Figure 2 - uploaded by Steven S. Seefeldt
Content may be subject to copyright.
Resulting fire severity models produced using classification tree analysis (CTA) with post-fire imagery (a), differenced imagery (b), and differenced models (c).
Source publication
Wildland fires are common in rangelands worldwide. The potential for high severity fires to affect long- term changes in rangelands is considerable, and for this reason assessing fire severity shortly after the fire is critical. Such assessments are typically carried out following Burned Area Emergency Response teams or similar protocols. These dat...
Contexts in source publication
Context 1
... CTA model developed using five post-fire satellite imagery layers (NIR, NDVI, NBR, biomass, and slope) correctly identified all 186 high fire severity validation areas (n = 186, user accuracy = 100%) ( Figure 2a, Table 1). Similarly, using five differenced input layers (dNIR, dNDVI, dNBR, differenced biomass, and the same slope topography layer) user accuracy was 99.5% ( Figure 2b, Table 2). ...
Context 2
... CTA model developed using five post-fire satellite imagery layers (NIR, NDVI, NBR, biomass, and slope) correctly identified all 186 high fire severity validation areas (n = 186, user accuracy = 100%) ( Figure 2a, Table 1). Similarly, using five differenced input layers (dNIR, dNDVI, dNBR, differenced biomass, and the same slope topography layer) user accuracy was 99.5% ( Figure 2b, Table 2). While overall accuracy intially appears to be better using differenced imagery (97.1% compared with 96.6%), this slight difference is considered insignificant. ...
Context 3
... authors argue that any differences reported here is not of practical significance. Map arithmetic techniques were used to identify those areas (pixels) where the two models predicted different fire severity levels (Figure 2c). Nearly all pixels exhibited agreement in predicted fire severity (98.5%). ...
Context 4
... it may appear that a disproportionately large number of validation sites were used in high fire severity areas (n=193, 92%; tables 1 and 2). However, as fire severity is primarily a function of the fire's behavior --which is closely tied to factors such as the amount and type of fuels and the weather during the fire ( Pyne et al. 1996) --the proportion of high fire severity sites (used for both modeling and validation) agreed well with field observations where 92% of the study area was recorded as high severity (Figure 2a, b). Indeed when the proportions of the random sampling locations were compared with proportions of fire severity areas, the results appear equitably distributed. ...
Citations
... Single satellite data have been widely used to detect characteristics of wildfires, and retrospectively map BA at a variety of spatial resolutions. At the early stage of fire monitoring, satellite data from coarse spatial resolution sensors, such as AVHRR (Barbosa et al. 1999;Dwyer et al. 2000), Along Track Scanning Radiometer (ATSR) (Eva and Lambin 1998), VEGETATION (Fraser et al. 2004;Weber, Seefeldt, and Moffet 2009;Chéret and Denux 2011), MODIS (Weber et al. 2008;Giglio et al. 2018;Alonso-Canas and Chuvieco 2015;Briones-Herrera et al. 2020; Bar et al. 2021), and those on geostationary satellites such as the Geostationary Operational Environmental Satellite (GOES) (Prins and Paul Menzel 1994) and Meteosat (Boschetti, Brivio, and Gregoire 2003), were mainly used to analyze fire activities over large regions. Meanwhile, the burnsensitive vegetation indices were developed based on the contrast between burned surfaces and unburned surfaces in the visible (0.4-0.7 μm), nearinfrared (0.7-1.5 μm), or middle infrared (1.5-4 μm) portions of the electromagnetic spectrum for burned area mapping (Key and Benson 2006;Miller and Thode 2007;Miller et al. 2009;Mallinis, Mitsopoulos, and Chrysafi 2017;Mpakairi, Lynnet Kadzunge, and Ndaimani 2020). ...
