Spatial variations in vegetation fires and emissions in South and Southeast Asia during COVID-19 and pre-pandemic.

Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, very few studies focused on vegetation fires and the changes during the COVID as compared to pre-pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations in SA/SEA during COVID-2020 and pre-pandemic (2012-2019). Results from the short-term 2020-COVID versus 2019-non-COVID year showed a decline in fire counts varying from - 2.88 to 79.43% in S/SEA. The exceptions in South Asia include Afghanistan and Sri Lanka, with a 152% and 4.9% increase, and Cambodia and Myanmar in Southeast Asia, with an 11.1% and 8.5% increase in fire counts in the 2020-COVID year. The burnt area decline for 2020 compared to 2019 varied from - 0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012-2020 pre-pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from - 0.24 to - 1, although most countries experienced a decrease in fire counts. The associated mean TPM emissions declined from ~ 2.31 Tg (0.73stdev) during 2012-2019 to 2.0 (0.65stdev)Tg in 2020 in South Asia and 6.83 (0.70stdev)Tg during 2012-2019 to 5.71 (0.69 stdev)Tg in 2020 for South East Asian countries. The study highlights variations in fires and emissions useful for fire management and mitigation.

Letter to the editor on "Nonlinear dynamics of fires in Africa over recent decades controlled by precipitation".

Time series of burned area versus cropland land cover type in sub-Saharan Africa do not demonstrate an inverse relationship between fire activity and cropland expansion in the most fire-prone continent, as was suggested by several authors.

Environmental and political implications of underestimated cropland burning in Ukraine.

Open burning is illegal in Ukraine, yet Ukraine has, on average, 300 times more fire activity per year (2001-2019) than most European countries. In 2016 and 2017, 47% of Ukraine was identified as cultivated area, with a total of 70% of land area dedicated to agricultural use. Over 57% of all active fires in Ukraine detected using space-borne Visible Infrared Imaging Radiometer Suite (VIIRS) during 2016 and 2017 were associated with pre-planting field clearing and post-harvest crop residue removal, meaning that the majority of these fires are preventable. Due to the small size and transient nature of cropland burns, satellite-based burned area (BA) estimates are often underestimated. Moreover, traditional spectral-based BA algorithms are not suitable for distinguishing burned from plowed fields, especially in the black soil regions of Ukraine. Therefore, we developed a method to estimate agricultural BA by calibrating VIIRS active fire data with exhaustively mapped cropland reference areas (42 958 fields). Our study found that cropland BA was significantly underestimated (by 30%-63%) in the widely used Moderate Resolution Imaging Spectroradiometer-based MCD64A1 BA product, and by 95%-99.9% in Ukraine's National Greenhouse Gas Inventory. Although crop residue burns are smaller and emit far less emissions than larger wildfires, reliable monitoring of crop residue burning has a number of important benefits, including (a) improving regional air quality models and the subsequent understanding of human health impacts due to the proximity of crop residue burns to urban locations, (b) ensuring an accurate representation of predominantly smaller fires in regional emission inventories, and (c) increasing awareness of often illegal managed open burning to provide improved decision-making support for policy and resource managers.

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales.

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire-prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region-specific seasonality, long-term trends, recent fire observations, and climate drivers representing both large-scale climate variability and local fire weather. We cross-validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near-real-time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

Trends in Vegetation fires in South and Southeast Asian Countries.

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012-2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.

Global validation of the collection 6 MODIS burned area product.

