Burned Area Index Research Articles

Burned Area Index의 동해안 대형 산불 피해지역 적용

Burned Area Index의 동해안 대형 산불 피해지역 적용

Methodology for burned areas delimitation and fire severity assessment using Sentinel-2 data. A case study of forest fires occurred in Spain between 2018 and 2023

The objective of this article was to develop a methodology for the burned areas delimitation and fire severity assessment in forest fires occurred in Spain between 2018 and 2022. As input data, this study was based on the use of Sentinel-2 spectral indices, which are characterized by having spectral bands in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, allowing a high distinction between burned and unburned areas, and between different fire severity degrees too. All possible combinations between Sentinel-2 bands applied to a spectral normalized difference index (SP) were analyzed, along with the most commonly used burn spectral indices in remote sensing as the Burned Area Index (BAI), the Burned Area Index for Sentinel-2 (BAIS2), the Mid-Infrared Burn Index (MIRBI), the Normalized Burn Ratio (NBR), the Relativized Burn Ratio (RBR) and the relative differential Normalized Burn Ratio (RdNBR). In addition, in order to delete confusions between burned area and the presence of other land cover areas, the Sentinel-2 Global Land Cover (S2GLC 2017) and the temporal differences between pre-fire and post-fire dates were obtained for each spectral index (dSP). The results were compared by: in the case of burned areas, the Emergency Mapping Service (EMS) and the Galicia forest service; in the case of fire severity, using field plots classified as in Ruiz-Gallardo et al. (2004) study (null, low, moderate and high severity). The final statistic results obtained showed that the dNBR2 spectral index (using B11 and B12 Sentinel-2 spectral bands) provided the highest results of burned area delimitation (7% of commission error and 3% omission error, respectively) whereas, the combination of the BAIS2, the NBR and the modified Normalized Burn Ratio (NBR2, using B7 and B12 Sentinel-2 spectral bands), used in areas with low, mix and full vegetation respectively, provided the highest results in fire severity assessment (kappa statistic, F1-score and Balanced Accuracy equal to 0.87, 0.86 and 0.92, respectively). The methodology developed in this work allows obtaining accurate maps of burned area and fire severity in Spain, contributing to the reinforcement of national forest fires statistics.

Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.

Assessment of the Analytic Burned Area Index for Forest Fire Severity Detection Using Sentinel and Landsat Data

The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation.

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.

Performance of heat spots obtained from satellite datasets to represent burned areas in Andean ecosystems of Cusco, Peru

The combustion of biomass is a prevalent practice in the Andes. Often, these burns escape control and escalate into wildfires. However, the investigation of unreported fire incidents has not received the same level of attention as reported wildfires. Satellite provide an alternative information source for studying wildfires. Given the scarcity of wildfire response tools, it is imperative to develop strategies to prevent burns in regions where human activity typically triggers wildfires. This study aims to assess the efficacy of heat spot data obtained from satellite datasets in identifying fire activity in the Peruvian Andes. The study utilized MODIS (MCD14DL product) and VIIRS (SUOMI and JPSS-1 products) satellite datasets to characterize fire activity through heat spot detection. Additionally, the study employed the normalized burned area index (NBR), a valuable indicator for mapping burned areas. Our findings indicate that MODIS and VIIRS heat spots demonstrate a high level of reliability in detecting active fires (commission errors of ∼1%). However, the detection of burned areas not captured by MODIS or VIIRS heat spots was significantly high (omission errors of ∼90%). Nevertheless, this detection deficiency decreased for larger burn areas (errors of omission between 10 and 30% for burned areas between 50 and 100 ha, using JPSS-1 VIIRS). These results suggest that satellite heat spots are inadequate for identifying burn practices, which encompass small-scale and short-duration fire activities (lasting only hours). The outcomes of this study enhance our understanding of the suitability of heat spot detection for wildfire prevention in the Peruvian Andes.

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making.

