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Comparison between MODIS net photosynthesis (PSN) (Tg) and Copernicus dry matter productivity (DMP, Tg DM) estimates that have been temporally integrated over MODIS burned areas that burned in successive years in the northern (a) and southern (b) African hemispheres. The grey shaded area represents the prediction interval and the blue shaded area is the slopes 95% confidence interval. Note that the x- and y-axis range differs among plots.
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African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data...
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Black carbon (BC) emissions from open biomass burning (BB) are known to have a considerable impact on the radiative budget of the atmosphere at both global and regional scales; however, these emissions are poorly constrained in models by atmospheric observations, especially in remote regions. Here, we investigate the feasibility of constraining BC...
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... The wide availability of a long time series of 1 km to 375 m resolution active fire registers (e.g., MODIS and VIIRS) represents an unprecedented opportunity to monitor the spatial patterns of fire intensity and associated fuel consumption and emissions (e.g., [51,52]). Active fires have been used to remotely monitor fire intensity through measurements of Fire Radiative Power (FRP), which is available at near-real time (10-15 min) from geostationary satellites such as GOES [82], SEVIRI [83][84][85][86] and Himawarii [87] and at daily time intervals from polar-orbiting satellites such as MODIS and VIIRS [51,52,83,88]. Several studies have demonstrated the potential of active fires' FRP to distinguish fire intensity differences between fire fronts or backfires, surface or crown fires (e.g., [64,89]) and even to monitor levels of suppression difficulty registered in field records (e.g., field-observed torching or extreme fire behavior) [90]. ...
Mapping potential fire intensity is a fundamental tool for fire management planning. Despite the wide use of Fire Radiative Power (FRP) as an indicator of expected fire intensity and fire emissions, very few studies have spatially analyzed the role of remotely sensed proxies of vegetation productivity to explain FRP. The current study aimed at modeling and mapping the relationships between aboveground biomass and Moderate Resolution Imaging Spectroradiometer (MODIS) maximum FRP, at 1 km pixel, in 2011–2020, for each of 46 fuel regions in the entirety of Mexico. Maximum FRP–biomass relationships supported a novel hypothesis of varying constraints of fire intensity. In lower-productivity areas, such as semiarid shrub- and grass-dominated ecosystems, fine fuel loads limited fire occurrence and FRP was positively related to biomass. In the more productive areas, such as temperate or tropical forests, a humped relationship of FRP against biomass was observed, suggesting an intermediate-productivity hypothesis of maximum fire intensity within those regions. In those areas, the highest fire intensity was observed in the intermediate biomass areas, where surface (timber understory) and crown fuel availability, together with higher wind penetration, can result in crown fires. On the contrary, within the most productive areas, the lowest intensity occurred, likely due to weather and fuel (timber litter) limitations.
... Comparisons of FRP are typically performed using fire clusters or aggregations Chatzopoulos-Vouzoglanis et al. 2022) due to the high variability in FRP measurements for individual pixels related to the location of a fire within the pixel Freeborn et al. 2014b;Li et al. 2018;Roberts et al. 2018b;Fu et al. 2020), and the along-scan pixel overlap, which leads to an overestimation of FRP on a per pixel basis (Schroeder et al. 2010). Freeborn et al. (2014a) used 0.5° grid aggregations when evaluating the SEVIRI Fire Thermal Anomaly (FTA) algorithm, comparing the seasonal sum of FRP within each grid cell to MODIS retrievals, while Roberts et al. (2005) intercompared total FRP retrievals from SEVIRI and MODIS, both in terms of fire clusters and regional-scale retrievals. ...
... VIIRS detection rates have been found to exceed 80% for fires larger than 10 ha in savannahs validated against Landsat-derived burned area maps, while the short duration and high spread rates of savannah fires resulted in a higher commission and omission rate than forest biomes (Oliva and Schroeder 2015). Low intensity fires are unlikely to be detected by GEO satellite products due to the far larger pixel footprint, resulting in significant omission errors (Roberts et al. 2018b). ...
