Differenced Normalized Burn Ratio Research Articles

ASSESSING AIR POLLUTANT EMISSIONS IN THE AFTERMATH OF THE 2021 FOREST FIRES IN MARMARIS AND MANAVGAT, TÜRKİYE: INSIGHTS FROM SATELLITE-BASED MONITORING

Abstract. In recent years, the occurrence of forest fires has become almost inevitable worldwide due to the severe impact of climate change. Consequently, it is imperative to examine the consequences of these fires, pinpoint the affected areas, and take required measures following post-fire incidents. With the advent of modern remote sensing satellites, which allow for the rapid monitoring of vast expanses, they offer a valuable data source for addressing such emergency scenarios. Therefore, the main objective of this study is to evaluate the extent of air pollutant gas emissions resulting from the forest fires that occurred in Marmaris and Manavgat, Türkiye, in the summer of 2021. To this aim, using the data obtained from the TROPOspheric Monitoring Instrument (TROPOMI) sensor onboard the Sentinel-5P satellite, the amount of major air pollutants nitrogen dioxide (NO2), carbon monoxide (CO) and aerosol were investigated after the forest fires considering three different non-fire time periods. The findings of the experiments indicated that in Manavgat, there was a substantial rise in NO2 and average CO levels by 260.43% and 107.07%, respectively, when compared to the 10-day period preceding the forest fire event. Similarly, in Marmaris, there was an increase of 203.63% in NO2 levels and a 102.47% rise in average CO levels during the same period. Positive absorbing aerosol index (AAI) values were also observed during the events, which means that the amount of UV-absorbing aerosols increased due to the fire. The differenced Normalized Burn Ratio (dNBR) maps derived from the Sentinel-2 MSI imagery were also used to investigate the severity of the forest fires, and to observe the relationship between the fire severity level and the air pollutants investigated.

Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India

Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.

MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa

The "Mapaton 2023," a collaborative initiative by the Peruvian Space Agency, Paraguayan Space Agency, and AMERIGEOS, introduces an innovative approach to analyze fire-prone areas in Arequipa, a pivotal endeavor for disaster prevention. Focused on the provinces of Arequipa and Caylloma, recent studies identified over 1,500 affected hectares by forest fires, emphasizing the urgency of employing satellite images and geospatial analysis techniques. Leveraging Landsat 8 satellite imagery, the research calculated indices, including the normalized difference vegetation index (NDVI) for vegetation analysis and the differenced normalized burn ratio (dNBR) for fire severity assessment. Results revealed varying impacts, with some areas exhibiting increased vegetation and others displaying significant damage. The use of ArcGIS online facilitated the presentation of geospatial data, emphasizing the utility of remote sensing in comprehending and addressing forest fires. Drawing insights from analogous studies in Mexico and the Amazon, this research underscores the importance of remote sensing and geospatial analysis in informing preventive measures against wildfires. The findings, crucial for environmental management, are recommended for sharing with relevant authorities, and the continued use of diverse satellite imagery sources is encouraged to enhance accuracy in monitoring and mitigating forest fires.

Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.

Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2

Burn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitoring but remains underutilized for fire impact estimation. Here, we explore the association between remotely sensed fire intensity metrics and spectral differencing severity indices to understand whether and where they describe similar wildfire effects. The commonly used Differenced Normalised Burn Ratio (dNBR) severity index was calculated for Advanced Himawari Imager (AHI − 2 km) and Sentinel-2 (20 m) data and compared to different Fire Radiative Power (FRP) metrics derived from fire hotspot detections from AHI data across Australia. The comparison was implemented through different stratifications based on biogeographical region, land cover, fire type, and percentage of AHI pixel burned (fire fractional cover). The results indicate that FRP and dNBR metrics do not correlate in most scenarios, noting correlations being marginally stronger for hotter fires. However, correlations become significantly stronger when data are grouped using fire type information and fire fractional cover, with correlations peaking (R = 0.75) for large fires that burned 41–60 % of an AHI pixel. In conclusion, remotely sensed fire intensity and severity proxies capture different aspects of wildfire impact, that only correlate with each other after using auxiliary data. Spectral differencing severity metrics have been used extensively during the past decades, however high-frequency fire intensity estimations have the potential to augment the existing information and reveal new ways of characterizing wildfire impact over large areas.