Wildfires have significant impacts on human lives, critical infrastructures, and Earth's ecosystems. Accurate and timely information on burned area (BA) affected by wildfires is vital to better understand the drivers of wildfire events, as well as its relevance for biogeochemical cycles, climate, and air quality, and to aid wildfire management. Single satellite data have been used to detect the characteristics of wildfires, retrospectively mapping BAs at a variety of spatial resolutions in previous studies. However, due to the trade-off between spatial and temporal resolutions, single-source satellite data are not sufficient to characterize the explicit dynamics of BAs at high resolutions in both space and time. Thus, a two-stage near real-time BA mapping method was developed in this study to take advantage of the high temporal frequency of coarse resolution sensors and the fine spatial resolution of medium resolution sensors in BA mapping by synergizing freely available coarse and medium spatial resolution (MSR) sensors. First, high temporal frequency sensors such as MODIS and VIIRS were used to identify wildfires and potential BAs. Then, multiple MSR sensors such as Sentinel-2A/2B, Landsat OLI, and Resourcesat AWiFS were synthesized for extracting the BAs with more spatial details in near real-time. We applied the method in California, USA, where wildfires occurred in northern and southern parts in 2017. The results showed that the proposed method is promising for BA mapping with an overall accuracy of 0.84 and 0.85 for wildfires in northern and southern California, respectively. Additionally, the proposed method greatly improved the frequency and reduced the latency, with an average interval of 3.5 days (3 days) and latency of 4 days (sub-daily) for wildfires in southern (northern) California. The extracted BAs illustrated accurate spatial details with MSR sensors. Our method can significantly take advantage of multi-source remote-sensing observations to accurately map the BAs of active wildfires in near real-time. More importantly, the method can be applied to other geographic regions where wildfires risk humans and ecosystems.
... Single satellite data have been widely used to detect characteristics of wildfires, and retrospectively map BA at a variety of spatial resolutions. At the early stage of fire monitoring, satellite data from coarse spatial resolution sensors, such as AVHRR (Barbosa et al. 1999;Dwyer et al. 2000), Along Track Scanning Radiometer (ATSR) (Eva and Lambin 1998), VEGETATION (Fraser et al. 2004;Weber, Seefeldt, and Moffet 2009;Chéret and Denux 2011), MODIS (Weber et al. 2008;Giglio et al. 2018;Alonso-Canas and Chuvieco 2015;Briones-Herrera et al. 2020; Bar et al. 2021), and those on geostationary satellites such as the Geostationary Operational Environmental Satellite (GOES) (Prins and Paul Menzel 1994) and Meteosat (Boschetti, Brivio, and Gregoire 2003), were mainly used to analyze fire activities over large regions. Meanwhile, the burnsensitive vegetation indices were developed based on the contrast between burned surfaces and unburned surfaces in the visible (0.4-0.7 μm), nearinfrared (0.7-1.5 μm), or middle infrared (1.5-4 μm) portions of the electromagnetic spectrum for burned area mapping (Key and Benson 2006;Miller and Thode 2007;Miller et al. 2009;Mallinis, Mitsopoulos, and Chrysafi 2017;Mpakairi, Lynnet Kadzunge, and Ndaimani 2020). ...
Wildfires have significant impacts on human lives, critical infrastructures, and Earth’s ecosystems. Accurate and timely information on burned area (BA) affected by wildfires is vital to better understand the drivers of wildfire events, as well as its relevance for biogeochemical cycles, climate, and air quality, and to aid wildfire management. Single satellite data have been used to detect the characteristics of wildfires, retrospectively mapping BAs at a variety of spatial resolutions in previous studies. However, due to the trade-off between spatial and temporal resolutions, single-source satellite data are not sufficient to characterize the explicit dynamics of BAs at high resolutions in both space and time. Thus, a two-stage near real-time BA mapping method was developed in this study to take advantage of the high temporal frequency of coarse resolution sensors and the fine spatial resolution of medium resolution sensors in BA mapping by synergizing freely available coarse and medium spatial resolution (MSR) sensors. First, high temporal frequency sensors such as MODIS and VIIRS were used to identify wildfires and potential BAs. Then, multiple MSR sensors such as Sentinel-2A/2B, Landsat OLI, and Resourcesat AWiFS were synthesized for extracting the BAs with more spatial details in near real-time. We applied the method in California, USA, where wildfires occurred in northern and southern parts in 2017. The results showed that the proposed method is promising for BA mapping with an overall accuracy of 0.84 and 0.85 for wildfires in northern and southern California, respectively. Additionally, the proposed method greatly improved the frequency and reduced the latency, with an average interval of 3.5 days (3 days) and latency of 4 days (sub-daily) for wildfires in southern (northern) California. The extracted BAs illustrated accurate spatial details with MSR sensors. Our method can significantly take advantage of multi-source remote-sensing observations to accurately map the BAs of active wildfires in near real-time. More importantly, the method can be applied to other geographic regions where wildfires risk humans and ecosystems.
