Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Detection of Burned Areas through Spectral Indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)

In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

Spatiotemporal changes in forest loss and its linkage to burned areas in China

Fire-induced forest loss has substantially increased worldwide over the last decade.In China,the connection between forest loss and frequent fi res on a national scale remains largely unexplored.In this study,we used a data set for a time-series of forest loss from the Global Forest Watch and for a MODIS-derived burned area for 2003–2015 to ascertain variations in forest loss and to explore its relationship with forest fi res(represented by burned areas)at the country-and forest-zone levels.We quantifi ed trends in forest loss during 2003–2015 using linear regression analysis and assessed the relation between forest loss and burned areas using Spearman’s correlation.Forest loss increased signifi cantly(264.8 km 2 a−1;R 2=0.54,p<0.01)throughout China,with an average annual increase of 11.4%during 2003–2015.However,the forest loss trend had extensive spatial heterogeneity.Forest loss increased mainly in the subtropical evergreen broadleaf forest zone(315.0 km 2 a−1;R 2=0.69,p<0.01)and tropical rainforest zone(38.8 km 2 a−1;R 2=0.66,p<0.01),but the loss of forest decreased in the cold temperate deciduous coniferous forest zone(−70.8 km 2 year−1;R 2=0.75,p<0.01)and the temperate deciduous mixed broadleaf and coniferous forest zone(−14.4 km 2 a−1;R 2=0.45,p<0.05).We found that 1.0%of China’s area had a signifi cant positive correlation(r≥0.55,p<0.05)with burned areas and 0.3%had a signifi cant negative correlation(r≤−0.55,p<0.05).In particular,forest loss had a signifi cant positive relationship with the burned area in the cold temperate deciduous coniferous forest zone(16.9% of the lands)and the subtropical evergreen broadleaf forest zone(7.8%).These results provide a basis for future predictions of fi re-induced forest loss in China.

Factorial analysis on forest canopy density restoration in the burned area of northern Great Xing＇an Mountains, China

The restoration of forest landscape has drawn much attention since thecatastrophic fire took place on the northern slope of Great Xing'an Mountains in 1987. Forest canopydensity, which has close relation to forest productivity, was selected as a key factor to find howmuch the forest quality was changed 13 years after fire, and how fire severity, regeneration way andterrain factors influenced the restoration of forest canopy density, based on forest inventory datain China, and using Kendall Bivariate Correlation Analysis, and Distances Correlation Analysis. Theresults showed that fire severity which was inversely correlated with forest canopy density gradewas an initial factor among all that selected. Regeneration way which did not remarkably affectforest canopy density restoration in short period, may shorten the cycle of forest succession andpromote the forest productivity of conophorium in the future. Among the three terrain factors, theeffect of slope was the strongest, the position on slope was the second and the aspect was the last.

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles.Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited,largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire,vegetation,climate,and anthropogenic factors.Here,using data from satellite-derived fire observations and ecosystem model simulations,we performed a comprehensive investigation of the spatial and temporal dynamics of China’s ecosystem fire disturbances and their carbon emissions over the past two decades(1997–2016).Satellite-derived results showed that on average about 3.47-4.53×10^(4) km^(2) of the land was burned annually during the past two decades,among which annual burned forest area was about 0.81-1.25×10^(4) km^(2),accounting for 0.33-0.51%of the forest area in China.Biomass burning emitted about 23.02 TgC per year.Compared to satellite products,simulations from the Energy Exascale Earth System Land Model(ELM)strongly overestimated China’s burned area and fire-induced carbon emissions.Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China’s Daxing’anling region and subtropical dry forest in South Yunnan,as revealed by both the satellite product and the model simulations.Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China’s ecosystem fire disturbances.Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences.Further studies are required to improve the methods of observing and modelling China’s ecosystem fire disturbances,which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

Distribution patterns of fire regime in the Pendjari Biosphere Reserve, West Africa

