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Eastern China agricultural area (111–123° E, 27–40° N), outlined by the white dashed box. The approximate area of the North China Plain and the Yangtze Plain are demarcated by the solid white boxes. The red circles depict numbers of MODIS active fire pixels detected between 2002 and 2015 per 1° grid cell (see legend lower right). Whilst most fires in the study region are agricultural fires, those towards the north of the wider region include forest fires. Yellow markers show locations of the data of Figs. 4, 5, 6, 8 and 9 (see legend upper right). Yellow outline shows the footprint of the VIIRS swath taken during the 85 s VIIRS SDR used to produce Fig. 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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We demonstrate a new active fire (AF) detection and characterisation approach for use with the VIIRS spaceborne sensor. This includes for the first-time joint exploitation of both 375 m I-Band and 750 m M-Band data to provide both AF detections and FRP (fire radiative power) retrievals over the full range of fire and FRP magnitudes. We demonstrate...
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... (1)), OBEs quickly resumed on 19 September owing to the dryness of moderately slow-drying fuels (DMC) and high fuel availability (BUI), highlighting the critical role of drought in facilitating overnight burning. However, non-OBEs can still occur when DMC and BUI were high and unaffected (see (2)). These non-OBEs are associated with periods of corresponding changes in the fast-reacting variables adverse to fire spread, such as relatively low temperature and increased RH. ...
Overnight fires are emerging in North America with previously unknown drivers and implications. This notable phenomenon challenges the traditional understanding of the ‘active day, quiet night’ model of the diurnal fire cycle1–3 and current fire management practices4,5. Here we demonstrate that drought conditions promote overnight burning, which is a key mechanism fostering large active fires. We examined the hourly diurnal cycle of 23,557 fires and identified 1,095 overnight burning events (OBEs, each defined as a night when a fire burned through the night) in North America during 2017–2020 using geostationary satellite data and terrestrial fire records. A total of 99% of OBEs were associated with large fires (>1,000 ha) and at least one OBE was identified in 20% of these large fires. OBEs were early onset after ignition and OBE frequency was positively correlated with fire size. Although warming is weakening the climatological barrier to night-time fires⁶, we found that the main driver of recent OBEs in large fires was the accumulated fuel dryness and availability (that is, drought conditions), which tended to lead to consecutive OBEs in a single wildfire for several days and even weeks. Critically, we show that daytime drought indicators can predict whether an OBE will occur the following night, which could facilitate early detection and management of night-time fires. We also observed increases in fire weather conditions conducive to OBEs over recent decades, suggesting an accelerated disruption of the diurnal fire cycle.
... It is also important to acknowledge that the Suomi NPP satellite's overpasses occur many hours later at night compared to Sentinel-3. The SLSTR satellite passes over the equator at 10 a.m. and 10 p.m., whereas VIIRS S-NPP passes at 1:30 a.m. and 1:30 p.m. [38]. In our case, VIIRS detected more fires in South/Southeast Asian countries, suggesting either fire persistence over longer durations or fires starting later than the SLSTR satellite's time of passing (10:00 p.m.). ...
Quantifying spatial variations and trends in nighttime fires is crucial for a comprehensive understanding of fire dynamics. Traditional fire monitoring typically focuses on daytime observations, but controlling nocturnal fires poses unique challenges due to reduced visibility. While several studies have focused on examining global and regional fire trends, very few studies have focused on nighttime fires, particularly in South/Southeast Asian (S/SEA) countries. In this study, we analyzed nighttime vegetation fires in S/SEA using VIIRS I-band (375 m) data, including a comparison with Sentinel-3A SLSTR data. The results suggested that ~28.25% of total fires occurred at night in SA, and 18.98% in SEA. In SA, a statistically significant (p =< 0.05) increase in nighttime fires was observed in Bangladesh. India showed a positive trend in nighttime fires, while Nepal, Pakistan, and Sri Lanka exhibited negative trends; however, these results were not statistically significant. In SEA, we detected statistically significant (p =< 0.05) decreases in nighttime fires in Cambodia, Indonesia, Malaysia, and Vietnam, with increases in Myanmar and the Philippines. Indonesia experienced the most substantial reduction in nighttime fires. Furthermore, VIIRS I-band detections were approximately 92–98 times higher than those of SLSTR-3A in S/SEA. Overall, our study offers valuable insights into nighttime fires and trends in S/SEA countries, which are useful for fire prevention, mitigation and management in the region.