Satellite remote sensing is a critical tool for continental and synoptic monitoring and mapping of savannah wildfires. Satellite active fire products, which report on the time and location of a fire and may further characterise fire by estimating fire radiative power (FRP), provide valuable utility for savannah fire management and carbon accounting. These applications require that satellite measurements are of high accuracy, which can only be determined through validation. However, acquiring reference data for validation that is a representative of the fire conditions at the time of satellite image capture is challenging, due to rapid changes in fire behaviour and the inherent safety considerations of collecting field data during fire events. This review explores traditional and contemporary methods used to assess the accuracy and consistency of fire detections and FRP derived from satellite data in savannah ecosystems, with a focus on the approaches and challenges in collecting suitable reference data for a phenomenon as dynamic, ephemeral, and hazardous as wildfire. From this synthesis, we present generalised frameworks for the validation and intercomparison of satellite active fire products within savannah ecosystems.
... Where stated within the article, six of the studies only sampled during one season, [25,31,33,[42][43][44]. This is despite differences in ambient air quality, meteorological differences across seasons, and changes in emission sources and rates of use over seasons; solid fuel burning for example increases in the winter for heating purposes [100][101][102]. De la Sota et al. [19], Kumar et al. [32] and Shezi et al. [52] stated that ambient air quality was not monitored in their respective studies irrespective of the potential of ambient pollutants entering into the indoor environment [99,103]. ...
... However, converting the product to biomass in kg/DM following the methodology of Oliva et al. [10] shows that the MODIS data underestimates above ground productivity by a factor of 2.7 on average, which would lead to substantially underestimated forage availability values with MODIS data and an amount that would not be able to support the field counted livestock population. This finding is supported by existing research stating a pronounced underestimation of the MODIS product, particularly in arid regions [115][116][117] and higher errors of the approach compared to our results [118]. Finally, reports of overestimation of the MODIS data exist [119]. ...
Dry rangelands provide resources for half of the world's livestock, but degradation due to overgrazing is a major threat to system sustainability. Existing carrying capacity assessments are limited by low spatiotemporal resolution and high generalization, which hampers applied ecological management decisions. This paper provides an example for deriving the carrying capacity and utilization levels for cold drylands at a new level of detail by including major parts of the transhumance system. We combined field data on vegetation biomass and communities, forage quality, productivity, livestock species and quantities, grazing areas and their spatiotemporal variations with Sentinel-2 and MODIS snow cover satellite imagery to develop maps of forage requirements and availability. These products were used to calculate carrying capacity and grazing potential in the Pamir-Hindukush Mountains. Results showed high spatial variability of utilization rates between 5% and 77%. About 30% of the area showed unsustainable grazing above the carrying capacity. Utilization rates displayed strong spatial differences with unsustainable grazing in winter pastures and at lower elevations, and low rates at higher altitudes. The forage requirements of wild herbivores (ungulates and marmots) were estimated to be negligible compared to livestock, with one tenth of the biomass consumption and no increase in unsustainably grazed pastures due to the wider distribution of animals. The assessment was sensitive to model parameterization of forage requirements and demand, whereby conservative scenarios, i.e. lower fodder availability or higher fodder requirements of livestock due to climate and altitude effects, increased the area with unsustainable grazing practices to 50%. The presented approach enables an in-depth evaluation of the carrying capacity and corresponding management actions. It includes new variables relevant for transhumance systems, such as the combination of forage quantity and quality or accessibility restrictions due to snow, and shows utilization patterns at high spatial resolutions. Regional maps allow the identification of unsustainable utilization areas, such as winter pastures in this study.
... Kremens et al., 2012;Sparks et al., 2017), but should be limited to situations where estimates of radiant energy release at a point are required. FRE estimates are best achieved from GEO data, because high imaging frequencies provide the best temporal sampling (Freeborn et al., 2009;Roberts and Wooster, 2008;Li et al., 2018;Ellicott et al., 2009;Roberts et al., 2018a). However, the typically coarser pixel areas of GEO sensors mean they often fail to detect the lower FRP component of a region's fire regime, and a single GEO imager provides neither global coverage nor high-quality observations at very high latitudes (Fig. 4). ...