Determination of fire intensity after forest fire by remote sensing: marmaris case study

Forest fires in Turkiye are a frequently encountered natural disaster, especially in recent years. After a fire, identifying the plant species to be used in the area and determining the intensity of the fire in the region are important for assessing the area’s capacity for natural regeneration. In this process, geographic information systems and remote sensing methods and techniques are commonly used tools for assessing burned areas and fire intensities following forest fires. In this study, one of Turkiye’s devastating fires, the Marmaris fire, which began on June 21, 2022, is examined. Landsat satellite images, geometrically and radiometrically corrected, were utilized to determine the intensity of the Marmaris fire. As part of the method, the NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), NDVI (Normalized Difference Vegetation Index), and dNDVI (Difference Normalized Difference Vegetation Index) indices were used for burned area detection. As a result of the study, information about the burned areas, fire intensities, and the regenerative capabilities of post-fire vegetation growth has been obtained. Furthermore, suitable pioneer plant species, considering the fire intensity, have been suggested for the restoration of burned landscape areas after the fire. This study is expected to serve as a guide for relevant public institutions and organizations, particularly the General Directorates of Forestry, enabling them to respond most effectively after fires and implement reforestation efforts considering the usage areas and growth capabilities of needle-leaved and broad-leaved plants.

OBSERVATION OF SPECTRAL INDICES PERFORMANCE FOR POST-FIRE FOREST MONITORING

Monitoring post-fire forest disturbances and subsequent recovery is vital for management and preservation purposes of the forest ecosystems. This study aimed to assess forests’ damages and regrowth dynamics after fire using remotely sensed data and to compare its reliability for post-fire monitoring in different forest environments. This paper compared forest regrowth dynamics applying selected spectral indices – Differenced Normalized Difference Vegetation Index (dNDVI), Differenced Normalized Burn Ratio (dNBR), and Differenced Disturbance Index (dDI). The post-fire environmental impact and recovery processes were performed on the territory of the three fires in Bulgaria – Ardino, Bistrishko branishte, and Perperek.

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO2) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment.

Fire Regimes of Utah: The Past as Prologue

(1) Background: Satellite monitoring of fire effects is widespread, but often satellite-derived values are considered without respect to the characteristic severity of fires in different vegetation types or fire areas. Particularly in regions with discontinuous vegetation or narrowly distributed vegetation types, such as the state of Utah, USA, specific characterization of satellite-derived fire sensitivity by vegetation and fire size may improve both pre-fire and post-fire management activities. (2) Methods: We analyzed the 775 medium-sized (40 ha ≤ area &lt; 400 ha) and 697 large (≥400 ha) wildfires that occurred in Utah from 1984 to 2022 and assessed burn severity for all vegetation types using the differenced Normalized Burn Ratio. (3) Results: Between 1984–2021, Utah annually experienced an average of 38 fires ≥ 40 ha that burned an annual average of 58,242 ha with a median dNBR of 165. Fire was heavily influenced by sagebrush and shrubland vegetation types, as these constituted 50.2% (17% SD) of area burned, a proportion which was relatively consistent (18% to 79% yr−1). Medium-sized fires had higher mean severity than large fires in non-forested vegetation types, but forested vegetation types showed the reverse. Between 1985 and 2021, the total area burned in fires ≥ 40 ha in Utah became more concentrated in a smaller number of large fires. (4) Conclusions: In Utah, characteristic fire severity differs both among vegetation types and fire sizes. Fire activity in the recent past may serve as an informative baseline for future fire, although the long period of fire suppression in the 20th century suggests that future fire may be more active. Fire managers planning prescribed fires &lt; 400 ha in forests may find the data from medium-sized fires more indicative of expected behavior than statewide averages or vegetation type averages, both of which are weighted to large fires.

