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Location of the 108 Landsat 5 TM and Landsat 7 SLC-on ETM+ scenes used for validation. The location of the scenes was selected by trained interpreters, with the involvement of the GOFC GOLD regional networks, and all the reference data were generated via visual interpretation according to the CEOS Cal/Val Protocol. The Landsat acquisition dates range from 2000 to 2011.
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The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board NASA's Terra and Aqua satellites have provided nearly two decades of global fire data. Here, we describe refinements made to the 500-m global burned area mapping algorithm that were implemented in late 2016 as part of the MODIS Collection 6 (C6) land-product reproces...
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... were detected on the same day of an active fire, and 68% within 2 days, which represents a substantial reduction in temporal uncertainty Confusion matrix and accuracy metrics for the MCD64A1 Collection 6 and Collection 5.1 and MCD45A1 Collection 5.1 products, considering all the in- dependent reference data derived from 108 Landsat path/rows (Fig. 8). Accu- racy metrics, which are described in the main text, consist of overall accuracy (OA), omission error (OE), commission error (CE), producer's accuracy (PA), user's accuracy (UA), and relative bias (B rel ...
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... Preliminary global validation A preliminary validation using a globally distributed independent reference data set ( Fig. 8) consisting of 108 Landsat scenes visually interpreted into burned, unburned, and unmapped classes was under- taken. Landsat 5 Thematic Mapper (TM) images acquired from 2000 to 2010, and Landsat 7 Enhanced Mapper Plus (ETM+) images sensed from 2000 to 2003 before the ETM+ scan line corrector failure (Markham et al., 2004), were used. ...
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African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data...
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... Recent data indicate that forest fires now result in an average of three million more hectares of tree cover loss per year compared to 2001, accounting for over one-quarter of all tree cover loss over the past two decades [1]. Annually, an average of more than 420 million hectares (Mha) of forest are burned globally [2,3], and the frequency and intensity of these fires have been increasing in recent times [4,5]. The escalation in both the frequency and intensity of these fires is alarming, with climate change, and particularly global warming, playing a significant role [6,7]. ...
Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.
... However, the role of wildfires as a critical factor in carbon sequestration/sources is often overlooked. To mitigate climate change and fully understand the carbon exchange mechanisms between terrestrial ecosystems and the atmosphere, it is essential to consider the impacts of wildfire 40 CO2 emissions on the Earth system (Chuvieco et al., 2019;Giglio et al., 2018;Kasischke et al., 1995;McGuire et al., 2001;Zhang et al., 2013). The significant differences in global wildfire CO2 emissions among countries highlight the complexity of wildfire CO2 emissions. ...
... The burned area data for wildfires with a spatial resolution of 500 m were sourced from MODIS-MCD64A1 burned area product (Giglio et al., 2018). The vegetation cover data were sourced from the China Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC), with a spatial resolution of 30 m (Xu et al., 2018). ...
Wildfires release large amounts of greenhouse gases into the atmosphere, exacerbating climate change and causing severe impacts on air quality and human health. Including carbon dioxide (CO2) emissions from wildfires in international assessments and national emission reduction responsibilities is crucial for global carbon reduction and environmental governance. In this study, based on a bottom-up approach and using satellite data, combined with emission factor and aboveground biomass data for different vegetation cover types (forest, shrub, grassland, cropland), the dynamic changes in CO2 emissions from wildfires in China from 2001 to 2022 were analyzed. The results showed that between 2001 and 2022, the total CO2 emissions from wildfires in China were 693.7 Tg (1 Tg = 1012 g), with an annual average of 31.5 Tg. The CO2 emissions from cropland and forest fires were relatively high, accounting for 46 % and 32 %, respectively. The yearly variation in CO2 emissions from forest and shrub fires showed a significant downward trend, while emissions from grassland fires remained relatively stable. In contrast, the CO2 emissions from cropland fires showed a clear upward trend. High CO2 emissions from wildfires were mainly concentrated in the eastern regions of Heilongjiang and Inner Mongolia Provinces in China, accounting for 44 % of the total annual emissions. Various factors such as daily cumulative sunshine hours (Spearman’s correlation coefficient, forest: -0.41, shrub:0.25; p < 0.001) and the normalized difference vegetation index (NDVI; Spearman’s correlation coefficient, forest: -0.35, shrub: 0.37; p < 0.001), influenced CO2 emissions from forest and shrub fires. Moreover, temperature (Spearman’s correlation coefficient, -0.45, p < 0.001) primarily affected CO2 emissions from grassland fires. The CO2 emissions from cropland fires negatively correlated with the gross domestic product (GDP) (Spearman’s correlation coefficient, -0.52, p < 0.001) and population density (Spearman’s correlation coefficient, -0.51, p < 0.001). China's policy management has been crucial in reducing CO2 emissions from wildfires. By accurately assessing CO2 emissions from wildfires, governments worldwide can better set CO2 reduction targets, take corresponding measures, and contribute to the global response to climate change.
