Validation of remote sensing of burn severity in south-eastern US ecosystems

2011 ◽  
Vol 20 (3) ◽  
pp. 453 ◽  
Author(s):  
Joshua J. Picotte ◽  
Kevin M. Robertson
Keyword(s):  
Remote Sensing ◽  
Wildland Fire ◽  
Ground Truth ◽  
Fire Effects ◽  
Burn Severity ◽  
Burned Area ◽  
Vegetation Types ◽  
South Eastern ◽  
Eastern Usa ◽  
Landsat Satellite

We assessed an existing method of remote sensing of wildland fire burn severity for its applicability in south-eastern USA vegetation types. This method uses Landsat satellite imagery to calculate the Normalised Burn Ratio (NBR) of reflectance bands sensitive to fire effects, and the change in NBR from pre- to post fire (dNBR) to estimate burn severity. To ground-truth ranges of NBR and dNBR that correspond to levels of burn severity, we measured severity using the Composite Burn Index at 731 locations stratified by plant community type, season of measurement, and time since fire. Best-fit curves relating Composite Burn Index to NBR or dNBR were used to determine reflectance value breakpoints that delimit levels of burn severity. Remotely estimated levels of burn severity within 3 months following fire had an average of 78% agreement with ground measurements using NBR and 75% agreement using dNBR. However, percentage agreement varied among habitat types and season of measurement, with either NBR or dNBR being advantageous under specific combinations of conditions. The results suggest this method will be useful for monitoring burned area and burn severity in south-eastern USA vegetation types if the provided recommendations and limitations are considered.

scholarly journals Assessing Post-Fire Effects on Soil Loss Combining Burn Severity and Advanced Erosion Modeling in Malesina, Central Greece

2021 ◽  
Vol 13 (24) ◽  
pp. 5160
Author(s):  
Ioanna Tselka ◽  
Pavlos Krassakis ◽  
Alkiviadis Rentzelos ◽  
Nikolaos Koukouzas ◽  
Issaak Parcharidis
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Prediction Models ◽  
Fire Effects ◽  
Burned Area ◽  
Strong Impact ◽  
Central Greece ◽  
High Soil ◽  
Post Fire Effects

Earth’s ecosystems are extremely valuable to humanity, playing a key role ecologically, economically, and socially. Wildfires constitute a significant threat to the environment, especially in vulnerable ecosystems, such as those that are commonly found in the Mediterranean. Due to their strong impact on the environment, they provide a crucial factor in managing ecosystems behavior, causing dramatic modifications to land surface processes dynamics leading to land degradation. The soil erosion phenomenon downgrades soil quality in ecosystems and reduces land productivity. Thus, it is imperative to implement advanced erosion prediction models to assess fire effects on soil characteristics. This study focuses on examining the wildfire case that burned 30 km2 in Malesina of Central Greece in 2014. The added value of remote sensing today, such as the high accuracy of satellite data, has contributed to visualizing the burned area concerning the severity of the event. Additional data from local weather stations were used to quantify soil loss on a seasonal basis using RUSLE modeling before and after the wildfire. Results of this study revealed that there is a remarkable variety of high soil loss values, especially in winter periods. More particularly, there was a 30% soil loss rise one year after the wildfire, while five years after the event, an almost double reduction was observed. In specific areas with high soil erosion values, infrastructure works were carried out validating the applied methodology. The approach adopted in this study underlines the significance of using remote sensing and geoinformation techniques to assess the post-fire effects of identifying vulnerable areas based on soil erosion parameters on a local scale.

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 Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Cara Applestein ◽  
Matthew J. Germino
Keyword(s):  
Remote Sensing ◽  
Great Basin ◽  
Satellite Imagery ◽  
Field Data ◽  
Burned Area ◽  
Sagebrush Steppe ◽  
Pixel Resolution ◽  
Sensing Applications ◽  
Steppe Ecosystems ◽  
Landsat Satellite

Abstract Background The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.

scholarly journals Rapid Response Tools and Datasets for Post-fire Erosion Modeling: Linking Remote Sensing and Process-based Hydrological Models to support Post-fire Remediation

2014 ◽  
Vol XL-1 ◽  
pp. 257-263
Author(s):  
M. E. Miller ◽  
W. J. Elliot ◽  
K. A. Endsley ◽  
P. R. Robichaud ◽  
M. Billmire
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Soil Properties ◽  
Burn Severity ◽  
Burned Area ◽  
Spatially Explicit ◽  
Observation Data ◽  
Sources Of Information ◽  
Modeling Tools ◽  
Induced Changes

Post-fire flooding and erosion can pose a serious threat to life, property, and municipal water supplies. Increased peak flows and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern to both resource managers and the public. To respond to this threat, interdisciplinary Burned Area Emergency Response (BAER) Teams are formed to assess potential erosion and flood risks. These teams are under tight deadlines as remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making these decisions is a burn severity map derived from remote sensing data (typically Landsat) that reflects fire induced changes in vegetative cover and soil properties. Slope, soils, land cover, and climate are also important parameters that need to be considered when accessing risk. Many modeling tools and datasets have been developed to assist BAER teams, but process-based and spatially explicit empirical models are currently under-utilized compared to simpler, lumped models because they are both more difficult to set up and require spatially explicit inputs such as digital elevation models, soils, and land cover. We are working to facilitate the use of models by preparing spatial datasets within a web-based tool that rapidly modifies model inputs using burn severity maps derived from earth observation data. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

scholarly journals Ajuste de metodologías para evaluar severidad de quemado en zonas semiáridas (SE peninsular): incendio Donceles 2012

