Normalized Burn Ratio Research Articles

Continuous burned area monitoring using bi-temporal spectral index time series analysis

Extreme wildfires are a major agent of disturbance in present-day forest ecosystems and, have serious impacts on society in terms of human casualties and economic losses. Prioritizing and implementing relief and recovery measures after fires require explicit spatial information on the disaster extent. Information for detecting and monitoring ecosystem disturbance due to wildfires is obtainable using time series analysis of remote sensing data. However, the accuracy of this information depends on many factors, such as the input spectral features and the scene characteristics. In this study, we appraise the use of three spectral indices for continuous burned area monitoring in mainland Greece using the Breaks For Additive Seasonal and Trend (BFAST) Monitor unsupervised algorithm. In addition to the commonly employed Normalized Burn Ratio (NBR) we evaluated the use of differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) generated from a 16-day Landsat-8 time series. The spatiotemporal Overall Accuracy (OA) is assessed using Copernicus Emergency Mapping Service (EMS) grading products and an independent visual interpretation procedure. The latter approach suggests that the using the dNBR (OA = 87.0 %) and RBR (OA = 86.9 %) indices is more efficient than using the NBR time series (OA = 66.4 %). Copernicus EMS validation data show that the RBR-based approach attains a higher OA (80.5 %) -higher than either dNBR (OA = 75.0 %) or NBR (OA = 72.1 %). Evaluating the temporal agreement with Copernicus EMS products, bi-temporal indices achieved mean time lags of 0.023 (dNBR), 0.019 (RBR) and −0.015 (NBR) years. Overall, RBR usage provides the most reliable input for consistent continuous burned area monitoring.

Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room

BackgroundDespite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types.ResultsNBR decline was significantly correlated with post-fire canopy mortality (r2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches.ConclusionsDifferences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.

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.

Elucidating factors driving post-fire vegetation recovery in the Mediterranean forests using Landsat spectral metrics

Wildfires represent one of the primary disturbance agents in the Mediterranean, significantly affecting the ecological integrity of forests. Therefore, understanding the spatial patterns of post-fire vegetation recovery is crucial to improving forest restoration planning and assessing the regeneration capacity of different forest stands that have been impacted by wildfires. In this study, we analysed post-fire vegetation recovery rates within the context of fire severity, pre-fire vegetation, and post-fire climate conditions, for different Mediterranean forest classes, namely, Mediterranean pine, holm, deciduous oak forests, sclerophyllous vegetation, and thermophilous shrublands. Basilicata, in Italy, was chosen as a study area, as it represents a wide range of forests.The Relative Recovery Indicator (RRI) was derived from Normalized Burn Ratio (NBR) patterns extracted from 30-meter Landsat time series for different wildfires that occurred during the 2004–2016 within Google Earth Engine (GEE) environment. A Linear Mixed Model (LMM) was used to test the effect of the different variables on the RRI. Results showed a general decrease in recovery rate within five-years post-fire for each forest cover class, which is mainly related to pre- and post-fire conditions. Pre-fire vegetation conditions significantly influenced post-fire vegetation recovery, especially in sclerophyllous and deciduous oak forests. Post-fire climate conditions (e.g., temperature) were also important predictors of vegetation recovery explaining the variation in post-fire RRI patterns. The proposed method could provide new insights into the restoration and management of forest ecosystems in the Mediterranean.

Assessment of peatland burning in Scotland during 1985–2022 using Landsat imagery

Abstract In the Scottish uplands, prescribed burning of moorland vegetation is widely practised either to boost gamebird numbers for recreational shooting or to improve livestock grazing. In recent years, this system of land management has become controversial due to concerns over the potential impacts on ecosystem services. However, there are limited data on the extent, distribution or frequency of burning and it is unclear whether there are long‐term trends in burning. Crucially, the extent of burning on peat soils is not well known. We used a time series of Landsat imagery covering 7750 km2 of moorland in Eastern Scotland to detect annual variation in area burnt from 1985 to 2022. Burnt areas were detected using annual changes in Normalised Burn Ratio. An accuracy evaluation conducted over eight sites covering 415 km2 using a combination of Google Earth imagery, and field studies suggested a user's accuracy of 90% and a producer's accuracy of 77%. We estimate an average annual mean area burnt of 61 km2 with large interannual variability and no significant change in area burnt over the 38‐year study period. We estimate that 32% of burning (19 km2 year−1) occurred on deep peat soils with no reduction in burning on deep peat after the revision of national guidelines (the Muirburn Code) in 2017 recommended ceasing this practice. We find that in Eastern Scotland there has been no significant change in moorland area burnt over the last four decades. The fractional area burnt that is on deep peat is a matter of management concern.

