Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderate–high R ²?=?0.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI–dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was nonlinear, while the GeoCBI–dNDVI (differenced NDVI) relationship appeared linear.     

Understanding burn severity sensing in Arctic tundra: exploring vegetation indices,suboptimal assessment timing and the impact of increasing pixel size

Little is known about how satellite imagery can be used to describe burn severity in tundra landscapes. The Anaktuvuk River Fire (ARF) in 2007 burned over 1000 km² of tundra on the North Slope of Alaska, creating a mosaic of small (1 m²) to large (>100 m²) patches that differed in burn severity. The ARF scar provided us with an ideal landscape to determine if a single-date spectral vegetation index can be used once vegetation recovery began and to independently determine how pixel size influences burn severity assessment. We determine and explore the sensitivity of several commonly used vegetation indices to variation in burn severity across the ARF scar and the influence of pixel size on the assessment and classification of tundra burn severity. We conducted field surveys of spectral reflectance at the peak of the first growing season post-fire (extended assessment period) at 18 field sites that ranged from high to low burn severity. In comparing single-date indices, we found that the two-band enhanced vegetation index (EVI2) was highly correlated with normalized burn ratio (NBR) and better distinguished among three burn severity classes than both the NBR and the normalized difference vegetation index (NDVI). We also show clear evidence that shortwave infrared (SWIR) reflectivity does not vary as a function of burn severity. By comparing a Quickbird scene (2.4 m pixels) to simulated 30 and 250 m pixel scenes, we are able to confirm that while the moderate spatial resolution of the Landsat Thematic Mapper (TM) sensor (30 m) is sufficient for mapping tundra burn severity, the coarser resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (250 m) is not well matched to the fine scale of spatial heterogeneity in the ARF burn scar.     

Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index

Remote sensing is the most practical method available to managers of fire-prone forests for quantifying and mapping fire impacts. Differenced Normalised Burn Ratio (ΔNBR) is among the most widely used spectral indices for the mapping of burn severity but is difficult to interpret in terms of fire-related changes in key biophysical attributes and processes. We propose to quantify burn severity as a change in the leaf area index (ΔLAI) of a stand. LAI is a key biophysical attribute of forests, and is central to understanding their water and carbon cycles. Previous studies have suggested that changes in canopy LAI may be a major contributor to ΔNBR and to the composite burn index (CBI) that is frequently used in combination with the NBR to assess burn severity on the ground. We applied remotely-sensed ΔLAI to map burn severity in jarrah (Eucalyptus marginata) forest in south-western Australia burnt during the January 2005 Perth Hills wildfires. Ground-based digital photography was used to measure LAI in typical stands representing the full range of canopy densities present in the study area as well as variation in the time since the last fire. Regression models for the prediction of LAI were developed using NBR, the Normalised Difference Vegetation Index (NDVI) or the Simple Ratio (SR) as the independent variable. All three LAI models had equally high coefficients of determination (R²: 0.87) and small root mean squared errors (RMSE: 0.27–0.28). ΔLAI was calculated as the difference between pre- and post-fire LAI, predicted using imagery from January 2004 and February 2005, respectively. The area affected by the January 2005 fire and the burn severity patterns within that area were mapped using ΔLAI and ΔNBR. Landscape patterns of burn severity obtained from differencing pre- and post-fire LAI were similar to those mapped by ΔNBR. We conclude that fire-affected areas and burn severity patterns in the northern jarrah forest can be objectively mapped using remotely-sensed changes in LAI, while offering the important advantage over NBR of being readily interpretable in the wider context of ecological forest management.     

GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data

Burn severity estimation is a key factor in the post-fire management. Previous studies using remotely sensed data to retrieve burn severity, as measured by the Composite Burn Index (CBI), have found inconsistencies, since spectral indices work well in some ecosystems but not in others. These inconsistencies may be caused by the lack of spectral uniqueness in the CBI definition, or by the performance of the spectral indices used. This paper analyses the former aspect, using a simulation analysis to study the relationships between the CBI and reflectance. Subsequently, a modified version of this index, called GeoCBI, is proposed to improve the retrieval of burn severity from remotely sensed data. GeoCBI takes into account the fraction of cover (FCOV) of the different vegetation strata used to compute the CBI. Moreover, it also includes the changes in the leaf area index (LAI) for the intermediate and tall tree strata (D+E). Field and simulation results show that GeoCBI is more consistently related to spectral reflectance than CBI for different ranges of burn severities, while keeping its ecological meaning.     