Predicting Spatially Explicit Composite Burn Index (CBI) from Different Spectral Indices Derived from Sentinel 2A: A Case of Study in Tunisia

Fire severity, which quantifies the degree of organic matter consumption, is an important component of the fire regime. High-severity fires have major ecological implications, affecting carbon uptake, storage and emissions, soil nutrients, and plant regeneration, among other ecosystem services. Accordingly, spatially explicit maps of the fire severity are required to develop improved tools to manage and restore the most damaged areas. The aim of this study is to develop spatially explicit maps of the field-based fire severity (composite burn index—CBI) from different spectral indices derived from Sentinel 2A images and using several regression models. The study areas are two recent large fires that occurred in Tunisia in the summer of 2021. We employed different spectral severity indices derived from the normalized burn ratio (NBR): differenced NBR (dNBR), relative differenced NBR (RdNBR), and relativized burn Ratio (RBR). In addition, we calculated the burned area index for Sentinel 2 (BAIS2) and the thermal anomaly index (TAI). Different tree decision models (i.e., the recursive partitioning regression method [RPART], bagging regression trees [Bagging], and boosted regression trees [BRT]), as well as a generalized additive model [GAM]), were applied to predict the CBI. The main results indicated that RBR, followed by dNBR, were the most important spectral severity indices for predicting the field-based CBI. Moreover, BRT was the best regression model, explaining 92% of the CBI variance using the training set of points and 88% when using the validation set. These results suggested the adequacy of RBR index derived from Sentinel 2A for assessing and mapping forest fire severity in Mediterranean forests. These spatially explicit maps of field-based CBI could help improve post-fire recovery and restoration efforts.

Investigation of Antalya forest fire's impact on air quality by satellite images using Google earth engine

Several fires occur in Türkiye every year for various reasons. Some of these fires are extinguished within a short period of time. But some of them get bigger due to various factors. Prolonged fires harm the biological ecosystem and nature to a great extent. Gases released into the atmosphere due to the fires negatively affect the life of living things.In this study, the number of fires in the Antalya-Manavgat region in 2021 was determined. For this aim, BAI (Burned Area Index), NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index) plant indexes were used to determine the amount of fire by using high resolution satellite images. In addition, the carbon monoxide (CO) and ultraviolet aerosol index (UVAI) values during the fire were investigated using satellite images. The precipitation and temperature values of the region between the years 2015–2021 were discussed. The emitted gases were correlated with the national air quality station data and when the destroyed area after the fire was determined by satellite, it was determined that the CO gas released in the fire produced meaningful results with the national air quality station.

Remote evaluation of the recovery of vegetation affected by forest fires

Forest fires alter ecosystem processes and interactions. However, not all fires are the same, some are more severe than others,their impact on the vegetation varying according to the degree of severity. Therefore, it is important to have information thatallows sizing these impacts and supports the implementation of management strategies. However, monitoring the impact of fireis not an easy task, much less monitoring the response of the ecosystem over the years. Accordingly, the objective of this workwas to implement satellite technology to monitor the dynamics of forest cover recovery in areas impacted by forest fires, inorder to direct ecosystem restoration strategies. For this, the difference (before and after the fire) of the normalized burningratio (dNBR) was defined to determine the severity of a fire that occurred in the Sian Ka'an Biosphere Reserve in 2019.Subsequently, the dynamic of ecosystem recovery was evaluated, through the analysis of a series of images, using thefollowing indexes: a) Normalized Difference Vegetation Index (NDVI); b) Normalized burn ratio (NBR); c) NormalizedThermal Burnup Ratio (NBRT); d) Burned Area Index (BAI). A change in the reflectance value was observed in the burnedarea after the fire, which was restored over time. However, although the values show a recovery of the vegetation, in the fieldthe ecosystem is made up of different species from those that were present before the fire, therefore, by means of the spectralsignature of the vegetation, an image was generated showing the difference in the density of vegetation, which favors theunderstanding of the effects of fire and helps define priority areas to direct restoration actions.

