Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at "being held" (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

Smart Forest Fire Prediction Market Outlook

According to our latest research, the global Smart Forest Fire Prediction market size reached USD 1.98 billion in 2024, driven by the rapid adoption of advanced technologies for wildfire management and prevention. The market is expected to grow at a CAGR of 18.4% during the forecast period, with the market size projected to reach USD 9.26 billion by 2033. This robust growth is primarily fueled by increasing incidences of forest fires globally, heightened environmental awareness, and government initiatives aimed at minimizing wildfire-related losses.

One of the principal growth drivers for the Smart Forest Fire Prediction market is the escalating frequency and severity of forest fires worldwide, attributed to climate change and human activities. The rising global temperatures and prolonged drought conditions have made forests more susceptible to wildfires, necessitating the deployment of advanced predictive technologies. Governments and environmental agencies are increasingly investing in smart solutions that leverage machine learning, IoT, and remote sensing to detect, predict, and manage forest fires proactively. The integration of these technologies not only enhances early warning capabilities but also significantly reduces response times, minimizing damage to biodiversity and human settlements.

Another significant factor propelling the market is the advancement in data analytics and artificial intelligence, which are revolutionizing how forest fire risks are assessed and managed. The ability of AI-powered platforms to analyze vast datasets from satellite imagery, weather stations, and ground sensors allows for more accurate forecasts and real-time monitoring. These predictive systems enable authorities to allocate resources more efficiently and implement targeted mitigation strategies. Moreover, the increasing availability of funding for research and development in this domain is fostering innovation, leading to the introduction of more sophisticated and user-friendly solutions tailored to the unique needs of different regions and end-users.

Furthermore, the collaborative efforts between public and private sectors are catalyzing market growth. Partnerships between technology providers, forestry departments, and research institutes are facilitating the deployment of integrated fire management systems. These collaborations are instrumental in overcoming technical and operational challenges, such as interoperability of devices and standardization of data formats. Additionally, the growing trend of smart city initiatives and the integration of forest fire prediction systems into broader disaster management frameworks are unlocking new opportunities for market expansion. As the technology matures and becomes more cost-effective, adoption is expected to surge across both developed and developing regions.

From a regional perspective, North America currently dominates the Smart Forest Fire Prediction market, accounting for the largest share in 2024 due to the high incidence of wildfires in the United States and Canada, coupled with substantial investments in advanced fire management technologies. Europe and the Asia Pacific regions are also witnessing significant growth, driven by increasing awareness and government-led initiatives. The Asia Pacific region, in particular, is projected to exhibit the fastest CAGR over the forecast period, supported by rising adoption in countries like Australia, India, and China. Meanwhile, Latin America and the Middle East & Africa are gradually embracing smart forest fire prediction solutions, although market penetration remains relatively lower due to budget constraints and limited technological infrastructure.

Component Analysis

The Component segment of the Smart Forest Fire Prediction market is categorized into Hardware, Software, and Services. The hardware segment comprises sensors, cameras, drones, and othe

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.

ABSTRACT Despite the existence of different fire danger indices, the use of an inefficient index can lead to making wrong decisions on the appropriate procedures for preventing and fighting forest fires, while a trusted prediction index can help the most quantification and allocation of resources for prevention. Thereat, the objective of this study is to analyze the efficiency of Fire Weather Index (FWI), Logarithmic of Telicyn Index, Nesterov Index, cumulative indexes of precipitation - evaporation (P-EVAP) and evaporation / precipitation (EVAP/P), Monte Alegre Index (FMA) and Monte Alegre Changed Index (FMA+) in the prediction of forest fires for the city of Viçosa (MG). The indices were compared using the method known as Skill Score (SS) taking into account the days that the indexes pointed to the risk of events with focus fire identified by satellite images on the 01/01/2005 to 31/12/2014 period. According to the results, the Logarithm of Telicyn Index (0.53257) as the most efficient for the study area, followed by the indices EVAP/P (0.46553), P-EVAP (0.43724), Nesterov (0.40445), FWI (0.39213), FMA+(0.34595) and FMA (0.28982).

Forest Fire Prediction Sensor Grid Market Outlook

According to our latest research, the global Forest Fire Prediction Sensor Grid market size reached USD 1.28 billion in 2024, reflecting the rapid adoption of advanced sensor technologies for wildfire management. The market is projected to grow at a robust CAGR of 17.4% from 2025 to 2033, with the total market size expected to reach USD 5.09 billion by 2033. This sustained growth is driven by the increasing frequency and severity of forest fires worldwide, which has intensified the demand for early detection, real-time monitoring, and predictive analytics solutions to mitigate environmental and economic losses.

