Normalized Burn Ratio Research Articles

Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data

Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented.

Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies.

Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity

BackgroundWildfires are increasingly impacting ecosystems worldwide especially in temperate dry habitats, often interplaying with other global changes (e.g., alien plant invasions). Understanding the ecological consequences of wildfires is crucial for effective conservation and management strategies. The aim of this study was to investigate the impacts of wildfire severity on plant community (both the canopy trees and herbaceous layer) and alien plant invasion, combining field observations and remotely sensed data.We conducted an observational study in the Karst forests (North-East Italy) 1 year after the large wildfire which affected the area in 2022. We assessed the impact through 35 field plots (200 m2 each) distributed among different fire severity (i.e., the loss of organic matter) classes assessed using the differenced normalized burn ratio (dNBR) calculated from satellite images. In each plot, tree species, diameter, vitality, resprouting capacity, and seedling density were measured. In addition, herb species richness (taxonomical diversity) was quantified, and plant cover was visually estimated. Functional diversity was also assessed considering six functional traits retrieved from databases.ResultsSome woody species (e.g., Quercus pubescens) showed a higher resistance to the fire (i.e., lower mortality rate), while others showed a higher resilience (i.e., recovery after fire through resprouting or seedlings, e.g., Cotinus coggygria). The transition to a shrub-dominated community (i.e., Cotinus coggygria) where fire severity was the highest underlines the dynamic nature of the post-fire succession. We detected a significant variation in the herbaceous plant community composition, diversity, and functional identity (i.e., community-weighted mean of trait) along the fire severity gradient. In particular, high-fire severity areas exhibited higher species richness compared to low-severity or unburned areas. Total alien plant cover increased with fire severity, while native cover remained constant. We also found shifts in species that enhance traits related to germination potential and growth strategy.ConclusionsOur results highlight the vulnerability of the forest stands to an increase in wildfire severity, resulting in significant mortality and changes in tree community structure. This study contributes to the understanding of ecological processes after wildfires using a novel remote sensing approach in a temperate forest, emphasizing the need for conservation strategies aimed at mitigating high severity wildfires.

Land cover and burn severity dynamics of the Ogan Komering Ilir peatlands from 2015 to 2023 using sar and optical datasets

Land cover changes and wildfires have had an increasing impact on the Ogan Komering Ilir Peatland ecosystems in South Sumatra, Indonesia. This study aims to understand the peatland land cover and burn severity dynamics from 2015 to 2023. The random forest method was applied to classify land cover, while the differenced Normalized Burn Ratio (dNBR) was used for mapping fire severity. We combined various satellite data to classify land cover, consisting of Landsat-8, Sentinel-1, and Sentinel-2. Landsat-8 or Sentinel-2 images were also used for the dNBR calculation. We revealed complex climate, human, and restoration interactions in land cover and burn severity fluctuations over 273,799 hectares of the study area from 2015 to 2023. The 2015 El Niño-induced drought led to 21,754 fire hotspots and 2.01% of the area in high-severity burns. In 2016, it reduced tree cover by 10.18% and increased bare/sparse vegetation by 6.11%. The 2019 El Niño event led to 7,893 fire hotspots, lessening unburned areas and worsening burns. Due to the extreme effects of the 2015 drought, restoration efforts between 2016 and 2020 significantly decreased fire hotspots in 2016. Tree cover stabilized, reaching 48.46% by 2020, whereas unburned areas rose to 69.46% in 2018, showing good recovery and lower fire severity. In 2021-2023, fire hotspots were modest relative to El Niño years but increased in 2023. After 2020, tree cover decreased, but other land cover classes fluctuated. Therefore, continual monitoring and adaptive management are critical for reducing negative consequences and increasing ecosystem resilience.

Multi-temporal assessment of a wildfire chronosequence by remote sensing

The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years•The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.

