Is burn severity related to fire intensity? Observations from landscape scale remote sensing

2013 ◽  
Vol 22 (7) ◽  
pp. 910 ◽  
Author(s):  
Heather Heward ◽  
Alistair M. S. Smith ◽  
David P. Roy ◽  
Wade T. Tinkham ◽  
Chad M. Hoffman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Burn Severity ◽  
Radiative Energy ◽  
Fire Intensity ◽  
Ecological Change ◽  
Fire Event ◽  
Radiative Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Burn Ratio

Biomass burning by wildland fires has significant ecological, social and economic impacts. Satellite remote sensing provides direct measurements of radiative energy released by the fire (i.e. fire intensity) and surrogate measures of ecological change due to the fire (i.e. fire or burn severity). Despite anecdotal observations causally linking fire intensity with severity, the nature of any relationship has not been examined over extended spatial scales. We compare fire intensities defined by Moderate Resolution Imaging Spectroradiometer Fire Radiative Power (MODIS FRP) products with Landsat-derived spectral burn severity indices for 16 fires across a vegetation structure continuum in the western United States. Per-pixel comparison of MODIS FRP data within individual fires with burn severity indices is not reliable because of known satellite temporal and spatial FRP undersampling. Across the fires, 69% of the variation in relative differenced normalized burn ratio was explained by the 90th percentile of MODIS FRP. Therefore, distributional MODIS FRP measures (median and 90th-percentile FRP) derived from multiple MODIS overpasses of the actively burning fire event may be used to predict potential long-term negative ecological effects for individual fires.

scholarly journals Forecasting the regional fire radiative power for regularly ignited vegetation fires

2021 ◽  
Author(s):  
Tero M. Partanen ◽  
Mikhail Sofiev
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Grid Cell ◽  
Radiative Energy ◽  
Fire Intensity ◽  
High Temporal Resolution ◽  
South Central ◽  
Radiative Power ◽  
Cloud Mask ◽  
Fire Radiative Power

Abstract. This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.

scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

scholarly journals Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018–2019 Using Multi-Source Satellite Products

2020 ◽  
Vol 12 (11) ◽  
pp. 1803 ◽  
Author(s):  
Mahlatse Kganyago ◽  
Lerato Shikwambana
Keyword(s):  
Forest Cover ◽  
Anthropogenic Activities ◽  
Management Strategies ◽  
Environmental Parameters ◽  
Fuel Load ◽  
Fire Intensity ◽  
Burned Area ◽  
Radiative Power ◽  
The Usa ◽  
Moderate Resolution Imaging Spectroradiometer

This study analysed the characteristics of the recent (2018–2019) wildfires that occurred in the USA, Brazil, and Australia using Moderate Resolution Imaging Spectroradiometer (MODIS) active fires (AF), fire radiative power (FRP, MW) and burned area (BA) products. Meteorological and environmental parameters were also analysed. The study found various patterns in the spatial distribution of fires, FRP and BA at the three sites, associated with various vegetation compositions, prevailing meteorological and environmental conditions and anthropogenic activities. We found significant fire clusters along the western and eastern coasts of the USA and Australia, respectively, while vastly distributed clusters were found in Brazil. Across all sites, significant fire intensity was recorded over forest cover (FC) and shrublands (SL), attributed to highly combustible tree crown fuel load characterised by leafy canopies and thin branches. In agreement, BA over FC was the highest in the USA and Australia, while Brazil was dominated by the burning of SL, characteristic of fire-tolerant Cerrado. The relatively lower BA over FC in Brazil can be attributed to fuel availability and proximity to highly flammable cover types such as cropland, SL and grasslands rather than fuel flammability. Overall, this study contributes to a better understanding of wildfires in various regions and the underlying environmental and meteorological causal factors, towards better wildfire disaster management strategies and habitat-specific firefighting.

scholarly journals Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica

2018 ◽  
Vol 10 (9) ◽  
pp. 1427 ◽  
Author(s):  
Papia Rozario ◽  
Buddhika Madurapperuma ◽  
Yijun Wang
Keyword(s):  
Remote Sensing ◽  
Costa Rica ◽  
National Park ◽  
Soil Cover ◽  
Risk Model ◽  
Burn Severity ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Normalized Burn Ratio ◽  
Palo Verde National Park