Forest Burned Area Detection Using a Novel Spectral Index Based on Multi-Objective Optimization

Forest fires cause environmental and economic damage, destroy large areas of land and displace entire communities. Accurate extraction of fire-affected areas is of vital importance to support post-fire management strategies and account for the environmental impact of fires. In this paper, an analytical burned area index, called ABAI, was proposed to map burned areas from the newly launched Sentinel-2 images. The innovation of this method is to separate the fire scars from other typical land covers by formulating different objective functions, which involved three main components: First, spectral differences between the burned land and other land covers were characterized by analyzing the spectral features of the existing burned area indices. Then, for each type of land cover, we formed an objective function by linear combination of bands with the values of band ratios. Second, all the objective functions and possible constraints were formulated as a multi-objective optimization problem, and then it was solved using a linear programming approach. Finally, the ABAI spectral index was achieved with the optimizing coefficients derived from the multi-objective problem. To validate the effectiveness of the proposed spectral index, three experimental datasets, clipped from Sentinel-2 images at different places, were tested and compared with baseline indices, such as normalized burned area (NBR) and burned area index (BAI) methods. Experimental results demonstrated that the injection of a green band to the spectral index has led to good applicability in burned area detection, where the ABAI can avoid most of the confusion presented by shadows or shallow water. Compared to other burned area indices, the proposed ABAI achieved the best classification accuracy, with the overall accuracy being over 90%. Visually, our approach significantly outperforms other spectral indexed methods, especially in confused areas covered by water bodies and shadows.

New techniques for old fires: Using deep learning to augment fire maps from the early satellite era

Disturbances such as fire play a critical role in forest ecosystems. However, anthropogenic fires can profoundly impact forests to the point of destabilizing ecosystems. In addition, fires have legacy effects on environments which may be observed in forests for decades after the fire is extinguished. Thus, understanding the extent of historic fires in a landscape is vital to understanding current forest structure and ecological processes (e.g., carbon sequestration capacity and provision of habitat) and, therefore, essential for informing land-management decisions. However, little work has been done to map forest fires pre 1980s due to the challenges of interpreting imagery from the 1970s-era Landsat Multispectral Scanner (MSS) platform. MSS imagery is distinguished from recent satellite missions through lower temporal, spatial, and spectral resolutions. Recent advances in image processing have brought the goal of high-quality MSS classifications within reach. In this study, we use deep learning, specifically UNet (a fully convolutional neural network (CNN)), to detect historic forest fires in MSS imagery for the forest-dominated regions of Quebec, Canada. While other studies have applied deep learning to present-day satellite data for land cover classification, hardly any work has specifically applied deep learning to MSS data for fire detection. We trained our UNet model on 206 MSS images that were labelled by applying thresholds to the Burned Area Index inside polygons drawn by the authors around burned areas. We then used the trained model to label burns in 5104 MSS images that were compiled to generate annual burned area maps. Our results identified (with a 95% confidence interval) 3503.95 ± 484.90 km2 of burns not previously reported in any database; this represents a 35.30 ± 3.94% increase in the total known burned area across the forest-dominated regions of Quebec between 1973 and 1982.

Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency, Indonesia)