... However, the application of EVI and NDVI to study post-fire vegetation alterations involves conceptual limitations related to the spectral bands that do not necessarily capture burn severity (Epting et al., 2005). Subsequently, the NBR was defined to identify and quantify the effects of fire on vegetation by including the mid-infrared band (Cocke et al., 2005, Epting et al., 2005, Roy et al., 2006, Walz et al., 2007, Loboda et al., 2007, Escuin et al., 2008, Weber et al., 2008. The difference between consecutive pre-and post-fire scenes (i.e. ...
We investigated the changes in hydrologic response in a forested catchment impacted by wildfire in Colorado U.S.A. from the storm event to the inter-annual scales. We also evaluated the utility of a remotely-sensed burn severity index to study post-fire shifts in streamflow. At the storm-scale, we evaluated hydrologic shifts through changes in the effective runoff (Q*/PTot), peak streamflow (Qpk) and response time (TR/TB) from multiple hydrographs, while at seasonal and inter-annual-scales we quantified hydrologic shifts through the runoff fraction (Q/PTot) and flow duration curves. Vegetation anomalies were monitored through comparisons of the Normalized Burn Ratio (NBR) between the burned and a hydrologically-similar, forested, neighboring, unburned catchment. We found short-term acute and long-term chronic transient streamflow shifts from the minute to the inter-annual scales. Flow duration curves indicate an order of magnitude increase in maximum flows. Event-average Q*/PTot increased by two orders of magnitude and Qpk increased by one order of magnitude relative to multiple representative pre-fire events of similar precipitation intensities. Decreases in TR/TB appear to be minimal. At the inter-annual scale, increases in the difference between simultaneous unburned and burned NBR are associated with increases in Q/PTot. A hydrologic recovery pathway is evident resembling a hysteresis effect driven by vegetation re-growth. Results illustrate the non-steady physical processes that increase flash-flooding risks post-fire in mountainous catchments and the utility of ΔNBR as a hydrologic predictor in ungauged watersheds.
... Wildfires have pervasive ecological and socioeconomic impacts, including biodiversity loss, erosion, infrastructure damage and human mortality (Lentile et al., 2006;Brewer et al., 2005;Rogan and Franklin, 2001). Understanding post-burn severity and effects is therefore fundamental to informing immediate emergency rehabilitation, and, future land management decisions for restoration projects (Brewer et al., 2005;Rogan and Franklin, 2001;Norton, 2006;Weber et al., 2008). Lentile et al. (2006) emphasise the terminological distinctions between fire intensity and burn severity; the former describing fire behaviour, primarily relating to energy release, and the latter referring to post-fire effects, defined as the scale of ecological variation produced by a wildfire (Brewer et al., 2005;Key and Benson, 2006;Miller and Thode, 2007). ...
... The index normalises differences in reflectance values from near-infrared (NIR) band 4 (0.76-0.90μm) and shortwave-infrared (SWIR) band 7 (2.08-2.35μm). Pre-fire, photosynthetically active vegetation typically has high NIR reflectance, illustrated by the classic red edge, and low SWIR reflectance; whereas post-fire areas typically have low NIR reflectance, due to vegetation charring and removal, and high SWIR reflectance (Eva and Lambin, 1998;Trigg and Flasse, 2000;Weber et al., 2008) ( Figure 1). (Humboldt State University, 2015). ...
... NASA Wrangler can deliver rapidlyassembled data to other RECOVER servers or allow users to download site-specific data packages for use in other GIS environments.0 nity [3,4]. In addition to observational data, Wrangler automatically gathers several dozen other data products, including information on the fire site's vegetation cover and type, agroclimatic zone, environmental site potential, fire regime condition class, geology, hydrology, soils, historic fires, topography, and evapotranspiration. ...
... Fire-history reconstruction may refer to a few decades for which spatially explicit information may be acquired from remote sensing observations, or to centennial or millennial scales where other tools (e.g., tree rings, charcoal analysis) are used to acquire the required data (Conedera et al., 2009). At short-term temporal scales (e.g., previous 30 years), numerous remote sensing studies can be found in the literature devoted to detection of fires, mapping of burned areas and burn severity, and monitoring vegetation recovery (López-Garcia and Caselles, 1991;Kasischke et al., 1992;Chuvieco and Martin, 1994;Viedma et al., 1997;Barbosa et al., 1998;Koutsias and Karteris, 1998;Rogan and Yool, 2001;Stroppiana et al., 2002;Roy et al., 2005;San-Miguel-Ayanz et al., 2005;Weber et al., 2008;Huang et al., 2009;Kontoes et al., 2009;Weber et al., 2009). In these studies, satellite data of multiple spatial, spectral, radiometric, and temporal resolutions constitute the prime source of information. ...