This study develops a site specific burn severity modelling using remote sensing techniques to develop severity patterns on vegetation and soil in the fire prone region of the Palo Verde National Park in Guanacaste, Costa Rica. Terrain physical features, soil cover, and scorched vegetation characteristics were examined to develop a fire risk model and to quantify probable burned areas. Spectral signatures of affected areas were captured through multi-spectral analysis; i.e., Normalized Burn Ratio (NBR), Landsat derived differenced Normalized Burn Ratio (dNBR) and relativized dNBR (RdNBR). A partial unmixing algorithm, Mixture Tuned Matched Filtering (MTMF) was used to isolate endmembers for scorched vegetation and soil. The performance of dNBR and RdNBR for predicting ground cover components was acceptable with an overall accuracy of 84.4% and Cohen’s Kappa 0.82 for dNBR and an overall accuracy of 89.4% and Cohen’s Kappa 0.82 for RdNBR. Landsat derived RdNBR showed a strong correlation with scorched vegetation (r2 = 0.76) and moderate correlation with soil cover (r2 = 0.53), which outperformed dNBR. The ecologically diverse and unique park area is threatened by wetland fires, which pose a potential threat to various species. Human induced fires by poachers are a common occurrence in such areas to gain access to these species. This paper aims to prioritize areas that are at a higher risk from fire and model spatial adaptations in relation to the direction of fire within the affected wetlands. This assessment will help wildlife personnel in managing disturbed wetland ecosystems.

scholarly journals Variations in Subpixel Fire Properties with Season and Land Cover in Southern Africa

2010 ◽  
Vol 14 (6) ◽  
pp. 1-29 ◽  
Author(s):  
Ted C. Eckmann ◽  
Christopher J. Still ◽  
Dar A. Roberts ◽  
Joel C. Michaelsen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Southern Africa ◽  
Ecological Impacts ◽  
Fire Season ◽  
Remotely Sensed Data ◽  
Radiative Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fire Properties ◽  
Aerosol Emissions

Abstract Some of the most widely used datasets for monitoring the world’s fires come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard NASA’s Terra and Aqua satellites. For virtually all remote sensing systems, including MODIS, pixels that contain fires comprise a mix of burning and nonburning components, each with sizes and temperatures that vary between pixels. Current remote sensing products provide little information about these subpixel components, severely limiting estimates of the gas and aerosol emissions and ecological impacts from the world’s fires. This study shows how multiple endmember spectral mixture analysis (MESMA) can estimate subpixel fire sizes and temperatures from MODIS and can overcome many limitations of existing methods for characterizing fire intensities from remotely sensed data, such as the fire radiative power (FRP) approach. This study used MESMA to estimate subpixel fire sizes and temperatures for MODIS scenes in southern Africa, analyzed how these sizes and temperatures varied with season and land cover, and compared these to analyses made with FRP. This study could be the first to analyze fire sizes and temperatures on a spatial scale as large as a MODIS scene and a temporal scale as large as a full fire season. The variations in MESMA estimates of fire temperature with season and land cover were more consistent than the FRP estimates. Based on these findings, MESMA appears to be more effective than FRP at capturing some variations in fire temperatures, which strongly influence the gas and aerosol emissions from fires, along with their effects on ecosystems.

scholarly journals Direct estimation of Byram's fire intensity from infrared remote sensing imagery

2017 ◽  
Vol 26 (8) ◽  
pp. 668 ◽  
Author(s):  
Joshua M. Johnston ◽  
Martin J. Wooster ◽  
Ronan Paugam ◽  
Xianli Wang ◽  
Timothy J. Lynham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Ancillary Data ◽  
Remote Sensing Imagery ◽  
Method Of Calculation ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Thermal Infrared Imagery ◽  
Remote Sensing Methods

Byram’s fire intensity (IB,tot; kWm–1) is one the most important and widely accepted metrics for quantifying wildfire behaviour. Calculation of IB,tot requires measurement of fuel consumption, heat of combustion and rate of spread; existing methods for obtaining these measurements are either inexact or at times impossible to obtain in the field. This paper presents and evaluates a series of remote sensing methods for directly deriving radiative fire intensity (IB,rad; kWm–1) using the Fire Radiative Power (FRP) approach applied to thermal infrared imagery of spreading vegetation fires. Comparisons between the remote sensing data and ground-sampled measurements were used to evaluate the various estimates of IB,tot, and to determine the radiative fraction (radF) of a fire’s emitted energy. Results indicate that the IB,tot along an advancing flame front can be reasonably estimated (and agrees with traditional methods of estimation (R2=0.34–0.73)) from appropriately collected time-series of remote sensing imagery without the need for ground sampling or ancillary data. We further estimate that the radF of the fire’s emitted energy varies between 0.15 and 0.20 depending on the method of calculation, which is similar to previous estimates.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