Post-fire vegetation response as a proxy to quantify the magnitude of burn severity in tropical peatland

In recent years, fires in tropical forests in Southeast Asia have become more frequent and widespread, resulting in an increased need to evaluate fire impacts at a landscape scale. We examine whether post-fire vegetation regrowth can be used as a proxy to evaluate burn severity in a peatland landscape in Central Kalimantan, Indonesian Borneo, that has been subject to frequent fires. Several single- and bi-temporal indices as well as spectral fraction endmembers derived from either a post-fire image or a combination of pre- and post-fire images obtained by the Landsat sensor were examined. Spectral data were correlated with vegetation variables obtained from in situ measurements collected 4 years after the last fire. Of the tested spectral data, the bi-temporal and single normalized burn ratio (dNBR and NBR) showed the strongest correlations with the sets of vegetation variables (i.e. total woody aboveground biomass, tree density, and number of trees <10 cm diameter at breast height (DBH)). The results of an analysis of variance (ANOVA) and Tukey's multiple comparison of means test confirmed that NBR, dNBR, and the normalized difference water index could delineate four regrowth classes, thus confirming their utility in separating areas subjected to a single fire from those affected by multiple fires (MFs) as well as for discrimination between fires of differing severity. The results (a) provide evidence of the long-lasting impact that MFs have on forest recovery in this ecosystem and (b) confirm that vegetation response can be used as a proxy to quantify burn severity in locations affected by MFs.     

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km². AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.     

MODIS-informed greenness responses to daytime land surface temperature fluctuations and wildfire disturbances in the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components.     

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002) and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. Satellite Pour 1’Observation de la Terre (SPOT)-VEGETATION 10-day Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat- TM/ETM+, SPOT-VEGETATION data and ground-based NDVI in identifying land-cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires that occurred in 1997 and 2002.     

Characterizing post-fire recovery of fynbos vegetation in the Western Cape Region of South Africa using MODIS data

Post-fire recovery trajectories of five fynbos vegetation stands in the Western Cape Region of South Africa were characterized using moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250 m data. Indices of NDVI recovery relative to pre-fire values or values from unburnt control plots indicated full recovery within 7 years and particularly rapid recovery in the first two post-fire years. Intra-stand variability of pixel NDVIs generally increased after fires and also exhibited a rapid recovery to pre-fire conditions. While stand age was the dominant determinant of NDVI recovery, drought interrupted the recovery pathways and this effect was amplified on drier, equator-facing slopes. Post-fire recovery characteristics of fynbos NDVI were found to be similar to those documented for chaparral vegetation in California despite contrasting rainfall and soil nutrient conditions in the two regions.     

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.     

Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Multi-temporal change detection is commonly used in the detection of changes to ecosystems. Differencing single band indices derived from multispectral pre- and post-fire images is one of the most frequently used change detection algorithms. In this paper we examine a commonly used index used in mapping fire effects due to wildland fire. Subtracting a post-fire from a pre-fire image derived index produces a measure of absolute change which then can be used to estimate total carbon release, biomass loss, smoke production, etc. Measuring absolute change however, may be inappropriate when assessing ecological impacts. In a pixel with a sparse tree canopy for example, differencing a vegetation index will measure a small change due stand-replacing fire. Similarly, differencing will produce a large change value in a pixel experiencing stand-replacing fire that had a dense pre-fire tree canopy. If all stand-replacing fire is defined as severe fire, then thresholding an absolute change image derived through image differencing to produce a categorical classification of burn severity can result in misclassification of low vegetated pixels. Misclassification of low vegetated pixels also happens when classifying severity in different vegetation types within the same fire perimeter with one set of thresholds. Comparisons of classifications derived from thresholds of dNBR and relative dNBR data for individual fires may result in similar classification accuracies. However, classifications of relative dNBR data can produce higher accuracies on average for the high burn severity category than dNBR classifications derived from a universal set of thresholds applied across multiple fires. This is important when mapping historic fires where precise field based severity data may not be available to aid in classification. Implementation of a relative index will also allow a more direct comparison of severity between fires across space and time which is important for landscape level analysis. In this paper we present a relative version of dNBR based upon field data from 14 fires in the Sierra Nevada mountain range of California, USA. The methods presented may have application to other types of disturbance events.     

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared-near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R² = 0.72) than one-year post-fire (R² = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.     

