Normalized Burn Ratio Index Research Articles

FOCOS DE CALOR E QUEIMADAS EM UNIDADES DE CONSERVAÇÃO FEDERAIS DO PANTANAL MATO-GROSSENSE NO PERÍODO DE 2016 – 2021

The Pantanal is one of the biomes that has suffered the most from burning, mainly due to its environmental characteristics. In view of this situation, this research sought to analyze the temporal space distribution of the sources of heat and recurrent fires in the Conservation Units Federal at the pantanal mato-grossense in the period 2016 to 2021 through Normalized Burn Ratio (NBR) and by the Forest Fires and Burns Program, made available by the National Institute for Space Research (INPE). Satellite images and analyzes of the NBR index were processed in a virtual platform (GEE) and a database was created, which were treated and generated two types of maps, the NBRs and the distribution of hotspots. The results showed that 2020 was the year with the highest occurrence of hotspots in the studied period, totaling 54,140 thousand hotspots inside of UCs. The UCs Mato-grossense Pantanal National Park, SESC-Pantanal Private Reserve of Natural Heritage Ecological Resort and the Taiamã Ecological Station, showed the highest area burned in the year 2021 throughout the study period. Compared to 2020, there was an increase in burned area in 2021 in the UCs, Taiamã Ecological Station, RPPN Reserve Jubran, RPPN Ecological Resort SESC-Pantanal, Mato-grossense Pantanal National Park. It has been shown that in UCs, RPPN Resort Farm Dorochê, RPPN Ecological Resort SESC-Pantanal, Mato-grossense Pantanal National Park the Low Severity, Low Moderate Severity and High Moderate Severity classes, which represent less dense vegetation, increased considerably from 2016 to 2021. The UCs that showed the highest burn were: RPPN Resort Farm Dorochê and Mato-grossense Pantanal National Park. The NBR in this study presented results that disagree with what actually happened in the UCs, showing that the period used in the study from July to October is not recommended, because the fires may not have occurred yet, however, it shows that for the following year when hot spots occurred, the NBR detects scars from the fires of the previous year.

Mapping Forest Fire-affected Areas Using Advanced Machine Learning Techniques in Damoh District of Central India

Abstract Forest fires can cause calamitous damage to the forest ecosystems and may affect climatic parameters, such as temperature, evapotranspiration, and precipitation by catalyzing changes in the local ecology of the region. The main research objective of this work was to quantify the different indices as well as to identify the changes in years 2011 and 2020 in pre- and post-fire, these parameters, such as Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Normalized Burn Ratio Index (NBR), associated with the severity of forest fires in the Damoh district of Madhya Pradesh. An attempt has been also made to understand the interrelationship of these parameters to gauge how these may correlate to determine the sensitivity to forest fires. In this context, we have incorporated advanced GIS methods for the identification of the pre-post fires for 2011 and 2020 year from Landsat 5 and Landsat 8 OLI/TIRS level -1 and Random Forest (RF) model in the Google Earth Engine (GEE) platform. Land Use Land Cover (LULC) map was categorized into five classes based on the satellite data sets. Our findings indicate elevated Land Surface Temperature (LST) values in the Northern and Central regions of the study area, reaching 32.0°C before the fire event. Subsequently, following the fire incident in the year 2011, LST escalated to 39.0°C. Similarly, in the southern and south-eastern regions of the Damoh district, LST peaked at approximately 43.0°C coinciding with the onset of a forest fire in 2020. Furthermore, our analysis revealed a negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST, whereas the Normalized Burn Ratio (NBR) displayed a positive correlation with LST. These results underscore the impact of LST on forest vegetation dynamics, with LST nearing 39.0°C indicating an increased risk of forest fires. The results of this study can be used by local administration to devise an efficient policy related to forest fire management.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

COMPARATIVE ANALYSES OF SECONDARY ECOLOGICAL SUCCESSION FOLLOWING WILDFIRES IN THREE DISTINCT FOREST TYPES. A STUDY CASE FROM MOGUER, SPAIN