Comparison between artificial restoration and natural recovery in vegetation regrowth following high-frequency fire disturbances in the Hengduan Mountains, Southwest China

The Hengduan Mountains in Southwest China are currently facing a recurring and escalating threat of forest fires, posing a significant challenge to vegetation restoration efforts given the ecological value of this region. However, the effects of artificial restoration compared to natural regeneration on vegetation recovery dynamics have been less explored. This study focused on two sites in the Hengduan Mountains, primarily composed of conifer forests. One site employed a mix of artificial restoration measures, including tree planting, aerial grass seeding, and fertilizer application. The other site, which is relatively drier and colder, relied on natural regeneration. Four fire events in these two sites were compared to examine the effects of artificial restoration on vegetation recovery using remotely sensed data from Landsat and MODIS. Fire severity was classified using the differenced Normalized Burn Ratio (dNBR) and validated with field samples. Vegetation greenness loss and recovery trajectory were quantified using the near-infrared reflectance of vegetation (NIRv). The effects of artificial restoration were further analyzed after employing unburnt controls to minimize the impact of climate fluctuations. Results showed that fire severity mapping achieved an overall accuracy of 82%. The average NIRv loss across the four fires was 30%, 49%, and 69% in areas of low, moderate, and high severity, respectively. In low severity areas, natural and artificial recovery rates were similar, at 21% and 26% respectively, three years post-fire. In moderate severity areas, artificial restoration achieved a 46% recovery rate compared to 31% for natural recovery. In high severity areas, artificial restoration resulted in a 63% recovery rate versus 45% for natural recovery. Although the differences in recovery patterns may also be attributed to inherent variations in the vegetation composition, physical environment, and their interactions, this study suggests that high severity areas benefit significantly from artificial intervention for rapid recovery. Future studies should include more controlled experiments at the plot scale to further elucidate the processes of vegetation response to different artificial restoration measures.

Influence of wildfires severity on tree composition and structure in Aberdare Afromontane Forest Ranges, Kenya

Wildfires strongly impacts on the forest ecosystems, with poorly understood consequences on the composition, diversity and structure within Afromontane forests of Eastern Africa. The impacts of wildfire on vegetation were investigated one year after the fire occurrence season. The burn severity was evaluated using Sentinel-2 satellite derived spectral index known as dNBR (difference Normalized Burn Ratio). This was used to delineate wildfire into low and high severity classes. We investigated the effects of wildfires using 36 plots of 15m radius distributed in high and low burn severities while others were laid in unburnt areas. Our findings indicated that tree height, basal area and density were significantly different along severity classes (p = 0.003, p=0.007 and p<0.000 respectively). However, diameter at breast Height was not significant along severity classes (p=0.185). The most ecologically important species based on Important Value Index (IVI) within the fire affected areas were Juniperus procera, Nuxia congesta, Olea europaea subsp. cuspidata, Protea kilimandscharicum and Scurtia myrtina. Among the five dominant tree species, the density of Protea kilimandscharicum and Olea europaea subsp. cuspidata were significant along the fire severities (p<0.05). Interaction of vegetation zones and wildfire severity indicated that only height was significant (p= 0.000) while all the other tree variables and species diversity index were insignificant; DBH (p=0.053), basal area (p=0.606), tree density (p=0.389), Shannon Weiner diversity index (p=0.843), Simpson dominance index (p=0.490) and Pielou's evenness index (p=0.248). Whereas this study was on a short-term post-fire effects on vegetation, it has shown remarkable findings which can guide restoration and management of fire affected landscapes. The study recommends evaluation of long-term regeneration and recovery to guide integrated fire management within Afromontane Forest Ecosystems.

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.

Environmental and climatic Interconnections: Impacts of forest fires in the Mato Grosso region of the Amazon

Wildfires in the Amazon biome of Mato Grosso cause extensive environmental, economic, and health damages, including biodiversity loss and high greenhouse gas emissions. This study used remote sensing to examine the relationship between fire severity and climatic factors, focusing on dNBR (Differenced Normalized Burn Ratio), precipitation, LST (Land Surface Temperature), SPI (Standardized Precipitation Index), NDVI (Normalized Difference Vegetation Index), and VCI (Vegetation Condition Index), analyzing data from 2001 to 2022. Statistical tests included Shapiro-Wilk, Tukey, regression kriging, Mann-Kendall for trend analysis, Pettitt for change points, and canonical variable tests. Regarding trends, only LST showed a significant trend starting in 2009, with the Northeast mesoregion showing the highest impact on temperature. dNBR correlated positively with NDVI and VCI, and negatively with precipitation and SPI. The northern mesoregion had a positive influence on dNBR and NDVI but negative for precipitation, SPI, and VCI. The southwestern mesoregion associated positively with dNBR and LST but negatively with the other variables. The Northeast and South-Central mesoregions showed positive correlations with most variables except dNBR and NDVI. These findings highlight the northern mesoregion's vulnerability due to its proximity to the central Amazon Forest and agri-cultural activity, indicating increased fire susceptibility with reduced humidity.

Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices

Fire plays a critical role in nutrient cycling and species diversity but also causes severe disturbances to ecosystems. Despite remote sensing has emerged as a valuable tool to study wildfires, a lack of information regarding post-fire dynamics still remains for certain regions. This study investigated the post-fire dynamics of four burned sites in the temperate forest of Chihuahua, northern Mexico from 2009 to 2017, based on the variability of spectral indices under varied topographic conditions of aspects and slopes. The Normalized Difference Vegetation index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR), and Normalized Difference Water index (NDWI) were utilized to assess vegetation recovery on the burned sites. One-way ANOVAs and Tukey´s tests were performed to evaluate separately the effects of aspect and slope on the indices’ variability (p < 0.05). In general, the NDVI measured the highest values of the burned sites along the evaluated lapse while the NDWI indicated the lowest measures. Results indicated significant effects of aspect on NDVI and SAVI (p < 0.05), while slope significantly influenced the four evaluated indices (p < 0.05).That in turn showed northern aspects and steeper slopes to have the highest post-fire vegetation recoveries. Furthermore, aspect only affected the bi-annual variability of SAVI (p < 0.05). However, the NBR and NDWI showed higher magnitudes of change among periods than the SAVI (i. e., recovery and non-recovery). Besides registering a continuous recovery of the burned sites from the wildfire events to 2015, this study revealed the NDVI and SAVI as the most useful spectral indices for assessing post-fire vegetation dynamics in the region. Besides aspect and slope, future studies could focus on the inclusion of additional indicators to more effectively assess post-fire ecosystem dynamics in northern Mexico.

Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning

The global escalation in forest fires, characterized by increasing frequency and severity, results from a complex interplay of natural and anthropogenic factors, exacerbated by climate change. These fires devastate habitats, threaten species, reduce biodiversity, disrupt natural cycles, and harm local ecosystems. The impacts are particularly damaging in biological reserves. The Similipal Biosphere Reserve (SBR) in Odisha State is one of India’s major forest fire hotspots, experiencing forest fires almost every year. The objective of this study is to develop a predictive model using Sentinel-2 MSI data and machine learning (ML) techniques to estimate the probability of forest fires in the SBR, India, thereby enhancing disaster management and prevention in the region. This research maps and quantifies forest fire intensity by leveraging ML algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest (RF). To develop a Forest Fire Probability (FFP) map, twenty conditioning factors, along with pre- and post-fire Normalized Burn Ratio (NBR) and delta Normalized Burn Ratio (dNBR), were utilized. Furthermore, four statistical methods—Mean Absolute Error, Mean Square Error, Root Mean Square Error, and Overall Accuracy—were employed to analyze the FFP. The results were validated using the Area Under Curve (AUC) method. The analysis identifies 2021 as the year with the highest incidence of forest fires, accounting for 29.19% of the occurrences. Among the models, the GBM exhibits superior performance, highlighting its efficacy in handling large, multidimensional datasets. Predictive mapping suggests that approximately 1400–1500 km2, or 25–30% of the studied area, faces a high to very high risk of forest fires.