Temporal burn scar evolution in tallgrass prairie based on field spectroscopy

In situ field spectroscopy samples were used to simulate several Moderate Resolution Imaging Spectroradiometer (MODIS) bands and indices commonly used for burned area detection. Each band or index was tested for its ability to differentiate between burned and unburned tallgrass prairie during several time periods from spring (when burning took place) to late summer (peak biomass) with three analysis of variance tests. The normalized difference vegetation index (NDVI), global environmental monitoring index (GEMI), global environmental monitoring index – burn scar (GEMI-B), and normalized burn ratio (NBR) indices, as well as MODIS band 7 (longwave mid-infrared; LWMIR), showed virtually no promise for differentiating burned from unburned areas for more than several days after the burn. Others, including the burned area index (BAI), Mid-infrared burn index (MIRBI), and MODIS bands 3 (red), 4 (near-infrared; NIR), 5 (longwave near-infrared; LWNIR), and 6 (shortwave mid-infrared; SWMIR) were able to differentiate between burned and unburned areas well into the growing season – in some cases, even through its entire length. The performance of particular bands and indices often depended on grazing, vegetation phenology, ash/char/soil reflectance, and factors that influenced pre-burn biomass.     

Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data

Wildfire is an important disturbance agent in Canada's boreal forest. Optical remotely sensed imagery (e.g., Landsat TM/ETM+), is well suited for capturing horizontally distributed forest conditions, structure, and change, while Light Detection and Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to characterize post-fire conditions. The objective of this study is to compare changes in forest structure, as measured with a discrete return profiling LIDAR, to post-fire conditions, as measured with remotely sensed data. Our research is focused on a boreal forest fire that occurred in May 2002 in Alberta, Canada. The Normalized Burn Ratio (NBR), the differenced NBR (dNBR), and the relative dNBR (RdNBR) were calculated from two dates of Landsat data (August 2001 and September 2002). Forest structural attributes were derived from two spatially coincident discrete return LIDAR profiles acquired in September 1997 and 2002 respectively. Image segmentation was used to produce homogeneous spatial patches analogous to forest stands, with analysis conducted at this patch level.In this study area, which was relatively homogenous and dominated by open forest, no statistically significant relationships were found between pre-fire forest structure and post-fire conditions (r < 0.5; p > 0.05). Post-fire forest structure and absolute and relative changes in forest structure were strongly correlated to post-fire conditions (r ranging from − 0.507 to 0.712; p < 0.0001). Measures of vegetation fill (VF) (LIDAR capture of cross-sectional vegetation amount), post-fire and absolute change in crown closure (CC), and relative change in average canopy height, were most useful for characterizing post-fire conditions. Forest structural attributes generated from the post-fire LIDAR data were most strongly correlated to post-fire NBR, while dNBR and RdNBR had stronger correlations with absolute and relative changes in the forest structural attributes. Absolute and relative changes in VF and changes in CC had the strongest positive correlations with respect to dNBR and RdNBR, ranging from 0.514 to 0.715 (p < 0.05). Measures of average inter-tree distance and volume were not strongly correlated to post-fire NBR, dNBR, or RdNBR. No marked differences were found in the strength or significance of correlations between post-fire structure and the post-fire NBR, dNBR, RdNBR, indicating that for the conditions present in this study area all three burn severity indices captured post-fire conditions in a similar manner. Finally, the relationship between post-fire forest structure and post-fire condition was strongest for dense forests (> 60% crown closure) compared to open (26-60%) and sparse forests (10-25%). Forest structure information provided by LIDAR is useful for characterizing post-fire conditions and burn induced structural change, and will complement other attributes such as vegetation type and moisture, topography, and long-term weather patterns, all of which will also influence variations in post-fire conditions.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.     

Fusing Landsat and MODIS data to retrieve multispectral information from fire-affected areas over tropical savannah environments in the Brazilian Amazon

ABSTRACT

In this study, the combination of surface reflectance products from Terra- Moderate Resolution Imaging Spectroradiometer and Landsat-Enhanced Thematic Mapper Plus sensors are explored through the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm within the framework of forest fire studies over tropical savannah environments. Thus, 60 fusion-derived images were generated from four spectral bands [red, near-infrared, shortwave infrared (SWIR₁ and SWIR₂)] and six spectral indices [normalized difference vegetation index, normalized difference moisture index, global environment monitoring index, soil-adjusted vegetation index, normalized burn ratio (NBR), and differenced normalized burn ratio (dNBR)] over two selected study sites. For all fusion processes performed, the actual Landsat images for the corresponding dates are available, which supports validation of the blended images. Additionally, integration of blended spectral indices in the immediate post-fire evaluation and the generation of fire severity were analysed. The blended bands presented correlation and Structure Similarity Index Measure (SSIM) values that were consistently higher than 0.819 and root mean square error values of less than 0.027, which confirms good accuracy levels obtained from the model. Similar correlation and SSIM accuracy levels were observed in the blended indices assessment for both study sites, which enables its values to be well-integrated for an analysis of the immediately post-fire date. However, the fire severity mapping from fused images needs to be carefully implemented since the dNBR index is generally less accurate than other blended indices. FSDAF fusion proved to be a useful alternative to retrieving multispectral information from savannah environments affected by fires.     