The primary growth factor for the Forest Fire Prediction Sensor Grid market is the alarming rise in wildfire incidents attributed to climate change, extended droughts, and deforestation. These factors have compelled governments, forestry management authorities, and environmental agencies to invest heavily in advanced sensor grids capable of providing timely alerts and actionable insights. The integration of IoT, AI, and remote sensing technologies into sensor grids has significantly improved the accuracy and speed of fire detection, making them indispensable tools for modern wildfire management strategies. Furthermore, the growing awareness among stakeholders regarding the ecological, economic, and human costs of forest fires continues to drive investments in innovative prediction and monitoring solutions.

Another significant growth driver is the rapid technological advancement in sensor hardware and data analytics platforms. The evolution of low-power, high-sensitivity sensors, coupled with robust communication modules and sophisticated AI-based analytics, has enabled the deployment of scalable and highly responsive sensor grids. These systems can now cover vast forested areas, offering real-time data transmission and predictive modeling capabilities that are crucial for early intervention. The proliferation of satellite-based monitoring and cloud-based analytics platforms further enhances the reach and efficiency of these sensor grids, enabling seamless integration with regional and national disaster management networks.

The market is also benefiting from supportive government policies and international collaborations aimed at strengthening wildfire preparedness and response. Many countries have launched strategic initiatives and funding programs to accelerate the deployment of forest fire prediction sensor grids, particularly in fire-prone regions. Public-private partnerships are fostering innovation, while cross-border collaborations facilitate the sharing of best practices and technological advancements. This ecosystem of support is catalyzing market growth, encouraging the development of next-generation sensor grids that leverage AI, machine learning, and remote sensing for more accurate and timely forest fire prediction.

Regionally, North America and Europe are leading the adoption of forest fire prediction sensor grids due to their advanced technological infrastructure and high incidence of wildfires. The Asia Pacific region is emerging as a significant growth market, driven by increasing wildfire occurrences in countries such as Australia, Indonesia, and India. Latin America and the Middle East & Africa are also witnessing steady adoption, supported by international aid and local government initiatives. The regional outlook for the market remains highly positive, with each region tailoring its adoption strategies to address unique environmental challenges and regulatory frameworks.

Component Analysis

The component segment of the Forest Fire Prediction Sensor Grid market encompasses sensors, communication modules, control units, software & analytics, and other auxiliary components. Sensors form the backbone of these systems, with advancements in temperature, smoke, gas, and humidity sensors enhancing detection accuracy and range. The market has witnessed a surge in demand for multi-parameter sensors capable of operating in harsh forest environments, providing reliable data under extreme weather conditions. Manufacturers

Data during wildfire seasons (May 1 - October 31) over the years 2008 - 2012 in the contiguous U.S. used for spatial causal analysis of wildland fire-contributed PM2.5. The two sources of PM2.5 data are monitor data from the EPA’s Air Quality System (AQS) and simulated PM2.5 from the CMAQ model. This dataset is associated with the following publication: Larsen, A., S. Yang, B. Reich, and A. Rappold. A spatial causal analysis of wildland fire-contributed PM2:5 using numerical model output. Annals of Applied Statistics. Institute of Mathematical Statistics, Beachwood, OH, USA, 16(4): 2714-2731, (2022).

The global forest wildfire detection system market is experiencing robust growth, driven by increasing frequency and intensity of wildfires globally, coupled with the urgent need for effective early warning systems. The market, currently valued in the hundreds of millions (a precise figure requires additional data, but considering similar technological markets, a reasonable estimate would place the 2025 market size at $500 million), is projected to experience a significant compound annual growth rate (CAGR) over the forecast period (2025-2033). This expansion is fueled by several key factors: advancements in sensor technology (e.g., thermal imaging, satellite monitoring), the development of sophisticated AI-powered analytics for faster and more accurate detection, and increasing government investments in forest management and conservation efforts. The integration of IoT devices, drones, and cloud computing further enhances the capabilities of these systems, leading to improved monitoring coverage and real-time data analysis. Both software and hardware components contribute significantly to the market's value, with software solutions (including data analytics platforms and alert systems) expected to exhibit faster growth due to their scalability and adaptability. Application-wise, the forest sector currently holds a larger market share compared to park applications, primarily due to the greater extent and severity of wildfire risks in vast forested areas. Despite the significant growth potential, the market faces certain constraints. High initial investment costs for deploying and maintaining these complex systems can be a barrier for some regions and smaller organizations. Furthermore, the effectiveness of these systems relies on reliable infrastructure, including robust communication networks and power supplies, which can be challenging to establish in remote, forested areas. Despite these challenges, the increasing economic and environmental costs associated with wildfires are driving strong demand for advanced detection technologies, leading to continued market expansion. Key players in the market are focusing on developing innovative solutions, expanding their geographical reach, and establishing strategic partnerships to capitalize on this growing demand. Competitive landscape is largely fragmented, with both established technology companies and specialized wildfire detection firms vying for market share. The market is poised for considerable growth, with a particular focus on regions with high wildfire risks and substantial investment in forest conservation.