This paper presents a Stage 3 validation of the recently released Collection 6 NASA MCD64A1 500 m global burned area product. The product is validated by comparison with Landsat 8 Operational Land Imager (OLI) image pairs acquired 16 days apart that were visually interpreted. These independent reference data were selected using a stratified random sampling approach that allows for probability sampling of Landsat data in both time and in space. A total of 558 Landsat 8 OLI image pairs (1116 images), acquired between March 1st, 2014 and March 19th , 2015, were selected and used to validate the MCD64A1 product. The areal accuracy of the MCD64A1 product was characterized at the 30 m resolution of the Landsat independent reference data using standard accuracy metrics derived from global and from biome specific confusion matrices. Because a probability based Stage 3 sampling protocol was followed, unbiased estimators of the accuracy metrics and associated standard errors could be used. Globally, the MCD64A1 product had an estimated 40.2% commission error and 72.6% omission error; the prevalence of omission errors is reflected by a negative estimated bias of the mapped global area burned relative to the Landsat independent reference data (-54.1%). Globally, the standard errors of the accuracy metrics were less than 6%. The lowest errors were observed in the boreal forest biome (27.0% omission and 23.9% estimated commission errors) where burned areas tend to be large and distinct, and remain on the landscape for long periods, and the highest errors were in the Tropical Forest, Temperate Forest, and Mediterranean biomes (estimated > 90% omission error and > 50% commission error). The product accuracy was also characterized at coarser scale using metrics derived from the regression between the proportion of coarse resolution grid cells detected as burned by MCD64A1 and the proportion mapped in the Landsat 8 interpreted maps. The errors of omission and commission observed at 30 m resolution compensate to a considerable extent at coarser resolution, as indicated by the coefficient of determination (r2 > 0.70), slope (> 0.79) and intercept (-0.0030) of the regression between the MCD64A1 product and the Landsat independent reference data in 3 km, 4 km, 5 km, and 6 km coarse resolution cells. The Boreal Forest, Desert and Xeric Shrublands, Temperate Savannah and Tropical Savannah biomes had higher r 2 and slopes closer to unity than the Temperate Forest, Mediterranean, and Tropical Forest biomes. The analysis of the deviations between the proportion of area burned mapped by the MCD64A1 product and by the independent reference data, performed using 3 km × 3 km and 6 km × 6 km coarse resolution cells, indicates that the large negative bias in global area burned is primarily due to the systematic underestimation of smaller burned areas in the MCD64A1 product.

Changes in Fire Activity in Africa from 2002 to 2016 and Their Potential Drivers.

While several studies have reported a recent decline in area burned in Africa, the causes of this decline are still not well understood. In this study, we found that from 2002 to 2016 burned area in Africa declined by 18.5%, with the strongest decline (80% of the area) in the Northern Hemisphere. One third of the reduction in burned area occurred in croplands, suggesting that changes in agricultural practices (including cropland expansion) are not the predominant factor behind recent changes in fire extent. Linear models that considered interannual variability in climate factors directly related to biomass productivity and aridity explained about 70% of the decline in burned area in natural land cover. Our results provide evidence that despite the fact that most fires are human-caused in Africa, increased terrestrial moisture during 2002-2016 facilitated declines in fire activity in Africa.

Spatial and temporal intercomparison of four global burned area products.

We characterize the agreement and disagreement of four publically available burned products (Fire CCI, Copernicus Burnt Area, MODIS MCD45A1, and MODIS MCD64A1) at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time. Our analysis, conducted using seven years of data (2005-2011), shows that estimates of burned area vary greatly between products in terms of total area burned, the location of burning, and the timing of the burning. We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally. Comparison with independent, contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime. Our results have implications for the use of global burned area products in fire ecology, management and emissions applications.

The Collection 6 MODIS burned area mapping algorithm and product.

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 reprocessing. The updated algorithm improves upon the heritage Collection 5.1 (C5.1) MCD64A1 and MCD45A1 algorithms by offering significantly better detection of small burns, a modest reduction in burn-date temporal uncertainty, and a large reduction in the extent of unmapped areas. Comparison of the C6 and C5.1 MCD64A1 products for fifteen years (2002-2016) on a regional basis shows that the C6 product detects considerably more burned area globally (26%) and in almost every region considered. The sole exception was in Boreal North America, where the mean annual area burned was 6% lower for C6, primarily as a result of a large increase in the number of small lakes mapped (and subsequently masked) at high latitudes in the upstream C6 input data. With respect to temporal reporting accuracy, 44% of the C6 MCD64A1 burned grid cells were de-tected on the same day as an active fire, and 68% within 2 days, which represents a substantial reduction in temporal uncertainty compared to the C5.1 MCD64A1 and MCD45A1 products. In addition, an areal accuracy assessment of the C6 burned area product undertaken using high resolution burned area reference maps derived from 108 Landsat image pairs is reported.

The collection 6 MODIS active fire detection algorithm and fire products.