... Hyer et al., 2013;Xu et al., 2017) and Africa (e. g. Roberts et al., 2009Roberts et al., , 2018a (Fig. 12). ...
Landscape fire is a widespread, somewhat unpredictable phenomena that plays an important part in Earth's biogeochemical cycling. In many biomes worldwide fire also provides multiple ecological benefits, but in certain circumstances can also pose a risk to life and infrastructure, lead to net increases in atmospheric greenhouse gas concentrations, and to degradation in air quality and consequently human health. Accurate, timely and frequently updated information on landscape fire activity is essential to improve our understanding of the drivers and impacts of this form of biomass burning, as well as to aid fire management. This information can only be provided using satellite Earth Observation approaches, and remote sensing of active fire is one of the key techniques used. This form of Earth Observation is based on detecting the signature of the (mostly infrared) electromagnetic radiation emitted as biomass burns. Since the early 1980's, active fire (AF) remote sensing conducted using Earth orbiting (LEO) satellites has been deployed in certain regions of the world to map the location and timing of landscape fire occurrence, and from the early 2000's global-scale information updated multiple times per day has been easily available to all. Geostationary (GEO) satellites provide even higher frequency AF information, more than 100 times per day in some cases, and both LEO- and GEO-derived AF products now often include estimates of a fires characteristics, such as its fire radiative power (FRP) output, in addition to the fires detection. AF data provide information relevant to fire activity ongoing when the EO data were collected, and this can be delivered with very low latency times to support applications such as air quality forecasting. Here we summarize the history of achievements in the field of active fire remote sensing, review the physical basis of the approaches used, the nature of the AF detection and characterization techniques deployed, and highlight some of the key current capabilities and applications. Finally, we list some important developments we believe deserve focus in future years.
... Despite this limitation, the single-channel method algorithm has become the de facto standard for the quantitative characterizations of active fires and has been implemented for both geostationary and polar-orbiting missions and is now available for the geostationary Meteosat Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor [18], Geostationary Operational Environmental Satellite (GOES) -E (East) and GOES-W (West) [19,20] and Himawari-8 [21] as well as the polar orbiting sensors Visible Infrared Imaging Radiometer Suite (VIIRS) ( [22,23], MODIS [24] and Sea and Land Surface Temperature Radiometer (SLSTR) [25,26]. The relationship between FRP and fuel consumption is used to drive the fire emissions component of the Copernicus Global Atmosphere Monitoring Services (CAMS) [27] and has been used to estimate fuel consumption per unit area burned [28][29][30]. This latter approach can be implemented through integration of (ideally continuous) FRP observations to yield the total fire radiative energy (FRE) over a fire event, which is then converted to fuel consumption (FC) through application of scaling factors, and finally divided by the burned area associated with the event. ...
... To derive fuel consumption (FC), we used temporal integration to estimate total fire radiative energy for a particular fire event following the conversion of FRE to fuel consumption described in [16], enhancing the conversion factor from FRE to FC as proposed by [28]. This enhancement is applied as FC derived from satellite FRP tends to underestimate true FC [28,29]. Temporal integration is derived from Meteosat FRP retrievals, based on an approach first presented by Boschetti and Roy for MODIS [30], and implemented for Meteosat by Roberts [29]. ...
... This enhancement is applied as FC derived from satellite FRP tends to underestimate true FC [28,29]. Temporal integration is derived from Meteosat FRP retrievals, based on an approach first presented by Boschetti and Roy for MODIS [30], and implemented for Meteosat by Roberts [29]. We use FRP derived from fire detections over the areas covered by the S-2 and VIIRS fire fronts and their connectors. ...
Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS over-passes. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire ra-diative energy release over the observed fire fronts. This was then converted to total fuel con-sumption, and, by making use of Sentinel- 2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fireline intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first di-rect estimate of Byram’s fireline intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also con-sidering upcoming sensor systems, are discussed.
