Grassland Fires Research Articles

Dominant C3 tussock grasses are resilient to the re‐introduction of fire in long‐unburned temperate grasslands

AbstractQuestionsThe recovery of dominant tussock grasses following fire is crucial as they contribute disproportionately to the structure and function of grasslands. To determine how resilient native grasslands are to the re‐introduction of fire, we quantify how fire affects the resprouting of the dominant C3 tussock grasses in long‐unburned grasslands (Austrostipa spp.) and compare this to the recovery of C4 tussock grasses (Themeda triandra) in grasslands that have had regular exposure to fire. We also quantify if these two grassland types burn in different ways as a result of their different dominant grass compositions.LocationTemperate native tussock grasslands on the Victorian volcanic plain, Australia.MethodsTo determine how fire affects tussock mortality, and whether this differs between C4 (regularly ) and C3 (long‐unburned) grasslands, tussocks were permanently marked pre‐fire and re‐censused for survival and growth at seven weeks after fire. To assess whether C3 grasslands burn in a different way to C4 grasslands, fire behaviour was quantified at 11 sites.ResultsFire behaviour was similar in both grassland types. Both C3 and C4 grass tussocks were resilient to fire, despite decades of fire suppression in C3 grasslands. Tussock mortality was low (<5%) in both C3 and C4 grasses. Survival was independent of basal circumference in C4 Themeda tussocks, but smaller‐sized tussocks (<10 cm circumference) were less likely to survive fire in C3 Austrostipa grasslands. Basal area declined in both grasses after fire but rate of tiller regrowth after fire was similar for both grass types.ConclusionLong‐unburned C3 grass tussocks were resilient to a single fire. Future studies should examine the response of C3 grasses to a fire regime that comprises frequent return intervals to which C4 grasses are resilient; this may indicate whether C3 grasses are sensitive to short fire‐return intervals despite resilience to an individual fire event.

Vegetation recovery after fire in mountain grasslands of Argentina

Fire is a natural disturbance occurring every few years in many grasslands ecosystems. However, since European colonization, fire has been highly reduced or even suppressed in Argentinean grasslands, fostering ignitable material accumulation. This has led to occasional catastrophic control-demanding fire events, extended for larger areas. The aims of this work are to study vegetation recovery and change after a non-natural fire event in mountain grasslands. The study area is located in the Ventania mountain system, mid-eastern Argentina. We studied vegetation recovery after fire (January 2014) in two different communities: grass-steppes (grasslands) and shrub-steppes (open low shrublands). We measured vegetation cover, species richness and bare ground percentage in burned and unburned areas 1, 4, 8, 11 and 23 months after fire. Vegetation surveys were also performed at the end of the growing season (December) 11 and 23 months after fire. Data were analyzed using regression analysis, ANOVA and multivariate analysis (NMS, PERMANOVA). Both communities increased their vegetation cover at the same rate, without differences between burned and unburned areas after two years. Species richness was higher in shrublands and their recovery was also faster than in grasslands. Considering functional composition, besides transient changes during the first year after fire, there were no differences in abundance of different functional vegetation groups two years after fire. At the same time, shrublands showed no differences in species composition, while grasslands had a different species composition in burned and unburned plots. Also, burned grassland showed a higher species richness than unburned grassland. Data shown mountain vegetation in Pampas grassland is adapted to fire, recovering cover and richness rapidly after fire and thus reducing soil erosion risks. Vegetation in mountain Pampas seems to be well adapted to fire, but in grasslands species composition has changed due to fire. Nonetheless, these changes seem to be not permanent since pre-fire species are still present in the area.

Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne, Australia

ABSTRACT As one of the main components of Grassland Fire Danger Index, grassland curing degree provides crucial information for determining grassland fire danger. Accurate estimates of grassland curing are critical for determining grassland fire risk. This research focuses on the use of Landsat 8 to estimate grassland curing. Results demonstrate that Landsat 8 observations can be used to estimate curing percentages as assessed by visual and ground sampling measurements. Grassland interannual variability for the Greater Melbourne region using Landsat 8 imagery from 2013 to 2019 is examined. Slight differences in curing times and degree are observed for sample sites surrounding Greater Melbourne due to climatic differences across the region. Precipitation is regarded as an essential variable affecting curing degree and this relationship is evident for all five sample sites. Landsat 8 curing results are compared to both visual observations and destructive sampling, the most accurate method, for accuracy assessment. At 95% confidence level, Landsat 8 estimations are no different from destructive ground sampling estimations. Overall, this study validates the use of Landsat 8 data as an effective and accurate way for grassland curing monitoring.