The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, on-board NASA's Terra and Aqua satellites, have provided more than a decade of global fire data. Here we describe improvements made to the fire detection algorithm and swath-level product that were implemented as part of the Collection 6 land-product reprocessing, which commenced in May 2015. The updated algorithm is intended to address limitations observed with the previous Collection 5 fire product, notably the occurrence of false alarms caused by small forest clearings, and the omission of large fires obscured by thick smoke. Processing was also expanded to oceans and other large water bodies to facilitate monitoring of offshore gas flaring. Additionally, fire radiative power (FRP) is now retrieved using a radiance-based approach, generally decreasing FRP for all but the comparatively small fraction of high intensity fire pixels. We performed a Stage-3 validation of the Collection 5 and Collection 6 Terra MODIS fire products using reference fire maps derived from more than 2500 high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. Our results indicated targeted improvements in the performance of the Collection 6 active fire detection algorithm compared to Collection 5, with reduced omission errors over large fires, and reduced false alarm rates in tropical ecosystems. Overall, the MOD14 Collection 6 daytime global commission error was 1.2%, compared to 2.4% in Collection 5. Regionally, the probability of detection for Collection 6 exhibited a ~3% absolute increase in Boreal North America and Boreal Asia compared to Collection 5, a ~1% absolute increase in Equatorial Asia and Central Asia, a ~1% absolute decrease in South America above the Equator, and little or no change in the remaining regions considered. Not unexpectedly, the observed variability in the probability of detection was strongly driven by regional differences in fire size. Overall, there was a net improvement in Collection 6 algorithm performance globally.

Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets.

In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.

Evaluating greenhouse gas emissions inventories for agricultural burning using satellite observations of active fires.

Fires in agricultural ecosystems emit greenhouse gases and aerosols that influence climate on multiple spatial and temporal scales. Annex 1 countries of the United Nations Framework Convention on Climate Change (UNFCCC), many of which ratified the Kyoto Protocol, are required to report emissions of CH4 and N2O from these fires annually. In this study, we evaluated several aspects of this reporting system, including the optimality of the crops targeted by the UNFCCC globally and within Annex 1 countries, and the consistency of emissions inventories among different countries. We also evaluated the success of individual countries in capturing interannual variability and long-term trends in agricultural fire activity. In our approach, we combined global high-resolution maps of crop harvest area and production, derived from satellite maps and ground-based census data, with Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of active fires. At a global scale, we found that adding ground nuts (e.g., peanuts), cocoa, cotton and oil palm, and removing potato, oats, rye, and pulse other from the list of 14 crops targeted by the UNFCCC increased the percentage of active fires covered by the reporting system by 9%. Optimization led to a different recommended list for Annex 1 countries, requiring the addition of sunflower, cotton, rapeseed, and alfalfa and the removal of beans, sugarcane, pulse others, and tuber-root others. Extending emissions reporting to all Annex 1 countries (from the current set of 19 countries) would increase the efficacy of the reporting system from 6% to 15%, and further including several non-Annex 1 countries (Argentina, Brazil, China, India, Indonesia, Thailand, Kazakhstan, Mexico, and Nigeria) would capture over 55% of active fires in croplands worldwide. Analyses of interannual trends from the United States and Australia showed the importance of both intensity of fire use and crop production in controlling year-to-year variations in agricultural fire emissions. Remote sensing provides an effective means for evaluating some aspects of the current UNFCCC emissions reporting system; and, if combined with census data, field experiments and expert opinion, has the potential to improve the robustness of the next generation inventory system.

Forecasting fire season severity in South America using sea surface temperature anomalies.

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Niño Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Continental-scale partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña period.

During the 1997 to 1998 El Niño, drought conditions triggered widespread increases in fire activity, releasing CH4 and CO2 to the atmosphere. We evaluated the contribution of fires from different continents to variability in these greenhouse gases from 1997 to 2001, using satellite-based estimates of fire activity, biogeochemical modeling, and an inverse analysis of atmospheric CO anomalies. During the 1997 to 1998 El Niño, the fire emissions anomaly was 2.1 +/- 0.8 petagrams of carbon, or 66 +/- 24% of the CO2 growth rate anomaly. The main contributors were Southeast Asia (60%), Central and South America (30%), and boreal regions of Eurasia and North America (10%).