Wildfires have become increasingly prevalent and destructive in forest ecosystems worldwide, necessitating a comprehensive understanding of post-fire recovery dynamics for effective conservation and management. Remote sensing technology, coupled with vegetation indices such as Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Red Vegetation Index (RVI), offers a powerful means to investigate these processes. In this study, we utilize remote sensing techniques to conduct a comparative analysis of secondary ecological succession following wildfires in three distinct forest types (Coniferous, Sclerophyll and Mixed) of a forest affected by fire near Moguer, Spain. Through the acquisition and analysis of multispectral satellite imagery, we monitored changes in vegetation health and recovery across the region of interest. The NBR index allowed us to assess the severity and extent of wildfire damage, while NDVI quantified vegetation greenness and regrowth. GRVI and RVI provided insights into subtle variations in vegetation composition and health. We identified distinct temporal and spatial patterns in post-fire recovery among the different forest types by applying these indices for the period between 2017 and 2021. Our findings underscore the significance of understanding the diverse responses of these ecosystems to wildfires. While common recovery patterns emerged, such as an initial decrease in NDVI followed by regeneration, variations were observed in the timing and magnitude of recovery. These distinctions are attributed to differences in species composition, fire adaptations, and ecological processes specific to each forest type. In conclusion, the utilization of NBR, NDVI, GRVI, and RVI indices allows for a more nuanced evaluation of post-fire recovery dynamics.

Burned area mapping in Dendang District, Tanjung Jabung Timur Regency using sentinel-2

Remote Sensing is a way to provide information efficiently both in terms of cost and time. In addition, the use of remote sensing in mapping the burned area can be carried out on a large scale but with a fast time for the prevention of land fires. Monitoring of forest fires is carried out to see locations that often become fire hotspots every year and to prevent frequent land and forest fires.The method used in this study is to map the burned area using multi-temporal data using the Normalized burning Ratio and Normalized burning Ratio 2 indices from Sentinel-2 images in May and September 2019. The trend data can be used to evaluate the moratorium on forest business permits or a peatland. Sentinel 2 has a higher spatial resolution of 20 meters compared to other multispectral images that can be accessed easily and free of charge, so it is suitable to be applied in areas that are not too large and minimizes the value of estimation errors, using Sentinel 2 imagery which has 13 channels will Selected several channels that can be used for index transformation, namely the NBR (Normalized Burn Ratio) and NBR2 (Normalized Burn Ratio2) indexes, both indices utilize Near Infrared, SWIR1, and SWIR2 channels which can provide information about the area after land fires,The research results obtained that burned areas are often identified as barren land, such as peat canals or vacant land. This is because the spectral reflection characteristics of objects in burnt areas are the same as those in non-vegetated land areas when the NBR index is transformed using the NIR, SWIR1 and SWIR2 channels.
 Keywords: Forest and land fires, NBR Indices, Sentinel-2.

Using dense Sentinel-2 time series to explore combined fire and drought impacts in eucalypt forests

Following one of the driest years on record, millions of hectares of forests in southeast Australia were burned in the 2019–2020 “Black Summer” wildfires. In addition to the areas burned, drought related canopy collapse, dieback and tree mortality was widely observed. In this paper, we present a method to map canopy damage due to drought and fire across a large area. Sentinel-2 satellite imagery was used in a monthly time series to highlight areas of forest where the Normalized Burn Ratio index was significantly below a pre-disturbance “stable” period. The stable period was defined as the 3 years prior to 2019 and the disturbance thresholds are based on bioregion specific standard deviations below pre-disturbance means. The novel methods enabled drought impacted forests to be identified, including those which were subsequently burned by wildfire. Across the 20 Mha of forests studied, 9.9 Mha (49%) fell below the disturbance threshold. Of that, 5.8 Mha was disturbed by fire and a further 4.1 Mha by drought outside of the fire extent. Within the fire extent, almost 0.9 Mha was identified as being significantly drought affected prior to being burned. An analysis of spectral recovery following substantial rainfall from February 2020 onward indicates that most of the areas impacted by both drought and fire have similar rates of recovery to those impacted only by fire. There are some areas, however, where the combined effects of the “double disturbance” appears to be hindering recovery. The methods presented here are easily transferrable and demonstrate an approach for monitoring forest disturbance at higher temporal and spatial scales than those typically used.