Accurate and up-to-date information about the burnt area is important in estimating environmental losses, prioritizing rehabilitation areas, and determining future planning strategies. The publicly available medium resolution optical Sentinel-2 satellite data provides a practical and effective solution for burnt area detection. In this study, we proposed two different approaches using mono-temporal and multi-temporal Sentinel-2 satellite imagery to detect burnt areas in Rokan Hilir Regency, Indonesia. The multi-temporal approaches utilized two different ensemble machine learning algorithms (Random Forest and XGBoost) and used six composite spectral indices of the differenced Normalized Burn Ratio (dNBR), differenced Normalized Burn Ratio 2 (dNBR2), differenced Normalized Difference Vegetation Index (dNDVI), differenced Soil Adjusted Vegetation Index (dSAVI), differenced Char Soil Index (dCSI), differenced Burnt area Index for Sentinel-2 (dBAIS2), and differenced Mid-infrared Burn Index (dMIRBI) as model inputs. The burnt areas are labeled by combining hotspots with confidence intervals above 95%, fire spots, and change detection methods. The XGBoost model achieved the best performance with an F1 score of 0.97 and an accuracy of 96%. Furthermore, we use the SHapley Additive exPlanations (SHAP) to quantify the contribution of each feature as well as its correlation with the target class. The dNBR, dMIRBI, and dNBR2 indices contribute the most to the XGBoost model. In comparison, this study also investigates and compares a mono-temporal approach with One-dimensional Convolutional Neural Network (CNN-1D) architecture and the performance obtained is slightly better than both machine learning models. Overall, both mono-temporal and multi-temporal approaches satisfactorily detect the burnt area.

Burned area determination using Sentinel-2 satellite images and the impact of fire on the availability of soil nutrients in Syria

The objective of this research is the identification of burned forest areas that occurred in Syria from September 2<sup>nd </sup>to October 15<sup>th</sup>, 2020. Forest fire risk classes were determined using Sentinel-2 images. Normalized Burn Ratio (NBR), Differenced Normalized Burn Ratio (dNBR), and Burned Area Index for Sentinel-2 (BAIS2), and Normalized Difference Vegetation Index (NDVI) were used for the identification how much the forests have been destroyed and to establish fire risk classes. According to the study results, the size of the vegetation area that was destroyed due to fire was determined, and the probability of the forest fire exposure of these areas was established. The fires also altered some chemical properties in the soil during the combustion process. Thus, this study was focused on the impact of fire on the availability of soil nutrients. Soil samples were collected from three depths (0–10 cm, 10–20 cm and 20–30 cm) under the forest land a month after the fire in three locations: Al-Fazeen, Sawda and Gard Al-rihan. Pine (Pinus brutia) trees cover these areas. The results of this study indicated that the fire increased pH, EC and sand, the fire also led to an increase in the solubility of the available major soil elements N, P and K. There was an increase in the solubility of the soil microelements Zn, Cu, Mn and Fe while the content of organic material and silt and clay ratio decreased at the three sites in comparison with unburned soil.

Оцінка поширення ландшафтних пожеж у Чорнобильській зоні відчуження в березні 2022 р. на основі моделі Ротермела і супутникових знімків Sentinel-2

The threat of occurrence and spread of landscape fires in Ukraine increases in the conditions of hostilities, therefore the possibility of terrestrial monitoring and suppression of wildfires is getting worser. The Chornobyl Exclusion Zone (ChEZ) has a special status due to the presence of radioactively contaminated areas that can become a source of dangerous emissions into the atmosphere from fires. Therefore, the implementation of satellite monitoring for active fires and the development of methods for predicting the behavior of fires and their consequences in the ChEZ by the computer modeling using GIS systems is relevant. The aim of this work was to estimate the size of burned areas (BA) due to the spread of fires in March 2022 during the period of its occupation by Russian troops using NRT data on active fires, obtained from the VIIRS and MODIS satellites from the NASA FIRMS as input. Calculations were made in two ways: a) fire propagation was simulated based on the well-known Rothermel model, implemented in the GRASS GIS open source software environment; b) actual BAs were defined using Normalized Burn Ratio (NBR) and Burned Area Index for Sentinel-2 (BAIS2) spectral indices, based on the involvement of Sentinel-2 bands. A comparison of the simulated areas and perimeters of scarses with the actual Bas, detected from Sentinel-2 satellite images, showed that the relative errors in BAs detection for individual groups of fires do not exceed 15%, which makes it possible to apply this approach for operative or scenario forecasting of the wildfire spread without significant loss in accuracy

Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.