... The cloud elimination process involved a seasonal filter followed by a temporal filter substituting cloudy pixels with clear pixels [45][46][47]. The seasonal filter effectively eliminated a significant portion of the clouds, while the remaining clouds were addressed using the temporal filter. ...
... The temporal filter operated under the assumption that snow cover remained constant even in continuous cloudy conditions, disregarding minimal melting that might occur. Sequential Equations (1)-(3) outlined the steps of the temporal filter, enabling the conversion of cloudy pixels to no snow if subsequent equations identified the pixels as no snow [45]. Equation (1) required replacing of the "OR" operator with "AND" to satisfy the condition for snow to no snow. ...
... The reason for applying the spatial filter after the temporal filter is its effectiveness in eliminating small or fragmented clouds. By examining the majority classification of the neighbouring non-cloudy pixels, the cloudy pixel is reclassified as either snow or no snow [45]. In cases where there is an equal number of no snow and snow pixels surrounding a particular pixel, that pixel is designated as snow. ...
Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.
... In 2014, Schroeder et al. developed a fire detection algorithm using VIIRS, which has superior mapping capabilities to MODIS . In 2017, Zhang et al. jointly used I-band at a spatial resolution of 375 m and M-band of 750 m from VIIRS to detect fire and its radiated power (FRP) for the first time, and were able to effectively detect small fires (Zhang et al., 2017). The Chinese GF-4 satellite has high temporal resolution and moderate spatial resolution, making it suitable for high-frequency forest fire monitoring. ...
With satellite remote sensing technology blooming, satellite remote sensing has become a common tool to detect forest fires, and played an important role in forest fire monitoring. This paper sort the research status and progress on satellite remote sensing monitoring for forest fires to provide directions and insights for subsequent research and applications. Through reviewing the literature on satellite remote sensing monitoring for forest fires, we present satellites and sensors for forest fire monitoring, describe forest fire monitoring methods through brightness temperature detection and smoke detection, and summarize current problems of satellite remote sensing monitoring of forest fires. Despite forest fire satellite remote sensing monitoring algorithms are becoming increasingly mature, it is not without problems such as slow migration of cloud detection algorithms, difficulties in unifying spatial and temporal characteristics, and difficulties in detecting small fires and low-temperature fires. Finally, in response to the problems identified, we list some recommendations with a view to providing useful references for future research on forest fire monitoring with satellite remote sensing.
... Satellite images and videos have been extensively used to fight wildfires, with various studies focusing on active fire detection and tracking. To address the limitations of the early thresholding models mentioned in the introduction [2,3,4], dynamic thresholding techniques have been utilized to adapt to local contextual conditions and minimize false alarms for smaller and cooler fires [19,20,21,22]. Contextual algorithms remain the most common approach for active fire detection due to their computational efficiency [23]. ...
The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data has become crucial for disaster management. Although several earth-observing satellites provide information about disasters, satellites in the geostationary orbit provide data at intervals as frequent as every minute, effectively creating a video from space. There are many techniques that have been proposed to identify anomalies in surveillance videos; however, the available datasets do not have dynamic behavior, so we discuss an anomaly framework that can work on very high-frequency datasets to find very fast-moving anomalies. In this work, we present a diffusion model which does not need any motion component to capture the fast-moving anomalies and outperforms the other baseline methods.
