, using a GIS-based spatial analyst and statistical techniques through the Kernel Density Estimation (KDE) and Getis-Ord Gi* with Inverse Distance Weighted (IDW) interpolation is performed, revealing fire risk zones at the city ward micro level. Using remote sensing, outputs of hotspot analysis are integrated with the built environment of Land Use Land Cover (LULC) to quantify the accurate built-up areas and population density of identified fire risk zones. KDE delineates 34 wards as hotspots, while Getis-Ord Gi* delineates 17 wards within the KDE hotspot, the central core areas having the highest built-up and population density. A temporal analysis reveals the maximum fires on Thursday during the hot afternoon hours from 12 noon to 5 p.m. The study outputs help decision makers for effective fire prevention and protection by deploying immediate resource allocations and proactive planning reassuring sustainable urban development. Furthermore, updating the requirement of the National Disaster Management Authority (NDMA) to build urban resilient infrastructure in accord with the Smart City Mission.
29
Kretinin, A., and Tatyana Bezrukova. "DIGITALIZATION OF FOREST PROTECTION MANAGEMENT BASED ON FOREST FIRE MONITORING." Actual directions of scientific researches of the XXI century: theory and practice 10, no. 1 (April 13, 2022): 139–52. http://dx.doi.org/10.34220/2308-8877-2022-10-1-139-152.
Повний текст джерелаАнотація:
The article analyzes measures to improve the effectiveness of management of forest protection from fires on the basis of information technology. The purpose of this article is to review and analyze the existing system of management of forest protection from fires, identifying the main management problems and forming recommendations to improve the existing system on the basis of digital technology. The relevance of the study lies in the fact that in recent years there has been a large number of forest fires, which brings irreparable damage to the Russian economy, so the question of the effectiveness of today's forest fire prevention system in our country is urgent. Innovative tools to improve the fight against forest fires are digital technologies. As part of the study of measures to improve the effectiveness of forest fire protection management clarified the conceptual apparatus, considered the concept of "digital technology", "forest fire". Based on the analysis of forest fire management, measures based on digital technology, which will improve the fire situation in the territory of forestry in the Russian Federation. Fighting forest fires is a dangerous job, which requires instant decisions based on immediately available information. The main digital tools used for fire protection are drones, laser scanning remote sensing of forests. The use of these tools will make it possible to quickly and smoothly determine the onset of forest fires, their area and determine on this basis the activities of fire departments and forestry organizations sequence of actions to eliminate forest fires. The more accurate real-time data firefighters have, such as fire location and condition, the location of hazards, and the number of forests covered by fire, the greater the probability of saving forests and the animals that live in their territory, ensuring the safety of firefighters and limiting fire damage. Consequently, digital technology, when fighting a forest fire, is the main and most important tool, and its implementation will reduce the risk of fire and damage from it.
30
Tian, Yuping, Zechuan Wu, Shaojie Bian, Xiaodi Zhang, Bin Wang, and Mingze Li. "Study on Spatial-Distribution Characteristics Based on Fire-Spot Data in Northern China." Sustainability 14, no. 11 (June 4, 2022): 6872. http://dx.doi.org/10.3390/su14116872.