An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems

Fire is used extensively in prairie grassland management in the Flint Hills region of the midwestern United States, particularly at the end of the dormant season (March–April). A model is used to manage grassland fires in the region to avoid deterioration of air quality beyond acceptable standards. Dormant season dry biomass is an important parameter in the model. The commonly used method for producing high-quality biomass estimates relies on clipping, drying and weighing small biomass samples, which is tedious, expensive and does not scale efficiently to larger areas to provide regional estimates. Small unmanned aircraft systems (sUAS) were used to develop a reliable and more efficient method of biomass estimation based on the correlation between biomass and vegetation canopy height derived from digital surface models (DSMs). A linear regression model was developed from data collected at 11 representative sites in the Kansas Flint Hills region, and the model was validated at two sites. Biomass and canopy heights derived from DSMs were correlated, with a Pearson product moment correlation value of 0.881 (P-value <0.001). Biomass estimated from clipped vegetation at two validation sites positively correlated with model-derived biomass estimates, resulting in linear regression R2-values of 0.90 and 0.74 and Pearson moment correlation coefficients of 0.99 (P<0.001) and 0.86 (P=0.003). The described sUAS method has the potential to increase the efficiency and reliability of dormant season grassland biomass estimates.

Observations of ozone formation in southern African savanna and grassland fire plumes

Open biomass burning is one of the largest sources of aerosols and reactive trace gases into the atmosphere, having a significant effect on earth's radiative budget and air quality. Biomass burning degrades air quality by increasing both particulate matter and ozone levels. Unlike aerosols, ozone is not directly emitted in fires but it is frequently formed in ageing plumes. This is not surprising, as many of the reactive trace gases emitted in biomass burning including nitrogen oxides, carbon monoxide and volatile organic compounds, act as precursors for ozone formation. Geographically, Africa is the most significant source region of biomass burning emissions. Here, we report rapid ozone formation in daytime savanna and grassland fire plumes observed in South Africa. We observed higher ozone production in more flaming cases for plumes fresher than 1.5h. However, in plumes aged >1.5h combustion characteristics have no effect, but ozone production is positively correlated with nitrogen oxides. Furthermore, the plumes with highest ozone production also had the strongest secondary aerosol formation during plume ageing. Additionally, we report emission factors of nitric oxide and sulphur dioxide for southern African savanna and grassland fires. SO2 emission factor was on average 1.1 g kg−1, which is two to three times higher than previous observations for savanna and grassland. On the other hand, the average NO emission factor (2.6 g kg−1) agrees well with previous observations.

Effects and behaviour of experimental fires in grasslands, savannas, and forests of the Brazilian Cerrado

An understanding of fire behaviour and its impacts on natural ecosystems is essential for developing management strategies, specifically for planning prescribed fires. It is well known that vegetation and microclimate factors are important predictors of fire behaviour. However, the relative importance of these factors and how fire behaviour responds to changes in these factors are still understood, especially in the Brazilian Cerrado, which has strong seasonality and high vegetation heterogeneity. Here, we compiled literature data from 65 experimental fire plots in three vegetation types in the Cerrado biome—grasslands, savannas, and forests—corresponding to a gradient of increasing of woody cover. First, we evaluated how aspects of fire behaviour (fire spread rate, fire intensity, and heat released) and of fire-associated emissions (fine fuel consumption, combustion factor, and carbon emissions associated with fine fuel consumption) vary according to vegetation type. Subsequently, we evaluated the relative importance of vegetation (represented by woody cover and fine fuel load) and microclimatic factors (represented by vapour pressure deficit [VPD] and wind speed) in determining the fire behaviour and fire-associated emissions. Using generalised linear models, we evaluated the simultaneous influences of vegetation and microclimate variables (only for independent and non-collinear variables) on fire behaviour and fire-associated emissions. We found that aspects of fire behaviour and fire-associated emissions differed among the vegetation types: compared to forests, grasslands and savannas showed higher fire spread rates and fire intensities, consumed more fine fuel biomass, had larger combustion factors, released more heat, and emitted more carbon. The results show that the vegetation type, VPD and fine fuel load are key metrics for explain fire behaviour and fire-associated emissions in the Cerrado. The VPD was found to be the primary driver of the fire spread rate and fire intensity for all vegetation types and fine fuel load was the primary driver of heat released, fine fuel consumption, combustion factor, and carbon emissions in all vegetation types.

