A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT)

Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R² = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBR_MT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBR_MT estimates resulted in a moderate-high coefficient of determination R² = 0.54. dNBR_MT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBR_MT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBR_MT accounts for both first- and second-order fire effects.     

Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems

Forest fires are one of the most important causes of environmental alteration in Mediterranean countries. Discrimination of different degrees of burn severity is critical for improving management of fire-affected areas. This paper aims to evaluate the usefulness of land surface temperature (LST) as potential indicator of burn severity. We used a large convention-dominated wildfire, which occurred on 19–21 September, 2012 in Northwestern Spain. From this area, a 1-year series of six LST images were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data using a single channel algorithm. Further, the Composite Burn Index (CBI) was measured in 111 field plots to identify the burn severity level (low, moderate, and high). Evaluation of the potential relationship between post-fire LST and ground measured CBI was performed by both correlation analysis and regression models. Correlation coefficients were higher in the immediate post-fire LST images, but decreased during the fall of 2012 and increased again with a second maximum value in summer, 2013. A linear regression model between post-fire LST and CBI allowed us to represent spatially predicted CBI (R-squared_adj > 85%). After performing an analysis of variance (ANOVA) between post-fire LST and CBI, a Fisher's least significant difference test determined that two burn severity levels (low-moderate and high) could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that summer post-fire LST from moderate resolution satellite data may be considered as a valuable indicator of burn severity for large fires in Mediterranean forest ecosytems.     

Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece

The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.     

Remote sensing of fire severity: assessing the performance of the normalized burn ratio

Several studies have used satellite data to map different levels of fire severity present within burned areas. Increasingly, fire severity has been estimated using a spectral index called the normalized burn ratio (NBR). This letter assesses the performance of the NBR against ideal requirements of a spectral index designed to measure fire severity. According to index theory, the NBR would be optimal for quantifying fire severity if the trajectory in spectral feature space caused by different levels of severity occurred perpendicular to the NBR isolines. We assess how well NBR meets this condition using reflectance data sensed before and shortly after fires in the South African savanna, Australian savanna, Russian Federation boreal forest, and South American tropical forest. Although previous studies report high correlation between fire severity measured in the field- and satellite-derived NBR, our results do not provide evidence that the performance of the NBR is optimal in describing fire severity shortly after fire occurrence. Spectral displacements due to burning occur in numerous directions relative to the NBR index isolines, suggesting that the NBR may not be primarily and consistently sensitive to fire severity. Findings suggest that the development of the next generation of methods to estimate fire severity remotely should incorporate knowledge of how fires of different severity displace the position of prefire vegetation in multispectral space.     

Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index

Several remote sensing studies have discussed the potential of satellite imagery as an alternative for extensive field sampling to quantify fire-vegetation impact over large areas. Most studies depend on Landsat image availability with infrequent image acquisition dates and consequently are limited for assessing intra-annual fire-vegetation dynamics or comparing different fire plots and dates. The control pixel based regeneration index (pRI) derived from SPOT-VEGETATION (VGT) normalized difference vegetation index (NDVI) is used in this study as an alternative to the traditional bi-temporal Landsat approach based on the normalized burn ratio (NBR). The major advantage of the pRI is the use of unburnt control plots which allow the expression of the intra-annual variation due to regeneration processes without external influences. In the comparison of Landsat and VGT data, (i) the inter-annual differences between the bi-temporal and control plot approach were contrasted and (ii) metrics of pRI were derived and compared with the inter-annual dynamics of both VGT and Landsat data. Results of these comparisons, demonstrate the overall similarity between NBR and NDVI data, stress the importance of the elimination of external influences (e.g., phenological variations), and emphasize the failure of including post-fire vegetation responses in bi-temporal Landsat assessments, especially in quickly recovering ecotypes with a strong annual phenological cycle such as savanna. This highlights the importance of using high frequency multi-temporal approaches to estimate fire-vegetation impact in temporally dynamic vegetation types.     

Detecting forest damage after a low-severity fire using remote sensing at multiple scales

Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R² = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R² = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha⁻¹ to 11.73 ± 1.84 Mg ha⁻¹, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level.     

Burned area mapping and post-fire impact assessment in the Kassandra peninsula (Greece) using Landsat TM and Quickbird data

The purpose of this study was to assess the environmental impacts of forest fires on part of the Mediterranean basin. The study area is on the Kassandra peninsula, prefecture of Halkidiki, Greece. A maximum likelihood supervised classification was applied to a post-fire Landsat TM image for mapping the exact burned area. Land-cover types that had been affected by fire were identified with the aid of a CORINE land-cover type layer. Results showed an overall classification accuracy of 95%, and 83% of the total burned area was ‘forest areas’. A normalized difference vegetation index threshold technique was applied to a post-fire Quickbird image which had been recorded six years after the fire event to assess the vegetation recovery and to identify the vegetation species that were dominant in burned areas. Four classes were identified: ‘bare soil’, ‘sparse shrubs’, ‘dense shrubs’ and ‘tree and shrub communities’. Results showed that ‘shrublands’ is the main vegetation type which has prevailed (65%) and that vegetation recovery is homogeneous in burned areas.     

