Burn Ratio Research Articles

THE EFFECTS OF LAND COVER CHANGES ON LAND SURFACE TEMPERATURES

Abstract. The aims of this study are to detect the land-cover maps and land surface temperatures using Landsat time series and analyse the relation between the land-cover and land surface temperatures (LST) and their changes in time. For these purposes initially, land-cover maps were generated rapidly using land cover indices and automatic thresholding. The land-cover indices used in this study are Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Index-Based Built-Up Index (IBI), Modified Bare-Soil Index (MBI), Plastic-Mulched Landcover Index (PMLI), Plastic Greenhouse Index (PGI) and Normalized Burn Ratio Thermal (NBRT) Index. Then, using the thermal bands of Landsat satellites, LST maps were created. Finally, the land-cover and LST changes were examined. The Kumluca district of Antalya, which includes extensive greenhouse areas as well as urban, vegetation, bareland, water, was selected as study area. Between the years 2004 and 2021, within the study area the greenhouse areas increased significantly, the urban area expanded and some areas exposed to fire, especially the fire in 2016. Therefore, the images within this time period were used. The overall accuracies for land-cover maps were computed as 76%, 79%, 79%, 89% and 86% for the years 2004, 2009, 2013, 2017 and 2021, respectively. The results obtained from the study reveal that while greenhouse and urban areas were increased, the vegetation areas were decreased significantly within this time period. In addition, generally increases were observed for LST values of all land-cover classes and the highest LST values were detected for the burned, bareland, urban and greenhouse areas.

MASK IMPORTANCE IN BURNED AREA MAPPING BASED ON REMOTE SENSING, GIS AND OPEN-SOURCE PRODUCTS

Abstract. Forest and field fires are an increasing problem for EU countries located in the southern parts of Europe. With each passing day, the need to develop a technological scheme for monitoring the territories affected by fires and the timely provision of open data in order to recover damages is increasing.In this study, the method used to study burned areas is based on the analysis of multispectral satellite images, the differences in terrain reflectivity and the use of indices to highlight the differences between areas with healthy vegetation and burned areas, taking into account their different signatures. Open source software - SNAP, QGIS, images from Sentinel-2 with bands in the green, red, near-infrared and short-wave infrared spectral bands were used for the purposes of the research. Data from the Inspire Directive was also used to analyze the types and characteristics of affected areas.In many cases, water bodies show a similar difference as the Normalized Burn Ratio (NBR) index, for this reason the need for their preliminary masking arises. Clouds that appear in each input image are also masked. A combined water and cloud mask is applied. To detect the water bodies, NDWI was used to maximize the reflectance of the water body in the green band and minimize the reflectance of the water body in the NIR range.

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

In this study, space-borne remote sensing (Landsat-8, MODIS) was employed to evaluate the effects of forest fires occurring on 05 July 2021, over Khenchela province, Algeria. Our objective is to understand the severity of damage caused by the fire and its implications for vegetation and land cover. Utilizing the Normalized Difference Vegetation Index (NDVI) from MODIS data and Landsat-8 imagery, we report changes in vegetation health and land cover. To identify areas affected by forest fires and evaluate the severity of damage, the Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) were calculated. Analysis showed that −1825.11 ha (1.21%) of the total area experienced severe burns, 3843.54 ha (2.54%) moderate to high severity burns, 3927.97 ha (2.59%) moderate to low severity burns and 9864.45 ha (6.51%) low severity burns. The area covered by vegetation decreased from 2014 to 2021, indicating a negative trend in vegetation cover over the study period.

Fuel build-up promotes an increase in fire severity of reburned areas in fire-prone ecosystems of the western Mediterranean Basin

