nasa-gesdisc/es-publications-researchareas huggingface dataset:
Observational evidence of wildfire-promoting soil moisture anomalies (2020)

Wildfires can destroy property and vegetation, thereby threatening peoples livelihoods and food security. Soil moisture and biomass are important determinants of wildfire hazard. Corresponding novel satellite-based observations therefore present an opportunity to better understand these disasters globally and across different climate regions. We sampled 9,840 large wildfire events from around the globe, between 2001 and 2018, along with respective surface soil moisture and biomass data. Using composites across fire events in similar climate regions, we show contrasting soil moisture anomalies in space and time preceding large wildfires. In arid regions, wetter-than-average soils facilitate sufficient biomass growth required to fuel large fires. In contrast, in humid regions, fires are typically preceded by dry soil moisture anomalies, which create suitable ignition conditions and flammability in an otherwise too wet environment. In both regions, soil moisture anomalies continuously decrease in the months prior to fire occurrence, often from above-normal to below-normal. These signals are most pronounced in sparsely populated areas with low human influence, and for larger fires. Resolving natural soil moisturefire interactions supports fire modelling and facilitates improved fire predictions and early warning.

Research areas: Wildfires
Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya (2021)

The recent episodes of forest fires in Brazil and Australia of 2019 are tragic reminders of the hazards of forest fire. Globally incidents of forest fire events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using a forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest fire. Model validation criteria like ROC curve, correlation coefficient, and Cohen's Kappa show a good predictive ability (AUC = 0.95, COR = 0.81,  = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

Research areas: Wildfires
A parameterization of long-continuing-current (LCC) lightning in the lightning submodel LNOX (version 3.0) of the Modular Earth Submodel System (MESSy, version 2.54) (2022)

Abstract. Lightning flashes can produce a discharge in which a continuing electrical current flows for more than 40 ms. Such flashes are proposed to be the main precursors of lightning-ignited wildfires and also to trigger sprite discharges in the mesosphere. However, lightning parameterizations implemented in global atmospheric models do not include information about the continuing electrical current of flashes. The continuing current of lightning flashes cannot be detected by conventional lightning location systems. Instead, these so-called long-continuing-current (LCC) flashes are commonly observed by extremely low-frequency (ELF) sensors and by optical instruments located in space. Reports of LCC lightning flashes tend to occur in winter and oceanic thunderstorms, which suggests a connection between weak convection and the occurrence of this type of discharge. In this study, we develop a parameterization of LCC lightning flashes based on a climatology derived from optical lightning measurements reported by the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS) between March 2017 and March 2020. We use meteorological data from reanalyses to develop a global parameterization that uses vertical velocity at the 450 hPa pressure level as a proxy for the ratio of LCC to total lightning in thunderstorms. We implement this parameterization into the LNOX submodel of the Modular Earth Submodel System (MESSy) for usage within the European Center HAMburg general circulation model (ECHAM)/MESSy Atmospheric Chemistry (EMAC) model and compare the observed and simulated climatologies of LCC lightning flashes using six different lightning parameterizations. We find that the best agreement between the simulated and observed spatial distribution is obtained when using a novel combined lightning parameterization based on the cloud-top height over land and on the convective precipitation over ocean.

Research areas: Atmospheric/Ocean Indicators
Observed changes in fire patterns and possible drivers over Central Africa (2020)

Abstract
            
              Fire is an integral part of Earths system that links regional and global biogeochemical cycles, human activities, and ecosystems. Global estimates for biomass burning indicate that Africa is responsible for ~70% of global burned area and ~50% of fire-related carbon emissions. Previous studies have documented an overall decline in burned area in the African continent, but changes in fire patterns, such as the frequency and size of different fire categories, have not been assessed. In this study, long-term fire trends were investigated using the latest burned area data from the MODerate resolution Imaging Spectroradiometer (MODIS) and the Global Fire Emission Database (GFED4s) over Central Africa (10E40E, 15N15S). A 3D (latitude, longitude, time) connected-component labeling algorithm was applied to identify individual fires and their sizes. The results show a decline in burned area by 2.73.2 Mha yr
              1
              (~1.3% yr
              1
              ) for the period 20032017, particularly in northern Central Africa. This decline was attributed to significant decreases in both fire frequency and size, particularly for large fires (>100 ha) which contribute to ~90% of the total burned area. Burned area declined in tropical savannas and grasslands but increased at the edges of the Congolese rainforest. A random forest regression model was applied to quantify the influences of climatic conditions, fuel availability, and agricultural activity on burned area changes. Overall, suppressed fuel, increased dry season length, and decreased rainfall contributed to significant declines in burned area in savannas and grasslands. At the edges of the southern Congolese rainforest, suppressed rainfall and warmer temperature were responsible for the increased burned area.