Повний текст джерелаАнотація:
Forest fires are an important disturbance in forest ecosystems and can affect the structure and function of forests. These must be mitigated, to eliminate the associated harmful impacts on forests and the environment as well as to have a healthy and sustainable environment for wildlife. The northern region of China (Heilongjiang, Jilin, Liaoning, and Hebei provinces) is one of the important deciduous broadleaf forests and boreal-forest ecosystems in China. Based on the monitoring of historical remote-sensing products, this study analyzes and explores the spatial- and temporal-distribution patterns of forest fires in Northern China in 2020 and 2021, providing a strong scientific basis for forest-fire prevention and management. The number of monthly forest fires in the northern region in 2020 and 2021 was counted, to obtain seasonal and interannual forest-fire variation. The results show that the number of forest fires occurring in Heilongjiang, Jilin, and Liaoning provinces in 2021 is smaller than that in 2020. The occurrence of forest fires is, mainly, concentrated in spring and autumn, especially in April and October. The number of forest fires that occurred in Hebei Province in 2020 and 2021 was almost the same, showing a slight increasing trend, especially with more growth in February. It is worth noting that Heilongjiang Province is the region with the highest number of forest fires, regardless of the comparison of the total number of forest fires in two years or the number of forest fires in a single year. Spatial-clustering analysis (Ripley’s K) was used to analyze the spatial-distribution pattern of forest fires, in each province of northern China, and the results showed that forest fires were significantly aggregated in all four provinces. The experimental analysis conducted in this paper can provide local forest managers and firefighting agencies with the opportunity to better plan for fighting fires and improve forest-management effectiveness. Based on mastering the characteristics of the spatial and temporal dynamics of forest fires, fire-prevention publicity and education should be strengthened, and scientific forest-fire-prevention measures should be applied to plan reasonable forest-protection policies. This will contribute towards a healthy and sustainable environment.
31
Fernández-Alonso, José María, Rafael Llorens, José Antonio Sobrino, Ana Daría Ruiz-González, Juan Gabriel Alvarez-González, José Antonio Vega, and Cristina Fernández. "Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain." Remote Sensing 14, no. 23 (November 30, 2022): 6063. http://dx.doi.org/10.3390/rs14236063.
Повний текст джерелаАнотація:
The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities.
32
Novais, Danilo Brito, Patrícia Carneiro Souto, Jacob Silva Souto, and José Augusto Da Silva Santana. "TEMPORARY SERIES OF HEAT SOURCES IN MESOREGIONS OF PARAÍBA, BRAZIL." FLORESTA 49, no. 2 (March 29, 2019): 181. http://dx.doi.org/10.5380/rf.v49i2.55112.
Повний текст джерелаАнотація:
The management of fire is a set of decisions directed to practices of prevention, verification and control of this agent that can modify the landscape. Remote sensing helps in understanding the phenomena that occur on the earth's surface. This work had the objective of analyzing the occurrences of heat sources recorded by satellites in the State of Paraíba (2000 – 2015). The occurrences of heat sources were selected by months of the year making it possible to visualize the frequency of heat sources in different locations. It is observable that the years with the highest number of heat sources in Paraíba were 2003, 2004 and 2009. In the six municipalities studied, 3.712 heat sources were recorded, with Cajazeiras municipality having the highest number of records (2.253 occurrences). It is concluded that the highest occurrence of heat sources inthe studied regions runs from September to December, where the Sertão Paraibano mesoregion was the one with the highest incidence of heat sources, being the most affected by fire. It is recommended, thus,the implementation of awareness programs that inform the citizens of rural and urban areas about the importance of adequate fire management in order to reduce heat sources in the region.
33
Alves, Raynon Joel Monteiro, Wanderson Gonçalves Gonçalves, Janaina Pinheiro Gonçalves, Stefani Silva Raulino, and Frank Bruno Baima de Sousa. "Spatial analysis of heat sources in Pará state - Brazil." Research, Society and Development 9, no. 11 (November 29, 2020): e66491110387. http://dx.doi.org/10.33448/rsd-v9i11.10387.
Повний текст джерелаАнотація:
The state of Pará has experienced a high occurrence of forest fires, which were strongly influenced by the El Niño phenomenon in 2015-2016. This study aims to analyze the conditions of heat sources in Pará, using the 2016 monthly bulletins provided by the Environment and Sustainability Secretariat. This data was analyzed through descriptive statistics. The IDW interpolation method was used to construct the density map, displaying the primary areas of concern. The results showed that the greatest detections were during the Amazonian summer, occurring during the second half of the year. Specifically, the municipalities of the Southwestern and Southeastern Para meso-regions were mainly the ones affected. Fire is used as a primary economic tool. The Southwest was the one region that presented the highest densities of hotspots. Although the results do not indicate the actual configuration of the events, because of the technical limitations of remote sensing, the information obtained in this study communicates ideas concerning prevention and action in the most affected areas. In loco studies are needed to determine precisely the causes of these occurrences.