... We used daily VIIRS active fire detections derived from the instrument's I-Band with 375 m nominal spatial resolution (Schroeder et al 2014). The I-Band pixel area is approximately ten-fold smaller than the Moderate Resolution Imaging Spectroradiometer (MODIS) pixel area at nadir, thus VIIRS is better suited for detecting small and low-intensity fires (Zhang et al 2017). We removed fire detections flagged as low confidence and used an interpolation algorithm to convert active fire observations into burned area estimates by grouping pixels separated by a maximum distance of 2000 m and a maximum time interval of 2 d and converting clusters of pixels to burned patches with a convex hull algorithm (see supplemental materials). ...
Ghana has retained a substantial area of tropical forests in an extensive network of protected reserves. These forests are impacted by land uses such as logging, mining, and agriculture as well as wildfires. We studied forest disturbance and recovery from 2013 to 2020 using annual maps of forest cover derived from Landsat imagery. Fire-associated disturbance was distinguished using VIIRS active fire data. We used boosted regression trees to model disturbances in closed and open forests as a function of climate variability, human accessibility, and landscape structure. A total of 3,562 km2 of forest reserves were disturbed, of which 17% (615 km2) were fire disturbances and 83% (2,946 km2) were non-fire disturbances. Of the total disturbed area, 68% was degradation (change from closed to open forest), 28% was open forest loss, and only 4% was closed forest loss. Over the same period, 2,702 km2 of forest reserves recovered, with 1,948 km2 of these recovering to closed-canopy forests. Fire disturbances were strongly associated with precipitation anomalies and occurred mostly in drier years, whereas non-fire disturbances had weaker relationships with precipitation. Disturbances in closed forests occurred in landscapes where closed forest cover was already low. In contrast, disturbances in open forests were most common in locations with intermediate levels of population pressure from nearby cities and proximity to non-forest land cover. The results support the idea that forest disturbance in Ghana is a multi-stage process involving degradation of closed forests followed by loss of the resulting open forests. Although non-fire disturbance rates are consistent from year to year, sharp increases in fire disturbance occur in drought years. Locations with the highest disturbance risk are associated with measurable indicators of climate, human pressure, and fragmentation, which can be used to identify these areas for conservation and forest restoration activities.
... This product was developed based on the MODIS thermal anomaly algorithm and can achieve a high spatial resolution of 375 m. Studies [11,33,34] ...
... This product was developed based on the MODIS thermal anomaly algorithm and can achieve a high spatial resolution of 375 m. Studies [11,33,34] Atmosphere Near Real-Time Capability program for Earth Observation System and can provide search ranges in terms of the country and time historical VNP14IMG fire product. In this study, we selected the VNP14IMG fire product, which occurred in Australia in 2019 and 2020, and extracted fire pixels from the study area in November and December 2019 and January 2020 according to time and latitude and longitude, Acquire_date, and Acquire_time information. ...
Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.
... These sensors are onboard polar satellites and acquire about two images per day, but these two sensors do not have a similar overpassing time. The pixel areas of the VIIRS's bands (375 m I-Bands and 750 m M-Bands) are smaller than that of SLSTR (with a spatial resolution of 1000 m), therefore this means that VIIRS has an improved active fire detection capability because it is able to find smaller thermal anomalies [Zhang et al., 2017]. In both cases, thermal anomalies are identified using a contextual algorithm which exploits MIR and TIR bands. ...
Satellite thermal remote sensing is widely used to detect and quantify the high-temperature vol- canic features produced during an eruption, e.g. released radiative power. Some space agencies provide Fire Radiative Power (FRP) Products to characterize any thermal anomaly around the world. In particular, Level-2 FRP Products of the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Sea and Land Surface Temperature Radiometer (SLSTR) are freely available online and they allow to monitor high-temperature volcanic features related to the dynamics of volcanic activity. Here, we propose the FastVRP platform developed in Google Colab to process automatically the FRP Products provided by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) space agencies. FastVRP was designed to monitor the volcanic radiative power (VRP) related to eruptive activity of Mt. Etna (Sicily, Italy). We compared the quality of these FRP Products during a number of recent paroxysmal lava fountains occurred at Etna volcano between February and March 2021. We highlighted the advantages and the limits of each sensor in monitor- ing intense volcanic eruptions lasting a few hours. Furthermore, we combine the mid-high spatial/ low temporal resolution VIIRS and SLSTR with the low spatial-high temporal resolution SEVIRI (Spinning Enhanced Visible and Infrared Radiometer Imager) to improve estimates of the energies released from each paroxysmal episode. In particular, we propose a fitting approach to enhance the accuracy of SEVIRI low spatial-high temporal resolution measurements exploiting the few acqui- sitions from VIIRS and SLSTR high spatial-low temporal resolution during lava fountain cooling phase. We validated the radiative power values forecasted from VIIRS and SLSTR with the radiative power values retrieved using MODIS (Moderate Resolution Imaging Spectroradiometer) sensor.