Burn Severity Drivers in Italian Large Wildfires

The increase of wildfire incidence in highly populated areas significantly enhances the risk for ecosystems and human lives, activities and infrastructures. In central and southern Italy, recent decades’ fire records indicate that 2007 and 2017 were extreme years in terms of the number of fires and total burned area. Among them, we selected large fire events and explored their features and drivers of burn severity. We used a standardized extraction procedure to identify large wildfires (>100 ha) from the MODIS burned areas database and Landsat multi-spectral images. We mapped burn severity with the Relative Difference Normalized Burn Ratio index and explored the main drivers of severity using topographic, land-cover and anthropogenic predictors. We selected 113 wildfires for a collective total burned area of over 100,000 ha. Large fires were more frequent in the southern than in the central and northern regions, especially in July and August. The average fire size was about 900 ha and occurred mainly in shrublands (30.4%) and broadleaf forests (19.5%). With a random forest model, we observed that the highest severity occurred in conifer plantations and shrublands, in highly populated areas and at lower elevations. Burn severity models, at the landscape or regional scales, can be very useful tools for pre- and post-fire forest management planning.

Short-Term Recovery of the Aboveground Carbon Stock in Iberian Shrublands at the Extremes of an Environmental Gradient and as a Function of Burn Severity

The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions.

Monitoring of Vegetation Disturbance around Protected Areas in Central Tanzania Using Landsat Time-Series Data

Understanding vegetation disturbance around protected areas (PAs) is critical as it significantly affects the sustainable conservation of wildlife. However, there is a lack of analyses of consistent long-term data on vegetation disturbance. In this study, the LandTrendr algorithm and Google Earth Engine were used to access satellite data and explore the vegetation dynamics history across the Ruaha–Rungwa landscape, Tanzania. We characterized vegetation disturbance patterns and change attributes, including disturbance occurrence trends, rate, and severity, by using each pixel’s normalized burn ratio index time series. Between 2000 and 2019, 36% of the vegetation was significantly disturbed by anthropogenic activities. The results of this study show that the disturbance trends, severity, and patterns are highly variable and strongly depend on the management approaches implemented in the heterogeneous landscape: Ruaha National Park (RNP), Rungwa–Kizigo–Muhesi Game Reserves (RKMGR), and the surrounding zones. The disturbance rates and severity were pronounced and increased toward the edges of the western RKMGR. However, the disturbance in the areas surrounding the RNP was lower. The characterization of the vegetation disturbance over time provides spatial information that is necessary for policy makers, managers, and conservationists to understand the ongoing long-term changes in large PAs.

Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape

The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.

Forest fires and carbon emission release in semi-arid regions of Indonesia: the evidence from medium resolution of satellite imagery

Forest fires are one of the causes of forest degradation and deforestation in Indonesia. Tropical savanna forests are one of the forest ecosystems found in the Indonesian region with a semi-arid climate type prone to drought and fire. Forest fires contribute to the release of greenhouse gas emissions into the atmosphere. The calculation method to estimate the burnt area is still an obstacle in calculating greenhouse gas emissions due to forest fires. This research aims to assess the burnt area in tropical savanna forest cover type using medium resolution satellite imagery data by utilizing the Normalized Burn Ratio index in four villages in East Sumba Regency and estimating greenhouse gas emissions these activities. The results showed that the NBR method does not produce an estimate of the burned area with a fair degree of accuracy because the characteristic of channel 7 (SWIR) is sensitive to vegetation moisture content. The savanna forest cover type is the type of land cover identified as the most dominant burned area. It produces the highest greenhouse gas emissions compared to other land cover types.

Uso de sensoriamento remoto para identificação de queimadas no Parque Estadual Encontro das Águas

Given the recognized socio-environmental importance of the Pantanal, the news reporting the devastating fires that occurred in the year 2020 in the region are of great concern. This work aims to identify and analyze the occurrence of fires in 2020 at the Encontro das Águas state park, through images from the Landsat-8 and Sentinel-2 satellites, besides comparing the normalized burn ratio (NBR) and normalized difference vegetation index (NDVI) spectral indexes to define the most suitable for this analysis. The comparison confirmed the expected results, pointing out the NBR as the most adequate index for the identification and classification of burned areas, not only because it is more sensitive to the burned area, but also for avoiding the occurrence of false positives. The images processed using the software QGIS for the NBR index showed an aggressive fire in the park in the second half of the year, especially in the months of August, September, and October. Furthermore, when the same period was analyzed for the year 2019, it becomes clear that the fires in the year 2020 lasted longer and were more devastating, with a total burned area approximately 11 times greater.