... This lag has been hypothesized to have been caused by a shift to more burning of woody biomass late in the fire season, causing increased emissions of less oxidized compounds such as CO and NH 3 (van der Werf et al., 2006;Zheng et al., 2018). Woody biomass is widely burned in the southern biomass burning region (Sinha et al., 2004), but is less important in the northern region (Roberts et al., 2018). NH 3 VCDs tend to begin their seasonal increase before the onset of the rainy season ( Figure 5), suggesting that fertilizer applications-which typically occur after the start of the rainy season-may not be a major contributor to the observed seasonal variation in NH 3 VCDs. ...
Ammonia (NH3) and nitrogen oxides (NOx: nitrogen dioxide [NO2] + nitric oxide [NO]) play important roles in atmospheric chemistry. Throughout most of Africa, emissions of these gases are predominantly from soils and biomass burning. Here we use observations of tropospheric NO2 vertical column densities (VCDs) from the Ozone Monitoring Instrument from 2005 through 2017 and atmospheric NH3 VCDs from the Infrared Atmospheric Sounding Interferometer from 2008 through 2017 to evaluate seasonal variation of NO2 and NH3 VCDs across Africa and in seven African ecoregions. In regions where mean annual precipitation (MAP) is under 500 mm yr⁻¹, we find that NO2 and NH3 VCDs are positively related to monthly precipitation, and where MAP is between 500 and 1,750 mm yr⁻¹ or higher, NO2 VCDs are negatively related to monthly precipitation. In dry ecoregions, temperature and precipitation were important predictors of NH3 and NO2 VCDs, likely related to variation in soil emissions. In mesic ecoregions, monthly NO2 VCDs were strongly related to burned area, suggesting that biomass burning drives seasonality. NH3 VCDs in mesic ecoregions were positively related to both monthly temperature and monthly carbon monoxide (CO) VCDs, suggesting that a mixture of soil and biomass burning emissions influenced NH3 seasonality. In northern mesic ecoregions, monthly temperature explained most of the variance in monthly NH3 VCDs, suggesting that soil sources, including animal excreta, determined NH3 seasonality. In southern mesic ecoregions, monthly CO VCDs explained more variation in NH3 VCDs than temperature, suggesting that biomass burning may have greater influence over NH3 seasonality.
... The approach will form the basis of a new fire emissions product to be delivered by the EUMETSAT Land Surface Analysis Satellite Application Facility (http://landsaf.meteo.pt), and will in future be extended back to 2004 using the full Meteosat SEVIRI FRP archive already exploited to study African fires by Roberts et al. (2018b). ...
We provide major updates to the ‘top down’ Fire Radiative Energy Emissions (FREM) approach to biomass burning emissions calculations, bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used ‘bottom up’ approaches. The FREM approach links satellite observations of fire radiative power (FRP) to emission rates of total particulate matter (TPM) via spatially varying smoke emissions coefficients (g.MJ⁻¹) – each derived from matchups of FRP and smoke plume aerosol optical depth (AOD). In the original FREMv1 approach, FRP data came from the geostationary Meteosat satellite and AOD data from the 10 km spatial resolution MODIS MOD04 aerosol product. However, the latter often performs quite poorly close to biomass burning sources due to its large 10 km pixels, bias at high MODIS view zenith angles, and saturation and/or removal of areas of high AOD - limitations introducing bias and uncertainty into the final FREM-derived smoke emissions estimates. We address each of these issues through a series of significant methodological and input data improvements, including exploitation of the 1 km MODIS MAIAC AOD product that performs far better close to fire sources. We use our FREMv2 methodology to generate a new pan-African fire emissions inventory for TPM and the carbonaceous gases CO2, CO and CH4, and our annual mean TPM emissions are within 11% of those of the MODIS-based FEER top-down approach, but significantly higher than those of GFASv1.2 and GFEDv4.1s (by 114% and 69% respectively) - agreeing with independent assessments that aerosol emissions of GFASv1.2 require upscaling by a factor of 2 to 3.4 to deliver matching magnitudes between modelled and observed AODs. From our carbonaceous emissions totals we map dry matter consumed (DMC) across Africa, and dividing this by the FireCCISFD11 20 m burned area product we provide one of the first data-driven pan-African maps of fuel consumption per unit area (kg.m⁻²) which in many areas is higher than in GFEDv4.1s. Our estimates represent the highest spatio-temporal resolution biomass burning emissions data yet available over Africa, and significantly advance the aim of a pan-tropical and mid-latitude inventory based on FRP from the global geostationary satellite network (Meteosat, Meteosat IOD, GOES and Himawari).