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 overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, 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 fire 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 direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Challenges of assessing fire and burn severity using field measures, remote sensing and modelling

2014 ◽  
Vol 23 (8) ◽  
pp. 1045 ◽  
Author(s):  
Penelope Morgan ◽  
Robert E. Keane ◽  
Gregory K. Dillon ◽  
Theresa B. Jain ◽  
Andrew T. Hudak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Simulation Models ◽  
Severity Index ◽  
Fire Effects ◽  
Burn Severity ◽  
Ecological Change ◽  
Continuous Variables ◽  
Field Methods ◽  
Soil Burn Severity ◽  
Effective Assessment

Comprehensive assessment of ecological change after fires have burned forests and rangelands is important if we are to understand, predict and measure fire effects. We highlight the challenges in effective assessment of fire and burn severity in the field and using both remote sensing and simulation models. We draw on diverse recent research for guidance on assessing fire effects on vegetation and soil using field methods, remote sensing and models. We suggest that instead of collapsing many diverse, complex and interacting fire effects into a single severity index, the effects of fire should be directly measured and then integrated into severity index keys specifically designed for objective severity assessment. Using soil burn severity measures as examples, we highlight best practices for selecting imagery, designing an index, determining timing and deciding what to measure, emphasising continuous variables measureable in the field and from remote sensing. We also urge the development of a severity field assessment database and research to further our understanding of causal mechanisms linking fire and burn severity to conditions before and during fires to support improved models linking fire behaviour and severity and for forecasting effects of future fires.

scholarly journals The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment

2021 ◽  
Vol 13 (8) ◽  
pp. 1459
Author(s):  
Michael Nolde ◽  
Simon Plank ◽  
Rudolf Richter ◽  
Doris Klein ◽  
Torsten Riedlinger
Keyword(s):  
Infrared Imaging ◽  
Global Scale ◽  
Fire Intensity ◽  
Small Satellite ◽  
Satellite Mission ◽  
Active Fire ◽  
Radiative Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Patterns And Processes ◽  
German Aerospace

Wildfires significantly influence ecosystem patterns and processes on a global scale. In many cases, they pose a threat to human lives and property. Through greenhouse gas emissions, wildfires also directly contribute to climate change. The monitoring of such events and the analysis of acquired data is crucial for understanding wildfire and ecosystem interactions. The FireBIRD small satellite mission, operated by the German Aerospace Center (DLR), was specifically designed for the detection of wildfires. It features a higher spatial resolution than available with other Earth-observation systems. In addition to the detection of active fire locations, the system also allows the derivation of fire intensity by means of the Fire Radiative Power (FRP). This indicator can be used as a basis to derive the amount of emitted pollutant, which makes it valuable for climate studies. With the FireBIRD mission facing its end of life in 2021, this study retrospectively evaluates the performance of the system through an inter-comparison with data from two satellite missions of the National Aeronautics and Space Administration (NASA) and discusses the potential of such a system. The comparison is performed regarding both geometrical and radiometric aspects, the latter focusing on the FRP. This study uses and compares two different methods to derive the FRP from FireBIRD data. The data are analyzed regarding six major fire incidents in different regions of the world. The FireBIRD results are in accordance with the reference data, showing a geometrical overlapping rate of 83% and 84% regarding MODIS (Moderate-resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) overpasses in close temporal proximity. Furthermore, the results show a positive bias in FRP of about 11% compared to MODIS.

scholarly journals A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers

2016 ◽  
Vol 113 (46) ◽  
pp. 13087-13092 ◽  
Author(s):  
John A. Gamon ◽  
K. Fred Huemmrich ◽  
Christopher Y. S. Wong ◽  
Ingo Ensminger ◽  
Steven Garrity ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Photosynthetic Activity ◽  
Spatial Scales ◽  
Optical Remote Sensing ◽  
Ecosystem Carbon ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Warming World ◽  
Evergreen Conifers

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying “photosynthetic phenology” from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a “chlorophyll/carotenoid index” (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA’s Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

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