High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery

The WorldView-3 (WV-3) sensor, launched in 2014, is the first high-spatial resolution scanner to acquire imagery in the shortwave infrared (SWIR). A spectral ratio of the SWIR combined with the near-infrared (NIR) can potentially provide an effective differentiation of wildfire burn severity. Previous high spatial resolution sensors were limited to data from the visible and NIR for mapping burn severity, for example using the normalized difference vegetation index (NDVI). Drawing on a study site in the Pine Barrens of New Jersey, USA, we investigate optimal processing methods for analysing WV-3 data, with a focus on the pre-fire minus post-fire differenced normalized burn ratio (dNBR). Although the imagery, originally acquired with a 3.7 m instantaneous field of view, was aggregated to 7.5 m pixels by DigitalGlobe due to current licensing constraints, a slight additional smoothing of the data was nevertheless found to help reduce noise in the multi-temporal dNBR imagery. The highest coefficient of determination (R²) of the regressions of dNBR with the field-based composite burn index was obtained with a dNBR ratio produced with the NIR1 and SWIR6 bands. Only a very small increase in R² was found when dNBR was calculated using the average of NIR1 and NIR2 for the NIR bands, and SWIR5 to SWIR8 for the SWIR bands. dNBR calculated using SWIR1 as the NIR band produced notably lower R² values than when either NIR1 or NIR2 were used. Differenced NDVI data was found to produce models with a much lower R² than dNBR, emphasizing the importance of the shortwave infrared region for monitoring fire severity. High spatial resolution dNBR data from WV-3 can potentially provide valuable information on finer details regarding burn severity patterns than can be obtained from Landsat 30 m data.     

Monitoring fire-affected areas using Thematic Mapper data

In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) and Multitemporal NDVI Classification (MNC). First, 11 Landsat-5 Thematic Mapper (TM) images of a forest area were radiometrically corrected to derive a multitemporal series of intercomparable images for each spring from 1984 to 1994. Then, in order to check the feasibility of the three approaches, they were used for mapping fire burns that occurred during 1992. The various procedures yielded different maps of burned areas; the MNC method seemed to be more reliable than the others, because it merges spectral data corresponding not only to 1992 (pre-fire) and 1993 (post-fire) but also to 1994 (the second year after the fires), which is key in the vegetation regeneration. Finally, this methodology was automated to yield an inventory of burned areas for each year during the period of study.     

Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery

Relatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R² of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.     

Forest recovery trends derived from Landsat time series for North American boreal forests

A critical component of landscape dynamics is the recovery of vegetation following disturbance. The objective of this research was to characterize the forest recovery trends associated with a range of spectral indicators and report their observed performance and identified limitations. Forest disturbances were mapped for a random sample of three major bioclimate zones of North American boreal forests. The mean number of years for forest to recover, defined as time required to for a pixel to attain 80% of the mean spectral value of the 2 years prior to disturbance, was estimated for each disturbed pixel. The majority of disturbed pixels recovered within the first 5 years regardless of the index ranging from approximately 78% with normalized burn ratio (NBR) to 95% with tasselled cap greenness (TCG) and after 10 years more than 93% of disturbed pixels had recovered. Recovery rates suggest that normalized differenced vegetation index (NDVI) and TCG saturate earlier than indices that emphasize longer wavelengths. Thus, indices such as NBR and the mid-infrared spectral band offer increased capacity to characterize different levels of forest recovery. The mean length of time for spectral indices to recover to 80% of the pre-disturbance value for pixels disturbed 10 or more years ago was highest for NBR, 5.6 years, and lowest for TCG, 1.7 years. The mid-infrared spectral band had the greatest difference in recovered pixels among bioclimate zones 1 year after disturbance, ranging from approximately 42% of disturbed pixels for the cold and mesic bioclimate zone to 60% for the extremely cold and mesic bioclimate zone. The cold and mesic bioclimate zone had the longest mean years to recover ranging from 1.9 years for TCG to 4.2 years for NBR, while the cool temperate and dry bioclimate zone had the shortest mean years to recover ranging from 1.6 years for TCG to 2.9 years for NBR suggesting differences in pre-disturbance conditions or successional processes. The results highlight the need for caution when selecting and interpreting a spectral index for recovery characterization, as spectral indices, based upon the constituent wavelengths, are sensitive to different vegetation conditions and will provide a variable representation of structural conditions of forests.