This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available wildland fire datasets that identify wildfire and prescribed fire areas across the United States. However, these datasets are all limited in some way. Time periods, spatial extents, attributes, and maintenance for these datasets are highly variable, and none of the existing datasets provide a comprehensive picture of wildfires that have burned since the 1800s. Utilizing a series of both manual processes and ArcGIS Python (arcpy) scripts, we merged 40 of these disparate datasets into a single dataset that encompasses the known wildfires within the United States from the 1800s to the present. These datasets were ranked by order of observed quality, and overlapping polygons in the same year were used individually or dissolved together with other polygons based on ranked quality (see individual steps in the polygon metadata for full details). The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.

Smart Forest Fire Prediction Market Outlook

According to our latest research, the global Smart Forest Fire Prediction market size reached USD 1.68 billion in 2024, driven by the growing imperative for advanced forest management and disaster mitigation technologies. The market is poised to expand at a robust CAGR of 19.7% from 2025 to 2033, with the forecasted market size expected to surpass USD 8.17 billion by 2033. This remarkable growth is fueled by rapid technological advancements, increasing climate-related fire risks, and heightened investments in smart environmental monitoring solutions.

The principal growth factor for the Smart Forest Fire Prediction market is the escalating frequency and severity of forest fires globally, attributed largely to climate change and human activities. As wildfires continue to devastate vast areas, causing significant ecological, economic, and human losses, there is a mounting demand for predictive technologies that can provide early warnings and facilitate prompt response. Governments and environmental agencies are increasingly adopting smart solutions—integrating hardware sensors, machine learning algorithms, and satellite imaging—to detect anomalies in forest conditions and predict fire outbreaks with greater accuracy. The ability to leverage real-time data from diverse sources and analyze it for actionable insights is transforming forest fire management strategies worldwide, driving substantial investment in this market.

Another key driver is the ongoing digitization and integration of Internet of Things (IoT) devices in forest ecosystems. IoT-enabled sensors and remote sensing technologies offer continuous, granular monitoring of forest parameters such as temperature, humidity, wind patterns, and smoke levels. These data streams, when combined with advanced analytics and artificial intelligence, empower stakeholders to assess fire risks dynamically and implement preemptive measures. The proliferation of cloud computing and edge processing further enhances the scalability and responsiveness of smart forest fire prediction systems, enabling seamless data sharing and collaboration among government agencies, research institutions, and field personnel.

Furthermore, supportive government policies, international collaborations, and increased funding for climate resilience initiatives are catalyzing the adoption of smart forest fire prediction technologies. Many countries are launching national programs to modernize forest management infrastructure and integrate advanced early warning systems. The involvement of environmental organizations, forestry departments, and research institutes in developing and deploying cutting-edge solutions is accelerating innovation within the sector. Additionally, partnerships with private technology providers are fostering the commercialization of scalable, cost-effective products and services, further propelling market expansion.

From a regional perspective, North America and Europe are leading the global Smart Forest Fire Prediction market, owing to their mature technology ecosystems, high wildfire incidence rates, and strong regulatory frameworks. The Asia Pacific region is witnessing the fastest growth, driven by rising awareness, government initiatives, and increasing investments in smart environmental monitoring. Latin America and the Middle East & Africa are also emerging as important markets, as these regions grapple with growing wildfire threats and seek to enhance their disaster response capabilities. This global momentum underscores the critical role of predictive technologies in safeguarding forests and communities against the escalating risks of wildfires.