Dynamics of forest stands changes on the territory of Skole Beskydy National Nature Park

The relevance of the study is determined by the issue of assessing the condition and utilization of forest cover within the territory of the Skole Beskydy National Nature Park, as well as the necessity to develop effective methods for monitoring and conserving forest ecosystems. The purpose of the study was to investigate the dynamics of forest cover changes within the Skole Beskydy National Nature Park, as well as to assess the losses of forest resources in this area and recommend the obtained series of images to enhance the forest management system. The research was conducted using a comprehensive approach combining various methods of studying forest cover changes and their comparison. The basis for applying remote sensing technologies was the use of specific multispectral satellite images combined with attribute information on forest land geospatial data. The most progressive methods of studying changes in forest plantations based on remote sensing data using geographic information systems have been demonstrated. Satellite images were obtained from the Landsat 8 satellite data set through the United States Geological Survey data portal. The normalized burn ratio and normalized difference vegetation index are calculated and compared. An analysis was also carried out using the Global Forest Watch online resource. As a result of the study, changes in forest cover were identified, an assessment of the changes was provided, and a map reflecting forest changes that occurred in the Skole Beskydy National Nature Park from 2000 to 2020 was developed. According to the results of the analysis, a low level of losses of forest plantations due to natural factors, including losses from fires, was established. The main part of forest losses is caused by anthropogenic factors. The practical significance of the results lies in the possibility of using the obtained series of images within public control and improving the forest management system

Assessment of Burn Severity and Monitoring of the Wildfire Recovery Process in Mongolia

Due to the intensification of climate change around the world, the incidence of natural disasters is increasing year by year, and monitoring, forecasting, and detecting evolution using satellite imaging technology are important methods for remote sensing. This study aimed to monitor the occurrence of fire disasters using Sentinel-2 satellite imaging technology to determine the burned-severity area via classification and to study the recovery process to observe extraordinary natural phenomena. The study area that was sampled was in the southeastern part of Mongolia, where most wildfires occur each year, near the Shiliin Bogd Mountain in the natural steppe zone and in the Bayan-Uul sub-province in the forest-steppe natural zone. The normalized burn ratio (NBR) method was used to map the area of the fire site and determine the classification of the burned area. The Normalized Difference Vegetation Index (NDVI) was used to determine the recovery process in a timely series in the summer from April to October. The results of the burn severity were demonstrated in the distribution maps from the satellite images, where it can be seen that the total burned area of the steppe natural zone was 1164.27 km2, of which 757.34 km2 (65.00 percent) was classified as low, 404.57 km2 (34.70 percent) was moderate-low, and the remaining 2.36 km2 (0.30 percent) was moderate-high, and the total burned area of the forest-steppe natural zone was 588.35 km2, of which 158.75 km2 (26.98 percent) was classified as low, 297.75 km2 (50.61 percent) was moderate-low, 131.25 km2 (22.31 percent) was moderate-high, and the remaining 0.60 km2 (0.10 percent) was high. Finally, we believe that this research is most helpful for emergency workers, researchers, and environmental specialists.

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community’s livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country’s financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017–2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress.

Monitoring and identifying forest burnt area using NBR Landsat and Landuse in Central Kalimantan

Forest fires are one of the phenomena that occur in peatlands in Indonesia. This incident is due to changes in landuse in peatland and occurs repeatedly over a certain period of time. This study aims to monitor and identify burnt areas and landuse changes in the Kahayan River and Sebangau Peat Hydrological Unit (PHU) Central Kalimantan. The analysis was carried out on the Landsat imagery data series and landcover data from Ministry of Environment and Forestry Indonesia from 2014-2019. Analysis conducted with identifying burnt area using the Normalized Burn Ratio (NBR) and intersected with landcover data in that area. The results of the analysis show that forest fires affect landuse changes. Landuses that were significantly affected by forest fires notably shrub swamps, plantations, and dry land farming. Landuse decrease in shrubs swamps by 18% (± 2000 ha) and increase in plantation land by 840% (± 4000 ha) and dry land farming by 6357% (± 9400 ha) prior to forest fires.

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.

Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine.

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.

Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M’diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M’diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region.

The Database of European Forest Insect and Disease Disturbances: DEFID2.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

CYCLE-GAN BASED FEATURE TRANSLATION FOR OPTICAL-SAR DATA IN BURNED AREA MAPPING

Abstract. For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas.