Research areas: Wildfires
Changes in Growing Season Phenology Following Wildfires in Alaska (2020)

Trends and geographic patterns of change in vegetation phenology metrics and snowmelt timing from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed across the state of Alaska for all wildfires that burned during the years 2004 and 2005. Phenology metric patterns (over the period 2000 to 2018) derived from the normalized difference vegetation index (NDVI) time-series at 250-m resolution tracked changes in the growing season length and integrated greenness cover over the past two decades. NDVI metrics showed that end of the growing season timing (EOST) and integrated greenness increased significantly in the majority of severely burned areas in Interior Alaska over the past decade particularly in low elevation zones below 500 m. In years with relatively early snowmelt dates (3 to 10 days earlier than the long-term mean), lower plant growth was observed over the ensuing growing season, potentially due to lower snow water inputs that can maintain available soil moisture levels for plant growth into the mid- and late-summer months. Statewide trends in MODIS phenology metrics indicate that the predominant vegetation cover type in most regrowing burned areas of Interior Alaska a decade post-fire is still deciduous shrub and young tree cover. Several large fires of special interest were identified to monitor with remote sensing and continue to track long-term recovery patterns and rates.

Research areas: Wildfires
Water quality and forest restoration in the Lake Tahoe basin: impacts of future management options (2022)

Land managers in the Lake Tahoe basin are considering increasing the use of prescribed fire and forest thinning to restore conditions that will be more resilient to wildfires. However, such restorative treatments also constitute disturbances that could increase sediment and nutrient loads. We examined whether the water-quality impacts from future treatments are likely to be lower compared to the potential impacts from future wildfires under various climate change scenarios. We applied an online interface for the Water Erosion Prediction Project (WEPP) model in combination with a landscape change model (LANDIS-II) to evaluate the effects of different combinations of thinning and prescribed burning on fine sediment (< 2 mm), very fine sediment (< 16 m), and phosphorus over time. First, we generated results based on historic weather data for soil disturbance conditions, including: an undisturbed baseline, a uniform thinning treatment; a uniform prescribed fire treatment; and uniform low, moderate, and high wildfire burn severity. Residual ground cover declined in that order, and expected loads of sediment and phosphorus increased. We then combined the estimated loads from hillslopes with projected management-disturbance regimes across each decade of the next century. We found that expected sediment and phosphorus loads were lower under the scenario that emphasized thinning, whereas scenarios that increased prescribed burning resulted in loads that were comparable to scenarios that involved less treatment. These results reflect the finding from the WEPP analysis that prescribed burning is expected to reduce ground cover more than is thinning. Our analysis supports efforts to increase fuel reduction treatments to mitigate future wildfires, but it also suggests that preventative treatments may not avoid a long-term decline in water quality as wildfires increase with climate change.

Research areas: Habitat Conversion/Fragmentation
Modeling the relationships between historical redlining, urban heat, and heat-related emergency department visits: An examination of 11 Texas cities (2021)

Place-based structural inequalities can have critical implications for the health of vulnerable populations. Historical urban policies, such as redlining, have contributed to current inequalities in exposure to intra-urban heat. However, it is unknown whether these spatial inequalities are associated with disparities in heat-related health outcomes. The aim of this study is to determine the relationships between historical redlining, intra-urban heat conditions, and heat-related emergency department visits using data from 11 Texas cities. At the zip code level, the proportion of historical redlining was determined, and heat exposure was measured using daytime and nighttime land surface temperature (LST). Heat-related inpatient and outpatient rates were calculated based on emergency department visit data that included ten categories of heat-related diseases between 2016 and 2019. Regression or spatial error/lag models revealed significant associations between higher proportions of redlined areas in the neighborhood and higher LST (Coef. = 0.0122, 95% CI = 0.00390.0205). After adjusting for indicators of social vulnerability, neighborhoods with higher proportions of redlining showed significantly elevated heat-related outpatient visit rate (Coef. = 0.0036, 95% CI = 0.00070.0066) and inpatient admission rate (Coef. = 0.0018, 95% CI = 0.00010.0035). These results highlight the role of historical discriminatory policies on the disparities of heat-related illness and suggest a need for equity-based urban heat planning and management strategies.

Research areas: Heat
Using TES retrievals to investigate PAN in North American biomass burning plumes (2018)

Abstract. Peroxyacyl nitrate (PAN) is a critical atmospheric reservoir for nitrogen oxide radicals, and plays a lead role in their redistribution in the troposphere. We analyze new Tropospheric Emission Spectrometer (TES) PAN observations over North America from July 2006 to July 2009. Using aircraft observations from the Colorado Front Range, we demonstrate that TES can be sensitive to elevated PAN in the boundary layer ( 750 hPa) even in the presence of clouds. In situ observations have shown that wildfire emissions can rapidly produce PAN, and PAN decomposition is an important component of ozone production in smoke plumes. We identify smoke-impacted TES PAN retrievals by co-location with NOAA Hazard Mapping System (HMS) smoke plumes. Depending on the year, 1532 % of cases where elevated PAN is identified in TES observations (retrievals with degrees of freedom (DOF) > 0.6) overlap smoke plumes during July. Of all the retrievals attempted in the July 2006 to July 2009 study period, 18 % is associated with smoke . A case study of smoke transport in July 2007 illustrates that PAN enhancements associated with HMS smoke plumes can be connected to fire complexes, providing evidence that TES is sufficiently sensitive to measure elevated PAN several days downwind of major fires. Using a subset of retrievals with TES 510 hPa carbon monoxide (CO) > 150 ppbv, and multiple estimates of background PAN, we calculate enhancement ratios for tropospheric average PAN relative to CO in smoke-impacted retrievals. Most of the TES-based enhancement ratios fall within the range calculated from in situ measurements.