34
Moya, Daniel, Giacomo Certini, and Peter Z. Fulé. "Fire regime and ecosystem responses: adaptive forest management in a changing world (Part 2)." International Journal of Wildland Fire 28, no. 7 (2019): 471. http://dx.doi.org/10.1071/wfv28n7_fo.
Повний текст джерелаАнотація:
Fire is an ecological factor in ecosystems around the world, made increasingly more critical by unprecedented shifts in climate and human population pressure. The knowledge gradually acquired on the subject is needed to improve fire behaviour understanding and to enhance fire management decision-making. This issue (Volume 28, issue 7, International Journal of Wildland Fire) is Part 2 of a special issue aimed at synthesising ongoing research on preventive management and post-fire restoration, including characterisation of the wildland–urban interface (WUI) and assessing the post-fire restoration of wilderness and WUI areas. Landscape management was also investigated using remote sensing techniques and simulation modelling to improve ecosystem resilience. As in Part 1 (Volume 28, issue 5, International Journal of Wildland Fire), the current issue covers diverse forest settings under scenarios of changing climate and land use. The broad geographical range of these studies highlights key similarities of wildfire issues around the world, but detailed data show unique local circumstances that must be considered. The new information from these six papers helps advance fire ecology and management during a period of rapid change.
35
Trenčanová, Bianka, Vânia Proença, and Alexandre Bernardino. "Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes." Remote Sensing 14, no. 5 (March 4, 2022): 1262. http://dx.doi.org/10.3390/rs14051262.
Повний текст джерелаАнотація:
In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land planning and the management for wildfire prevention. Here, we propose methods to simplify and automate the segmentation of shrub cover in high-resolution RGB images acquired by UAVs. The main contribution is a systematic exploration of the best practices to train a convolutional neural network (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes. Several semantic segmentation models were trained and tested in partitions of the provided data with alternative methods of data augmentation, patch cropping, rescaling and hyperparameter tuning (the number of filters, dropout rate and batch size). The most effective practices were data augmentation, patch cropping and rescaling. The developed classification model achieved an average F1 score of 0.72 on three separate test datasets even though it was trained on a relatively small training dataset. This study demonstrates the ability of state-of-the-art CNNs to map fine-grained land cover patterns from RGB remote sensing data. Because model performance is affected by the quality of data and labeling, an optimal selection of pre-processing practices is a requisite to improve the results.
36
Guo, Kai, Wei Wang, Shixiang Tian, Juntao Yang, Zebiao Jiang, and Zhangyin Dai. "Research on Optimization Technology of Cross-Regional Synergistic Deployment of Fire Stations Based on Fire Risk." Sustainability 14, no. 23 (November 25, 2022): 15725. http://dx.doi.org/10.3390/su142315725.
Повний текст джерелаАнотація:
Regional planning and development of urban agglomerations such as the Beijing-Tianjin-Hebei Region, the Yangtze River Delta, the Guangdong-Hong Kong-Macao Greater Bay Area and the Chengdu-Chongqing Twin Cities provide a good opportunity for fire rescue across administrative regions. This study is aimed at investigating the optimization technology of cross-regional synergistic deployment of fire stations. To achieve this aim, with the Yangtze River Delta integrated demonstration zone taken as the research object, urban fire risk was assessed by means of range standardization, iterative equations and expert scoring and weighting on the basis of population density, road density, water source distribution and urban POI data and urban remote sensing images. Besides, different fire response times were set with reference to the classified regional fire risk levels. Furthermore, the status of fire stations was evaluated based on the coverage-maximized model, and the cross-regional synergistic deployment of fire stations was optimized based on the facility point-minimized model. Finally, the deployment was tested using the maximized coverage rate. The following results were obtained: High-risk regions are mainly distributed in areas with dense population and high-rise buildings. The fire station coverage rates of single administrative regions are all lower than 80%; in contrast, 31 more regions are covered under cross-regional synergistic deployment. Based on the facility point minimization model and the maximum coverage model, on the basis of retaining the existing fire stations, when 17 new fire stations are built, 90% of the high-risk fire areas in the study area can be covered within 3 min, and the coverage of medium-risk areas and low-risk areas can be increased to 70%, which can better meet the fire risk prevention and control needs of the Yangtze River Delta integrated demonstration area.