... Дополнительная верификация проводилась на основе информационных продуктов детектирования выгоревших площадей по данным спутниковой системы MODIS FireCCI51 разрешения 250 м [13], MCD64A1 разрешения 500 м [14] и по данным Landsat разрешения 30 м [15]. Ни один из указанных информационных продуктов, как и их совокупность, не позволяют картографировать гари с необходимой точностью (рис. ...
Регулярные ландшафтные пожары приводят к существенным изменениям состояния ландшафтов, поэтому картографирование выгоревших площадей, определение длительностей пирогенных сукцессий и межпожарных интервалов являются важным этапом исследований экосистем. В данном исследовании выполнено картографирование выгоревших площадей на территории Терско-Кумской низменности в Северо-Западном Прикаспии за 2001-2020 гг. на основе спутниковых данных Landsat и различных информационных продуктов детектирования тепловых аномалий и гарей. Всего идентифицировано более 13 тыс. гарей. Установлено значимое снижение горимости как зональных (Ногайская степь), так и интразональных ландшафтов дельтовой и приморской частей территории исследований вследствие ухудшения условий увлажнения. Продолжающееся падение Каспийского моря сопровождается небольшим увеличением горимости прибрежных ландшафтов из-за их обсыхания и увеличения площадей, занятых высокопродуктивной околоводной растительностью. Полученные результаты позволят в дальнейшем оценить влияние пирогенного воздействия на экосистемы, а также оценить эффективность проводимой в регионе противопожарной профилактики.
... gov) (Zhang et al. 2021). We combined the monthly global 500 m grid product with 1 km of MODIS active fire observations to enhance the spatial analysis of the MCD64A1 Version 6 Burned Area data product (Giglio et al. 2018). This product facilitates the identification of per-pixel burned areas, detecting thermal anomalies and fire locations at a moderate resolution (Katagis and Gitas 2022). ...
Vegetation fires have major impacts on the ecosystem and present a significant threat to human life. Vegetation fires consists of forest fires, cropland fires, and other vegetation fires in this study. Currently, there is a limited amount of research on the long-term prediction of vegetation fires in Pakistan. The exact effect of every factor on the frequency of vegetation fires remains unclear when using standard analysis. This research utilized the high proficiency of machine learning algorithms to combine data from several sources, including the MODIS Global Fire Atlas dataset, topographic, climatic conditions, and different vegetation types acquired between 2001 and 2022. We tested many algorithms and ultimately chose four models for formal data processing. Their selection was based on their performance metrics, such as accuracy, computational efficiency, and preliminary test results. The model’s logistic regression, a random forest, a support vector machine, and an eXtreme Gradient Boosting were used to identify and select the nine key factors of forest and cropland fires and, in the case of other vegetation, seven key factors that cause a fire in Pakistan. The findings indicated that the vegetation fire prediction models achieved prediction accuracies ranging from 78.7 to 87.5% for forest fires, 70.4 to 84.0% for cropland fires, and 66.6 to 83.1% for other vegetation. Additionally, the area under the curve (AUC) values ranged from 83.6 to 93.4% in forest fires, 72.6 to 90.6% in cropland fires, and 74.2 to 90.7% in other vegetation. The random forest model had the highest accuracy rate of 87.5% in forest fires, 84.0% in cropland fires, and 83.1% in other vegetation and also the highest AUC value of 93.4% in forest fires, 90.6% in cropland fires, and 90.7% in other vegetation, proving to be the most optimal performance model. The models provided predictive insights into specific conditions and regional susceptibilities to fire occurrences, adding significant value beyond the initial MODIS detection data. The maps generated to analyze Pakistan’s vegetation fire risk showed the geographical distribution of areas with high, moderate, and low vegetation fire risks, highlighting predictive risk assessments rather than historical fire detections.
... Two data sets, FireCCISFD20 and FIRECCIS310, are part of the European Space Agency's Climate Change Initiative Program, utilizing surface reflectance data from Copernicus Sentinel-2 (FireCCISFD20 or S2-product: Chuvieco et al., 2022) and Sentinel-3 (FIRECCIS310 or S3-product: Lizundia-Loiola et al., 2022) satellites at 20 and 300 m resolutions, respectively. The third data set, MCD64A1C6 (Giglio et al., 2018), is based on Moderate Resolution Imaging Spectroradiometer (MODIS) data at 500 m resolution (M-product). The reported burned area data for all three data sets is provided in Table S3 in Supporting Information S1 for all of Africa and for southern Africa, and further subdivided for three major aggregated ecosystems based on MODIS MCD12Q1C6 landcover (Friedl & Sulla-Menashe, 2019). ...
