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Frequency of VIIRS 375 m fi re pixels with (dashed line) and without (solid line) coincident 750 m active fi re detection for daytime (a) and nighttime (b) data during 1 – 30 August 2013. The horizontal axis indicates the number of VIIRS 375 m fi re detections contained within each 750 m pixel footprint. Density plot showing the size of coincident fi re pixel clusters detected using VIIRS 375 m and 750 m daytime (c) and nighttime (d) data.
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Context 1
... ARP product, and a staggering 25-fold increase in the absolute number of nighttime fire pixels detected compared to the 750 m fire product. The number of coincident daytime detections produced by both data sets (i.e., one 750 m Active Fires ARP fire pixel with a minimum of one and a maximum of four overlapping 375 m nominal confidence fire pixel; Fig. 7a-b, dashed lines) accounted for 43% and 83% of the 375 m and 750 m fire pixels, respectively. Further consideration of 375 m low confidence daytime fire pixels helped increase the rate of coincident detections to 90% of the 750 m fire pixels. Meanwhile, the number of coincident nighttime detections accounted for 10% and 98% of the 375 m and 750 m fire ...
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... the rate of coincident detections to 90% of the 750 m fire pixels. Meanwhile, the number of coincident nighttime detections accounted for 10% and 98% of the 375 m and 750 m fire pixels produced, respectively. These statistics reflect a significantly higher rate of fire detection using the 375 m data at night compared to the 750 m product ( Fig. 7a-b; solid ...
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... in day and nighttime performance observed for the two VIIRS fire detection data sets also applied to spatially coincident clusters of contiguous fire pixels. Fig. 7c-d shows density plots of spatially coincident fire pixel clusters detected by the 375 m and 750 m fire algorithms, ranging in size from 1 to 10 contiguous pixels in each case. Coincident fire pixel clusters in the nighttime data were largely skewed towards the lower part of the plot area, indicating the occur- rence of multiple ...
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... with smaller 750 m pixel clusters often containing less than two contiguous pixel elements. The occurrence of lower intensity fires at night may help explain the results above, since those fires could fall below the detection limit of the 750 m product as suggested by the flat response of the 750 m product to smaller 375 m fire pixel clusters (Fig. 7b). Meanwhile, the occurrence of large 375 m fire pixel clusters with 1-2 750 m coincident pixels suggests potential omission errors in the IDPS product. The VIIRS 750 m Active Fires ARP product generated by IDPS is a replica of the Collection 4 MODIS Fire and Thermal Anomalies algorithm, modified to accept VIIRS data format. ...
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... Conversely, outputs from Hong et al. (2022) and were 1.6 and 2.1 times our estimates, respectively. This was because their datasets were mainly based on VIIRS and JMA-Himawari-8 fire products, which included a substantial number of interference sources and thus caused a severe overestimation of CRBinduced emissions [60]. Despite a large inconsistency between these outputs, we can conclude that our estimation output lay in a reasonable range, given the resolution advantage and improved accuracy of NSMC-Himawari-8 fire products compared with MODIS and JMA fire products. ...
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... Five high-resolution bands, covering the visible to infrared wavelengths (0.64 μm-11.45 μm), are used to detect the hotspots and calculate their respective Fire Radiative Power (FRP) [MW], measuring the radiative energy emission rate per unit of time [50]. This provides important information for the wildfire spread modeling process, as it helps to visualize the active flaming front and thermal characteristics of a fire over time [27]. ...