In July 1983, a large wildfire occurred on the island of Karpathos in Greece. However, only a general sketch of the burn perimeter was available and this lacked detailed spatial information, particularly for unburned patches within the fire scar perimeter. A study was undertaken to correctly map the area burned using USGS-archived Landsat data by applying several digital image processing techniques. This paper summarizes and discusses the main findings of that study and provides some general recommendations on the use of remote sensing and archived Landsat data for reconstructing fire history. Remote sensing along with geographic information systems can provide an excellent framework for fast, reliable data capture, measurement, and synthesis, all of which are essential for thorough eco-environmental analysis. Satellite data of multiple types offer an unlimited source of information due to their rich spectral and spatial information content. Satellite mapping of burned areas is considered a standard technique in creating maps of fire scars at multiple scales as a function of the satellite sensor's geometric resolution.
... Higher values indicate the presence of vegetation and wetter conditions. Temporal differencing of NBR (dNBR) subtracts pre-fire from post-fire NBR images (dNBR = NBR pre-fire -NBR post-fire ) so that areas unaffected by fire will have near-zero values while positive values indicate vegetation stressed or reduced in the post-fire image Benson, 1999, 2006;Miller and Yool, 2002;Weber et al., 2008). Cocke et al. (2005) found dNBR to be sensitive to the ...
Forest fires have profound impacts on the spatial distribution of vegetation type and density. This research analyzes the impacts of the 2002 Ponil Fire in New Mexico on landscape patterns using a moving-window analysis of landscape metrics. Categorically derived landscape metrics and a measure of fire severity—the Normalized Burn Ratio—are used to produce a quantitative, spatial distribution of landscape change. While gross land-cover change summaries and landscape-metric changes indicate a more heterogeneous landscape following the fire, the moving-window approach demonstrates the oversimplification of landscape-scale metrics and summaries. The moving-window approach indicates that the majority of areas in the landscape were unchanged in mean patch size, whereas mean (and median) patch size increased according to landscape-level measures. Contrary to expectations, average patch density and richness were also nearly unchanged. The moving-window approach is particularly helpful in analyzing large fires with considerable variability in severity, allowing greater insight into the relationship between fire severity and landscape composition and structure in post-fire landscapes. The moving-window approach also can guide researchers and managers to specific areas of a landscape where large changes have occurred and where evidence for understanding the process driving that change is most likely to be found.
Purpose: To develop the automated processing technology of the low and medium spatial resolution multispectral satellite images, which provides increasing reliability and efficiency of determining the area of forest burnt. Methodology: We used the methods of assessment of the state of vegetation on the burnt area, a complex of different vegetation indices and satellite image classification methods. The method of fire-sites decoding included the following main steps: selection of the image showing the area before and after a fire and calculation of vegetation indices. Findings: The efficient technology of automated estimation of the area affected by forest fires, using satellite imagery, has been suggested. The technique allows us to fulfill all the processing procedure by means of the web-service that provides current and reliable data about the fire effects. Using the developed method we analyzed the multispectral images with low spatial resolution from Terra and Aqua satellites (instrument MODIS) in order to identify areas and periods of active burning in the selected area (California). Then we processed the multispectral images with medium spatial resolution from Landsat 8 satellite (shooting device OLI) to define accurately the boundaries of the burnt areas and to calculate their size. The area of the forest burnt during the period from July 30 to August 12 in 2015 exceeds 41 477 ha. Originality: In contrast to the existing methods of determining the burned areas by means of satellite images and using the Differential Normalized Bum Index (dNBR), the proposed technology allows determining the areas burnt more accurately through independent determination of optimal binarization thresholds for each image. In addition it is possible to analyze the temporal changes in the affected forest areas for long periods of observation by using vector layers with attribute information. The essential advantage of the technique is the high degree of automation of the satellite images processing and the use of remote sensing data which are freely available on the Internet. Practical value: The developed technology allows creating a web service for regular space monitoring of the consequences of forest fires. The users of such a service may be state monitoring bodies, state and private insurance companies, energy and oil refining companies, municipal services, private companies, fanners, TV and radio companies and other mass media as well as population living close to the territories affected by forest fires. © Hnatushenko V.V., Hnatushenko Vik.V., Mozgovyi D.K., Vasiliev V.V.. 2016.
RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.
