Active Fire Products Research Articles

Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States

Biomass burning is an important source of atmospheric greenhouse gases and aerosols, and its emissions can be estimated using Fire Radiative Power (FRP) retrievals from polar-orbiting and geostationary satellites. Accurate and timely estimation of biomass-burning emissions (BBE) requires high-spatiotemporal-resolution FRP that is characterized by accurate diurnal FRP cycle. This study is to estimate hourly reliable BBE in a 0.25° × 0.3125° grid across the conterminous United States (CONUS) to be used in chemical transport models for air quality forecast. To do this, this study for the first time fused FRP retrievals from the Geostationary Operational Environmental Satellite (GOES) with those from Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 after GOES FRP was angularly adjusted and was further calibrated against MODIS FRP. The FRP data was obtained from Terra and Aqua MODIS 1 km active fire products with fire observations of four times a day and from 4 km GOES WF_ABBA (WildFire Automated Biomass Burning Algorithm) fire products for GOES-W (GOES-11 and 15) and GOES-E (GOES-13) with observations every 5–15 min across the CONUS from 2011 to 2015. The diurnal FRP cycles at an interval of 15 min for a grid were reconstructed using the ecosystem-specific diurnal FRP climatology and actually available MODIS-GOES fused FRP, which were applied to estimate hourly BBE across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varied significantly in magnitude and shape among 45 CONUS ecosystems. The biomass burning released 717 Gg particulate matter smaller than 2.5 μm in diameter (PM2.5) in the CONUS each year; however, it presented significant temporal (diurnal, seasonal, and interannual) and spatial variations. Finally, the BBE estimates were evaluated using available data sources and compared well (a difference of ∼4%) with emissions derived from Landsat burned areas in the western CONUS and with hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California (difference < 1%). The BBE estimates showed similar seasonal variation to six available BBE inventories but with variable magnitude.

Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

Missing emissions from post-monsoon agricultural fires in northwestern India: regional limitations of MODIS burned area and active fire products

A rising source of outdoor emissions in northwestern India is crop residue burning, occurring after the monsoon (kharif) and winter (rabi) crop harvests. In particular, post-monsoon rice residue burning, which occurs annually from October to November and is linked to increasing mechanization, coincides with meteorological conditions that enhance short-term air quality degradation. Here we examine the Global Fire Emissions Database (GFED), whose bottom-up emissions are based on the 500-m burned area product, MCD64A1, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Using a household survey from 2016, we find that MCD64A1 tends to underestimate burned area in many surveyed villages, leading to poor representation of small, scattered fires and consequent spatial biases in model results. To more accurately allocate such small fires and resolve sub-village heterogeneity, we use an experimental hybrid MODIS-Landsat method (ModL2T) to map burned area at 30-m spatial resolution, which results in 44 ± 21% higher burned area than MCD64A1 and up to 105 ± 52% increase in dry matter emissions over GFEDv4s. In our validation and assessments, we find that ModL2T performs better relative to MCD64A1 in terms of bias and omission error, but may introduce commission error due to conflation of burning with harvest and still underestimate burned area due to Landsat’s coarse temporal resolution (every 16 days). We conclude that while MODIS and Landsat provide more than two decades worth of observations, their spatio-temporal resolution is too coarse to overcome several region-specific challenges: small median landholding size (1–3 ha), quick harvest-to-sowing turnover period, prevalence of partial burning, and increasing haziness. To further constrain agricultural fire emissions in northwestern India and improve model estimates of associated public health impacts, integration of finer resolution imagery, as well as better understanding of the spatial patterns in burn rates, burn practices, and fuel loading, is requisite.

Geometric accuracy of remote sensing images over oceans: The use of global offshore platforms