Interaction between fire and fragmentation in the successional stages of coastal dune grasslands of the southern Pampas, Argentina

Vegetation’s increased vulnerability to extrinsic disturbances is an important but less studied effect of natural habitat fragmentation. Fire is part of the evolutionary history of grassland ecosystems, but fragmentation by forest plantations can alter the fire regime and influence their resilience. This study compares the successional trajectories after fire in continuous and fragmented grassland in terms of composition and abundance of plant species. Grassland fragments of varying sizes (0.1 to 2.5 ha) surrounded by a forest matrix and grassland controls of an equivalent area in adjacent, non-fragmented sites were selected. Fire was associated with an increase in the abundance of exotic plants in the fragmented grassland whereas the continuous grasslands were much more resistant to invasion. These differences in the species composition between fragments and continuous areas, which were limited to the smaller areas before the fire, were observed one year after the fire throughout the range of sizes analyzed. These results show the impact of fragmentation on grassland resilience and how the effects of this process become evident even months after a disturbance, highlighting the synergistic effect of habitat fragmentation and biological invasions, two factors identified as the main forces of biodiversity erosion.

Wildfires front dynamics: 3D structures and intensity at small and large scales

The 3D structure of a fire front propagating through a homogeneous porous solid-fuel layer was studied numerically at laboratory and field scales. At laboratory scale, wind-tunnel fires propagating through laser-cut cardboard fuel were numerically reproduced, while at field scale, simulations of grassland fires with quasi-infinite fire front were carried out for different wind speeds. These simulations were performed using FIRESTAR3D, based on a multiphase formulation that includes the main physical phenomena governing fire behavior. An unsteady RANS approach and a Large Eddy Simulation (LES) approach were used to simulate the reactive turbulent flow, whereas turbulent combustion was modeled using Eddy Dissipation Concept (EDC). Unlike other 3D wildfire tools available in the community, such as FIRETEC and WFDS, the model is based on an implicit, low-Mach number resolution of the governing equations, and makes no empirical assumptions in the resolution of the radiative transfer equation. The comparison with the experimental data concerned mainly the Rate of Spread (ROS) of fire, the fireline intensity, the flame-zone depth, and the wavelength characterizing the crest-and-trough structure of the fire front along the transverse direction. Particular attention was drawn to the similitude in the fire front dynamics between small and large scales. In order to highlight the physical mechanisms responsible for this dynamics, a dimensional analysis was carried out by introducing Byram's convection number NC based on the fireline intensity and Froude's numbers Fr based on the characteristic wavelength of the fire-front structure. The analysis shows that all the results (wind-tunnel fires and grassland fires, experimental and numerical) collapsed on a single scaling law in the form Fr=NC−2/3.

Ecological engineers’ nests benefit plant recovery following fire in a semiarid grassland, New Mexico, USA