Various fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate.
... The development and application of sustainable fire management approaches in fireprone savanna landscapes poses recognised environmental, ecologic and attendant socioeconomic challenges. Savanna fires, especially from tropical biomes in northern and southern hemisphere Africa, account for the vast majority of contemporary global burnt area and fire carbon emissions, lit mostly by people under fairly severe late dry season conditions for an array of agricultural and subsistence purposes (van der Werf et al. 2017;Giglio et al. 2018;Ramo et al. 2021). Building on extensive Australian experience with the undertaking of Kyoto-compliant, commercial 'savanna burning' greenhouse gas (GHG) emissions abatement projects, particularly by reducing the extent and severity of late dry season (LDS) fires through prescribed early dry season (EDS) management (Russell-Smith et al. 2013a;Edwards et al. 2021), it has been suggested that a similar approach could provide tangible environmental and societal benefits in comparable savanna landscape settings elsewhere (ISFMI (International Savanna Fire Management Initiative) 2015; Lipsett-Moore et al. 2018;Moura et al. 2019;Russell-Smith et al. 2013bTear et al. 2021). ...
... Selection of plot locations was based initially on time-since-last-burnt mapping derived from 5 years of Landsat 30 m fire history mapping and, for analysis purposes as presented here, ultimately checked with reference to 10 years prior manual Landsat (30 m pixel) fire mapping following procedures described by Evans and Russell-Smith (2019). The Collection 6 MODIS 500 m burnt area data (after Giglio et al. 2018) were used for identifying potential sites for EDS 2022 fieldwork in northern Zambia, followed by checking from the Landsat archive following plot establishment. It is well recognised that the MODIS 500 m product significantly under-represents (by at least 50%) fire extent in African contexts, especially burnt patches <100 ha (Roteta et al. 2019;Ramo et al. 2021). ...
Background and aims. To assess development of a robust emissions accounting framework for expansive miombo woodland savannas covering ~2 million km 2 of southern Africa that typically are burnt under relatively severe late dry season (LDS) conditions. Methods. A detailed site-based study of fuel accumulation, combustion and greenhouse gas (GHG) emission factor parameters under early dry season (EDS) and LDS conditions along a central rainfall-productivity and associated miombo vegetation structural and floristics gradient, from lower rainfallsites in northern Botswana to higher rainfall sites in northern Zambia. Key results. Assembled field data inform core components of the proposed emissions reduction framework: fuel and combustion conditions sampled across the vegetation/productivity gradient can be represented by three defined Vegetation Fuel Types (VFTs); fuel accumulation, combustion and emissions parameters are presented for these. Applying this framework for an illustrative case, GHG emissions (t CO 2-e) from EDS fires were one-third to half those of LDS fires per unit area in eligible miombo VFTs. Conclusions. Our accounting framework supports undertaking EDS fire management to significantly reduce emissions and, realistically, burnt extent at landscape scales. We consider application of presented data to development of formal emissions abatement accounting methods, linkages with potential complementary woody biomass and soil organic carbon sequestration approaches, and necessary caveats concerning implementation issues.
... Wildfire studies tend to use either fire power (from MODIS or VIIRS) or burned-area-based datasets to quantify fire severity. Burned area is determined by MODIS from a time series of the burn-sensitive vegetation index, which compares daily surface reflectances (Giglio et al., 2018). Fire power is the radiated energy from fires over time, and MODIS determines this quantity by comparing the brightness temperature of a fire pixel with the background brightness temperature (Peterson et al., 2013). ...
Wildfires in the southwestern United States, particularly in northern California (nCA), have grown in size and severity in the past decade. As they have grown larger, they have been associated with large emissions of absorbing aerosols and heat into the troposphere. Utilizing satellite observations from MODIS, CERES, and AIRS as well as reanalysis from MERRA-2, the meteorology associated with fires during the wildfire season (June–October) was discerned over the nCA-NV (northern California and Nevada) region during the period 2003–2022. Wildfires in the region have a higher probability of occurring on days of positive temperature (T) anomalies and negative relative humidity (RH) anomalies, making it difficult to discern the radiative effects of aerosols that are concurrent with fires. To attempt to better isolate the effects of large fire emissions on meteorological variables, such as clouds and precipitation, variable anomalies on high fire emission days (90th percentile) were compared with low fire emission days (10th percentile) and were further stratified based on whether surface relative humidity (RHs) was anomalously high (75th percentile) or low (25th percentile) compared with typical fire season conditions. Comparing the simultaneously high fire emission and high RHs data with the simultaneously low fire emission and high RHs data, positive tropospheric T anomalies were found to be concurrent with positive AOD anomalies. Further investigation found that due to shortwave absorption, the aerosols heat the atmosphere at a rate of 0.041 ± 0.016 to 0.093 ± 0.019 K d−1, depending on whether RH conditions are anomalously positive or negative. The positive T anomalies were associated with significant negative 850–300 hPa RH anomalies during both 75th percentile RHs conditions. Furthermore, high fire emission days under high RHs conditions are associated with negative CF anomalies that are concurrent with the negative RH anomalies. This negative CF anomaly is associated with a significantly negative regional precipitation anomaly and a positive net top-of-atmosphere radiative flux anomaly (a warming effect) in certain areas. The T, RH, and CF anomalies under the simultaneously high fire emission and high RHs conditions compared with the simultaneously low fire emission and high RHs conditions have a significant spatial correlation with AOD anomalies. Additionally, the vertical profile of these variables under the same stratification is consistent with positive black carbon mass mixing ratio anomalies from MERRA-2. However, causality is difficult to discern, and further study is warranted to determine to what extent the aerosols are contributing to these anomalies.