Simulation of Soil Organic Carbon Dynamics in Postfire Boreal Forests of China by Incorporating High-Resolution Remote Sensing Data and Field Measurement

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO2 emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management.

Caution is needed across Mediterranean ecosystems when interpreting wall-to-wall fire severity estimates based on spectral indices

The use of comprehensive, multi-strata fire severity metrics measured in the field, such as the composite burn index (CBI), is the most commonly used approach to calibrate remote sensing-based products and obtain wall-to-wall fire severity estimates. However, the extent to which specific ecological changes as measured by individual CBI attributes per stratum actually drive the spectral variability in Mediterranean fire-prone ecosystems has not been deciphered yet. In addition, it is not clear if these relationships are consistent across different Mediterranean ecosystems, particularly when fire effects across strata can be decoupled. To bridge these knowledge gaps, we examined the fire severity divergence among CBI strata in different types of Mediterranean forest (both conifer- and broadleaf-dominated) and shrubland ecosystems with 240 plots distributed across five wildfires in the westernmost part of the Mediterranean Basin and Chile. Then, we compared the relationship of two spectral fire severity indices with the CBI scores by strata. Finally, we used a wrapper algorithm (VSURF) around Random Forest regression to assess the potential contribution and redundancy of individual CBI attributes in shaping the spectral response of fire severity indices computed from multispectral Sentinel-2 data. The Relativized Burn Ratio (RBR) index fitted CBI field data better than the differenced Normalized Burn Ratio (dNBR). Fire effects were strongly decoupled and, for example, the substrate stratum experienced the lowest severity in conifer forests and shrublands, whereas the upper strata remained largely unaffected in broadleaf forests. This has substantial implications since we found differential responses of the RBR signal to fire effects by strata as measured by distinct individual CBI attributes. Specifically, the fire impact on the green vegetation fraction of upper strata in broadleaf and conifer forests, and on the substrate stratum in shrublands, were identified as non-redundant and most contributing fire effects shaping the spectral signal variability in burned Mediterranean ecosystems. These results, together with decoupled fire severity effects across strata, suggest that aggregated CBI metrics may obscure ecologically relevant fire-induced changes, and noticeable disconnects may exist between wall-to-wall fire severity estimates and actual fire effects in the field.

Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts

Precise quantification of forest fire impacts is critical for management strategies in support of post-fire mitigation. In this regard, optical remote sensing imagery in combination with spectral unmixing has been widely used to measure fire severity by means of fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), charcoal (CH) and further ground components such as ash, bare soil and rocks. However, most unmixing analyses have made use of a single post-fire image without accounting for the pre-fire state. We aim to assess fire severity from Sentinel-2 data using a bi-temporal spectral unmixing analysis that provides a quantitative fire impact description and is oriented towards the process of change by including pre-fire and post-fire information. Unmixing was based on Random Forest Regression (RFR) modeling using synthetic training data from a bi-temporal spectral library. We describe fire severity as changes associated with the combustion of photosynthetic vegetation (PV–CH fraction) and dieback of photosynthetic vegetation (PV-NPV fraction). Unburned forest was mapped as stable photosynthetic vegetation (PV-PV fraction). We evaluated our approach on a forest fire that burned in a temperate forest region in eastern Germany in 2018. Independent validation was carried out based on reference fractions obtained from very high-resolution (VHR) imagery such as Plante Scope, SPOT6, orthophotos, aerial photos, and Google Earth. The results underline the effectiveness of our unmixing approach, with Root Mean Squared Errors (RMSE) of 0.072 for PV-CH, 0.09 for PV-NPV, and 0.08 for PV-PV fractions. Most of the errors were caused by spectral similarity between charcoal and shadow effects caused by trees, and the coloring of foliage and NPV in the late phenological season of the post-fire Sentinel-2 image. Based on the two-dimensional feature space of PV-CH and PV-NPV fractions, we calculated two metrics to characterize fire impacts: distance, an indicator of disturbance severity (sum of combustion and dieback), and angle, a measure of disturbance composition (gradient between combustion and dieback). Furthermore, we compared the fraction-based metrics with the difference Normalized Burn Ratio (dNBR). Since the dNBR is most sensitive to combustion and presence of charcoal, it does not fully characterize fire-related vegetation loss associated with dieback. The bi-temporal fraction-based indices provide more ecologically meaningful information on fire severity, particularly for regions that are less prone to severe wildfires such as Central Europe.