37
Laneve, Giovanni, Lorenzo Fusilli, Guido Bernini, and Juan Suarez Beltran. "Preventing Forest Fires Through Remote Sensing: Achievements of the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area Project." IEEE Geoscience and Remote Sensing Magazine 8, no. 3 (September 2020): 37–49. http://dx.doi.org/10.1109/mgrs.2019.2906948.
Повний текст джерела
38
Lasaponara, Rosa, Antonio Lanorte, and Stefano Pignatti. "Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data." Earth Interactions 10, no. 13 (May 1, 2006): 1–11. http://dx.doi.org/10.1175/ei165.1.
Повний текст джерелаАнотація:
Abstract The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; 2) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; and 3) accuracy assessment for the performance evaluation based on the comparison of MIVIS-based results with ground truth. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.
39
Rodríguez-Puerta, Francisco, Rafael Alonso Ponce, Fernando Pérez-Rodríguez, Beatriz Águeda, Saray Martín-García, Raquel Martínez-Rodrigo, and Iñigo Lizarralde. "Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images." Drones 4, no. 2 (June 11, 2020): 21. http://dx.doi.org/10.3390/drones4020021.
Повний текст джерелаАнотація:
Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.
40
Akbulak, Cengiz, Hasan Tatlı, Gurcu Aygün, and Bülent Sağlam. "Forest fire risk analysis via integration of GIS, RS and AHP: The Case of Çanakkale, Turkey." Journal of Human Sciences 15, no. 4 (November 16, 2018): 2127. http://dx.doi.org/10.14687/jhs.v15i4.5491.
Повний текст джерелаАнотація:
Forest fire is one of the high-risk natural disasters in the north-western Anatolia section of Turkey. This paper suggests a new approach based on Geographic Information Systems (GIS), Remote Sensing (RS) and Analytical Hierarchy Process (AHP) for the development of forest fire-risk model. The proposed approach includes human factors as well as environmental factors. In this context, the 12 variables defined under anthropogenic and physical factors in the proposed model are the slope, elevation, aspect, vegetation type, crown closure, Normalized Difference Vegetation Index (NDVI), distance to road, settlement, and agricultural areas, population density, previous fires, and Canadian Forest Fire Weather Index (FWI). For each variable, a layer was created in the GIS database environment. GIS-layers were classified, considering the risk of potentially generating forest-fire of the relevant variables. In addition, to generate risk maps, the weights used in these GIS-layers were obtained by applying the AHP technique. One of the major results of the study shows that the rates of “extreme”, “very high”, “high”, and “moderate” risk areas are 3.87%, 63.46%, 32.13% and 0.53%, respectively. Another important result is that there are not observed the so called “no risk" and "low risk" classes in the region. The results let us to make a conclusion that the natural and human factors having significant contributions the region to be fire-prone. Yet, these results also indicate that rather than emphasizing forest-fire preparedness and mitigation, policy-makers manage forest-fires through reactive, crisis-oriented approaches. In contrast to crisis-based management plans, this study suggests that risk-based preventive plans should be developed and implemented.
41
Che, Xinyue, Ke Diao, and Kangzhe Zhou. "Evaluation of environmental recovery and vulnerability in the Mohe area by using mathematical modeling and remote sensing techniques." E3S Web of Conferences 308 (2021): 02003. http://dx.doi.org/10.1051/e3sconf/202130802003.