Post-Fire Vegetation (Non-)Recovery across the Edges of a Wildfire: An Unexplored Theme

Wildfires have a significant influence on ecosystems globally, shaping vegetation, biodiversity, landscapes, soil properties, and other ecosystem processes. Despite extensive research on different aspects of wildfires, the edges of burned areas remain understudied, even though they involve complex dynamics. In this study, we analyzed the post-fire vegetation recovery across the edges of a large wildfire in a Mediterranean area. The investigations were focused on patches of woodlands that, in a previous study, showed a normalized burn ratio (NBR) decline one year after the fire. Field vegetation surveys were carried out in areas characterized by different NBR recovery rates and in areas outside the burned area as controls. Five hypotheses were tested, identifying delayed tree mortality as a key factor linked to NBR decline, particularly in low-severity fire zones in proximity to the fire edges. Delayed mortality, observed predominantly near the edges, may also affect unburned or less severely burned patches within the main fire perimeter, highlighting the need for ongoing monitoring. As these areas play a crucial role in the post-fire succession and vegetation dynamics, understanding the second-order effects of a fire is imperative for effective ecosystem management. This study underscores the importance of the long-term assessment of fire impacts, emphasizing the necessity of field surveys alongside remote sensing. Continued observation is essential to elucidate the enduring impacts of wildfires and to facilitate informed restoration strategies.

Long-term assessment of historical disturbances in the dry land ecosystems of Mutis Timau Nature Reserve, East Nusa Tenggara, Indonesia

Long-term assessment using historical satellite imagery is critical for gaining meta-information and identifying trends in land cover change. Informed policymaking about this assessment ensures forest sustainability and mitigates climate change to consider the identified trends in land cover changes and historical disturbances. Therefore, this study aimed to describe the trend of land cover change and historical disturbances in Mutis Timau Nature Reserve (CA Mutis Timau), Indonesia using the LandTrendr platform. The land cover classification is based on SNI 7645-1:2014, with a 1:50,000 scale. The data was processed using Google Earth Engine’s LandTrendr platform, which covered the vegetation index, specifically the normalized burn ratio (NBR). The results showed that the trend of land cover change in CA Mutis Timau remained stable from 1986 to 2023. These disturbance locations have low slopes and are located near neighborhoods. In specifics, the Eucalyptus forest land cover experienced the largest land cover change between 1986 and 2023, losing 945.68 ha of vegetation, or 8% of the total area of CA Mutis Timau. Over a duration of 38 years, from 1986 to 2023, the Eucalyptus forest had a cover change that turned into marble stone, barren land, grassland, and shrubs. Forest management policies, such as establishing a nature reserve area on Mount Mutis areas, had an impact on the long-term sustainability of vegetation land cover.

Sentinel 2 based burn severity mapping and assessing post-fire impacts on forests and buildings in the Mizoram, a north-eastern Himalayan region

The Increasing frequency and severity of forest fires worldwide highlights the need for more effective Burnt area mapping. Finding the effects of fire on vegetation and putting mitigation methods in place, depends on post-fire evaluation. In this study, the location of the burned regions and the severity of the fire were determined using high-resolution multi-spectral images from Sentinel 2 on Google Earth Engine (GEE) platform. Three widely used fire severity indices—differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR)—based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR—were computed and compared based on their accuracy using very high-resolution planet imagery fire points and equal number of random non fire points. Maps also validated with active fires, ground based photos and crowdsourced images. The accuracy (AUC) of the RdNBR map was 85%, RBR - 84% and dNBR −82%. The RdNBR index demonstrated highest level of accuracy. Then the loss to vegetation using pre-fire and post-fire NDVI was analysed. The analysis of pre-fire and post-fire NDVI provided insights into the extent of vegetation loss. The analysis of vegetation loss offered valuable information regarding the impact of fire on the affected areas. Google building dataset was used to monitor the percent of buildings under threat due to these fires. Around 8.77% of buildings were found in high severity region. Accurate mapping aids post-fire evaluation, guided mitigation strategies, and enhanced forest management and ecological restoration.

Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires.