The geometric accuracy of tens of millions of scenes of medium-resolution remote sensing (RS) images collected in the past 45 years has been systematically evaluated for land scenes, but the accuracy of ocean scenes is poorly known due to the lack of ground control points (GCPs). In this study, the locations of offshore platforms are first derived from time-series of Landsat-8 OLI images, and are then used as offshore reference points to systematically assess the geometric performance of RS images covering offshore oil/gas development areas. An inventory of 16,131 offshore platforms at the global scale is established, and then a novel method using the position-invariant characteristic of offshore platforms and the coherent characteristic of the geometric shift among tie-points (i.e. between sensed points from to-be-assessed images and the corresponding OLI-derived reference points) is developed for assessing the geometric accuracy of Landsat and other RS images. The method has been applied to 112,935 Landsat scenes (~1.87% of the entire archive) over oceans. The results indicate an optimal performance of Landsat OLI images (both pre-collection and Collection-1) but a less reliable performance of Landsat TM/ETM+ L1TP images. Approximately 50% of TM L1GS and ETM+ L1GT images have at least 2 pixels of geometric error. The new reference points inventory and the developed method were also applied to many other low-resolution and finer-resolution imagery (e.g. VIIRS Night-fire product, Terra/Aqua MODIS active fire product, ENVISAT ASAR, ALOS-1 PALSAR, Sentinel-1 SAR, Sentinel-2 MSI, the National Agriculture Imagery Program (NAIP) aerial images, and images from several Chinese satellites), and a quantitative description of the geometric accuracy of these sensors is also presented. The findings suggest that the new offshore reference point inventory is probably useful to help establish more robust offshore GCPs for U.S. Geological Survey (USGS) GCP library and further improve the ongoing USGS Global GCP improvement plan and European Space Agency Global Reference Image plan.

A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data

Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.

Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States

With growing concern about the impacts of fires on ecosystems and economies, satellite products are increasingly being used to understand fire regimes. Concurrently, where available, agency records of fires have also been used to assess fire regimes. Yet, it remains unclear if these independent datasets measure the same fires, which raises concerns about the interpretation and benchmarking of models derived from these products. Here, we present a novel product intercomparison of the MODIS burned area and active fire products across the conterminous United States using nearly 250,000 agency reported wildfires as reference data to model consistencies and inconsistencies between all three datasets. We compared agency reported wildfires from the Fire Program Analysis fire occurrence database to the MODIS products to identify which fires were detected vs. omitted by MODIS products relative to agency fire records, and by agency fire records relative to MODIS. We created generalized linear models as a function of fire attributes (e.g. size) and environmental variables (e.g. cloud cover) to predict MODIS detection of agency wildfires, and anthropogenic variables (e.g. agriculture) to predict agency detection of MODIS fires. We modeled fire detection probability separately for MODIS burned area and active fire products, and for the eastern and western U.S. Overall, we found that MODIS product detection rates ranged from 3.5% to 23.4% of all documented agency wildfires >1 ha, and that likelihood of detection increased with fire size. Agency detection rates ranged from 23.5% to 48% of MODIS burned area and active fires. Under ideal conditions, the MODIS active fire product had a 50% probability of detecting a wildfire that grew to at least 10 ha (eastern U.S.) – 78 ha (western U.S.), while the burned area product had a 50% probability of detecting a wildfire that grew to at least 169 ha (eastern U.S.) – 234 ha (western U.S.). Cloud cover and leaf area index were significant predictors of MODIS fire detection, while state boundaries were significant predictors of agency fire detection. This analysis presents an important assessment of the fire attributes and ground conditions that influence MODIS fire detection relative to extensive and increasingly used ground-based wildfire records. The large discrepancy in records of fire occurrence between MODIS and agency fire datasets highlights the need for this type of analysis into the types of fires likely to be included in each database.

Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products

ABSTRACTThe AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) is a recently developed algorithm designed to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. At present, the AHI-FSA algorithm has only been tested on a number of case study fires in Western Australia. Initial results demonstrate potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes), multi-resolution fire-line detections. This paper intercompares AHI-FSA across the Northern Territory of Australia (1.4 million km2) over a ten-day period with the well-established fire products from LEO (Low Earth Orbiting) satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses the difficulties and solutions when comparing high temporal frequency fire products with existing low temporal resolution LEO satellite products. The results indicate that the multi-resolution approach developed for AHI-FSA is successful in mapping fire activity at 500 m. When compared to the MODIS, daily AHI-FSA omission error was only 7%. High temporal frequency data also results in AHI-FSA observing fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement.