Повний текст джерелаАнотація:
In the Greater Khingan Range, wildfires in forests were frequent and severe. The wildfire in the Greater Khingan Range in 1987 was one of the severest wildfires in human history, and the study is primarily based on this natural disaster. Mohe is a representative region in the Greater Khingan Range field related to wildfire cases. Many indicators affect the relationship between wildfire and forests, such as topography, climate change, and human behaviors. This paper used remote sensing techniques, the AHP model, and the entropy model to study the environmental fragility of forests in the region of Mohe. Present paper used NDVI images from 1987, 1992, 1997, 2002 to detect the vegetation coverage change in this area and found out its potential problems that need to be paid attention to. NDVI images in the paper showed that the vegetation coverage in the region of Mohe was generally low. Therefore, the results indicated that it is necessary to make prevention and conservation in the region of Mohe. By collecting dem images and data from fire yearbooks within these years, the paper summarized seven indicators: vegetation coverage, number of fires, area of damaged forest, number of injured people, slope, altitude, and temperature. Then the paper used the AHP model to calculate the ratio of each indicator affecting wildfire and scored on indicators to observe the quality of the environment under different indicators. AHP tables in the paper showed that the influence of slope and altitude were weak on a wildfire in this region because their scores were constant. Forest quality in 1987 was relatively low, and the trend dramatically increased after this year; however, it decreased again from 1997 to 2002. Besides the AHP model, the paper also provided an entropy model by using the same parameters. Compared to the AHP model, the entropy model was more objective. Although its scores were all higher than the AHP model, the trends of the two models were similar.
42
Pradeep, G. S., Nilanchal Patel, Sekhar L. Kuriakose, R. S. Ajin, Valeria-Ersilia Oniga, A. Rajaneesh, Pratheesh C. Mammen, Megha K. Prasad, S. Nikhil, and Jean Homian Danumah. "Forest Fire Risk Zone Mapping of Eravikulam National Park in India." Croatian journal of forest engineering 43, no. 1 (November 24, 2021): 199–217. http://dx.doi.org/10.5552/crojfe.2022.1137.
Повний текст джерелаАнотація:
Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.
43
Pio, Fernanda Paula Bicalho, and Eliane Maria Vieira. "Determinação das Áreas Atingidas por Queimadas em Bacias Hidrográficas por meio do Índice De Queimada (NBR), Estudo de Caso da Bacia do Rio Piracicaba-MG." Revista Brasileira de Geografia Física 13, no. 1 (February 29, 2020): 087. http://dx.doi.org/10.26848/rbgf.v13.1.p087-101.
Повний текст джерелаАнотація:
O desenvolvimento da humanidade e consequente alteração no uso do solo vem tornando áreas cada vez mais susceptíveis à incêndios florestais, fato agravado pela prática de queimadas. Eventos de queima são considerados preocupantes devido a abrangência dos impactos que influenciam, inclusive nas mudanças climáticas. Assim, técnicas de sensoriamento remoto podem ser empregadas para identificação e espacialização de áreas queimadas. A gravidade dos impactos gerados torna visível a importância de estudos capazes de detectar cicatrizes de queimadas a fim contribuir com o desenvolvimento de técnicas de monitoramento, conscientização, prevenção e recuperação de áreas afetadas. Assim, o presente estudo objetivou gerar o índice de queimada (NBR) bem como sua variação (NBR) e o Índice Normalizado de Diferença de Água (NDWI) para distinção entre áreas queimadas e áreas úmidas para a região da bacia hidrográfica do rio Piracicaba, Minas Gerais, utilizando cenas Landsat 8 órbita/ponto 217/074 e 218/074 dos meses de agosto e setembro de 2016. A eficiência do método para detecção de áreas queimadas foi analisada a partir da comparação com polígonos de queimadas do banco de dados do INPE e pontos de ocorrência de incêndio fornecidos pelo 4º Pelotão de Bombeiros Militar de Itabira. Os resultados da NBR foram classificados em níveis de severidade e mostraram-se eficientes para a detecção de áreas queimadas quando comparados com polígonos de registro de queimadas do INPE. Com o cálculo do índice para toda a bacia pôde-se perceber regiões com maior ocorrência das classes moderada e alta severidade nas porções noroeste e nordeste da bacia. Determination of Areas Affected by Burns in Watersheds by the Queimada Index (Nbr), Case Study of the Piracicaba-MG River Basin ABSTRACTThe development of mankind and consequent alteration in land use has made areas increasingly susceptible to forest fires, a fact aggravated by the practice of burning. Burning events are considered worrisome due to the extent of the impacts that