AbstractQuestionsAnts and rodents are recognized as ecosystem engineers worldwide creating soil‐modified nests and adjacent edges with superior resources. The influence of Dipodomys spectabilis (rat) and Pogonomyrmex rugosus (ant) on nests and edges would be similar in burned and unburned habitat on functional groups’ cover, density, number of inflorescences and species number. We hypothesize that annual and perennial dicots would recover faster on rat patches than on burned grassland, dicots and grasses would recover faster on ant patches than on burned grassland, and annual dicots would recover faster on rat than on ant patches, whereas grasses would recover faster on ant than on rat patches.LocationSemiarid grassland, Sevilleta National Wildlife Refuge, New Mexico, USA.MethodsWe analyzed annual and perennial dicots, grasses and subshrubs in burned and unburned plots at 80 nests during cool‐ and warm‐seasons two years post‐fire. Data were collected at nest top (mound), edge, and 20 m (control) from the edge.ResultsCover, density and species richness of different functional groups were low on animal mounds in unburned and burned plots. Ant edges had greater perennial dicot cover, grass cover and richness while rat edges had lower grass density and richness than control sites at unburned and burned plots. Perennial dicot density and richness recovered faster at rat mounds and edges than control. Ant edges yielded faster recovery of grass inflorescences than control. Dicots recovered faster on rat than ant mounds whereas grasses grew faster on ant edges.ConclusionsRats create nests enhancing density of recovering dicots and ant edges facilitate reproduction of recovering grasses. Equal regeneration between animal nests and grassland occurred commonly when fire reduced grass competition. Dysochory, granivory and fire intensity may influence recovery. Nests may be sources of reestablishment and seed dispersal. Recovery of grassland may be amplified as plants infill from engineered nests.

Soil fungal community changes in response to long-term fire cessation and N fertilization in tallgrass prairie

Abstract In grasslands, fire management and fertilization are established drivers of plant community change, but associated soil fungal responses are less well defined. We predicted that soil fungal communities would change seasonally, that decades of fire cessation and nitrogen (N) fertilization would alter fungal distributions, and that plant and fungal community change would be correlated. Surface soils were sampled monthly for 1 y from a 30-y fire by fertilization experiment to evaluate fungal community dynamics and assess correlation with plant community heterogeneity. ITS gene community composition was seasonally stable, excepting increased arbuscular mycorrhizal fungal summer abundance in the burned, fertilized treatment. Long-term treatments affected soil fungal and plant communities, with correlated heterogeneity patterns. Despite woody encroachment in the fire cessation treatment, soil fungal communities did not resemble those of forests. This study provides evidence supporting the strength of feedbacks between fungal and plant community change in response to long-term grassland fire and N management changes.

Dynamic Wildfire Navigation System

Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 km 2 in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance.

Estimation of emissions from biomass burning in China (2003–2017) based on MODIS fire radiative energy data

Abstract. Biomass burning plays a significant role in air pollution and climate change. In this study, we used a method based on fire radiative energy (FRE) to develop a biomass burning emission inventory for China from 2003 to 2017. Daily fire radiative power (FRP) data derived from 1 km MODIS Thermal Anomalies/Fire products (MOD14/MYD14) were used to calculate FRE and combusted biomass. Available emission factors were assigned to four biomass burning types: forest, cropland, grassland, and shrubland fires. The farming system and crop types in different temperate zones were taken into account in this research. Compared with traditional methods, the FRE method was found to provide a more reasonable estimate of emissions from small fires. The estimated average annual emission ranges, with a 90 % confidence interval, were 91.4 (72.7–108.8) Tg CO2 yr−1, 5.0 (2.3–7.8) Tg CO yr−1, 0.24 (0.05–0.48) Tg CH4 yr−1, 1.43 (0.53–2.35) Tg NMHC yr−1, 0.23 (0.05–0.45) Tg NOx yr−1, 0.09 (0.02–0.17) Tg NH3 yr−1, 0.03 (0.01–0.05) Tg SO2 yr−1, 0.04 (0.01–0.08) Tg BC yr−1, 0.27 (0.07–0.49) Tg OC yr−1, 0.51 (0.19–0.84) Tg PM2.5 yr−1, 0.57 (0.15–1.05) Tg PM10 yr−1, where NMHC, BC, and OC are nonmethane hydrocarbons, black carbon, and organic carbon, respectively. Forest fires are determined to be the primary contributor to open fire emissions, accounting for 45 % of the total CO2 emissions (average 40.8 Tg yr−1). Crop residue burning ranked second place with a large portion of 39 % (average 35.3 Tg yr−1). During the study period, emissions from forest and grassland fires showed a significant downward trend. Crop residue emissions continued to rise during 2003–2015 but dropped by 42 % in 2015–2016. Emissions from shrubland were negligible and little changed. Forest and grassland fires are concentrated in northeastern China and southern China, especially in the dry season (from October to March of the following year). Plain areas with high crop yields, such as the North China Plain, experienced high agricultural fire emissions in harvest seasons. Most shrubland fires were located in Yunnan and Guangdong provinces. The resolution of our inventory (daily, 1 km) is much higher than previous inventories, such as GFED4s and GFASv1.0. It could be used in global and regional air quality modeling.