Research areas: Air Quality
Environmental and political implications of underestimated cropland burning in Ukraine (2021)

Abstract
            Open burning is illegal in Ukraine, yet Ukraine has, on average, 300 times more fire activity per year (20012019) 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 Ukraines 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.

Research areas: Environmental Impacts
Unfolding the contribution of environmental and anthropogenic variables in forest fire over western Himalayan fire regime (2021)

In last few decades, a surge of uncontrolled wild and forest fire has been observed over biomes, mostly from tropical and subtropical regions. The present study has disentangled the contribution of different environmental and anthropogenic factors in forest fire over the western Himalayan (Uttarakhand and Himachal Pradesh) fire regime, which is an active fire hotspot in India. Fire-CCI v5.1 data was used to labelled fire and non-fire pixel. The climatic (e.g. maximum and minimum temperature, precipitation, solar radiation, vapour pressure, wind speed, water vapour deficit, soil moisture and palmer drought index), physiographic (elevation, slope, aspect and roughness), anthropogenic (population density and human modification) and locational (latitude and longitude) variables were utilized to unfold their contribution in forest fire by the aid of Random Forest (RF) a machine learning technique. After parameterization, a 10-fold cross-validation RF model was built over the whole dataset and the average overall accuracy, precision, recall, F-1 score and overall accuracy were estimated as 0.94 (0.002), 0.86 (0.003), 0.91 (0.002) and 0.91 (0.002), respectively. Furthermore, the whole dataset (2005-2018) was divided into two parts, training set (2005-2017) and testing (2018), to get a robust model. The testing accuracy (overall accuracy = 0.82, precision =0.79, recall = 0.95, F1 score = 0.86 and area under curve (AUC) = 0.95) suggested a reliable performance of RF model in forest fire classification (fire and non-fire). The contributions of the selected variables were retrieved from the feature importance of the RF model. The maximum temperature exhibited the highest importance, followed by elevation, minimum temperature and location variable (latitude and longitude). The population density and human modification (gHM) are moderately contributing to western Himalayan forest fire. Keywords: Forest fire; Western Himalaya; Random Forest

Research areas: Wildfires
Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction (2018)

To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m2) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m2 resolution).

Research areas: Wildfires
2019-20 Australian Bushfires and Anomalies in Carbon Monoxide Surface (2021)

In Australia, bushfires are a natural part of the countrys landscape and essential for the regeneration of plant species; however, the 201920 bushfires were unprecedented in their extent and intensity. This paper is focused on the 201920 Australian bushfires and the resulting surface and column atmospheric carbon monoxide (CO) anomalies around Wollongong. Column CO data from the ground-based Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) site in Wollongong are used together with surface in situ measurements. A systematic comparison was performed between the surface in situ and column measurements of CO to better understand whether column measurements can be used as an estimate of the surface concentrations. If so, satellite column measurements of CO could be used to estimate the exposure of humans to CO and other fire-related pollutants. We find that the enhancements in the column measurements are not always significantly evident in the corresponding surface measurements. Topographical features play a key role in determining the surface exposures from column abundance especially in a coastal city like Wollongong. The topography at Wollongong, combined with meteorological effects, potentially exacerbates differences in the column and surface. Hence, satellite column amounts are unlikely to provide an accurate reflection of exposure at the ground during major events like the 20192020 bushfires.

Research areas: Wildfires
A low-order dynamical model for fire-vegetation-climate interactions (2022)

Abstract
            Climate conditions play a key role in determining the occurrence and severity of wildfires. Despite the impacts of wildfires on ecosystems, human livelihoods, and air quality, little is known conceptually about how natural or anthropogenic shifts in climate may influence the fire activity on a regional or global scale. Here, we introduce a new low order dynamical model that describes the nonlinear interactions between climate, vegetation (fire fuel) and fire probabilities. This 1-dimensional model describes the influence of precipitation and temperature on burned area and fuel availability. Estimating key parameters from observations, the model successfully reproduces the spatio-temporal variability of wildfire occurrences, particularly, in semi-arid regions in Africa, South America, and northern Australia. The fidelity of the model translates into a high degree of longer-term predictability of fire conditions in these vulnerable regions. Our new low-order modeling framework may provide guidance to forestry managers to assess fire risks under present and future climate conditions.

Research areas: Wildfires
Mapping Wetland Burned Area from Sentinel-2 across the Southeastern (2021)

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (20162019) across the Southeastern US (>290,000 km2). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters (n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.

Research areas: Wildfires