scholarly journals Timing Constraints on Remote Sensing of Wildland Fire Burned Area in the Southeastern US

2011 ◽  
Vol 3 (8) ◽  
pp. 1680-1690 ◽  
Author(s):  
Joshua J. Picotte ◽  
Kevin Robertson
Keyword(s):  
Remote Sensing ◽  
Wildland Fire ◽  
Burned Area ◽  
Timing Constraints ◽  
Southeastern Us

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 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.

Minimizing the cost of wildland fire suppression: a model with uncertainty in predicted flame length and fire-line width produced

1994 ◽  
Vol 24 (6) ◽  
pp. 1253-1259 ◽  
Author(s):  
Romain Mees ◽  
David Strauss ◽  
Richard Chase
Keyword(s):  
Line Width ◽  
Wildland Fire ◽  
Fire Suppression ◽  
Flame Length ◽  
Burned Area ◽  
Expected Cost ◽  
Control Time ◽  
The Cost ◽  
Attack Strategy

We describe a model that estimates the optimal total expected cost of a wildland fire, given uncertainty in both flame length and fire-line width produced. In the model, a sequence of possible fire-line perimeters is specified, each with a forecasted control time. For a given control time and fire line, the probability of containment of the fire is determined as a function of the fire-fighting resources available. Our procedure assigns the resources to the fire line so as to minimize the total expected cost. A key feature of the model is that the probabilities reflect the degree of uncertainty in (i) the width of fire line that can be built with a given resource allocation, and (ii) the flame length of the fire. The total expected cost associated with a given choice of fire line is the sum of: the loss or gain of value of the area already burned; the cost of the resources used in the attack; and the expected loss or gain of value beyond the fire line. The latter is the product of the probability that the chosen attack strategy fails to contain the fire and the value of the additional burned area that would result from such a failure. The model allows comparison of the costs of the different choices of fire line, and thus identification of the optimal strategy. A small case study is used to illustrate the procedure.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty

2011 ◽  
Vol 11 (24) ◽  
pp. 12973-13000 ◽  
Author(s):  
S. P. Urbanski ◽  
W. M. Hao ◽  
B. Nordgren
Keyword(s):  
United States ◽  
Biomass Burning ◽  
Emission Factor ◽  
Emission Inventory ◽  
Wildland Fire ◽  
Western United States ◽  
Burned Area ◽  
Emission Inventories ◽  
Fuel Loading ◽  
Global Modeling

Abstract. Biomass burning emission inventories serve as critical input for atmospheric chemical transport models that are used to understand the role of biomass fires in the chemical composition of the atmosphere, air quality, and the climate system. Significant progress has been achieved in the development of regional and global biomass burning emission inventories over the past decade using satellite remote sensing technology for fire detection and burned area mapping. However, agreement among biomass burning emission inventories is frequently poor. Furthermore, the uncertainties of the emission estimates are typically not well characterized, particularly at the spatio-temporal scales pertinent to regional air quality modeling. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires, WF) in the contiguous United States (CONUS). The model combines observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua satellites, meteorological analyses, fuel loading maps, an emission factor database, and fuel condition and fuel consumption models to estimate emissions from WF. WFEI was used to estimate emissions of CO (ECO) and PM2.5 (EPM2.5) for the western United States from 2003–2008. The uncertainties in the inventory estimates of ECO and EPM2.5 (uECO and uEPM2.5, respectively) have been explored across spatial and temporal scales relevant to regional and global modeling applications. In order to evaluate the uncertainty in our emission estimates across multiple scales we used a figure of merit, the half mass uncertainty, ũEX (where X = CO or PM2.5), defined such that for a given aggregation level 50% of total emissions occurred from elements with uEX ũEX. The sensitivity of the WFEI estimates of ECO and EPM2.5 to uncertainties in mapped fuel loading, fuel consumption, burned area and emission factors have also been examined. The estimated annual, domain wide ECO ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual, domain wide EPM2.5 were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual WF emissions were a significant share of total emissions from non-WF sources (agriculture, dust, non-WF fire, fuel combustion, industrial processes, transportation, solvent, and miscellaneous) in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, WF emissions were ~20% of total (WF + non-WF) CO emissions and ~39% of total PM2.5 emissions. During the months with the greatest fire activity, WF accounted for the majority of total CO and PM2.5 emitted across the study region. Uncertainties in annual, domain wide emissions was 28% to 51% for CO and 40% to 65% for PM2.5. Sensitivity of ũECO and ũEPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50% of total ECO with an uncertainty <133% and half of total EPM2.5 with an uncertainty <146%. ũECO and ũEPM2.5 are reduced by more than half at the scale of global modeling applications (Δ x = 100 km, Δ t = 30 day) where 50% of total emissions are estimated with an uncertainty <50% for CO and <64% for PM2.5. Uncertainties in the estimates of burned area drives the emission uncertainties at regional scales. At global scales ũECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 plays the dominant role in ũEPM2.5. Our analysis indicates that the large scale aggregate uncertainties (e.g. the uncertainty in annual CO emitted for CONUS) typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of regional scale modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

Factors influencing large wildland fire suppression expenditures

2008 ◽  
Vol 17 (5) ◽  
pp. 650 ◽  
Author(s):  
Jingjing Liang ◽  
Dave E. Calkin ◽  
Krista M. Gebert ◽  
Tyron J. Venn ◽  
Robin P. Silverstein
Keyword(s):  
Wildland Fire ◽  
Fire Suppression ◽  
Burned Area ◽  
Private Land ◽  
Fire Size ◽  
Department Of Agriculture ◽  
Factors Influencing ◽  
The Us ◽  
Large Fires ◽  
Parsimonious Model

There is an urgent and immediate need to address the excessive cost of large fires. Here, we studied large wildland fire suppression expenditures by the US Department of Agriculture Forest Service. Among 16 potential non-managerial factors, which represented fire size and shape, private properties, public land attributes, forest and fuel conditions, and geographic settings, we found only fire size and private land had a strong effect on suppression expenditures. When both were accounted for, all the other variables had no significant effect. A parsimonious model to predict suppression expenditures was suggested, in which fire size and private land explained 58% of variation in expenditures. Other things being equal, suppression expenditures monotonically increased with fire size. For the average fire size, expenditures first increased with the percentage of private land within burned area, but as the percentage exceeded 20%, expenditures slowly declined until they stabilised when private land reached 50% of burned area. The results suggested that efforts to contain federal suppression expenditures need to focus on the highly complex, politically sensitive topic of wildfires on private land.

scholarly journals MAPPING OF BURNED AREA USING PRESENCE AND BACKGROUND LEARNING FRAMEWORK ON THE GOOGLE EARTH ENGINE PLATFORM

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 37-41
Author(s):  
D. Attaf ◽  
K. Djerriri ◽  
D. Mansour ◽  
D. Hamdadou
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Google Earth ◽  
The Other ◽  
Burned Area ◽  
Support Vector ◽  
Classification Techniques ◽  
Burned Areas ◽  
Google Earth Engine ◽  
One Class Classification

<p><strong>Abstract.</strong> Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.</p>

Sign in / Sign up

Export Citation Format

Share Document