Component Analysis

The Smart Forest Fire Prediction market by component is segmented into hardware, software, and services, each playing a distinct yet interdependent role in the overall ecosystem. Hardware components, including environmental sensors, cameras, drones, and satellite receivers, form the backbone of data acquisition infrastructure. These devices are deployed across forested landscapes to capture real-time environmental variables and detect early signs of fire hazards. Advances in sensor miniaturization, energy efficiency, and wireless connectivity have significantly improved the coverage and reliability of hardware installations, enabling continuous monitoring even in remote and inaccessible regions. The demand for robust and scalable hardware solutions is expected to remain strong, as governments and organizations priori

2000-2020 TURKEY WILDFIRE DATA / NASA

IT CONTAINS THE DATA NEEDED TO RESEARCH LATEST FOREST FIRES IN TURKEY.

PAY ATTENTION TO THE DATE INTERVALS.

Content

Data on recent forest fires in Turkey, published with permission from NASA Portal. The data was created based on the hotspots and obtained from the satellite.

3 SEPARATE SATELLITE DATA:

• MODIS C6.1
• SUOMI VIIRS C2
• J1 VIIRS C1

GENERAL ATTRIBUTES

• Latitude Center of nominal 375 m fire pixel

• Longitude Center of nominal 375 m fire pixel

• Bright_ti4 (Brightness temperature I-4) VIIRS I-4: channel brightness temperature of the fire pixel measured in Kelvin.

• Scan (Along Scan pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Track (Along Track pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Acq_Date (Acquisition Date) Date of VIIRS acquisition.

• Acq_Time (Acquisition Time) Time of acquisition/overpass of the satellite (in UTC).

• Satellite N Suomi National Polar-orbiting Partnership (Suomi NPP)

• Confidence This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.

Please note:

Low confidence nighttime pixels occur only over the geographic area extending from 11° E to 110° W and 7° N to 55° S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.

• Version Version identifies the collection (e.g. VIIRS Collection 1) and source of data processing: Near Real-Time (NRT suffix added to collection) or Standard Processing (collection only). "1.0NRT" - Collection 1 NRT processing. "1.0" - Collection 1 Standard processing.

• Bright_ti5 (Brightness temperature I-5) I-5 Channel brightness temperature of the fire pixel measured in Kelvin.

• FRP (Fire Radiative Power) FRP depicts the pixel-integrated fire radiative power in MW (megawatts). Given the unique spatial and spectral resolution of the data, the VIIRS 375 m fire detection algorithm was customized and tuned in order to optimize its response over small fires while balancing the occurrence of false alarms. Frequent saturation of the mid-infrared I4 channel (3.55-3.93 µm) driving the detection of active fires requires additional tests and procedures to avoid pixel classification errors. As a result, sub-pixel fire characterization (e.g., fire radiative power [FRP] retrieval) is only viable across small and/or low-intensity fires. Systematic FRP retrievals are based on a hybrid approach combining 375 and 750 m data. In fact, starting in 2015 the algorithm incorporated additional VIIRS channel M13 (3.973-4.128 µm) 750 m data in both aggregated and unaggregated format.

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

http://cedadocs.ceda.ac.uk/770/1/SEVIRI_FRP_documentdesc.pdf

• Type (Inferred hot spot type)

0 = presumed vegetation fire

1 = active volcano

2 = other static land source

3 = offshore detection (includes all detections over water)

• DayNight (Day or Night)

D= Daytime fire

N= Nighttime fire

annual burned area from MODIS fire dataemerging hotspots analysis on FIRMS fire dataMethods:we used Google use and MODIS Fire data to assess forest fire trends over time.

This is a regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data.

Data Set Information:

In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transform. Four different input setups were used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority.

Attribute Information:

For more information, read [Cortez and Morais, 2007].

1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9
2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9
3. month - month of the year: 'jan' to 'dec'
4. day - day of the week: 'mon' to 'sun'
5. FFMC - FFMC index from the FWI system: 18.7 to 96.20
6. DMC - DMC index from the FWI system: 1.1 to 291.3
7. DC - DC index from the FWI system: 7.9 to 860.6
8. ISI - ISI index from the FWI system: 0.0 to 56.10
9. temp - temperature in Celsius degrees: 2.2 to 33.30
10. RH - relative humidity in %: 15.0 to 100
11. wind - wind speed in km/h: 0.40 to 9.40
12. rain - outside rain in mm/m2 : 0.0 to 6.4
13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform).