Risk assessment using transfer learning for grassland fires

A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods.

Biomass-burning smoke heights over the Amazon observed from space

Abstract. We characterise the vertical distribution of biomass-burning emissions across the Amazon during the biomass-burning season (July–November) with an extensive climatology of smoke plumes derived from MISR and MODIS (2005–2012) and CALIOP (2006–2012) observations. Smoke plume heights exhibit substantial variability, spanning a few hundred metres up to 6 km above the terrain. However, the majority of the smoke is located at altitudes below 2.5 km. About 60 % of smoke plumes are observed in drought years, 40 %–50 % at the peak month of the burning season (September) and 94 % over tropical forest and savanna regions, with respect to the total number of smoke plume observations. At the time of the MISR observations (10:00–11:00 LT), the highest plumes are detected over grassland fires (with an averaged maximum plume height of ∼1100 m) and the lowest plumes occur over tropical forest fires (∼800 m). A similar pattern is found later in the day (14:00–15:00 LT) with CALIOP, although at higher altitudes (2300 m grassland vs. 2000 m tropical forest), as CALIOP typically detects smoke at higher altitudes due to its later overpass time, associated with a deeper planetary boundary layer, possibly more energetic fires, and greater sensitivity to thin aerosol layers. On average, 3 %–20 % of the fires inject smoke into the free troposphere; this percentage tends to increase toward the end of the burning season (November: 15 %–40 %). We find a well-defined seasonal cycle between MISR plume heights, MODIS fire radiative power and atmospheric stability across the main biomes of the Amazon, with higher smoke plumes, more intense fires and reduced atmospheric stability conditions toward the end of the burning season. Lower smoke plume heights are detected during drought (800 m) compared to non-drought (1100 m) conditions, in particular over tropical forest and savanna fires. Drought conditions favour understory fires over tropical forest, which tend to produce smouldering combustion and low smoke injection heights. Droughts also seem to favour deeper boundary layers and the percentage of smoke plumes that reach the free troposphere is lower during these dry conditions. Consistent with previous studies, the MISR mid-visible aerosol optical depth demonstrates that smoke makes a significant contribution to the total aerosol loading over the Amazon, which in combination with lower injection heights in drought periods has important implications for air quality. This work highlights the importance of biome type, fire properties and atmospheric and drought conditions for plume dynamics and smoke loading. In addition, our study demonstrates the value of combining observations of MISR and CALIOP constraints on the vertical distribution of smoke from biomass burning over the Amazon.

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a >50% probability of obtaining weekly clear observations.

Characterizing grassland fire activity in the Flint Hills region and air quality using satellite and routine surface monitor data

Prescribed grassland fires in the Flint Hills region of central Kansas and northern Oklahoma are a common tool for land management. Local to regional scale impacts on air quality from grassland fires in this region are not well understood, which is important as these types of prescribed fires may increase in the future to preserve broader areas of native grasses in the central U.S. Routine air quality and deposition measurements from sites in and near the Flint Hills were examined for coincident increases during periods of increased prescribed grassland fires. Prescribed fire activity in this region was quantified using satellite detections and multiple publicly available data products of area burned information. March and April comprise over half (41 to 93%) of all annual fire detections in the Flint Hills region seen from satellites between 2007 and 2018 excluding drought years. Annual total fire detections in this region range between 1 and 12 thousand and account for approximately 3% of all fire detections in the contiguous U.S. Annual acres burned ranged from 0.2 to 2 million acres based on U.S. EPA's National Emission Inventory, which accounts for 4 to 38% of grasslands in the area. A comparison of weekly standardized anomalies suggests a relationship between periods of increased grassland fire activity and elevated levels of PM2.5 organic carbon, elemental carbon, and potassium. Daily 1-hr maximum ozone (O3), ammonia (NH3), sulfur dioxide (SO2), and oxidized nitrogen gases measured at Konza Prairie also had increased levels when prescribed grassland fire activity was highest. This detailed characterization of prescribed fire activity in the Flint Hills and associated air quality impacts will benefit future efforts to understand changes in atmospheric composition due to changing land management practices.

Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands

Quantitative estimations of the fractional cover of photosynthetic vegetation (fPV), non-photosynthetic vegetation (fNPV) and bare soil (fBS) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale fPV, fNPV and fBS estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term fPV, fNPV and fBS estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of fPV, fNPV and fBS in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and fNPV based on the sample scale. The linear correlation between the dead fuel index (DFI) and fNPV was best (R2 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the fPV, fNPV and fBS (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The fPV, fNPV and fBS values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands.

Grassland Fire Effects on Steel Fence Posts

Rangeland fire effects on flora, fauna, soils, and water have been studied widely. Fire effects information is limited on rangeland developments such as fencing. Fencing is an integral part of rangeland infrastructure and receives much attention when there is a wildfire or even discussion about conducting prescribed fires. Due to known fence age and fire history, we conducted a study on the Oklahoma State University Research Range located near Stillwater, Oklahoma. Five randomly selected individual metal T posts were sampled from a common fence line of known age and fire treatment. The T posts ranged from 4 to 35 yr since fence construction, with fire activity of 0, 1, 3, or 12 prescribed fires and 0, 1, or 2 wildfire occurrences. Each T post was tested in situ at 40 cm, 80 cm, and 120 cm above ground level for hardness and paint adhesion as set forth by American Society for Testing and Materials (ASTM) International standards for hot wrought metal fence posts. Our results found no differences in post hardness, with all posts tested being higher than the ASTM minimum standard of 83 Rockwell B Hardness Scale. We also found that paint adhesion between burned and unburned T posts did not vary, with age of post being the only significant predictor variable for adhesion. Overall, our results provide evidence that T posts exposed to numerous fires and fire types did not suffer negative effects. This shows that concerns about wildfire and prescribed fires in grasslands having negative effects on metal T posts are unfounded.

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real­-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.

Factors influencing nesting ecology of lesser prairie‐chickens

ABSTRACTLesser prairie‐chicken (Tympanuchus pallidicinctus) populations have declined since the 1980s. Understanding factors influencing nest‐site selection and nest survival are important for conservation and management of lesser prairie‐chicken populations. However, >75% of the extant population is in the northern extent of the range where data on breeding season ecology are lacking. We tested factors influencing fine‐scale and regional nest‐site selection and nest survival across the northern portion of the lesser prairie‐chicken range. We trapped and affixed satellite global positioning system and very high frequency transmitters to female lesser prairie‐chickens (n = 307) in south‐central and western Kansas and eastern Colorado, USA. We located and monitored 257 lesser prairie‐chicken nests from 2013 to 2016. We evaluated nest‐site selection and nest survival in comparison to vegetation composition and structure. Overall, nest‐site selection in relation to vegetation characteristics was similar across our study area. Lesser prairie‐chickens selected nest microsites with 75% visual obstruction 2.0–3.5 dm tall and 95.7% of all nests were in habitat with ≥1 dm and ≤4 dm visual obstruction. Nests were located in areas with 6–8% bare ground, on average, avoiding areas with greater percent cover of bare ground. The type of vegetation present was less important than cover of adequate height. Nest survival was maximized when 75% visual obstruction was 2.0–4.0 dm. Nest survival did not vary spatially or among years and generally increased as intensity of drought decreased throughout the study although not significantly. To provide nesting cover considering yearly variation in drought conditions, it is important to maintain residual cover by managing for structural heterogeneity of vegetation. Managing for structural heterogeneity could be accomplished by maintaining or strategically applying practices of the Conservation Reserve Program, using appropriate fire and grazing disturbances in native working grasslands, and establishing site‐specific monitoring of vegetation composition and structure. © 2018 The Wildlife Society.