influence, including climate change. Thus, remote sensing techniques can be used to identify and spatialize burned areas. The severity of the impacts generated makes visible the importance of studies capable of detecting burn scars in order to contribute to the development of monitoring techniques, awareness, prevention and recovery of affected areas. Thus, the present study aimed to generate the burn rate (NBR) as well as its variation (ΔNBR) and the Normalized Water Difference Index (NDWI) to distinguish between burned areas and wetlands for the Piracicaba river basin region, Minas Gerais, using Landsat 8 scenes orbit / point 217/074 and 218/074 from the months of August and September 2016. The efficiency of the method for the detection of burned areas was analyzed from the comparison with burned polygons of the INPE and fire occurrence points provided by the 4th Itabira Military Fire Squad. The results of ΔNBR were classified in severity levels and were efficient for the detection of burned areas when compared to INPE burn logs. With the calculation of the index for the whole basin it was possible to perceive regions with higher occurrence of the moderate and high severity classes in the northwest and northeast portions of the basin.Keywords: burned, sevirity of fire, remote sensing, burn rate
44
Ahmad, Firoz, and Laxmi Goparaju. "Analysis of forest fire and climate variability using Geospatial Technology for the State of Telangana, India." Environmental & Socio-economic Studies 7, no. 1 (March 1, 2019): 24–37. http://dx.doi.org/10.2478/environ-2019-0003.
Повний текст джерелаАнотація:
Abstract The dynamic changes in the regimes of forest fires are due to the severity of climate and weather factors. The aim of the study was to examine the trend of forest fires and to evaluate their relationship with climate parameters for the state of Telangana in India. The climate and forest fire data were used and uploaded to the GIS platform in a specified vector grid (spacing: 0.3° x 0.3°). The data were evaluated spatially and statistical methods were applied to examine any relationships. The study revealed that there was a 78% incidence of forest fires in the months of February and March. The overall forest fire hotspot analysis (January to June) of grids revealed that the seven highest forest fire grids retain fire events greater than 600 were found in the north east of Warangal, east of Khammam and south east of Mahbubnagar districts. The forest fire analysis significantly followed the month wise pattern in grid format. Ten grids (in count) showed a fire frequency greater than 240 in the month of March and of these, three grids (in count) were found to be common where the forest fire frequency was highest in the preceding month. Rapid seasonal climate/weather changes were observed which significantly enhanced the forest fire events in the month of February onwards. The solar radiation increased to 159% in the month of March when compared with the preceding month whereas the relative humidity decreased to 47% in the same month. Furthermore, the wind velocity was found to be highest (3.5 meter/sec.) in the month of February and precipitation was found to be lowest (2.9 mm) in the same month. The analysis of Cramer V coefficient (CVC) values for wind velocity, maximum temperature, solar radiation, relative humidity and precipitation with respect to fire incidence were found to be in increasing order and were in the range of 0.280 to 0.715. The CVC value for precipitation was found to be highest and equivalent to 0.715 and showed its strongest association/relationship with fire events. The significant increase in precipitation not only enhances the moisture in the soil but also in the dry fuel load lying on the forest floor which greatly reduces the fuel burning capacity of the forest. The predicted (2050) temperature anomalies data (RCP-6) for the month of February and March also showed a significant increase in temperature over those areas where forest fire events are found to be notably high in the present scenario which will certainly impact adversely on the future forest fire regime. Findings from this study have their own significance because such analyses/relationships have never be examined at the state level, therefore, it can help to fulfill the knowledge gap for the scientific community and the state forest department, and support fire prevention and control activities. There is a need to replicate this study in future by taking more climate variables which will certainly give a better understanding of forest fire events and their relationships with various parameters. The satellite remote sensing data and GIS have a strong potential to analyze various thematic datasets and in the visualization of spatial/temporal paradigms and thus significantly support the policy making framework.