气候、植被和地形对大兴安岭林火烈度空间格局的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 气候、植被和地形对大兴安岭林火烈度空间格局的影响 DOI: 10.5846/stxb201902140264 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31570462）；江西省教育厅科学技术研究项目（GJJ160275） Effects of climate, vegetation, and topography on spatial patterns of burn severity in the Great Xing'an Mountains Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在北方森林中火干扰是森林景观变化的主导因素。林火烈度作为衡量林火动态的重要指标，较为直观地反映了火干扰对森林生态系统的破坏程度，其空间格局深刻地影响着森林景观中的多种生态过程（如树种组成、种子扩散以及植被的恢复）。解释林火烈度空间格局有助于揭示林火干扰后森林景观格局的形成机制，对预测未来林火烈度空间格局以及制定科学合理林火管理策略均有重要意义。基于LandsatTM/ETM遥感影像，将2000-2016年大兴安岭呼中林区的36场火的林火烈度划分为未过火、轻度、中度、重度4个等级。采用FRAGSTAT景观格局分析软件从类型水平上计算了斑块所占景观面积比、面积加权平均斑块面积、面积加权平均斑块分维数、面积加权边缘面积比、斑块密度5个景观指数，以对林火烈度空间格局进行了定量化描述。并且采用随机森林模型，分析了气候、地形、植被对林火烈度空间格局的影响及其边际效应。通过研究得出以下结果：（1）相对于未过火、轻度、以及中度火烧斑块，重度火烧斑块的面积更大、形状更简单；（2）海拔对重度火烧斑块的空间格局起着至关重要的作用，其次是坡向、坡度、植被覆盖度、相对湿度、温度等；（3）随着海拔的升高，面积加权平均斑块面积和面积加权平均斑块分维数的边际效应曲线呈上升趋势，而面积加权边缘面积比和斑块密度呈下降趋势；除了面积加权平均斑块面积外，都受到火前植被覆盖度的影响，且植被覆盖度为0.2-0.3范围内，重度火烧斑块在景观中所占比例最大。总的来看，2000-2016年大兴安岭呼中森林景观中重度火烧斑块与未过火、轻度以及中度火烧斑块存在显著差异性。相对于气候，地形和植被对于塑造重度火烧斑块空间格局具有重要作用。因此，应针对重度火烧区域进行可燃物处理，从景观层面上合理配置森林斑块，从而降低高烈度森林大火发生的风险。 Abstract:Fire is a major driver of forest landscape change in boreal forests. Burn severity is one of the main indexes for measuring the damage degree of fire on forest ecosystems. Spatial patterns of burn severity affect numerous ecological processes (e.g., species composition, seed dispersal, and vegetation restoration). Explaining spatial patterns of burn severity is conducive to reveal the formation mechanism of forest landscape patterns after fire, which is of great significance for predicting spatial patterns of burn severity in the future and formulating scientific fire management strategies. Based on Landsat TM/ETM remote sensing images, we mapped the burn severity of 36 fires that occurred between 2000 and 2016 in Huzhong forest region of the Great Xing'an Mountains by calculating the post-fire Normalized Burn Ratio index (NBR) and classified the fires into unburned, low, moderate and high severity classes. For each fire, we calculated five landscape metrics to quantitatively describe spatial patterns of burn severity at the class level using the FRAGSTATS program. The landscape pattern metrics were percentage of landscape (PLAND), area-weighted mean patch size (AREA_AM), area-weighted mean fractal dimension index (FRAC_AM), perimeter-area ratio (PARA_AM), and patch density (PD). Using Random Forest models, we analyzed the relative importance and marginal effects of weather, topography, and vegetation variables on determining spatial patterns of burn severity. The results showed that:1) compared with unburned, low-, and moderate-severity patches, the high-severity patches were more larger and simpler in shape. 2) Elevation played an important role in shaping spatial patterns of burn severity, followed by aspect, slope, vegetation coverage, relative humidity, and temperature. 3) With the increase in elevation, the marginal effect curve of area-weighted mean patch area and area-weighted mean patch fractal dimension showed an obvious increasing trend, whereas area-weighted perimeter-area ratio and patch density exhibited a decreasing trend. In addition to area-weighted mean patch area, all of them were affected by pre-fire vegetation coverage. When pre-fire vegetation coverage ranged fom 0.2 to 0.3, the proportion of high-severity patches in the landscape were the largest. In general, the high-severity patches differed significantly from unburned, low- and moderate-severity patches for five spatial pattern metrics. Topography and vegetation were more important in shaping the spatial pattern of high-severity patches than climate. Therefore, it would be urgent to implement forest fuel treatment in high-severity areas. It is necessary to allocate different forest patches reasonably from the landscape level, then to reduce the risk of high-severity forest large fires. 参考文献 相似文献 引证文献

Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation

The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand km2 per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data.

Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: a case study on a large fire in NW Spain

This study analyses the suitability of remote sensing data from different sources (Landsat 7 ETM+, MODIS and Meteosat) in evaluating the effect of fuel conditions on fire severity, using a megafire (11891ha) that occurred in a Mediterranean pine forest ecosystem (NW Spain) between 19 and 22August 2012. Fire severity was measured via the delta Normalized Burn Ratio index. Fuel conditions were evaluated through biophysical variables of: (i) the Visible Atmospherically Resistant Index and mean actual evapotranspiration, as proxies of potential live fuel amount; and (ii) Land Surface Temperature and water deficit, as proxies of fuel moisture content. Relationships between fuel conditions and fire severity were evaluated using Random Forest models. Biophysical variables explained 40% of the variance. The Visible Atmospherically Resistant Index was the most important predictor, being positively associated with fire severity. Evapotranspiration also positively influenced severity, although its importance was conditioned by the data source. Live fuel amount, rather than fuel moisture content, primarily affected fire severity. Nevertheless, an increase in water deficit and land surface temperature was generally associated with greater fire severity. This study highlights that fuel conditions largely determine fire severity, providing useful information for defining pre-fire actions aimed at reducing fire effects.

Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

The purpose of this paper is to identify the burned areas that occurred in Italy during the summer of 2017 using change detection analysis techniques. This task is possible thanks to continuous, free and open availability of the multispectral images obtained by Sentinel-2 satellites. Indeed, comparing the satellite images of the same scene recorded at different times, it was possible to evaluate the landscape change. In this paper, the Direct Comparison change detection technique was applied to the analysis and identification of burned area using several Remote Sensing indexes. In particular, in order to achieve this aim, NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) were used. By case study in South Italy region (Sorrento peninsula), using images derived from Sentinel-2A imagery, it was possible to identify the burned areas in a specific period and evaluate the performance of the two indexes. In fact, after having constructed the confusion matrix for the two tested indexes, through the use of methods that indicate the quality of a thematic map (User's Accuracy, Producer's Accuracy, Overall Accuracy and Kappa coefficient), the percentage values for each remote sensing index analyzed were compared. The analysis of the different methods revealed, from one side the high quality of the results achievable by NBR index, on the other side, it was shown how, in some areas, the NDVI was inadequate for the recognition of burned areas.

Potencial de las imágenes UAV como datos de verdad terreno para la clasificación de la severidad de quema de imágenes Landsat: aproximaciones a un producto útil para la gestión post incendio

<p>Mapping fire severity is determinant to understand landscape evolution after a wildfire and provides useful information for decision making during post fire management. Quantitative fire severity mapping from relative changes in Normalized Burn Ratio index (RdNBR) is not actually being incorporated into decision making processes, being more useful the categorization in severity levels (high, moderate and low). However, the most common mapping severity methodologies based on the definition of RdNBR thresholds from field information are not always possible due to lack of field data or because the published thresholds are unsatisfactory in new regions. The boom in the use of UAVs (Unmanned Aerial Vehicle) has raised these platforms as potential tools for validation of remote sensing data. This paper presents the potential of UAVs as ground truth information in forest fires. From the photointerpretation of high resolution RGB images, the Aerial Severity Proportion Index (ASPI) has been created. Non-linear regression models between RdNBR and ASPI allows to delimitate of thresholds for the classification of Landsat images and to obtain qualitative severity maps. Validation with random points presents a kappa index of 0,5 and a relative accuracy of 70,8%. Therefore, UAV images become a very useful tool for wildfire severity mapping and for fill the gap between remote sensing information and expensive field ground campaigns</p>

Fuel treatments and landform modify landscape patterns of burn severity in an extreme fire event.

