The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires

2004 ◽  
Vol 13 (2) ◽  
pp. 227 ◽  
Author(s):  
Chris J. Chafer ◽  
Mark Noonan ◽  
Eloys Macnaught
Keyword(s):  
Vegetation Index ◽  
Fire Severity ◽  
Energy Levels ◽  
Satellite Image ◽  
Visual Assessment ◽  
Fuel Load ◽  
Fire Intensity ◽  
Simple Method ◽  
Affected Area ◽  
Normalised Difference Vegetation Index

Using pre- and post-fire satellite imagery from SPOT2, we examined the fire severity and intensity of the Christmas 2001 wildfires in the greater Sydney Basin, Australia. We computed a Normalised Difference Vegetation Index (NDVI) from the two satellite images captured before (November 2001) and after (January 2002) the wildfires, then subtracted the later from the former to produce a difference image (NDVIdiff) which was subsequently classified into six fire severity classes (unburnt, low, moderate, high, very high and extreme severity). We then tested the fire severity classification on 342 sample sites within the 225 000ha fire affected area using a qualitative visual assessment guide. We found that the NDVIdiff classification produced an accuracy of at least 88% (K hat = 0.86), with the greatest discrepancy being between the low and moderate classification. Knowledge of rate of spread over some of the affected area, coupled with a complete knowledge of fuel loads, was used to retrospectively model fire intensity, which in areas of extreme fire intensity, produced heat energy levels exceeding 70 000 kW m–1. Importantly, we found no positive effect of topography on fire severity, in fact finding an inverse relationship between slope and fire severity and no effect due to aspect. Further analysis showed that flat to moderate slopes less than 18° across all aspects suffered the greatest vegetal destruction, and there was no relationship between north-westerly aspects and fire severity. We also introduce a relatively simple method for estimating fuel load biomass using a combination of satellite image and rapid field assessment. We found 79% accuracy for this method based on 125 sample sites. It is postulated that this type of analysis can greatly improve our understanding of the spatial impact of fire, how natural areas within the fire ground were impacted, and how remote sensing and GIS technologies can be efficiently used in fire management planning and post-fire analysis.

Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity

2006 ◽  
Vol 15 (2) ◽  
pp. 213 ◽  
Author(s):  
Kate A. Hammill ◽  
Ross A. Bradstock
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Vegetation Type ◽  
Fire Severity ◽  
Image Resolution ◽  
Fire Intensity ◽  
Vegetation Types ◽  
Open Forest ◽  
Normalised Difference Vegetation Index ◽  
Severity Class

Fire intensity affects ecological and geophysical processes in fire-prone landscapes. We examined the potential for satellite imagery (Satellite Pour l’Observation de la Terre [SPOT2] and Landsat7) to detect and map fire severity patterns in a rugged landscape with variable vegetation near Sydney, Australia. A post-fire, vegetation-based indicator of fire intensity (burnt shrub branch tip diameters, representing the size of fuel consumed) was also used to explore whether fire severity patterns can be used to retrospectively infer patterns of fire intensity. Six severity classes (ranging from unburnt to complete crown consumption) were defined using aerial photograph interpretation and a field assessment across five vegetation types of varying height and complexity (sedge-swamp, heath, woodland, open forest, and tall forest). Using established Normalised Difference Vegetation Index (NDVI) differencing methodology, SPOT2 and Landsat7 imagery yielded similar broad-scale severity patterns across the study area. This was despite differences in image resolution (10 m and 30 m, respectively) and capture dates (2 months and 9 months apart, respectively). However, differences in the total areas mapped for some severity classes were found. In particular, there was reduced differentiation between unburnt and low-severity areas and between crown-scorched and crown-consumed areas when using the Landsat7 data. These differences were caused by fine-scale classification anomalies and were most likely associated with seasonal differences in vegetation condition (associated with time of image capture), post-fire movement of ash, resprouting of vegetation, and low sun elevation. Relationships between field severity class and NDVIdifference values revealed that vegetation type does influence the detection of fire severity using these types of satellite data: regression slopes were greater for woodland, forest, and tall forest data than for sedge-swamp and heath data. The effect of vegetation type on areas mapped in each fire severity class was examined but found to be minimal in the present study due to the uneven distribution of vegetation types in the study area (woodland and open forest cover 86% of the landscape). Field observations of burnt shrub branch tips, which were used as a surrogate for fire intensity, revealed that relationships between fire severity and fire intensity are confounded by vegetation type (mainly height). A method for inferring fire intensity from remotely sensed patterns of fire severity was proposed in which patterns of fire severity and vegetation type are combined.