UTILIZING LANDSAT 8 IMAGERY FOR MAPPING OF BURNED AREAS USING THE NORMALIZE DIFFERENCE VEGETATION INDEX (NDVI) AND NORMALIZE BURN RATIO (NBR) METHODS

This study aims (1) to map changes in the area of ​​forest land in the western part of Bengkalis Regency in 2016 and 2021, (2) to determine the distribution of the area of ​​forest burned in the western part of Bengkalis Regency, (3) to determine the severity of forest fires in the District of Bengkalis West Bengkalis.This study used the NDVI (Normalized Difference Vegetation Index) method by Huete et. Al by compositing band 5 (NIR) and band 4 (Red) on Landsat 8 imagery which was processed using ArcGIS software before and after a forest fire. As well as the NBR (Normalized Burn Ratio) and dNBR (Difference Normalized Burn Ratio) methods by Eidenshink et al by compositing band 5 (NIR) and band 7 (SWIR) on Landsat 8 images processed using QGIS software. For sampling using random sampling method and accuracy test using overall accuracy, user&amp;#39;s accuracy, producer&amp;#39;s accuracy, and kappa analysis. The results of this study are (1) the area of ​​forest land in Bengkalis Regency continues to decrease every year, in 2016 the area of ​​forest land903,920 ha and 2021 the total forest area is463,441 ha. (2)The area of ​​forest land burned due to forest fires in Bengkalis Regency, which burned the least was 267.43 ha, while it was 1468.93 ha and the most extensive was 2186.53 ha.(3) Based on one forest fire distribution map, it is divided into 7 fire severity classes, namely high post-fire regrowth, low post-fire regrowth, no burning, low, medium-high and very high and the most dominant forest fire level is low-high.

Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.

Forest fire area detection using Sentinel-2 data: Case of the Beni Salah national forest ‒ Algeria

Forest cover plays an important role in terms of biodiversity and the environment. The Beni Salah national forest in its part which is located in the Guelma province in the extreme northeast of Algeria is an illustrative example where forest fires represent the chronic phenomenon which weighs heavily on this forest. The present study comes after a forest fire that occurred in 2021, when 3 000 ha of this forest were ravaged by forest fires according to the conservation of forests of Guelma. The main objective of this research is to map the severity of burns and estimate the severely burned area using Sentinel-2 satellite images based on remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Differenced Normalized Difference Vegetation Index (dNDVI), Normalized Burn Ratio (NBR), Differenced Normalized Burn Ratio (dNBR), Green Normalized Difference Vegetation Index (GNDVI), Differenced Green Normalized Difference Vegetation Index (dGNDVI), Burn Area Index (BAI) and Relativized Burn Ratio (RBR). The result obtained revealed that 28.23% of the study area represents a seriously burned area. The established burn severity map is a real decision-making tool, but it still has certain limitations.

A New Method for the Rapid Determination of Fire Disturbance Events Using GEE and the VCT Algorithm—A Case Study in Southwestern and Northeastern China

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices.

