Remote Sensing of Fire Severity: Assessing the Performance of the Normalized Burn Ratio

2006 ◽  
Vol 3 (1) ◽  
pp. 112-116 ◽  
Author(s):  
D.P. Roy ◽  
L. Boschetti ◽  
S.N. Trigg
Keyword(s):  
Remote Sensing ◽  
Fire Severity ◽  
Normalized Burn Ratio

scholarly journals Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1459
Author(s):  
Yolanda Sánchez Sánchez ◽  
Antonio Martínez Graña ◽  
Fernando Santos- Francés
Keyword(s):  
Remote Sensing ◽  
Fire Severity ◽  
High Mountain ◽  
Mountain Areas ◽  
High Severity ◽  
Natural Reserve ◽  
Remote Sensing Techniques ◽  
Normalized Burn Ratio ◽  
The Moment ◽  
Very High

Soil erosion is one of the most important environmental problems of the moment, especially in areas affected by wildfires. In this paper, we study pre-fire and post-fire erosion using remote sensing techniques with Sentinel-2 satellite images and LiDAR. The Normalized Burn Ratio is used to determine the areas affected by the fire that occurred on 18 August 2016 in the Natural Reserve of Garganta de los Infiernos (Cáceres). To calculate the erosion, the multi-criteria analysis is carried out from the RUSLE. Once all calculations were performed, there was a considerable increase in sediment production from 16 June 2016 (pre-fire) with an erosion of 31 T/ha·year to 16 June 2017 of 74 T/ha·year for areas of moderate fire severity, and an increase from 11 T/ha·year in 2016 to 70 T/ha·year for areas with a very high severity. From the NDVI, it was possible to verify that this also affected the recovery of post-fire vegetation, decreasing the NDVI index 0.36 in areas of moderate severity and 0.53 in areas of very high severity.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Instantaneous spatio-temporal rate of spread of fast spreading wildfires - a new approach from visible and thermal image processing

2021 ◽  
Author(s):  
Benjamin Schumacher ◽  
Katharine Melnik ◽  
Marwan Katurji ◽  
Veronica Clifford ◽  
Jiawei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Stand Structure ◽  
Fire Severity ◽  
Thermal Infrared ◽  
Fire Spread ◽  
New Approach ◽  
Rate Of Spread ◽  
Fast Spreading ◽  
Fire Experiments

<p>The rate of spread (ROS) of wildfires is an important parameter for understanding fire-atmospheric interactions and developing fire-spread models, but it is also vital for firefighting operations to ensure the safety of firefighters (Plucinski 2017, Stow 2019). Spatial ROS observations are usually carried out by using visible and thermal satellite imagery of wildfires estimating the ROS on a time scale of hours to days for large fires (>100 ha) or repeated passing with an airborne thermal infrared imager for higher spatial and temporal resolution (Viedma et al. 2015, Stow 2014). For fire experiments in highly controlled conditions like laboratory fires or during light fuel prescribed burns, ROS estimation usually involves lag-correlation of temperature point measurements (Finney 2010, Johnston 2018). However, these methodologies are not applicable to fast-spreading grass or bush fires because of their temporal and spatial limitations. Instantaneous spatial ROS of these fires is needed to understand rapid changes in connection with the three major drivers of the fire: fuel, topography and atmospheric forcings.</p><p>We are presenting a new approach towards a spatial ROS product which includes newly developed image tracking methods based on thermal and visible imagery collected from unmanned aerial vehicles to estimate instantaneous, spatial ROS of fast spreading grass or bush fires. These techniques were developed using imagery from prescribed wheat-stubble burns carried out in Darfield, New Zealand in March 2018 (Finney 2018). Results show that both the visible and thermal tracking techniques produce similar mean ROS; however they differ in limitations and advantages. The visible-spectrum tracking method clearly identifies the flaming zone and provides accurate ROS measurements especially at the fire front. The thermal tracking technique is superior when resolving dynamics and ROS within the flaming zone because it resolves smaller scale structures within the imagery.</p><p> </p><p>References:</p><p>Finney, M. et al. 2010: An Examination of Fire Spread Thresholds in Discontinuous Fuel Beds.” International Journal of Wildland Fire, 163–170.</p><p>Finney, M. et al. 2018: New Zealand prescribed fire experiments to test convective heat transfer in wildland fires. In Advances in Forest Fire Research, Imprensa da Universidade de Coimbra: Coimbra, 2018.</p><p>Johnston, J. M., et al. 2018:  Flame-Front Rate of Spread Estimates for Moderate Scale Experimental Fires are Strongly Influenced by Measurement Approach. Fire 1: 16–17</p><p>Plucinski M., et al. 2017: Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1-12.</p><p>Stow, D., et al. 2014: Measuring Fire Spread Rates from Repeat PassAirborne Thermal Infrared Imagery. Remote Sensing Letters 5: 803–881.</p><p>Stow, D., et al. 2019: Assessing uncertainty and demonstrating potentialfor estimating fire rate of spread at landscape scales based on time sequential airbornethermal infrared imaging, International Journal of Remote Sensing, 40:13, 4876-4897</p><p>Viedma, O., et al. 2015:  Fire Severity in a Large Fire in a Pinus Pinaster Forest Is Highly Predictable from Burning Conditions, Stand Structure, and Topography. Ecosystems18: 237–250.</p>

scholarly journals Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

2019 ◽  
Vol 11 (24) ◽  
pp. 2963
Author(s):  
Christopher L. Kibler ◽  
Anne-Marie L. Parkinson ◽  
Seth H. Peterson ◽  
Dar A. Roberts ◽  
Carla M. D’Antonio ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Species Composition ◽  
Vegetation Cover ◽  
Spatial And Temporal Variation ◽  
Conifer Species ◽  
Burn Scar ◽  
Green Vegetation ◽  
Normalized Burn Ratio

Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011–2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses.

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 ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

2016 ◽  
Vol XLI-B8 ◽  
pp. 763-769
Author(s):  
H. Tonbul ◽  
T. Kavzoglu ◽  
S. Kaya
Keyword(s):  
Geographical Information Systems ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Fire Severity ◽  
Cost Effective ◽  
Geographical Information ◽  
Burned Area ◽  
Elevation Model ◽  
Normalized Burn Ratio ◽  
Vegetation Regeneration

Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

scholarly journals Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs

2005 ◽  
Vol 97 (1) ◽  
pp. 92-115 ◽  
Author(s):  
Alistair M.S. Smith ◽  
Martin J. Wooster ◽  
Nick A. Drake ◽  
Frederick M. Dipotso ◽  
Michael J. Falkowski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Severity

scholarly journals Remote sensing techniques to assess active fire characteristics and post-fire effects

2006 ◽  
Vol 15 (3) ◽  
pp. 319 ◽  
Author(s):  
Leigh B. Lentile ◽  
Zachary A. Holden ◽  
Alistair M. S. Smith ◽  
Michael J. Falkowski ◽  
Andrew T. Hudak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Severity ◽  
Fire Behavior ◽  
Fire Effects ◽  
Ecological Effects ◽  
Fire Intensity ◽  
Active Fire ◽  
Post Fire Effects ◽  
Remote Sensing Techniques ◽  
Fire Characteristics

Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.

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