Pendjari Biosphere Reserve(PBR),a primary component of the W-Arly-Pendjari transboundary biosphere reserve,represents the largest intact wild ecosystem and pristine biodiversity spot in West Africa.This savannah ecosystem has long been affected by fire,which is the main ecological driver for the annual rhythm of life in the reserve.Understanding the fire distribution patterns will help to improve its management plan in the region.This study explores the fire regime in the PRB during 2001–2021 in terms of burned area,seasonality,fire frequency,and mean fire return interval(MFRI)by analysing moderate resolution imaging spectroradiometer(MODIS)burned area product.Results indicated that the fire season in the PBR extends from October to May with a peak in early dry season(November–December).The last two fire seasons(2019–2020 and 2020–2021)recorded the highest areas burned in the PBR out of the twenty fire seasons studied.During the twenty years period,8.2%of the reserve burned every 10–11 months and 11.5%burned annually.The largest part of the reserve burned every one to two years(63.1%),while 8.3%burned every two to four years,5.8%burned every four to ten years,and 1.9%burned every ten to twenty years.Only 1.3%of the entire area did not fire during the whole study period.Fire returned to a particular site every 1.39 a and the annual percentage of area burned in the PBR was 71.9%.The MFRI(MFRI<2.00 a)was low in grasslands,shrub savannah,tree savannah,woodland savannah,and rock vegetation.Fire regime must be maintained to preserve the integrity of the PBR.In this context,we suggest applying early fire in tree and woodland savannahs to lower grass height,and late dry season fires every two to three years in shrub savannah to limit the expansion of shrubs and bushes.We propose a laissez-faire system in areas in woodland savannah where the fire frequency is sufficient to allow tree growth.Our findings highlight the utility of remote sensing in defining the geographical and temporal patterns of fire in the PBR and could help to manage this important fire prone area.

Contribution of biophysical and climate variables to the spatial distribution of wildfires in Iran

This study investigated the relationship between climate and biophysical variables in burned areas in Iran.The fire burned area(FBA)product(Fire CCI 5.1.1),land surface temperature(MOD11C3C),vegetation index(MOD13A1),and climate variables such as temperature,wind speed,relative humidity,and volumetric soil moisture from the ERA5 reanalysis dataset were used.Pearson correlation coefficient was used to determine the relationship between biophysical and climate variables and fire occurrence.The results show that FBA increased by 1.7 hectares/decade from 2001 to 2020.The high FBA in 2010(the black summer of Iran)was due to high temperatures and significant heatwaves that led to extensive wildfires.Although anthropogenic activities are considered a significant cause of wildfires,several variables,including increased temperatures,less precipitation,relative humidity,and wind speed and direction,contribute to the extent and occurrence of wildfires.The country’s FBA hotspot is in the Arasbaran region during the summer season.Temperature and relative humidity are the most significant variables influencing the occurrence of wildfires.The results show the vulnerability of Iran s forests and their high potential for fires.Considering the frequency of fire occurrences in Iran and the limited equipment,fire prevention plans should be carried out by applying proper management in high-risk regions.

Monitoring fire regimes and assessing their driving factors in Central Asia

Relatively little is known about fire regimes in grassland and cropland in Central Asia.In this study,eleven variables of fire regimes were measured from 2001 to 2019 by utilizing the burned area and active fire product,which was obtained and processed from the GEE(Google Earth Engine)platform,to describe the incidence,inter-annual variability,peak month and size of fire in four land cover types(forest,grassland,cropland and bare land).Then all variables were clustered to define clusters of fire regimes with unique fire attributes using the K-means algorithm.Results showed that Kazakhstan(KAZ)was the most affected by fire in Central Asia.Fire regimes in cropland in KAZ had the frequent,large and intense characters,which covered large burned areas and had a long duration.Fires in grassland mainly occurred in central KAZ and had the small scale and high-intensity characters with different quarterly frequencies.Fires in forest were mainly distributed in northern KAZ and eastern KAZ.Although fires in grassland underwent a shift from more to less frequent from 2001 to 2019 in Central Asia,vigilance is needed because most fires in grassland occur suddenly and cause harm to humans and livestock.