Wildfire spread models are an essential tool for mitigating catastrophic effects associated with wildfires. However, current operational models suffer from significant limitations regarding accuracy and transferability. Recent advances in the availability and capability of Earth observation data and artificial intelligence offer new perspectives for data-driven modeling approaches with the potential to overcome the existing limitations. Therefore, this study developed a data-driven Deep Learning wildfire spread modeling approach based on a comprehensive dataset of European wildfires and a Spatiotemporal Graph Neural Network, which was applied to this modeling problem for the first time. A country-scale model was developed on an individual wildfire time series in Portugal while a second continental-scale model was developed with wildfires from the entire Mediterranean region. While neither model was able to predict the daily spread of European wildfires with sufficient accuracy (weighted macro-mean IoU: Portugal model 0.37; Mediterranean model 0.36), the continental model was able to learn the generalized patterns of wildfire spread, achieving similar performances in various fire-prone Mediterranean countries, indicating an increased capacity in terms of transferability. Furthermore, we found that the spatial and temporal dimensions of wildfires significantly influence model performance. Inadequate reference data quality most likely contributed to the low overall performances, highlighting the current limitations of data-driven wildfire spread models.
... The emergence of improved remote sensing techniques enables new air quality monitoring methods at larger scales that can be used more efficiently along with field-based techniques [23]. Satellite-based data offer global coverage at sub-daily frequency acquisitions of environmental data that can be used to retrieve various fire indicators, such as burned areas, smoke density, and the location and intensity of fire hotspots [24,25]. In order to obtain a broader understanding of the spatial and temporal distribution of PM 2.5 , remotely sensed aerosol optical depth (AOD) products can be used to approximate PM 2.5 concentrations [22,26,27]. ...
... In the current study, the active fire hotspot product from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m is examined over the period of 2012-2020, in relation to different land cover classes in the five Mekong countries [24]. The VIIRS sensor is a publicly available, near-real-time fire hotspot detector with a higher spatial resolution compared to its predecessor, the MODIS fire hotspot product [24]. ...
... In the current study, the active fire hotspot product from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m is examined over the period of 2012-2020, in relation to different land cover classes in the five Mekong countries [24]. The VIIRS sensor is a publicly available, near-real-time fire hotspot detector with a higher spatial resolution compared to its predecessor, the MODIS fire hotspot product [24]. As a result, VIIRS can detect smaller and less intense fire pixels. ...
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... The active fire/hotspots (ACFs), which are defined as areas where anomalous heat is detected at the surface, can be obtained from VNP14IMGTDL_NRT data. The VIIRS is a sensor on the United States Suomi National Polarorbiting Partnership (Suomi NPP) and National Oceanic and Atmospheric Administration (NOAA-20) polarorbiting satellites (Schroeder et al., 2014). Since the shortwave infrared (SWIR) signal of VIIRS channels is sensitive to the near-infrared radiation emitted by burning objects and since thermal infrared (TIR) channels can detect the heat emitted by fires, VIIRS has the ability to provide ACF products. ...
The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.
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... As a result, 15,199 global inventories of industrial heat sources (accuracy 77%) operating between 2012 and 2016 in Liu's product were detected. The NPP VIIRS active fire product (ACF) with a 375-m resolution for daily/ nighttime thermal anomalies 22 was applied to identify IHS based on a density-based spatial-temporal clustering model to detect much smaller IHS in China 1,18 . A total of 4,143 IHS operating between 2012 and 2017 were identified by Ma 1 , and 2,055 high-confidence IHS were identified by Li 18 in 2016 in China. ...
The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012–2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user’s accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.
... (accessed on 16 October 2023). The VIIRS instruments have five Image (I) bands and a day/night band, as well as eight mid-IR and four TIR bands (with one mid-IR and one TIR band with 375 m GSD) primarily for weather observations [183]. The VIIRS has 16 (M, moderate resolution) bands, for which nadir views are collected at 750 m GSD. ...
Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth’s status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth’s surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.
... Land Surface Temperature (LST) is one of the most important parameters for regional and global energy exchange, and the radiative balance of the surface-atmosphere system. The temporal and spatial information of Land Surface Temperature has not only been widely used to study the urban heat island effect, detect geothermal resources, and monitor fire points, but it also directly affects important parameters like soil moisture and surface evapotranspiration [1][2][3][4][5]. Satellite-based thermal infrared (TIR) remote sensing plays an important role in quickly obtaining large-scale LST, due to its direct relationship with the thermal remote sensing signal [6][7][8]. ...
Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.
... Since Sentinel-2 does not have thermal infrared band, whose detection capability of cropland burning was not tested in this paper. Satellites boarded with thermal infrared bands, such as Landsat (Schroeder et al., 2016), visible infrared scanner (VIIRS) (Schroeder et al., 2014) and MODIS (Giglio et al., 2009), have been proven to be effective in burning detection. Another benefit of using the thermal infrared band for burned area detection is that the cropland burning time can be provided. ...
The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle, and an important source of atmospheric trace gasses and aerosols. Accurate estimation of cropland burned area is both crucial and challenging, especially for the small and fragmented burned scars in China. Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument (MSI) data and its effectiveness was tested taking Songnen Plain, Northeast China as a case using satellite image of 2020. We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator, and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product. The overall accuracy of the single variable logistic regression was 77.38% to 86.90% and 73.47% to 97.14% for the 52TCQ and 51TYM cases, respectively. In comparison, the accuracy of the burned area map was improved to 87.14% and 98.33% for the 52TCQ and 51TYM cases, respectively by multiple variable logistic regression of Sentind-2 images. The balance of omission error and commission error was also improved. The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection, offering a highly automated process with an automatic threshold determination mechanism. This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit. It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.
... For example, the data collected by satellite sensor fire incidents are acquired whether a person witnesses and reports the event, a limiting factor of other datasets. The downloaded data are already analyzed in each of these datasets to represent possible wildfires as point vector data, with one point in each pixel identified as containing a fire [43,44]. The MODIS and two VIIRS datasets have a confidence value associated with each point representing a fire. ...
This study explores the relationship between wildfire exposure, social vulnerability, and community resilience across the 26 states east of the Mississippi River. This work centers around one research question: are there spatial differences in wildfire exposure that disproportionately impact disadvantaged communities in the Eastern United States over the recent period (2000-2020)? Employing remotely sensed wildfire data and ancillary datasets, we analyze and map the extensive wildfire exposure in the Eastern United States and compare it with spatial metrics of social vulnerability and community resilience to examine the social burdens of wildfire exposure in the Eastern U.S. A discernible wildfire exposure pattern emerges, with the Southeast bearing the highest exposure levels, largely attributed to human-caused and prescribed burning. By establishing a measure of disadvantaged counties using social vulnerability and community resilience, we identify regions where wildfire exposures could have the most adverse impact-areas characterized by highly vulnerable populations and limited community capacity to respond effectively to potential events. In evaluating wildfire risk, we conclude that considering not only exposure levels but also the inclusion of disadvantaged areas (incorporating social vulnerability and community resilience) is essential for understanding the disparate impact of wildfires on individuals and the communities where they live.
... In line with this, the standard MCD14ML collection 6.1 product from MODIS onboard Terra and Aqua provides the systematic detection of active fires worldwide at 1-km resolution from November 2000 to the present-day [57]. In contrast, the 375 m standard VIIRS VNP14IMGML collection 1 product offers better accuracy in detecting active fires from January 2012 onwards [58]. These datasets were provided as feature point data in the ESRI Shapefile format by the Fire 6 Information for Resource Management System (FIRMS) (https://firms.modaps.eosdis.nasa.gov/download/, ...
... Ideally, the MODIS sensor routinely detects active fires of ~ 0.1 ha and, under ideal observational conditions, fires up to ~ 50 m 2 in size can be captured, but those of lesser extents or with low emitted fire radiative power (FRP) may not be detected [83]. Conversely, the VIIRS sensor has a minimum theoretical detectable fire size of ~ 20 m 2 at 1 200 K fires burning in the daytime and one half at 900 K instantaneous fires at night time [58]. ...
We reconstruct four decades of past fire patterns (BA product) at fine resolution following international standards.
We demonstrate its consistency against readily available global and regional products.
Our product shows high agreement with ground-based BA estimates from forest services.
We detect an increasing rate in burned area terms over the years.