45
Cao, Duanguang, Hanfa Xing, Man Sing Wong, Mei-Po Kwan, Huaqiao Xing, and Yuan Meng. "A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images." Remote Sensing 13, no. 19 (September 29, 2021): 3898. http://dx.doi.org/10.3390/rs13193898.
Повний текст джерелаАнотація:
Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.
46
Tian, X. M., D. Liu, S. L. Fu, D. C. Wu, B. X. Wang, Z. Wang, and Y. Wang. "CHARACTERIZATION OF SPRING AIR POLLUTION OF BEIJING IN 2019 USING ACTIVE AND PASSIVE REMOTE SENSING INSTRUMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 153–58. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-153-2019.
Повний текст джерелаАнотація:
Abstract. As the capital of China, the Beijing area needs to be paid special attention to its air quality. We used active remote sensing instrument (ground-based lidar), combined with passive remote sensing instrument (VIIRS onboard the NPP spacecraft), to study the serious pollution event over Beijing in spring of 2019. At the same time, the ground-based particulate matter (PM) data and the meteorological element data were analyzed. It is found that the ratio of concentrations of PM2.5 to PM10 is very large during the pollution period. The mean value of ratio is 0.75464, indicated it is fine particulate matter pollution. The Range correction signal (RCS) of lidar is very large in the layer below 0.5 km. But the volume depolarization ratio (VDR) is much less than 0.05. It indicated it is anthropogenic urban aerosols. The change in the aerosol optical depth (AOD) of VIIRS during pollution is consistent with the change in optical properties observed by lidar. The backward trajectory model of HYSPLIT shows that the pollutant came from the Hebei area where industrial pollution is serious, and the local meteorological conditions in Beijing are conducive to the continuous accumulation of pollutants. This work can deepen the understanding of the mechanism of haze formation and can help and support pollution prevention work.
47
Strunk, Jacob, Petteri Packalen, Peter Gould, Demetrios Gatziolis, Caleb Maki, Hans-Erik Andersen, and Robert J. McGaughey. "Large Area Forest Yield Estimation with Pushbroom Digital Aerial Photogrammetry." Forests 10, no. 5 (May 7, 2019): 397. http://dx.doi.org/10.3390/f10050397.
Повний текст джерелаАнотація:
Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.
48
Berdengalieva, Asel. "Analysis of the lower Volga floodplain landscapes burning according to active fire and burnt areas satellite data." InterCarto. InterGIS 28, no. 1 (2022): 346–58. http://dx.doi.org/10.35595/2414-9179-2022-1-28-346-358.