Mapping burn severity and uncovering spread patterns of the 2021 Varibopi wildfire

This work unveils how the wildfire of August 3rd, 2021 that initiated at the northeastern part of Attica (Varibopi), spread and burned during the four days of its active propagation. This fire is a characteristic large-scale event that affected the wildland-urban interface of Athens, revealing weaknesses of both pillars of fire management: prevention and suppression. Initially, we provide a short description of how and under which conditions (vegetation and weather) the fire initiated and propagated after debriefing reports, news articles and satellite data. Then, we applied the difference Normalized Burn Ratio on a pair of Sentinel 2A satellite images (one right before and another taken one year later) to map the extended burn severity and delineate the fire perimeter. Finally, using the Minimum Travel Time Algorithm, we simulated major spread vectors and rate of spread to answer how the fire would have been evolved in the absence of active fire suppression. We found that from the 8,445-ha included in the final delineated perimeter, 1,000 ha were unburned, 4,715 ha experienced low or moderate-low burn severity, and 2,730 moderate-high or high burn severity. Without suppression, the fire could have escaped from three directions to the west with high spread rate, potentially affecting the Parnitha National Park. The most imminent post-fire issue is not whether the conifer vegetation will recover, but if during the next decade vegetation management measures are not applied in selected locations, then a new ignition can spread faster and burn a more extended area compared to the 2021 fire.

Assessment of Small-Extent Forest Fires in Semi-Arid Environment in Jordan Using Sentinel-2 and Landsat Sensors Data

The objective of this study was to evaluate the separability potential of Sentinel-2A (MultiSpectral Instrument, MSI) and Landsat (Operational Land Imager, OLI and Thermal Infrared Sensor, TIRS) derived indices for detecting small-extent (&lt;25 ha) forest fires areas and severity degrees. Three remote sensing indices [differenced Normalized Burn Ratio (dNBR), differenced Normalized Different Vegetation Index (dNDVI), and differenced surface temperature (dTST)] were used at three forest fires sites located in Northern Jordan; Ajloun (total burned area 23 ha), Dibbeen (burned area 10.5), and Sakeb (burned area 15 ha). Compared to ground reference data, Sentinel-2 MSI was able to delimit the fire perimeter more precisely than Landsat-8. The accuracy of detecting burned area (area of coincidence) in Sentinel-2 was 7%–26% higher that Landsat-8 OLI across sites. In addition, Sentinel-2 reduced the omission area by 28%–43% and the commission area by 6%–38% compared to Landsat-8 sensors. Higher accuracy in Sentinel-2 was attributed to higher spatial resolution and lower mixed pixel problem across the perimeter of burned area (mixed pixels within the fire perimeter for Sentinel-2, 8.5%–13.5% vs. 31%–52% for Landsat OLI). In addition, dNBR had higher accuracy (higher coincidence values and less omission and commission) than dNDVI and dTST. In terms of fire severity degrees, dNBR (the best fire index candidate) derived from both satellites sensors were only capable of detecting the severe spots “severely-burned” with producer accuracy &gt;70%. In fact, the dNBR-Sentinel-2/Landsat-8 overall accuracy and Kappa coefficient for classifying fire severity degree were less than 70% across the studied sites, except for Sentinel-dNBR in Dibbeen (72.5%). In conclusion, Sentinel-dNBR and Landsat promise to delimitate forest fire perimeters of small-scale (&lt;25 ha) areas, but further remotely-sensed techniques are require (e.g., Landsat-Sentinel data fusion) to improve the fire severity-separability potential.

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 (&gt;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.

Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province

Sentinel-2 imagery can identify forest and land fires in underground parts, surface fires, and crown fires. The dNBR and RBR spectral indices on Sentinel-2 images proved accurate in identifying. This study analyzed the index value for burned area mapping in wetland areas using Sentinel-2 imagery data in 2019 and hotspot data from the MODIS data. The indices used to identify the burned area and the severity of the fire was the differenced normalized burn ratio (dNBR) and relativized burn ratio (RBR). Visual validation tests were performed by comparing RGB composite images to check the appearance before and after combustion with dNBR and RBR results. The dNBR value accuracy was 91.5%, and for a kappa, the accuracy was 89.58%. The RBR accuracy was 92.9%, and the kappa accuracy was 0.91. The results confirmed that in the Banjarbaru area, RBR was more accurate in identifying burned areas than dNBR; both indices can be used for burned area mapping in wetland areas.