A fast spectral recovery does not necessarily indicate post-fire forest recovery

BackgroundClimate change has increased wildfire activity in the western USA and limited the capacity for forests to recover post-fire, especially in areas burned at high severity. Land managers urgently need a better understanding of the spatiotemporal variability in natural post-fire forest recovery to plan and implement active recovery projects. In burned areas, post-fire “spectral recovery”, determined by examining the trajectory of multispectral indices (e.g., normalized burn ratio) over time, generally corresponds with recovery of multiple post-fire vegetation types, including trees and shrubs. Field data are essential for deciphering the vegetation types reflected by spectral recovery, yet few studies validate spectral recovery metrics with field data or incorporate spectral recovery into spatial models of post-fire vegetation recovery. We investigated relationships between spectral recovery and field measurements of post-fire recovery (16 to 27 years post-fire) from 99 plots in mixed conifer forests of the Blue Mountains, USA. Additionally, using generalized linear mixed effects models, we assessed the relative capacities of multispectral, climatic, and topographic data to predict field measurements of post-fire recovery.ResultsWe found that a fast spectral recovery did not necessarily coincide with field measurements of forest recovery (e.g., density of regenerating seedlings, saplings, and young trees and % juvenile conifer cover). Instead, fast spectral recovery often coincided with increases in % shrub cover. We primarily attributed this relationship to the response of snowbrush ceanothus, an evergreen shrub that vigorously resprouts post-fire. However, in non-trailing edge forests—where it was cooler and wetter and fast-growing conifers were more common—rapid spectral recovery coincided with both increases in % shrub cover and forest recovery. Otherwise, spectral recovery showed potential to identify transitions to grasslands, as grass-dominated sites showcased distinctly slow spectral trajectories. Lastly, field measurements of post-fire forest recovery were best predicted when including post-fire climate and multispectral data in predictive models.ConclusionsDespite a disconnect between a fast spectral recovery and forest recovery, our results suggest that including multispectral data improved models predicting the likelihood of post-fire forest recovery. Improving predictive models would aid land managers in identifying sites to implement active reforestation projects.Graphical Photo credit: J. Celebrezze

Razvoj metode bi-normaliziranog omjera opožarenosti na događaju katastrofalnog šumskog požara 14. srpnja 2022. godine kod Vodica, Hrvatska

The use of multispectral sensors, such as MSI, on the Sentinel-2 satellite for rapid assessment of the area and the burning degree after large forest fires utilises standard method of determining the Normalized Burn Ratio (NBR) before and after the fire, from which the difference of the Normalized Burn Ratio (dNBR) is calculated. Applied to areas with sparse vegetation, the standard method of NBR gives poor results. To cope with low vegetation areas, a so-called “relativizing dNBR” was introduced where an empirical expression divides the difference of the NBRs in two channels by a quotient slightly greater than the pre-fire NBR. In this paper, a forest fire near Vodice, Croatia, which occurred on July 14, 2022, and which grew into a catastrophic fire that left about 30 km2 of burnt area, was studied. As this is an area of Mediterranean vegetation, relatively sparse, instead of the relativization, an attempt was made to introduce a new formula for calculating the normalized ratio of burning, which included two differences in spectral radiances (instead of one). In addition to the standard difference in the near-infrared and short-wave infrared part of the spectrum, a difference between the near-infrared and the green visible part of the spectrum was used simultaneously. These new, bi-Normalized Burn Ratio, retain information about the spectral radiance in the green visible band and react to surfaces where at least a little vegetation cover remained after the fire, which ultimately gives lower burn categories, with the same burned area detected compared to the standard method.

Exploring the ethnobiological practices of fire in three natural regions of Ecuador, through the integration of traditional knowledge and scientific approaches

This study examines the convergence between traditional and scientific knowledge regarding the use of fire and its potential to trigger wildfires, with possible impacts on ecosystems and human well-being. The research encompasses three distinct natural regions of Ecuador: the coast, the highlands, and the Amazon. Data on traditional fire use were collected through semi-structured interviews with 791 members from five local communities. These data were compared with climatic variables (rainfall (mm), relative humidity (%), wind speed (km/h), and wind direction) to understand the climatic conditions conducive to wildfires and their relationship with human perceptions. Furthermore, the severity of fires over the past 4 years (2019–2022) was assessed using remote sensing methods, employing the Normalized Burn Ratio (NBR) and the difference between pre-fire and post-fire conditions (NBR Pre-fire–NBR Post-fire). The results revealed a significant alignment between traditional knowledge, climatic data, and many fires, which were of low severity, suggesting potential benefits for ecosystems. These findings not only enable the identification of optimal techniques and timing for traditional burns but also contribute to human well-being by maintaining a harmonious balance between communities and their environment. Additionally, they provide valuable insights for the development of more inclusive and effective integrated fire management strategies in these natural areas of Ecuador.