scholarly journals Urban Fire Severity and Vegetation Dynamics in Southern California

2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Lauren E. H. Mathews ◽  
Alicia M. Kinoshita
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Current Knowledge ◽  
Fire Severity ◽  
Satellite Image ◽  
Burn Severity ◽  
Native Vegetation ◽  
Riparian Areas ◽  
The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

Mapping prescribed fire severity in south-east Australian eucalypt forests using modelling and satellite imagery: a case study

2017 ◽  
Vol 26 (6) ◽  
pp. 491 ◽  
Author(s):  
John Loschiavo ◽  
Brett Cirulis ◽  
Yingxin Zuo ◽  
Bronwyn A. Hradsky ◽  
Julian Di Stefano
Keyword(s):  
Satellite Imagery ◽  
Prescribed Fire ◽  
Vegetation Index ◽  
Fire Severity ◽  
Kappa Statistic ◽  
Fire Spread ◽  
Management Tool ◽  
Eucalypt Forest ◽  
Normalised Difference Vegetation Index ◽  
Eucalypt Forests

Accurate fire severity maps are fundamental to the management of flammable landscapes. Severity mapping methods have been developed and tested for wildfire, but need further refinement for prescribed fire. We evaluated the accuracy of two severity mapping methods for a low-intensity, patchy prescribed fire in a south-eastern Australian eucalypt forest: (1) the Normalised Difference Vegetation Index (NDVI) derived from RapidEye satellite imagery, and (2) PHOENIX RapidFire, a fire-spread simulation model. We used each method to generate a fire severity map (four-category: unburnt, low, moderate and severe), and then validated the maps against field-based data. We used error matrices and the Kappa statistic to assess mapping accuracy. Overall, the satellite-based map was more accurate (75%; Kappa±95% confidence interval 0.54±0.06) than the modelled map (67%; Kappa 0.40±0.06). Both methods overestimated the area of unburnt forest; however, the satellite-based map better represented moderately burnt areas. Satellite- and model-based methods both provide viable approaches for mapping prescribed fire severity, but refinements could further improve map accuracy. Appropriate severity mapping methods are essential given the increasing use of prescribed fire as a forest management tool.

scholarly journals A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

2021 ◽  
Vol 13 (23) ◽  
pp. 4739
Author(s):  
Marcio D. DaSilva ◽  
David Bruce ◽  
Patrick A. Hesp ◽  
Graziela Miot da Silva
Keyword(s):  
Vegetation Index ◽  
Fire Severity ◽  
Coastal Dune ◽  
Burn Severity ◽  
Soil Conditions ◽  
Initiation Mechanism ◽  
Disturbance Index ◽  
Dune Systems ◽  
Normalised Difference Vegetation Index ◽  
Vegetation Loss

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems.

Spectral analysis of charcoal on soils: implicationsfor wildland fire severity mapping methods

2010 ◽  
Vol 19 (7) ◽  
pp. 976 ◽  
Author(s):  
Alistair M. S. Smith ◽  
Jan U. H. Eitel ◽  
Andrew T. Hudak
Keyword(s):  
Spectral Analysis ◽  
Soil Type ◽  
Large Scale ◽  
Vegetation Index ◽  
Wildland Fire ◽  
Fire Severity ◽  
Mapping Method ◽  
Normalised Difference Vegetation Index ◽  
National Programs

Recent studies in the Western United States have supported climate scenarios that predict a higher occurrence of large and severe wildfires. Knowledge of the severity is important to infer long-term biogeochemical, ecological, and societal impacts, but understanding the sensitivity of any severity mapping method to variations in soil type and increasing charcoal (char) cover is essential before widespread adoption. Through repeated spectral analysis of increasing charcoal quantities on six representative soils, we found that addition of charcoal to each soil resulted in linear spectral mixing. We found that performance of the Normalised Burn Ratio was highly sensitive to soil type, whereas the Normalised Difference Vegetation Index was relatively insensitive. Our conclusions have potential implications for national programs that seek to monitor long-term trends in wildfire severity and underscore the need to collect accurate soils information when evaluating large-scale wildland fires.

Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data

2011 ◽  
Vol 20 (5) ◽  
pp. 690 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber ◽  
Jamey Anderson ◽  
Bhushan Gokhal
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Late Summer ◽  
Thematic Mapper ◽  
Fuel Load ◽  
Landsat Thematic Mapper ◽  
Change Analysis ◽  
Active Growth ◽  
Normalised Difference Vegetation Index

In order to monitor wildfires at broad spatial scales and with frequent periodicity, satellite remote sensing techniques have been used in many studies. Rangeland susceptibility to wildfires closely relates to accumulated fuel load. The normalised difference vegetation index (NDVI) and fraction of photosynthetically active radiation (fPAR) are key variables used by many ecological models to estimate biomass and vegetation productivity. Subsequently, both NDVI and fPAR data have become an indirect means of deriving fuel load information. For these reasons, NDVI and fPAR, derived from the Moderate Resolution Imaging Spectroradiometer on-board Terra and Landsat Thematic Mapper imagery, were used to represent prefire vegetation changes in fuel load preceding the Millennial and Crystal Fires of 2000 and 2006 in the rangelands of south-east Idaho respectively. NDVI and fPAR change maps were calculated between active growth and late-summer senescence periods and compared with precipitation, temperature, forage biomass and percentage ground cover data. The results indicate that NDVI and fPAR value changes 2 years before the fire were greater than those 1 year before fire as an abundance of grasses existed 2 years before each wildfire based on field forage biomass sampling. NDVI and fPAR have direct implication for the assessment of prefire vegetation change. Therefore, rangeland susceptibility to wildfire may be estimated using NDVI and fPAR change analysis. Furthermore, fPAR change data may be included as an input source for early fire warning models, and may increase the accuracy and efficiency of fire and fuel load management in semiarid rangelands.

Automated spaceborne detection of degraded vegetation around Monchegorsk, Kola Peninsula, Russia

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 107-112 ◽  
Author(s):  
W. G. Rees
Keyword(s):  
Time Series ◽  
Kola Peninsula ◽  
Industrial Pollution ◽  
Satellite Images ◽  
Vegetation Index ◽  
Simple Method ◽  
Data Analyst ◽  
Normalised Difference Vegetation Index ◽  
The Town ◽  
Analysis Of Time Series

ABSTRACTThis paper develops a simple method for the detection of ‘vegetation anomalies’, locations where the amount of vegetation, estimated through the use of the normalised difference vegetation index (NDVI), is significantly lower than expected on the basis of topographic factors alone. The method is developed and tested using satellite imagery from the area around the town of Monchegorsk on the Kola Peninsula, Russia. This area has been subject to heavy levels of airborne industrial pollution for many years and the intended purpose of the method is to allow the extent of pollution damaged vegetation to be estimated with as few operational decisions as possible by the data analyst, thus suiting it for automation and for the analysis of time-series of satellite images. While the work described in this paper is to some extent preliminary, it does establish that spatial variations in the NDVI of undisturbed vegetation can, at least in the study area, be modelled satisfactorily using topographic variables, and that negative departures from this modelled variation are very strongly associated with industrially mediated damage.

scholarly journals The spatial dimensions of Savanna fire ecology in Kruger National Park, South Africa

2015 ◽  
Author(s):  
◽  
Daniel Godwin
Keyword(s):  
South Africa ◽  
National Park ◽  
Fire Severity ◽  
Kruger National Park ◽  
Tree Cover ◽  
Annual Precipitation ◽  
Fuel Load ◽  
Fire Intensity ◽  
Mean Annual Precipitation ◽  
Woody Cover

Savannas are thought to be bistable with forests potentially occurring above [about]650 mm / yr of Mean Annual Precipitation (MAP) due to the limiting effects of fire on tree cover. This is predicated on two assumptions: 1) fires increasingly limit woody cover in more mesic (> 650 MAP) savannas and 2) increasing tree cover produces feedbacks that reduce fire intensity. These assumptions are investigated in a spatially explicit framework. We use Kruger National Park (KNP), South Africa as our study system, in part due to the wide range of frequency of fires, the large variation in natural communities and rainfall, and the large body of previous research for comparisons and modeling efforts. To investigate whether tree cover produces feedbacks on fire intensity, we measured fire behavior as a function of grass fuel load and woody cover in experimental burns within KNP. We found weak but positive relationships (not negative, as assumed) between woody cover and fire intensity, independent of grass fuel load, and no relationship between tree cover and grass fuel load. At a landscape scale, we modeled the factors predicted to drive fire severity in KNP. We observed that fireline intensity is a strong predictor of many estimations of fire severity in small fires, but across larger fires, rainfall and woody cover likewise can predict impacts on herbaceous consumption and woody cover, respectively. Lastly, to investigate whether trees escape fire less often in more mesic savannas, we used a stochastic model parameterized with real data. After a review of published growth rates, we modeled fire escape probability using mean annual precipitation, fire frequency, and fireline intensity values across KNP. When accounting for species turnover across rainfall gradients, we found a nearly flat relationship between the probability of individuals escaping fire and rainfall. Our research challenges two key assumptions for fire-mediated bistability of mesic savannas.

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