Повний текст джерелаАнотація:
Landscape fires have significantly intensified in the last two decades. A lot of research is devoted to forest fires, while much less attention is paid to the analysis of the burning of non-forest lands. The floodplain landscapes of the arid zone, which include the Volga-Akhtuba floodplain with the Volga delta, are practically not covered by studies of the fire regime. The aim of the work is to establish the spatio-temporal regularities of the burning of the floodplain landscapes of the Volga in its lower reaches according to the detection of active burning and burnt areas based on Earth remote sensing technologies. The work used MCD14ML (FIRMS), MCD64A1, FireCCI51 and GABAM data for 2001–2020, the first three of which are based on MODIS data, the last one is based on Landsat data. Each of the products has both omissions and false definitions and artifacts. Nevertheless, a joint analysis of all the data makes it possible to obtain a fairly reliable assessment of the flammability. In total, during the study period, the total area of burned areas ranged from 2.9 million hectares to 4.8 million, according to estimates of different products. The average long-term flammability is 9.2 % of the study area. The maximum burning rate was noted in 2019 (15.4 %), and the minimum—in 2016 (2.8 %). These years are characterized by the shortest and longest duration of periods of maximum flood flow, respectively. The influence of the hydrological situation on the burning of floodplain landscapes is confirmed by the correlation analysis. A significant correlation between the fire rate and the flood level and its duration has been established. The higher the maximum flood level of the floodplain and the longer the water stays on the floodplain, the lower the flammability. This is especially pronounced in the Volga delta, where, due to the reduction in flood costs and the drop in the level of the Caspian Sea, the drying of floodplain landscapes is intensifying. This leads to the intensification of fires. In addition to the hydrological situation, the weather affects the flammability. At the same time, the main climatic factor is atmospheric moisture. The more rainfall, the less fire. The air temperature does not affect the size of the burnt area, since the temperatures remain quite high throughout the entire warm period. Trends in hydrological changes are aimed at reducing the water content of the flood, which will lead to the drying of the floodplain against the backdrop of a continuing drop in the level of the Caspian Sea. With the existing system of fire prevention, we should expect a further increase in the burning of landscapes.
49
Sulova, Andrea, and Jamal Jokar Arsanjani. "Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine." Remote Sensing 13, no. 1 (December 22, 2020): 10. http://dx.doi.org/10.3390/rs13010010.
Повний текст джерелаАнотація:
Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.
50
Marchenko, T. A., A. I. Radin, and A. N. Razdaivodin. "Retrospective and current state of forest territories of the border areas of the Bryansk region exposed to radioactive contamination." Radiatsionnaya Gygiena = Radiation Hygiene 13, no. 2 (June 25, 2020): 6–18. http://dx.doi.org/10.21514/1998-426x-2020-13-2-6-18.
Повний текст джерелаАнотація:
The aim of the study is to analyze the accumulated data on the study of forest territories of the border regions of the Bryansk region that have been exposed to radioactive contamination for their involvement in economic activity, as well as the possible transfer of radioactive materials in forest fires. The area of recorded and unaccounted forests was estimated according to the “Forest Plan of the Bryansk Region for the period 2019-2028” and the results of the analysis of remote sensing data of the earth, the assessment of radiation pollution – according to the radiation surveys of the forest fund and radioecological monitoring of forests, assessment of cesium-137 content – according to radiation monitoring. In the course of the work, the dynamics of the transition of forests from the range of a high level of radioactive contamination to lower ones from 1991 to 2018 slightly changes the total area of contaminated forests by regions was revealed. Top-level values of cesium-137 content in the main types of forest combustible materials, which a dangerous factor is contributing to a significant increase in the content of radiocesium in atmospheric air and the transfer of radionuclides beyond the limits of radioactive contamination zones in a forest fire. The most radiation-hazardous is the forest litter, which contains more than 70% of the total cesium-137 reserve in forest combustible materials, the values of which reach values of 224 kBq / kg in the Krasnogorsk district of the Bryansk region. The obtained forecast of cesium-137 content in the forest litter by the zones of radioactive contamination of forests in the most polluted areas of the Bryansk region for the period up to 2046 indicates the preservation of a high degree of radioactive contamination of forests in the Krasnogorsk and Novozybkovsky districts after more than 60 years after the Chernobyl accident power plants. Due to the high class of natural fire hazard of forests in the south-west of the Bryansk region and the high risk of fires in contaminated areas, it is necessary to assess the degree of danger in the prevention and suppression of radioactive forest fires, especially criterion of the absorbed dose for workers in order to avoid the deterministic effect.