Showing papers by "Louis Giglio published in 2022"

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

Abstract: Abstract 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.

Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)

Abstract: Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500 m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e. fire-related forest loss, was modelled based on the overlap between 30 m forest loss data and MODIS burned area and active fire detections. Using this new 500 m model, we calculated global average carbon emissions from fire of 2.1±0.2 (±1σ interannual variability, IAV) Pg C yr−1 during 2002–2020. Fire-related forest loss accounted for 2.6±0.7 % (uncertainty range =1.9 %–3.3 %) of global burned area and 24±6 % (uncertainty range =16 %–31 %) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13±4 % of global emissions. Our global fire emissions estimate was higher than the 1.5 Pg C yr−1 from GFED4 and similar to 2.1 Pg C yr−1 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500 m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30 m and finer show a substantial amount of burned area that remains undetected with 500 m sensors, suggesting that global carbon emissions from fire are likely higher than our 500 m estimates. The ability to model fire emissions at 500 m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.

Comment on Otón et al. Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018). Fire 2021, 4, 74

Abstract: In a recent study, Otón et al. [...]

Assessment of satellite orbit-drift artifacts in the long-term AVHRR FireCCILT11 global burned area data set

Abstract: The recent 1982–2018 FireCCILT11 burned area (BA) product derived from NOAA AVHRR data is examined with regard to its suitability for long-term BA analyses. We focus on identifying NOAA AVHRR satellite orbit-drift artifacts within the FireCCILT11 BA time series since the occurrence of such artifacts can render any BA data set inappropriate for long-term analyses. We show that significant orbit-drift artifacts are present in the FireCCILT11 product over numerous large spatial patches located on every continent except Antarctica. In addition, the BA mapped by the FireCCILT11 in the United States is compared to independent ground-based records compiled by the National Interagency Fire Center. Prior to 2001, the FireCCILT11 product drastically overestimated BA within this region, to the point of falsely suggesting an abrupt radical change in fire regime at the turn of the century. Our findings indicate that caution is required when using the FireCCILT11 product for long-term BA studies, particularly within the tropics or the United States for the periods 1982–2000 and 2018. Studies requiring gridded BA data for these years, particularly at local and regional scales, should explicitly consider AVHRR orbit-drift artifacts that may result in FireCCILT11-reported BA that is substantially different (for example, several times greater) than the actual BA.

Impacts of large-scale refugee resettlement on LCLUC: Bidi Bidi refugee settlement, Uganda case study

Abstract: Uganda is the third-largest refugee-hosting country partly due to its open-door policy—deemed one of the most progressive. When new refugees arrive, refugee settlements are established rapidly, and irreversible changes to the landscape are inevitable. We utilize satellite data to map land cover (LC), land use, and burned area (BA) to assess their relationship in the context of large-scale refugee resettlement in Bidi Bidi—Uganda’s largest refugee settlement. We show inevitable dramatic changes in LC, e.g. built-up residential zones increased from 1.8% to 30%, while cropland increased from less than 0.7%–25.6% during our study period (2015–2019). In contrast, BA that affected more than 80% of the area was drastically reduced during the establishment phase (August–December 2017). Substantial reduction in BA was observed predominantly within the residential zones, but outside of the zones, BA was hardly affected by the arrival of refugees. Our study shows that these changes in LC and BA are mainly missing in the readily accessible satellite-derived data products, which impede assessment, planning, and implementation of humanitarian response programs. We discuss the importance of mapping at the appropriate spatial and temporal scales and the importance of context, sector, and geographic domain knowledge expertise in developing critical information products for informing programs to support vulnerable populations.

A remote sensing-based approach to estimating the fire spread rate parameter for individual burn patch extraction

Abstract: ABSTRACT For the past two decades, satellite-derived activef fire data have been used in a multitude of operational applications and in a large and growing body of research on the role of fire within the Earth system. More recent work with satellite-based active fire data has been directed toward estimating what are in effect broad-scale fire spread rates that are in turn used as an important temporal parameter for the extraction of individual-fire boundaries from burned area maps. Here we use data mining to identify active fire clusters that serve as an input to a fire spread reconstruction algorithm to derive optimal global fire spread rates suitable for fire-perimeter extraction. The spread rates calculated for the active fire clusters, which are useful for applications beyond perimeter extraction, correlate with the spread rates based on reference fire boundaries (R 2 = .82, NRMSE = 2.6%) and are generally compatible with other studies, despite key differences in data acquisition methods and quantities measured.