... Fire activity across Africa is highly variable and has been previously attributed to changes in weather patterns, shifting plant communities, and human activity 3,5,[8][9][10][11] . Variation in the extent of fire burned area is widely believed to be dependent on vegetation composition and distribution 1 , as well as air and soil controls on fuel drying 12 . ...
Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.
... African vegetation is broadly known to be sensitive to climate variability (mostly in arid and semi-arid environments) (Anyamba et al., 2014;Papagiannopoulou et al., 2017) or recent CO2 fertilization (tropical regions) (Nemani et al., 2003;Zhu et al., 2016), many non-climatic factors are also reported to influence its dynamics at different spatiotemporal scales. These include land-use change and fragmentation Hobbs et al., 2008;Song et al., 2018), land management (Kiage, 2013;Stevens et al., 2016), land grabbing (Friis and Reenberg, 2010;Sulieman, 2015;Ykhanbai et al., 2014), population growth (Pricope et al., 2013), change in fire, grazing and herbivore patterns (Andela et al., 2017;Archibald and Hempson, 2016a;Roberts et al., 2018), conflicts (Bromley, 2010;Gorsevski et al., 2012), urban expansion and infrastructures (Alkemade et al., 2013;Dobson et al., 2010). While these non-climatic factors are responsible for different forms of alteration of rangeland ecosystem structure and, therefore, functioning (e.g., soil-atmosphere exchanges of water, nutrient, and carbon, ecological interaction among species, livestock-wildlife interactions, energy fluxes), nowadays, the spatial location and extent where, in Africa, climate is the predominant or subordinate driver of long-term rangeland vegetation dynamics are still unknown. ...
Rangelands are domestic or wildlife grazing lands including grasslands, woodlands, shrublands, and some extent of deserts. In Africa, rangelands cover approximately 28% (ca. 8,300,000 km2) of the continent, where they provide essential ecosystem services (e.g., meat and dairy products, water, shade, recreation, pollination) in support of the livestock rearing activities of some 270 million people. The rangelands of Africa are found where most of global rural poverty and hunger are concentrated. In other words, they occur in countries defined as some of the most vulnerable to climate change and anthropogenic transformations. Studying the way ecosystems respond to these disturbances should prioritise developing regions that directly support millions of people. However, the response of African rangelands to global environmental change, and therefore their capacity to sustain people’s livelihood, has not been studied in detail. Based on three decades of optical and microwave satellite data, and a dynamic global vegetation model, this study represents the first African-scale assessment of long-term rangeland vegetation dynamics. Overall, findings revealed that African rangelands greened- up between 1982 and 2015 (ca. 3,500,000 km2 greening vs. ca. 700,000 km2 browning), thus supporting the recent evidence of a greening Earth. In addition, while most (ca. 2,400,000 km2) changes in rangeland vegetation resulted to be controlled by climate (climatic-driven rangelands), there exist substantial areas (ca. 1,800,000 km2) where this is not the case (non-climatic-driven rangelands). This evidence may imply that many biogeochemical models, where climate is the main input information for vegetation growth simulations, might not capture the complete trajectory of current and future changes in biosphere-atmosphere interactions. Importantly, the investigation of long-term changes in the vegetation composition highlighted that a switch in the woody and herbaceous vegetation coverage occurred. While climatic greening (ca. 2,200,000 km2) resulted from positive trends in both woody and herbaceous cover, non-climatic greening (ca. 1,400,000 km2) was associated with an increase in woody cover and a concomitant decline in herbaceous vegetation. Opposite evidence, i.e., decreased woody cover and increased herbaceous vegetation, was observed in non-climatic browning rangelands (ca. 400,000 km2). These results suggest that while greening boosts climate change mitigation via high carbon uptake, the encroachment of woody species likely shortens the resources available to pastoral communities. On the other hand, woody-controlled browning attenuates carbon sequestration rates, but higher herbaceous cover may inform of potential more forage for pastoralists.