The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia

Abstract. In the southern Amazon relationships have been established among drought, human activities that cause forest loss, fire, and smoke emissions. We explore the impacts of recent drought on fire, forest loss, and atmospheric visibility in lowland Bolivia. To assess human influence on fire, we consider climate, fire, and vegetation dynamics in an area largely excluded from human activities since 1979, Noel Kempff Mercado National Park (NK) in northeastern Bolivia. We use data from five sources: the Moderate Resolution Imaging Spectroradiometer Collection 6 active fire product (2001–2015) (MODIS C6), Global Fire WEather Database (GFWED) data (1982–2015), MODIS land cover data (2001–2010), MODIS forest loss data (2000–2012), and the regional extinction coefficient for the southwestern Amazon (i.e., Bext), which is derived from horizontal visibility data from surface stations at the World Meteorological Organization (WMO) level (1973–2015). The Bext is affected by smoke and acts as a proxy for visibility and regional fire emissions. In lowland Bolivia from 2001 to 2015, interannual Drought Code (DC) variability was linked to fire activity, while from 1982 to 2015, interannual DC variability was linked to Bext. From 2001 to 2015, the Bext and MODIS C6 active fire data for lowland Bolivia captured fire seasonality, and covaried between low- and high-fire years. Consistent with previous studies, our results suggest Bext can be used as a longer-term proxy of regional fire emissions in southwestern Amazonia. Overall, our study found drought conditions were the dominant control on interannual fire variability in lowland Bolivia, and fires within NK were limited to the Cerrado and seasonally inundated wetland biomes. Our results suggest lowland Bolivian tropical forests were susceptible to human activities that may have amplified fire during drought. Human activities and drought need to be considered in future projections of southern Amazonian fire, in regard to carbon emissions and global climate.

Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data

ABSTRACTA vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire's absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4 μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75% of cases. Potential improvements in detection time of up to 6 h over LEO products are also demonstrated.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Deforestation-Induced Fragmentation Increases Forest Fire Occurrence in Central Brazilian Amazonia

Amazonia is home to more than half of the world’s remaining tropical forests, playing a key role as reservoirs of carbon and biodiversity. However, whether at a slower or faster pace, continued deforestation causes forest fragmentation in this region. Thus, understanding the relationship between forest fragmentation and fire incidence and intensity in this region is critical. Here, we use MODIS Active Fire Product (MCD14ML, Collection 6) as a proxy of forest fire incidence and intensity (measured as Fire Radiative Power—FRP), and the Brazilian official Land-use and Land-cover Map to understand the relationship among deforestation, fragmentation, and forest fire on a deforestation frontier in the Brazilian Amazonia. Our results showed that forest fire incidence and intensity vary with levels of habitat loss and forest fragmentation. About 95% of active fires and the most intense ones (FRP &gt; 500 megawatts) were found in the first kilometre from the edges in forest areas. Changes made in 2012 in the Brazilian main law regulating the conservation of forests within private properties reduced the obligation to recover illegally deforested areas, thus allowing for the maintenance of fragmented areas in the Brazilian Amazonia. Our results reinforce the need to guarantee low levels of fragmentation in the Brazilian Amazonia in order to avoid the degradation of its forests by fire and the related carbon emissions.

How Well Does the ‘Small Fire Boost’ Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning?

The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.

Shortwave IR Adaption of the Mid-Infrared Radiance Method of Fire Radiative Power (FRP) Retrieval for Assessing Industrial Gas Flaring Output

The radiative power (MW) output of a gas flare is a useful metric from which the rate of methane combustion and carbon dioxide emission can be inferred for inventorying purposes and regular global surveys based on such assessments are now being used to keep track of global gas flare reduction efforts. Several multispectral remote sensing techniques to estimate gas flare radiative power output have been developed for use in such surveys and single band approaches similar to those long used for the estimation of landscape fire radiative power output (FRP) can also be applied. The MIR-Radiance method, now used for FRP retrieval within the MODIS active fire products, is one such single band approach—but its applicability to gas flare targets (which are significantly hotter than vegetation fires) has not yet been assessed. Here we show that the MIR-Radiance approach is in fact not immediately suitable for retrieval of gas flare FRP due to their higher combustion temperatures but that switching to use data from a SWIR (rather than MWIR) spectral channel once again enables the method to deliver unbiased FRP retrievals. Over an assumed flaring temperature range of 1600–2200 K we find a maximum FRP error of ±13.6% when using SWIR observations at 1.6 µm and ±6.3% when using observations made at 2.2 µm. Comparing these retrievals to those made by the multispectral VIIRS ‘NightFire’ algorithm (based on Planck Function fits to the multispectral signals) we find excellent agreement (bias = 0.5 MW, scatter = 1.6 MW). An important implication of the availability of this new SWIR radiance method for gas flare analysis is the potential to apply it to long time-series from older and/or more spectrally limited instruments, unsuited to the use of multispectral algorithms. This includes the ATSR series of sensors operating between 1991–2012 on the ERS-1, ERS-2 and ENVISAT satellites and such long-term data can be used with the SWIR-Radiance method to identify key trends in global gas flaring that have occurred over the last few decades.

Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity in the Arctic tundra from 2001–2015

Recent years have seen an increased frequency of wildfire events in different parts of Arctic tundra ecosystems. Contemporary studies have largely attributed these wildfire events to the Arctic’s rapidly changing climate and increased atmospheric disturbances (i.e. thunderstorms). However, existing research has primarily examined the wildfire–climate dynamics of individual large wildfire events. No studies have investigated wildfire activity, including climatic drivers, for the entire tundra biome across multiple years, i.e. at the planetary scale. To address this limitation, this paper provides a planetary/circumpolar scale analyses of space-time patterns of tundra wildfire occurrence and climatic association in the Arctic over a 15 year period (2001–2015). In doing so, we have leveraged and analyzed NASA Terra’s MODIS active fire and MERRA climate reanalysis products at multiple temporal scales (decadal, seasonal and monthly). Our exploratory spatial data analysis found that tundra wildfire occurrence was spatially clustered and fire intensity was spatially autocorrelated across the Arctic regions. Most of the wildfire events occurred in the peak summer months (June–August). Our multi-temporal (decadal, seasonal and monthly) scale analyses provide further support to the link between climate variability and wildfire activity. Specifically, we found that warm and dry conditions in the late spring to mid-summer influenced tundra wildfire occurrence, spatio-temporal distribution, and fire intensity. Additionally, reduced average surface precipitation and soil moisture levels in the winter–spring period were associated with increased fire intensity in the following summer. These findings enrich contemporary knowledge on tundra wildfire’s spatial and seasonal patterns, and shed new light on tundra wildfire–climate relationships in the circumpolar context. Furthermore, this first pan-Arctic analysis provides a strong incentive and direction for future studies which integrate multiple datasets (i.e. climate, fuels, topography, and ignition sources) to accurately estimate carbon emission from tundra burning and its global climate feedbacks in coming decades.

Accuracy assessment of MODIS active fire products in southern African savannah woodlands

AbstractSatellite remote sensing offers a cost‐effective method for monitoring fire occurrence in savannah systems, for proper fire management. However, the ability of satellite fire products to detect active fire is known to vary depending on the terrestrial ecosystems and sensor characteristics. In this study, the overall accuracy, kappa coefficient of agreement and true skill statistic (TSS) were used to assess the accuracy of two MODIS fire products (MOD14A1 and MCD14ML) to detect active fire at two savannah woodland sites dominated by Baikiaea plurijuga and Brachystegia spiciformis in Zimbabwe. In both sites, MOD14A1 with a coarse spatial resolution of 1 km had a poor index of agreement with ground fire data (kappa = 0, TSS = 0 and overall accuracy ≤ 0.4). By contrast, a moderate to strong agreement between MCD14ML and active fires measured on the ground was observed at both study sites (overall accuracy ≥ 0.7, kappa ≥ 0.6 and TSS ≥ 0.6; Table ). It was therefore concluded that MCD14ML, with a spatial resolution of 375 m, is a more suitable product for detecting active fires in both Baikiaea plurijuga and Brachystegia‐dominated savannah woodlands of southern Africa compared to MOD14A1.

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA’s Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer’s accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites’ sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems.