Sensitivity of Spectral Indices on Burned Area Detection using Landsat Time Series in Savannas of Southern Burkina Faso

Accurate and efficient burned area mapping and monitoring are fundamental for environmental applications. Studies using Landsat time series for burned area mapping are increasing and popular. However, the performance of burned area mapping with different spectral indices and Landsat time series has not been evaluated and compared. This study compares eleven spectral indices for burned area detection in the savanna area of southern Burkina Faso using Landsat data ranging from October 2000 to April 2016. The same reference data are adopted to assess the performance of different spectral indices. The results indicate that Burned Area Index (BAI) is the most accurate index in burned area detection using our method based on harmonic model fitting and breakpoint identification. Among those tested, fire-related indices are more accurate than vegetation indices, and Char Soil Index (CSI) performed worst. Furthermore, we evaluate whether combining several different spectral indices can improve the accuracy of burned area detection. According to the results, only minor improvements in accuracy can be attained in the studied environment, and the performance depended on the number of selected spectral indices.

AN UNSUPERVISED METHOD BASED ON FIRE INDEX ENHANCEMENT AND GRNN FOR AUTOMATED BURNED AREA MAPPING FROM SINGLE-PERIOD REMOTE SENSING IMAGERY

Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

Exploring the utility of Sentinel-2 MSI derived spectral indices in mapping burned areas in different land-cover types

The difference in structure and composition of landcover types requires accurate mapping of burned areas for post-fire ecological assessments. Spectral indices for burned area mapping are mostly hard-coded to particular environments. However, the best post-fire spectral index to use for burned area mapping in most unstudied landcover types is not known. In this study, out of nine burned mapping indices optimised using the red-edge band, we tested which index outperformed the others in different land cover types. We used the Random Forest (RF) classifier to detect burned areas from Sentinel 2A imagery in four study sites and assessed the classification accuracy. We found out that, the Burned Area Index (BAI) and Global Environmental Monitoring Index (GEMI) spectral indices outperformed other indices in open shrublands, evergreen forest and in needle-leaved and semi-deciduous forests. The lowest performing spectral indices in the four study sites were Optimised Soil Adjusted Vegetation Index (OSAVI), Normalise Burn Ratio (NBR), and Normalise Difference Vegetation Index (NDVI). We recommend that for future studies, researchers and ecologists should use BAI and GEMI in mapping fires in open shrublands, evergreen, needle-leaved and semi-deciduous forests. Our results provide necessary insight for burned mapping algorithms and the accurate estimation of post-fire carbon emission with uni-temporal spectral indices in open shrublands, evergreen, needle-leaved and semi-deciduous forests.

Testing the utility of the blue spectral region in burned area mapping: Insights from savanna wildfires

Monitoring ecosystem events such as wildfires with remote sensing is fundamental to natural resources management. However, precisely delineating burned areas with remote sensing remains a challenge for post-fire ecological assessment. Burned area mapping methods with spectral indices are affected by several interferences such as soil background, water surfaces and atmospheric effects. In this study, we test the applicability of a normalized difference spectral index with the shortwave infrared and blue spectral bands in accurately mapping burned areas. The spectral index was tested in two study sites and compared to six spectral indices commonly used in burned area mapping. Random forest (RF), a machine learning algorithm, was used to classify the spectral indices and map burned areas. Although the True Skill Statistic (TSS) was relatively low, we found out that the Optimized Soil Adjusted Vegetation Index (OSAVI) and Normalized Burned Index (NBI) performed better than all the other spectral indices considered in the two study sites. The Burned Area Index (BAI) was the third highest performing spectral index. OSAVI and NBI performed well in the study sites plausibly because they were adjusted for soil effects and the blue spectral band was included to account for atmospheric effects. Our results indicate the need to adjust spectral indices for soil, water and atmospheric effects when using them for burned area mapping. The study also adds rudimentary knowledge on the potential of the blue spectral band in monitoring burned area vegetation.