2017 ◽  
pp. 103 ◽  
Author(s):  
E. Gómez-Sánchez ◽  
J. De las Heras ◽  
M. Lucas-Borja ◽  
D. Moya
Keyword(s):  
Remote Sensing ◽  
Fire Severity ◽  
Ground Truth ◽  
Burn Severity ◽  
Restoration Planning ◽  
Soil Burn Severity ◽  
Landsat 7 ◽  
Large Fires ◽  
Emergency Actions ◽  
Short Time

<p>Post-fire management should be based on a proper evaluation of fire damage (burn severity), mainly for Large Fires (&gt;500 ha). Several methodologies have been developed based on remote sensing information validated with fieldwork. The most widespread techniques was the assessment of fire severity indices obtained from remote sensing. It allow a quick assessment of large areas at affordable costs, although the analysis of soil burn severity and the degree of agreement with the ground truth is not fully reliable. Our study case was the Donceles fire (summer 2012, Hellín, Albacete). The post-fire restoration planning, emergency actions, was based on cartographic information of burn severity. To optimize results in a short time and low budget, we applied methodologies in a similar way other similar fires in the Mediterranean peninsular area. We assessed burn severity by using spectral indices (NDVI, dNBR, RdNBR and RBR) and images from Landsat-7 (including banded) and Deimos-1. For each index, we developed both supervised and unsupervised classifications, using field data as training areas. The highest overall reliability values were found for dNBR (79%) and NBR (71%), obtaining low values with RdNBR. In all cases, the reliability was higher using the supervised classification, so using real-ground data to identify the categories of severity to be discriminated. We conclude the need to extend fire studies in our area to improve the reliability of the fire severity assessment obtained from spectral indexes, thus establishing a protocol of data collection and standard methodology of calculation adapted to the characteristics of the region.</p>

scholarly journals Exploring the Potential of Sentinels-1 & 2 of the Copernicus Mission in Support of Rapid and Cost-effective Wildfire Assessment

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Konstantinos P. Ferentinos
Keyword(s):  
Soil Erosion ◽  
Wildland Fire ◽  
Burn Severity ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Support Vector ◽  
Fire Dynamics ◽  
Burnt Area ◽  
The Impact ◽  
Sentinel 2

The present study explores the use of the recently launched Sentinel-1 and -2 data of the Copernicus mission inwildfire mapping with a particular focus on retrieving information on burnt area, burn severity as well as inquantifying soil erosion changes. As study area, the Sierra del Gata wildfire occurred in Spain during the summerof 2015 was selected. First, diverse image processing algorithms for burnt area extraction from Sentinel-2 datawere evaluated. In the next step, burn severity maps were derived from Sentinel-2 data alone, and the synergybetween Sentinel-2 &amp; Sentinel-1 for this purpose was evaluated. Finally, the impact of the wildfire to soilerodibility estimates derived from the Revised Universal Soil Loss Equation (RUSLE) model implemented to theacquired Sentinel images was explored. In overall, the Support Vector Machines (SVMs) classifier obtained themost accurate burned area mapping, with a derived accuracy of 99.38%. An object-based SVMs classificationusing as input both optical and radar data was the most effective approach of delineating burn severity,achieving an overall accuracy of 92.97%. Soil erosion mapping predictions allowed quantifying the impact ofwildfire to soil erosion at the studied site, suggesting the method could be potentially of a wider use. Our resultscontribute to the understanding of wildland fire dynamics in the context of the Mediterranean ecosystem, demonstratingthe usefulness of Sentinels and of their derived products in wildfire mapping and assessment.

Evaluating the Mid-Infrared Bi-spectral Index for improved assessment of low-severity fire effects in a conifer forest

2018 ◽  
Vol 27 (6) ◽  
pp. 407 ◽  
Author(s):  
T. Ryan McCarley ◽  
Alistair M. S. Smith ◽  
Crystal A. Kolden ◽  
Jason Kreitler
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Canopy Cover ◽  
Fire Effects ◽  
The United States ◽  
Burn Severity ◽  
Conifer Forest ◽  
Mid Infrared ◽  
Spectral Changes ◽  
Spectral Separability

Remote sensing products provide a vital understanding of wildfire effects across a landscape, but detection and delineation of low- and mixed-severity fire remain difficult. Although data provided by the Monitoring Trends in Burn Severity (MTBS) project are frequently used to assess severity in the United States, alternative indices can offer improvement in the measurement of low-severity fire effects and would be beneficial for future product development and adoption. This research note evaluated one such alternative, the Mid-Infrared Bi-Spectral Index (MIRBI), which was developed in savannah ecosystems to isolate spectral changes caused by burning and reduce noise from other factors. MIRBI, differenced MIRBI (dMIRBI) and burn severity indices used by MTBS were assessed for spectral optimality at distinguishing severity and the ability to differentiate between unburned and burned canopy in a conifer forest. The MIRBI indices were better at isolating changes caused by burning and demonstrated higher spectral separability, particularly at low severity. These findings suggest that MIRBI indices can provide an enhanced alternative or complement to current MTBS products in high-canopy-cover forests for applications such as discernment of fire perimeters and unburned islands, as well as identification of low-severity fire effects.

ORDOVICIAN GLACIATION IN NE AFRICA: FIELD AND REMOTE SENSING-BASED EVIDENCES FROM THE SOUTH EASTERN DESERT OF EGYPT

2018 ◽  
Author(s):  
Mohamed Samy Elhebiry ◽  
◽  
Mohamed Sultan ◽  
Mohamed Sultan ◽  
A.E. Kehew ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Eastern Desert ◽  
The South ◽  
South Eastern ◽  
Ne Africa

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Sign in / Sign up

Export Citation Format

Share Document