Continent‐level drivers of African pyrodiversity

Pyrodiversity, which describes fire variability over space and time, is believed to increase habitat heterogeneity and thereby promote biodiversity. However, to date there is no standardised metric for quantifying pyrodiversity, and so broad geographic patterns and drivers of pyrodiversity remain unexplored. We present the first generalizable method to quantify pyrodiversity, and use it to address the fundamental questions of what drives pyrodiversity, which fire attributes constrain pyrodiversity under different conditions, and whether pyrodiversity is spatial grain‐dependent. We linked the MODIS burned area and active fire products to measure fire size, seasonal timing, return interval, and intensity for 2.2 million individual fires in sub‐Saharan Africa from 2000–2015. We then quantified pyrodiversity as a four‐dimensional hypervolume described by fire attributes within a grid cell, for any spatial grain of analysis. Environmental (rainfall, vegetation, soils, and topography) and human‐associated (cattle biomass, cropland area, and human population density) variables were assessed as potential drivers of pyrodiversity. Rainfall was the main environmental driver of pyrodiversity, with higher pyrodiversity in drier regions (&lt; 650 mm yr–1). Pyrodiversity was not strongly associated with human‐associated variables across Africa. Rainfall and a human influence index had clear but contrasting effects on the variability of fire size, seasonal timing, return interval, and intensity. Our analyses show that fire size and seasonal timing constrain pyrodiversity in wetter regions, whereas none of the fire attributes pose a strong constraint in drier regions. We found no evidence that pyrodiversity was spatial grain‐dependent when recalculated at 5‐minute grain increments from 15 to 120 minutes. We hypothesise that the strongest positive effect of pyrodiversity on biodiversity in all its forms will occur at intermediate precipitation (650–1300 mm yr–1), where fire plays an important role in shaping vegetation structure and where pyrodiversity is still quite high.

Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes

Fire products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery provide timely information for wildfire detection, monitoring, and characterization at the global scale. However, in Alaskan boreal forest fires, their lower effectiveness in detecting residual fire once the high-intensity fire front has passed limits their practical use for regional or local fire management decisions. Using data acquired during Alaska's 2016 fire season, we analyzed the performance of the MODIS-based MOD14/MYD14, and the more recent VIIRS I-band active fire products. A comparison with the fire perimeter and properties data published by the Alaska Interagency Coordination Center (AICC) shows that both MODIS and VIIRS fire products successfully detect all fires larger than approximately 200–300ha. For fires smaller than this threshold, the VIIRS I-band product offers higher detection likelihood. To map burn areas containing both low- and high-intensity active fire, we developed the VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL). We apply this algorithm to regions of known Alaskan boreal forest fires and validate it using events mapped by fire management agencies and detected on closely-timed Landsat imagery. We find that for Alaska, an example of a high-latitude region, VIFDAHL more accurately captures the fire spread, can differentiate well between low- and high-intensity fires, and can detect 30–90% more fire pixels compared to the MODIS and VIIRS global fire products.

Study on Climate and Grassland Fire in HulunBuir, Inner Mongolia Autonomous Region, China.

Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal and spatial variability of grassland fire. The purpose of this paper is to study the effects of monthly time scale climate factors on the occurrence of grassland fire in HulunBuir, located in the northeast of the Inner Mongolia Autonomous Region in China. Based on the logistic regression method, we used the moderate-resolution imaging spectroradiometer (MODIS) active fire data products named thermal anomalies/fire daily L3 Global 1km (MOD14A1 (Terra) and MYD14A1 (Aqua)) and associated climate data for HulunBuir from 2000 to 2010, and established the model of grassland fire climate index. The results showed that monthly maximum temperature, monthly sunshine hours and monthly average wind speed were all positively correlated with the fire climate index; monthly precipitation, monthly average temperature, monthly average relative humidity, monthly minimum relative humidity and the number of days with monthly precipitation greater than or equal to 5 mm were all negatively correlated with the fire climate index. We used the active fire data from 2011 to 2014 to validate the fire climate index during this time period, and the validation result was good (Pearson’s correlation coefficient was 0.578), which showed that the fire climate index model was suitable for analyzing the occurrence of grassland fire in HulunBuir. Analyses were conducted on the temporal and spatial distribution of the fire climate index from January to December in the years 2011–2014; it could be seen that from March to May and from September to October, the fire climate index was higher, and that the fire climate index of the other months is relatively low. The zones with higher fire climate index are mainly distributed in Xin Barag Youqi, Xin Barag Zuoqi, Zalantun Shi, Oroqen Zizhiqi, and Molidawa Zizhiqi; the zones with medium fire climate index are mainly distributed in Chen Barag Qi, Ewenkizu Zizhiqi, Manzhouli Shi, and Arun Qi; and the zones with lower fire climate index are mainly distributed in Genhe Shi, Ergun city, Yakeshi Shi, and Hailar Shi. The results of this study will contribute to the quantitative assessment and management of early warning and forecasting for mid-to long-term grassland fire risk in HulunBuir.