Assessment of Post-fire Soil Erosion Risk in Fire-Affected Watersheds Using Remote Sensing and GIS

Soil erosion is a prominent cause of land degradation and desertification in Mediterranean countries. The detrimental effects of soil erosion are exemplified in climate (in particular climate change), topography, human activities, and natural disasters. Forest fires, which are an integral part of Mediterranean ecosystems, are responsible for the destruction of above-and below-ground vegetation that protects against soil erosion. Under this perspective, the estimation of potential soil erosion, especially after fire events, is critical for identifying watersheds that require management to prevent sediment loss, flooding, and increased ecosystem degradation. The objective of this study was to model the potential post-fire soil erosion risk following a large and intensive wildland fire, in order to prioritize protection and management actions at the watershed level in a Mediterranean landscape. Burn severity and preand post-fire land cover/uses were mapped using an ASTER image acquired two years before the fire, air photos acquired shortly after the fire, and a Landsat TM image acquired within one month after-fire. We estimated pre-and post-fire sediment loss using an integrated GIS-based approach, and additionally we analyzed landscape erosion patterns. The overall accuracy of the severity map reached 83%. Severe and heavy potential erosion classes covered approximately 90% of the total area following the fire, compared to 55% before. The fire had a profound effect on the spatial erosion pattern by altering the distribution of the potential erosion classes in 21 out of 24 watersheds, and seven watersheds were identified as being the most vulnerable to post-fire soil erosion. The spatial pattern of the erosion process is important because landscape cover heterogeneity induced especially by fire is a dominant factor controlling runoff generation and erosion rate, and should be considered in post-fire erosion risk assessment.     

Relative suitability of indices derived from Landsat ETM+ and SPOT 5 for detecting fire severity in sagebrush steppe

Remote sensing indices of burn area and fire severity have been developed and tested for forest ecosystems, but not sparsely vegetated, desert shrub-steppe in which large wildfires are a common occurrence and a major issue for land management. We compared the performance of remote sensing indices for detecting burn area and fire severity with extensive ground-based cover assessments made before and after the prescribed burning of a 3 km² shrub-steppe area. The remote sensing indices were based on either Landsat 7 ETM+ or SPOT 5 data, using either single or multiple dates of imagery. The indices delineating burned versus unburned areas had better overall, User, and Producer's accuracies than indices delineating levels of fire severity. The Soil Adjusted Vegetation Index (SAVI) calculated from SPOT had the greatest overall accuracy (100%) in delineating burned versus unburned areas. The relative differenced Normalized Burn Ratio (RdNBR) using Landsat ETM+ provided the highest accuracies (73% overall accuracy) for delineating fire severity. Though SPOT's spatial resolution likely conferred advantages for determining burn boundaries, the higher spectral resolution (particularly band 7, 2.21 μm) of Landsat ETM+ may be necessary for detecting differences in fire severity in sparsely vegetated shrub-steppe.     

Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days).     

Assessing Forest Fire and Vegetation Recovery in the Black Hills,South Dakota

After 110 years of sustained fire suppression, the 2000 Jasper fire consumed about 33,785 ha (83,500 acres), or 12% of the Black Hills National Forest. We mapped the severity of the Jasper fire using Landsat imagery and then investigated post-fire vegetation regeneration conditions using field data, Quickbird imagery, and regression modeling. We found that fire scar and severity could be delineated and mapped accurately based on remotely sensed and field-acquired data. Results also revealed that vegetative recovery relative to burn severity, topography, and soil factors could be modeled effectively using local geographically weighted regression (GWR). Further regeneration assessment revealed that severely or heavily burned areas were more rapidly re-vegetated with grasses, forbs, and woody shrubs in the short term. The field survey showed that prescribed burns retard crown fires and that after eight years ponderosa pine seedlings have not re-established.     