Quantitative dynamics on stimulating regeneration and sowing seedlings of Larix gmelinii in Daxing'an Mountains

To understand the quantitative dynamics and death reason of stimulating regeneration seedlings is significant for stimulating the natural regeneration ofLarix gmelinii and implement of conservation project of natural forest. This paper summarized location observations and directly-seeding simulation experiments of six permanent sample plots that were set up after the seed bumper harvest year ofLarix gmelinii in 1989. The study showed that stimulating natural regeneration seedlings had a large mortality in the first three years, especially in the first year of seedling emergence. After three years seedlings died less and stepped into the stable regeneration stage. A large number of seedlings died of sunscald as the primary death reason. For those areas of good site conditions and rich soil, damping-off would cause seedlings to death in large quantities. The task of stimulating regeneration is mainly to get rid of the litter (forest floor) on burned areas. By means of promoting measures, emergence rate of sown seeds would be several times and dozens of times higher than that of seed shedding on the condition of retention of forest floor. Promoting the regeneration need to select the suitable site against great slope and low-lying lands; at the same time, be careful of the avoidance of frost heaving by depression water.

The effect of climate factors on the size of forest wildfires(case study:Prague-East district,Czech Republic)

This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions.Data from the Prague-East District of the Czech Republic was used for model derivation.Individual burned forest area was hypothesized as a function of water vapor pressure,air temperature and wind speed.Wind speed was added to enhance predictions of the size of forest wildfires,and further improvements to the utility of prediction methods were added to the regression equation.The results show that if the air temperature increases,it may contain less water and the fuel will become drier.The size of the burned area then increases.If the relative humidity in the air increases and the wind speed decreases,the size of the burned area is reduced.Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests.

Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia,China

Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.

Wildfires in Botswana and Their Frequency of Occurrence

Fires play an essential part in the maintenance of the environment, but amplified fire activity often leads to adverse effects in the environment such as destruction of property and loss of life. Botswana has experienced wildfires that are caused by humans intentionally and unintentionally. Some of these wildfires grow into mega fires such as the 2008 wildfires. Data of wildfires reported in Botswana from the Department of Forestry and Range Resources and the frequency of occurrence was studied and analyzed. It shows the period of 2006-2017, Ghanzi and Ngamiland districts were mostly affected by wildfires. These districts have protected land such as the wildlife parks which may lead to the outbreak of natural wildfires that burns unnoticed due to minimal movements of people. The wildfires reported in each district show an increase over the years and this may affect the smooth running of operations.

Spatial and temporal intercomparison of four global burned area products

We characterize the agreement and disagreement of four publically available burned products(Fire CCI,Copernicus Burnt Area,MODIS MCD45A1,and MODIS MCD64A1)at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time.Our analysis,conducted using seven years of data(2005–2011),shows that estimates of burned area vary greatly between products in terms of total area burned,the location of burning,and the timing of the burning.We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally.Comparison with independent,contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime.Our results have implications for the use of global burned area products in fire ecology,management and emissions applications.

Solid rocket motor propellant grain burnback simulation based on fast minimum distance function calculation and improved marching tetrahedron method

To efficiently compute arbitrary propellant grain evolution of the burning surface with uniform and non-uniform burning rate for solid rocket motor,a unified framework of burning surface regression simulation has been developed based on minimum distance function.In order to speed up the computation of the mini-mum distance between grid nodes of grain and the triangular mesh of burning surface,a fast distance querying method based on the equal size cube voxel structure was employed.An improved marching tetrahedron method based on piecewise linear approximation was carried out on second-order tetrahedral elements,achieved high-efficiency and adequate accuracy of burning surface extraction simultaneously.The cases of star grain,finocyl grain,and non-uniform tube grain were studied to verify the proposed method.The observed result indicates that the grain burnback computation method could realize the accurate simulation on unstructured tetrahedral mesh with a desirable performance on computational time.

Interannual variability and climatic sensitivity of global wildfire activity

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources;2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations;3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs;and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.