BackgroundFire-vegetation feedbacks can modulate the global change effects conducive to extreme fire behavior and high fire severity of subsequent wildfires in reburn areas by altering the composition, flammability traits, and spatial arrangement of fuels. Repeated, high-severity wildfires at short return intervals may trigger long-term vegetation state transitions. However, empirical evidence about these feedbacks is absent in fire-prone ecosystems of the western Mediterranean Basin, where the response of fire activity has been enhanced by contemporary socioeconomic and land-use changes. Here, we evaluated whether fire severity differs between initial burns and subsequent wildfires in reburn areas (fire-free periods = 10–15 years) of maritime pine and Aleppo pine forests, holm oak woodlands, and shrublands in the western Mediterranean Basin, and whether there is a relationship between the severity of such interactive wildfire disturbances. We also tested how the type of ecosystem and changes in vegetation structure after the initial wildfires influence these relationships. We leveraged Landsat-based fire severity estimates for initial and last wildfires using the Relativized Burn Ratio (RBR) and Light Detection and Ranging (LiDAR) data acquired before the last wildfire.ResultsFire severity of the last wildfire was significantly higher than that of the initial wildfire for each dominant ecosystem type in reburn areas. These differences were very pronounced in maritime pine forests and shrublands. For consistency, the same patterns were evidenced for the fire severity in reburn and first-entry areas of the last wildfire for each dominant ecosystem type. Fire severity of the last wildfire in forests and woodlands (particularly maritime pine-dominated) raised with increasing severity of the previous wildfire to a greater extent than in shrublands. Pre-fire fuel density in the lower vegetation strata (up to 4 m high in maritime and Aleppo pine forests, as well as in shrublands, and up to 2 m high in holm oak forests) was significantly higher in reburn than in first-entry areas of the last wildfire.ConclusionsOur results suggest that land managers should promote more fire-resistant landscapes to high fire severity by minimizing fuel build-up and thus fire hazard through pre-fire fuel reduction treatments such as prescribed burning.

Quantifying forest resilience post forest fire disturbances using time-series satellite data.

Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study.

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

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

Assessing Burned Areas in Sikkim, India through Satellite Mapping

Aim of study: Fire impacts biodiversity and ecosystems, and is crucial for understanding fire causes. This paper aimed to assess burned areas and severity levels in Sikkim's forest fire incidence data from 2004-2019. 
 Area of the study: The study area for the work is the state of Sikkim, situated in the Himalayan Mountain's North-eastern region. 
 Material and methods: Landsat 8 and Landsat 5 satellite image were used for the study and Standard vegetation indices like Delta Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) are computed. Also, a linear regression analysis was performed between weather parameters like temperature (℃), wind (Km/h), rainfall (mm) on burn severity (dNBR classes) of forest fires in Sikkim between the year 2009-2019.
 Main results: According to the findings, out of 557 numbers forest fire incidents in Sikkim between 2004 and 2019, 250 numbers were classified as Unburned (46.21 %), 199 numbers as Enhanced Regrowth, Low (35.72 %), and 43 numbers as Enhanced Regrowth, High (7.94 %), while 32 numbers were classified as Low Severity (5.92 %), 9 numbers as Moderate-Low Severity (1.66 %), 5 numbers as Moderate-High Severity (0.92 %), and 2 numbers as High Severity (0.36 %). It was found that the wind (r=0.80, Slope=0.57, SD=0.70) and rainfall (r=0.77, Slope=-0.18, SD=7.00) showed a strong positive and strong negative linear relationships respectively in influencing the burn severity (dNBR). While, temperature (r=0.69, Slope=0.74, SD=0.01) plays a moderate positive role in influencing the burn severity (dNBR).
 Highlights: The study has shown the effectiveness of burn area mapping and remote sensing data products in analyzing forest fire regions with limited resources and diverse landforms and vegetation. Researchers will be able to identify the regions affected by forest fires and those that have not recovered since the fire. Goal of this research is to improve forest fire planning and management by fostering aid to the responsible authorities to evaluate the pattern of vegetation degradation in burn regions and estimate the impact of forest fires

An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R2 = 0.79 and R2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

Mapping planted forest age using LandTrendr algorithm and Landsat 5–8 on the Loess Plateau, China

Forest age is a key parameter for understanding the water and carbon cycles and carbon sequestration potential of planted forest ecosystems. However, estimating forest age on a large scale is difficult. This study aims to determine whether the distribution of forest age can be mapped over a large area using the LandTrendr (LT) algorithm. With the LT algorithm, the initial year of forestland (breakpoints) can be determined rather accurately through the yearly trajectory of the normalized burn ratio (NBR) index in the revegetated forest. The results show that the LT algorithm is a convenient, efficient, and reliable method for identifying forests age. Moreover, a comparison with ground truth data on the Chinese Loess Plateau (LP) revealed that the overall accuracy of the error confusion matrix was 89 %, with a root-mean-square error (RMSE) of 2.14 years. Using the LT algorithm, we revealed that the forestland on the Chinese LP in 2020 was dominated by planted forests over 30 years old, accounting for approximately 78.27 %. The forestland area increased by approximately 20,386 km2 from 1990 to 2020, and the increase occurs primarily around the original forestland in the eastern and southern parts of the Chinese LP. This study provides an important parameter for assessing and quantifying biomass and carbon sequestration through afforestation on the Chinese LP.

Fire Regimes of Utah: The Past as Prologue

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

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.

Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires

Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63–76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20–40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.

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.