Variation in tree mortality and regeneration affect forest carbon recovery following fuel treatments and wildfire in the Lake Tahoe Basin, California, USA

Background

Forest fuel treatments have been proposed as tools to stabilize carbon stocks in fire-prone forests in the Western U.S.A. Although fuel treatments such as thinning and burning are known to immediately reduce forest carbon stocks, there are suggestions that these losses may be paid back over the long-term if treatments sufficiently reduce future wildfire severity, or prevent deforestation. Although fire severity and post-fire tree regeneration have been indicated as important influences on long-term carbon dynamics, it remains unclear how natural variability in these processes might affect the ability of fuel treatments to protect forest carbon resources. We surveyed a wildfire where fuel treatments were put in place before fire and estimated the short-term impact of treatment and wildfire on aboveground carbon stocks at our study site. We then used a common vegetation growth simulator in conjunction with sensitivity analysis techniques to assess how predicted timescales of carbon recovery after fire are sensitive to variation in rates of fire-related tree mortality, and post-fire tree regeneration.

Results

We found that fuel reduction treatments were successful at ameliorating fire severity at our study site by removing an estimated 36% of aboveground biomass. Treated and untreated stands stored similar amounts of carbon three years after wildfire, but differences in fire severity were such that untreated stands maintained only 7% of aboveground carbon as live trees, versus 51% in treated stands. Over the long-term, our simulations suggest that treated stands in our study area will recover baseline carbon storage 10?C35?years more quickly than untreated stands. Our sensitivity analysis found that rates of fire-related tree mortality strongly influence estimates of post-fire carbon recovery. Rates of regeneration were less influential on recovery timing, except when fire severity was high.

Conclusions

Our ability to predict the response of forest carbon resources to anthropogenic and natural disturbances requires models that incorporate uncertainty in processes important to long-term forest carbon dynamics. To the extent that fuel treatments are able to ameliorate tree mortality rates or prevent deforestation resulting from wildfire, our results suggest that treatments may be a viable strategy to stabilize existing forest carbon stocks.     

应用迁移学习的林火烈度初始评估研究

林火发生后,开展森林生态系统烈度信息的初始评估,能够为灾后生态修复管理措施的快速实施提供定量依据。为了改善传统林火烈度评估模型的时效性,本研究利用历史过火区域的实地调查数据,构建基于迁移学习的烈度评估模型,并将其应用于2020年3月30日发生的西昌泸山森林大火烈度初始评估研究中。研究结果表明：迁移学习算法能够将源区域和目标区域的遥感影像光谱转换为多个新的特征变量,在这些新特征变量构成的投影空间中,源区域和目标区域样本具有相似的特征分布。在此基础上,基于源区域历史实地调查数据构建的烈度评估模型,能够迁移应用于目标区域的烈度评估。在本研究林火烈度的初始评估中,基于迁移学习的烈度评估模型精度较高,总体精度为71.20%,Kappa系数为0.64。与该模型对比,未进行迁移学习的支持向量回归模型精度较低,其总体精度为58.00%,Kappa系数为0.48。同时,基于dNDVI、dLST和dNBR指数的经验回归模型精度最低,其总体精度分别为：20.80%、34.8%和24.80%,Kappa系数分别为：0.01、0.19和0.06。本研究可为林火灾后管理措施的快速响应,提供一种新的思路和参考。     

A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests

The regular and consistent measurements provided by Earth observation satellites can support the monitoring and reporting of forest indicators. Although substantial scientific literature espouses the capabilities of satellites in this area, the techniques are under-utilised in national reporting, where there is a preference for aggregating ad hoc data. In this paper, we posit that satellite information, while perhaps of low accuracy at single time steps or across small areas, can produce trends and patterns which are, in fact, more meaningful at regional and national scales. This is primarily due to data consistency over time and space. To investigate this, we use MODIS and Landsat data to explore trends associated with fire disturbance and recovery across boreal and temperate forests worldwide. Our results found that 181 million ha (9 %) of the study area (2 billion ha of forests) was burned between 2001 and 2018, as detected by MODIS satellites. World Wildlife Fund biomes were used for a detailed analysis across several countries. A significant increasing trend in area burned was observed in Mediterranean forests in Chile (8.9 % yr⁻¹), while a significant decreasing trend was found in temperate mixed forests in China (-2.2 % yr⁻¹). To explore trends and patterns in fire severity and forest recovery, we used Google Earth Engine to efficiently sample thousands of Landsat images from 1991 onwards. Fire severity, as measured by the change in the normalized burn ratio (NBR), was found to be generally stable over time; however, a slight increasing trend was observed in the Russian taiga. Our analysis of spectral recovery following wildfire indicated that it was largely dependent on location, with some biomes (particularly in the USA) showing signs that spectral recovery rates have shortened over time. This study demonstrates how satellite data and cloud-computing can be harnessed to establish baselines and reveal trends and patterns, and improve monitoring and reporting of forest indicators at national and global scales.     

Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

联合多源遥感数据监测四川木里县森林火灾

森林火灾既严重影响森林生态系统的稳定,还威胁到人类生命财产安全。传统监测森林火灾方法,覆盖范围小,难以及时监测小面积火灾。遥感卫星能大范围精确监测火情,提高了监测方法的时效性,但使用单一卫星数据源很容易受到云雨等客观环境因素影响,降低监测的时效性。本文以四川木里藏族自治县"330森林火灾"区域为对象,开展多源卫星遥感数据对小范围火灾联合监测的研究。首先,充分挖掘高分四号高时空分辨率和中红外火烧敏感波段优势,联合烟幕、温度和植被指数时序变化确定火烧时间与位置;然后,使用Sentinel-2数据监测不同火烧区域光谱信息;接着,使用Sentinel-2数据提取dNBR(differenced Normalized Burn Ratio),提出了基于最大类间方差算法(OTSU)分步骤确定不同程度火烧迹地与面积的方法;最后,建立Sentinel-1A极化比值PR (Polarization Ratio)和NDVI之间关系,利用微波雷达突破云雨限制。结果表明:(1)高分四号联合IRS(InfraRed Scanner)和PMS(Panchromatic Multispectral Sensor)能够实时监测小范围火灾;(2)根据火点位置,确定火灾蔓延期间NDVI下降(由0.7降低至0.25),确定起火时间(3月30日);(3)火灾区域与未受灾区,以及不同类型火烧迹地之间的光谱在490—2200 nm范围存在差异;(4)基于OTSU算法自动确定阈值,确定林地损失面积41.56公顷(dNBR=0.35),精度达94.67%,提取林地过火未损失面积66.56公顷(dNBR=0.10),精度达90.94%,林地损失区域基本符合实际调查结果;(5)火灾前后极化比值由6.6 dB升高至10.8 dB,NDVI与PR经线性回归,R2=0.58,验证R2=0.50。联合多源卫星监测森林火灾,能提高森林火灾监测的时效性,避免了云雨等复杂环境的影响。研究成果能为小火点的及时识别和灾害评估提供参考,其应用可为林火应急响应提供技术支撑。     

Influences of forest roads and their edge effects on the spatial pattern of burn severity

Previous research has shown that forest roads are an important feature in many landscapes and have significant effects on wildfire ignition and cessation. However, forest road effects on burn severity have not been studied at the landscape level. Therefore, the overarching goal of our study is to identify the influences of road edge effects on the spatial patterns of burn severity. We analyzed six fires within the Okanogan–Wenatchee National Forest on the eastern slope of the Cascades mountain range of central Washington.We generated two categories for assessing road variables: (1) Primary Road Effect Zone (area within 150 m of the nearest road) and (2) Secondary Road Effect Zone (area from 150 m to 300 m to the nearest road). A regular sampling grid including one out of every 9 cells was created for each fire.These grids were intersected with burn severity data in the form of the Relative Differenced Normalized Burn Ratio (RdNBR), road distance category, stream distance, elevation, slope, terrain shape index, heat load index, canopy cover, and fuel type. We fit spatial regression models with RdNBR as the dependent variable.We found that high burn severity is less likely to occur in the Primary Road Effect Zone for most fires, although one fire exhibited the opposite relationship. Forest road edge effects were hypothesized to be an important determinant of burn severity because fragmentation created by roads alters the roadside fuel profile and environment and because road corridors create barriers to fire spread. Recognizing roadside effects on burn severity patterns highlights the need for further study of the range of effects that roads have on fuels and the fire environment and the potential for incorporating road effects into landscape-level assessments of fire risk.     

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well-illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing a small image-to-image difference in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p < 0.001). The original c-correction method improved the optimality of badly illuminated pixels while deteriorating the optimality of well-illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination–reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that deviated up to two months from the ideal anniversary date scheme the modified c-correction technique resulted in a more reliable change detection.     

Correcting the Fire Scar Perimeter of a 1983 Wildfire Using USGS-Archived Landsat Satellite Data

In July 1983, a large wildfire occurred on the island of Karpathos in Greece. However, only a general sketch of the burn perimeter was available and this lacked detailed spatial information, particularly for unburned patches within the fire scar perimeter. A study was undertaken to correctly map the area burned using USGS-archived Landsat data by applying several digital image processing techniques. This paper summarizes and discusses the main findings of that study and provides some general recommendations on the use of remote sensing and archived Landsat data for reconstructing fire history. Remote sensing along with geographic information systems can provide an excellent framework for fast, reliable data capture, measurement, and synthesis, all of which are essential for thorough eco-environmental analysis. Satellite data of multiple types offer an unlimited source of information due to their rich spectral and spatial information content. Satellite mapping of burned areas is considered a standard technique in creating maps of fire scars at multiple scales as a function of the satellite sensor's geometric resolution.