Under a rapidly warming climate, a critical management issue in semiarid forests of western North America is how to increase forest resilience to wildfire. We evaluated relationships between fuel reduction treatments and burn severity in the 2006 Tripod Complex fires, which burned over 70,000 ha of mixed-conifer forests in the North Cascades range of Washington State and involved 387 past harvest and fuel treatment units. A secondary objective was to investigate other drivers of burn severity including landform, weather, vegetation characteristics, and a recent mountain pine beetle outbreak. We used sequential autoregression (SAR) to evaluate drivers of burn severity, represented by the relative differenced Normalized Burn Ratio index, in two study areas that are centered on early progressions of the wildfire complex. Significant predictor variables include treatment type, landform (elevation), fire weather (minimum relative humidity and maximum temperature), and vegetation characteristics, including canopy closure, cover type, and mountain pine beetle attack. Recent mountain pine beetle damage was a statistically significant predictor variable with red and mixed classes of beetle attack associated with higher burn severity. Treatment age and size were only weakly correlated with burn severity and may be partly explained by the lack of treatments older than 30 years and the low rates of fuel succession in these semiarid forests. Even during extreme weather, fuel conditions and landform strongly influenced patterns of burn severity. Fuel treatments that included recent prescribed burning of surface fuels were particularly effective at mitigating burn severity. Although surface and canopy fuel treatments are unlikely to substantially reduce the area burned in regional fire years, recent research, including this study, suggests that they can be an effective management strategy for increasing forest landscape resilience to wildfires.

Biophysical and spectral responses to various burn treatments in the northern mixed-grass prairie

Over the past century, environmental managers have worked to suppress fire throughout various biomes. Today, burning is generally considered beneficial to many ecosystems, though the precise effects are not entirely understood. This research aims to further our understanding of the biophysical effects of fire in C3 dominated mixed-grass prairies and to find an effective remote sensing approach for differentiating between burn treatments in late spring. Biophysical properties including vegetation cover, biomass, soil properties, canopy height, Leaf Area Index (LAI), and individual wavebands of the SPOT-5 sensor as well as two vegetation indices (Normalized Difference Vegetation Index and Normalized Burn Ratio Index) derived from SPOT-5 images were compared among unburned sites and sites with various burning treatments in Grasslands National Park, Canada. Results showed that significantly higher soil temperature and lower LAI were found in burned sites. Dead material biomass, total biomass, and percentage of standing dead cover were significantly lower in burned and grazed sites. The percentage of bare ground cover was the only biophysical variable to show significant changes in all burning treatments. Burned and unburned sites could not be distinguished based on assessing the changes of individual wavebands of the SPOT-5 sensor and the two vegetation indices. Models based on linear combinations of spectral indices were developed for biophysical properties that show significant changes in burned sites. The agreement between model simulated biophysical properties and field measured values suggest the feasibility of remote sensing to assess or monitor post-fire effects in mixed grasslands.

TerraSAR-X Data for Burn Severity Evaluation in Mediterranean Forests on Sloped Terrain

TerraSAR-X (TSX) dual-polarized synthetic aperture radar (SAR) data from a test site in Spain have been investigated to determine the relationship between forest burn severity and SAR backscatter. The role of the local incidence angle on the backscatter coefficient has been also studied. Burn severity was estimated by means of composition burn index plots and the remotely sensed differenced normalized burn ratio index. To infer the potential of the TSX data for burn severity assessment, the determination coefficients obtained from linear regression analysis have been used. At horizontal transmit horizontal receive (HH) polarization, backscatter increased for slopes oriented toward the sensor and areas affected by high burn severity, whereas, at horizontal transmit vertical receive (HV) polarization, higher backscatter occurred for slopes oriented away from the sensor in areas of low burn severity. The dependence of the backscatter coefficient on topography for areas affected by forest fire has been confirmed. The HH backscatter presented a clear descending trend with the increase in local incidence angle, whereas the HV backscatter presented an ascending trend. The determination coefficients showed that, at HH polarization, better estimates of burn severity are obtained at low local incidence angles, whereas, for HV polarization, the best estimates are obtained at high local incidence angles. The dual-polarized X-band SAR data showed potential for burn severity estimation in the Mediterranean environment if local incidence angle is accounted for.