Assessment for the severity of forest areas burnt by fire in the Phu Kradueng National Park by retrieving data from the Landsat 8 OLI Satellite

Forest fire is a problem that has been more and more severe due to the changing climate and other factors caused by humans. This study aimed at developing operation forms that are up-to-date for the analysis of and assessment of the severity of forest fire burnt fire in the Phu Kradueng National Park by retrieving data from the Landsat 8 OLI Satellite. The operation methods are as follows: 1) bring the data from the Landsat 8 OLI Satellite before and after forest fire incident to be adjusted on part of the wave radiation, 2) perform the analysis to find the Normalized Burn Ratio (NBR) and ∆NBR, 3) assess for the severity of forest areas burnt by forest fire in 7 levels, and 4) check for the accuracy of forest areas burnt by forest fire by using the method of visual interpretation into the data obtained from the Landsat 8 OLI Satellite that has passed the False Color Composite process from SWIR - NIR - Red wave ranges (RGB:754); the location was specified by creating 90 random points, dividing into forest area for 30 points, forest area burnt by forest fire for 30 points, and other areas that are not forest for 30 points. After that, consider the overall accuracy and Kappa statistics. It was found from the operation that NBR and ∆NBR can be well used to assess for the severity of the burning, and are consistent with the situations that occur. Also, there is consistency in the accuracy of the burnt forest area from forest fire by the method of visual interpretation.

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.

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.&#x0D; Keywords: Forest and land fires, NBR Indices, Sentinel-2.

Identifying Burnt Areas in Forests and Land Fire Using Multitemporal Normalized Burn Ratio (NBR) Index on Sentinel-2 Satellite Imagery

Identifying Burnt Areas in Forests and Land Fire Using Multitemporal Normalized Burn Ratio (NBR) Index on Sentinel-2 Satellite Imagery

Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments

Although wildfires are a common disturbance factor to the environment, some of them can cause significant environmental and socioeconomic losses, affecting ecosystems and people worldwide. The wildfire identification and assessment of their effects on damaged forest areas is of great importance for provision of effective actions on their management and preservation. Forest regrowth after a fire is a continuously evolving and dynamic process, and the accuracy assessment of different remote sensing indices for its evaluation is a complicated task. The implementation of this task cannot rely on the standard procedures. Therefore, we suggested a method involving delineation of dynamic boundaries between conditional categories within burnt forest areas by application of spectral reflectance characteristics (SRC). This study compared the performance of firmly established for fire monitoring differenced vegetation indices—Normalized Difference Vegetation Index (dNDVI) and Normalized Burn Ratio (dNBR) and tested the capabilities of tasseled cap-derived differenced Disturbance Index (dDI) for post-fire monitoring purposes in different forest environments (Boreal Mountain Forest (BMF), Mediterranean Mountain Forest (MMF), Mediterranean Hill Forest (MHF)). The accuracy assessment of the tree indices was performed using Very High Resolution (VHR) aerial and satellite data. The results show that dDI has an optimal performance in monitoring post-fire disturbances in more difficult-to-be-differentiated classes, whereas, for post-fire regrowth, the more appropriate is dNDVI. In the first case, dDI has an overall accuracy of 50%, whereas the accuracy of dNBR and dNDVI is barely 35% and 36%. Moreover, dDI shows better performance in 16 accuracy metrics (from 17). In the second case, dNDVI has an overall accuracy of 59%, whereas those of dNBR and dDI are 55% and 52%, and the accuracy metrics in which dNDVI shows better performance than the other two indices are 11 (from 13). Generally, the studied indices showed higher accuracy in assessment of post-fire disturbance rather than of the post-fire forest regrowth, implicitly at test areas—BMF and MMF, and contrary opposite result in the accuracy at MHF. This indicates the relation of the indices’ accuracy to the heterogeneity of the environment.

Using Landsat time series and bi-temporal GEDI to compare spectral and structural vegetation responses after fire

Passive and active spaceborne remote sensing technologies play a key role in monitoring forests across large areas, particularly when combining the advantages of both sensor technologies. This study investigates the link between spectral and structural change metrics following forest fire disturbance, collected from Landsat satellites and the Global Ecosystem Dynamics Investigation (GEDI) mission, respectively. The relationships were analysed across 1849 GEDI sampling locations (footprints), spread across south-east Australian forests. To assess structural change on the footprint level, simulated pre-fire GEDI observations were compared with real GEDI observations from one year after the fires. Results show relatively strong fire responses across Landsat spectral indices, with a median decline to between 46.1 % (Normalised Burn Ratio 2) and 77 % (Normalised Difference Vegetation Index) of pre-fire levels. GEDI’s structural change metrics demonstrated a markedly different response, with most showing an even more pronounced decline. In contrast, canopy height demonstrated a less substantial decline, dropping to 82.7 %. Results also suggest that fire severity and forest type impact the fire response of some of the examined spectral and structural metrics. In particular, taller forests and increased fire severity were associated with a more pronounced post-fire decline. The findings of this study highlight the large variation of forest structural responses to fire and their divergence from spectral change metrics, and emphasise the potential of integrating GEDI observations into wall-to-wall spectral forest change monitoring. The concept of bi-temporal GEDI observations as demonstrated in this study is promising, as it captures both pre- and post-disturbance structure on the footprint level and might enhance modelling of forest structural change in future approaches.