... Remote sensing has emerged as a practical alternative for detecting fire occurrences, burned areas, and burn severity, as it provides timely regional and global coverage . A number of remote sensing products such as MODIS MOD/MYD14 and MCD64 (Giglio et al. 2018), Fire CCI (Chuvieco et al. 2018), and GFED (van der Werf et al. 2017) offer continuous spatial and temporal information of active fire and burned area at coarse spatial resolutions. These products encounter difficulties in detecting small and highly fragmented fires due to their pixel size (Roteta et al. 2019). ...
Information on the spatiotemporal distribution of human-caused wildfires on a national scale is crucial for developing susceptibility map and facilitating decision-making in fire risk management. In China, it is difficult to access a national dataset of wildfires based on ground records at a fine spatiotemporal scale for understanding fire regimes. This study explores the use of judgment documents as a potential alternative long-term key data source, considering human-caused wildfire events as criminal offense in certain cases. Judgment documents are readily accessible, regularly updated, and provide a diverse range of reliable information. To extract information on human-caused wildfires, we develop a rule-based approach for judgment documents. The results indicate the feasibility of the method, allowing for the effective extraction of specific information of event such as the start date and time, location, burn area, and ignition cause. A comparison reveals limited consistency in the spatiotemporal distributions between the novel data and conventional datasets. Notably, judgment documents provide richer attribute information and identify additional human-caused wildfires challenging to capture using conventional datasets. Despite inherent limitations, information sourced from judgment documents proves valuable data for characterizing national patterns of wildfire occurrence at a fine scale.
... Remote sensing involves the collection of information about an object, area, or phenomenon without direct physical contact (Giglio et al., 2018). It relies on sensors mounted on various platforms, such as satellites, airplanes, drones, or ground-based instruments to capture electromagnetic radiation reflected or emitted from the Earth's surface. ...
This book serves as a comprehensive guide to navigating the intricacies of modern agriculture, offering a wealth of knowledge and insights aimed at fostering sustainability and resilience in farming practices. From precision agriculture to biotechnology, each chapter delves into key aspects of agricultural innovation, providing practical strategies for optimizing crop management, enhancing soil health, and adapting to changing environmental conditions. Through a holistic approach that encompasses soil-centric practices, integrated pest management, and climate-smart agriculture, farmers are empowered to cultivate resilient agroecosystems that thrive in the face of uncertainty.
At the core of this exploration lies the concept of precision agriculture, which revolutionizes crop management by leveraging technology and data to optimize resource use and maximize yields. Through the integration of satellite imagery, soil sensors, and advanced analytics, farmers gain unprecedented insights into crop behavior, allowing for informed decision-making and targeted interventions. Meanwhile, biotechnology emerges as a powerful tool for engineering crop traits that promote sustainability, from drought tolerance to pest resistance. By harnessing the potential of genetic modification and gene editing, farmers can cultivate crops that are better adapted to their environment while reducing reliance on chemical inputs.
Furthermore, the book emphasizes the importance of soil health management as a foundational pillar of sustainable agriculture. By adopting practices that enhance soil fertility, promote microbial diversity, and mitigate erosion, farmers can ensure the long-term productivity and resilience of their land. This holistic approach extends beyond the field, encompassing post-harvest technologies, organic farming practices, remote sensing applications, and water management innovations. Together, these strategies offer a roadmap for cultivating a more sustainable and resilient agricultural future, where farmers can thrive in harmony with the environment while meeting the challenges of a changing world.
... To analyse the impact of the Black Summer bushfires on the LAI and albedo, we use version 6.1 of MODIS Global Burned Area Product (MCD64A1) product at a 500-m spatial resolution to identify burned areas and the date the fire commenced at each location ( Fig. 1b- Boschetti et al., 2019;Giglio et al., 2018). This dataset provides similar spatial and temporal patterns of the Black Summer bushfires burned regions as the Fire Extent and Severity Mapping product (FESM, https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severit ...