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('forest_fires', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Summary: Hotspots and burned areas in forest ecosystemsStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) K: Standard K-ESS3-1 - Earth and Human Activity - Use a model to represent the relationship between the needs of different plants or animals (including humans) and the places they liveGrade level(s) K: Standard K-ESS3-1 - Earth and Human Activity - Use a model to represent the relationship between the needs of different plants or animals (including humans) and the places they liveGrade level(s) 3: Standard 3-LS4-1 - Biological Evolution: Unity and Diversity - Analyze and interpret data from fossils to provide evidence of the organisms and the environments in which they lived long ago.Grade level(s) 6-8: Standard MS-ESS3-2 - Earth and Human Activity - Analyze and interpret data on natural hazards to forecast future catastrophic events and inform the development of technologies to mitigate their effectsGrade level(s) 6-8: Standard MS-ESS3-2 - Earth and Human Activity - Analyze and interpret data on natural hazards to forecast future catastrophic events and inform the development of technologies to mitigate their effectsGrade level(s) 9-12: Standard HS-ESS2-2 - Earth’s Systems - Analyze geoscience data to make the claim that one change to Earth’s surface can create feedbacks that cause changes to other Earth systemsMost frequently used words:firesforestareasburnedalsoApproximate Flesch-Kincaid reading grade level: 9.9. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

Analysis of ‘National USFS Fire Occurrence Point (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7f30c6f5-71eb-4789-b5c7-67dcbc9f0ee3 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The FireOccurrence point layer represents ignition points, or points of origin, from which individual USFS wildland fires started. Data are maintained at the Forest/District level, or their equivalent, to track the occurrence and the origin of individual USFS wildland fires. Forests are working to include historical data, which may be incomplete.

--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Forest Fire Dataset is an image classification dataset consisting of images related to wildfires and smoke. It is designed to serve as visual training material for the development of fire and smoke detection algorithms. The dataset includes two classification labels: 'fire' for wildfire images and 'smoke' for smoke-related images.

2) Data Utilization (1) Characteristics of the Forest Fire Dataset: • The dataset contains images of fires and smoke captured in various environments, making it suitable for the development of early detection and classification systems. • Most of the images are sourced from the wildfire detection dataset released by the University of Science and Technology of China (USTC), and they contain a wide range of visual features reflecting real wildfire scenarios.

(2) Applications of the Forest Fire Dataset: • Development of wildfire and smoke recognition AI models: Can be used to train image-based artificial intelligence models that automatically classify the presence of fire or smoke. • Experiments for disaster response system development: Useful as foundational data for building technologies such as forest surveillance, CCTV video analysis, and real-time alert systems. • Environmental research and climate change applications: Can be used to analyze wildfire occurrence patterns and assess the effectiveness of fire detection algorithms under climate change scenarios.

This data set provides regional-scale estimates of down woody materials (DWM) that contribute to prescribed and wild land fire fuels. DWM is classified into three successive layers: (1) branches and logs (fine and coarse woody material), (2) litter, and (3) duff. Additionally, live and dead understory shrubs and herbs are included with forest floor measurements. Duff includes the dark, partly decomposed organic material (where plant forms are unrecognizable) above mineral soil. On top of duff is litter, which includes recognizable plant parts such as leaves and flowers but not branches. Branches are separated into three size classes of fine woody material (FWM): <6, 6 to 25, and 26 to 76 mm in diameter. These classes correspond to 1-hour, 10-hour, and 100-hour fire fuel classes, respectively. The U.S. Department of Agriculture Forest Inventory and Analysis (FIA) program currently measures variables related to DWM on a Phase 3 (P3) subsample of its Phase 2 (P2) plots. Investigators have used P3 and P2 FIA data to estimate DWM for all plots in the eastern half of the FIA database. Residuals for the separate DWM class estimates are available with the data set, as described in Chojnacky, D.C., Mickler, R.A., Heath, L.S., Woodall, C.W. 2004. Estimates of down woody materials in eastern US forests. Environmental Management 33(Supplement 1): S44-S55. Also see Woodall, C.W.; Heath, L.S.; Smith, J.E. 2008. National inventories of down and dead woody material forest carbon stocks in the United States: Challenges and opportunities. Forest Ecology and Management. 256: 221-228. DWM data are available online. After developing accurate models of down woody materials by fuel class, location, and other forest attributes, investigators will next link the models to PEcon, a species-level forest growth and economics model, to predict down woody materials and fuel loads in response to changes in climate, land use, species composition, and timber markets.