... The Remote Sensing (RS) and Geographic Information Systems (GIS) techniques played an important role in understanding and estimating cropland fire areas, detecting active fire and its behavior, and mapping burned areas [23][24][25]. Satellite-based remote sensing data provide opportunities to monitor cropland residue burning in a large area [26], and this method has been used for more than two decades for the mapping and monitoring of global forest and cropland fires [27][28][29][30]. In many parts of the world, the monitoring and mapping of global forest fires have been reported [29][30][31][32], but reports on cropland fires in the scientific literature are limited, especially in Central India [8]. ...
... Satellite-based remote sensing data provide opportunities to monitor cropland residue burning in a large area [26], and this method has been used for more than two decades for the mapping and monitoring of global forest and cropland fires [27][28][29][30]. In many parts of the world, the monitoring and mapping of global forest fires have been reported [29][30][31][32], but reports on cropland fires in the scientific literature are limited, especially in Central India [8]. In this study, we quantified spatiotemporal patterns with monthly and seasonal patterns of cropland residue burning, as well as air pollutant emissions, from cropland residue burning in Chhattisgarh for the last 21 years (2001-2021). ...
Cropland residue burning is one of the major causes of the emission of greenhouse gases and pollutants into the atmosphere, and is a major global environmental problem. This study analyzes the spatiotemporal changes in greenhouse gas emissions from cropland residue burning in Chhattisgarh, India. The Moderate Resolution Imaging Spectroradiometer (MODIS) active fire data was analyzed over a 21-year (2001–2021) period, and associated greenhouse gas emissions were estimated. A total of 64,370 fire points were recorded for all land cover types. The number of cropland fires increased from 49 to 1368 between 2001 and 2021, with a burning peak observed between December and March. Fires in cropland areas contributed to 32.4% (19,878) of the total fire counts in the last 21 years. The total estimated emissions of greenhouse gases between 2001 and 2021 ranged from 421.5 to 37,233 Gg, with an annual rate of emission of 8972 Gg from wheat residue burning, and from 435.45 to 64,108.1 Gg, with an annual emission of 15,448.16 Gg from rice residue burning. The Chhattisgarh plain region was the cropland fire hotspot of the state. The present study indicates increased cropland residue-burning activity in Chhattisgarh. Therefore, there is an immediate need to develop sustainable alternative methods for agricultural residue management and eco-friendly methods for the disposal of crop residues.
... where FRP is in unit MW, L 4 is the VIIRS I4 spectral radiance of the fire pixel (W m −2 sr −1 µm −1 ), L 4 is the mean background radiance, A is the area of the VIIRS pixel (m 2 ), σ is the Stefan-Boltzmann constant (5.6704 × 10 −8 W m −2 K −4 ), τ 4 is the atmospheric transmittance of the I4 spectral channel, and the empirical constant of a can be calculated based on the power physic law of radiance and temperature (3.2146 × 10 −9 W m −2 sr −1 µm −1 K −4 ) for VIIRS [36]. To analyze the thermal anomalies of the VIIRS active fire, the Digital Elevation Model from Shuttle Radar Topography Mission data (DEM-SRTM)-National Aeronautics and Space Administration (NASA) at 30 m spatial resolution was used for topographical analysis to characterize the landscape terrain for each type of thermal anomaly. ...
In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km2. High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires.