The global forest fire camera market is experiencing robust growth, driven by increasing wildfires globally and the escalating need for effective early detection and prevention systems. The market size in 2025 is estimated at $500 million, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, technological advancements in camera technology, such as improved thermal imaging capabilities and enhanced analytics, provide more accurate and timely detection of fire outbreaks even in challenging conditions like dense forests or at night. Secondly, governments and environmental agencies worldwide are investing heavily in advanced forest fire monitoring and management systems, thereby driving demand for sophisticated fire detection cameras. Thirdly, increasing awareness about the devastating economic and environmental consequences of wildfires is also boosting the adoption of these systems. The market is segmented by camera range (2-3 km, 4-5 km, etc.) and application (forest, garden, orchard, etc.), with the forest application segment dominating due to its high susceptibility to wildfires. The North American region is currently the largest market, followed by Asia Pacific, driven by significant investments in forest fire prevention in countries like the United States and China. Significant restraints to market growth include the high initial investment costs associated with implementing these systems, particularly in remote areas. However, the long-term cost-benefit analysis, including the prevention of catastrophic wildfires, is encouraging adoption. Further challenges involve the maintenance and operational costs, especially in harsh weather conditions and rugged terrain. Despite these challenges, the market is poised for continued expansion driven by technological advancements, government regulations, and rising environmental concerns. The increasing adoption of cloud-based data analytics and AI-powered fire detection algorithms will further enhance the market growth in the coming years. The competitive landscape is characterized by both established players and emerging innovative companies focusing on providing solutions tailored to specific needs and geographical contexts.

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period between 1984 and the current MTBS release. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon of the location of all currently inventoried and mappable MTBS fires occurring between calendar year 1984 and the current MTBS release for the continental United States, Alaska, Hawaii and Puerto Rico. Map Service Feature Layer

LOCAL INVESTIGATIVE WILDFIRE DATA / ANTALYA-MUGLA

IT CONTAINS THE DATA NEEDED TO RESEARCH LATEST FOREST FIRES IN TURKEY.

PAY ATTENTION TO THE DATE INTERVALS.

Content

Data on recent forest fires in Turkey, published with permission from NASA Portal. The data was created based on the hotspots and obtained from the satellite.

3 SEPARATE SATELLITE DATA:

• MODIS C6.1
• SUOMI VIIRS C2
• J1 VIIRS C1

GENERAL ATTRIBUTES

• Latitude Center of nominal 375 m fire pixel

• Longitude Center of nominal 375 m fire pixel

• Bright_ti4 (Brightness temperature I-4) VIIRS I-4: channel brightness temperature of the fire pixel measured in Kelvin.

• Scan (Along Scan pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Track (Along Track pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Acq_Date (Acquisition Date) Date of VIIRS acquisition.

• Acq_Time (Acquisition Time) Time of acquisition/overpass of the satellite (in UTC).

• Satellite N Suomi National Polar-orbiting Partnership (Suomi NPP)

• Confidence This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.

Please note:

Low confidence nighttime pixels occur only over the geographic area extending from 11° E to 110° W and 7° N to 55° S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.

• Version Version identifies the collection (e.g. VIIRS Collection 1) and source of data processing: Near Real-Time (NRT suffix added to collection) or Standard Processing (collection only). "1.0NRT" - Collection 1 NRT processing.

"1.0" - Collection 1 Standard processing.

• Bright_ti5 (Brightness temperature I-5) I-5 Channel brightness temperature of the fire pixel measured in Kelvin.

• FRP (Fire Radiative Power) FRP depicts the pixel-integrated fire radiative power in MW (megawatts). Given the unique spatial and spectral resolution of the data, the VIIRS 375 m fire detection algorithm was customized and tuned in order to optimize its response over small fires while balancing the occurrence of false alarms. Frequent saturation of the mid-infrared I4 channel (3.55-3.93 µm) driving the detection of active fires requires additional tests and procedures to avoid pixel classification errors. As a result, sub-pixel fire characterization (e.g., fire radiative power [FRP] retrieval) is only viable across small and/or low-intensity fires. Systematic FRP retrievals are based on a hybrid approach combining 375 and 750 m data. In fact, starting in 2015 the algorithm incorporated additional VIIRS channel M13 (3.973-4.128 µm) 750 m data in both aggregated and unaggregated format.

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

http://cedadocs.ceda.ac.uk/770/1/SEVIRI_FRP_documentdesc.pdf

• Type (Inferred hot spot type)

0 = presumed vegetation fire

1 = active volcano

2 = other static land source

3 = offshore detection (includes all detections over water)

• DayNight (Day or Night)

D= Daytime fire

N= Nighttime fire