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.     

Prototyping an artificial neural network for burned area mapping on a regional scale in Mediterranean areas using MODIS images

Each year thousands of ha of forest land are affected by forest fires in Southern European countries such as Spain. Burned area maps are a valuable instrument for designing prevention and recovery policies. Remote sensing has increasingly become the most widely used tool for this purpose on regional and global scales, where a large variety of techniques and data has been applied. This paper proposes a semiautomatic method for burned area mapping on a regional scale in Mediterranean areas (the Iberian Peninsula has been used as a study case). A Multi-layer Perceptron Network (MLPN) has been designed and applied to MODIS/Terra Surface Reflectance Daily L2G Global 500m SIN Grid multitemporal composite monthly images. The compositing criterion was based on maximum surface temperature. The research covered a six year period (2001–2006) from June to September, when most of the forest fires occur. The resulting burned area maps have been validated using official fire perimeters and compared with MODIS Collection 5 Burned Area Product (MCD45A1). The MLPN shown as an effective method, with a commission error of 29.1%, in the classification of the burned areas, while the omission error was of 14.9%. The results were compared with the MCD45A1 product, which had a slightly higher commission error (30.2%) and a considerably higher omission error (26.2%), indicating a high underestimation of the burned area.     

Satellite-based automated burned area detection: A performance assessment of the MODIS MCD45A1 in the Brazilian savanna

Burnings, which cause major changes to the environment, can be effectively monitored via satellite data, regarding both the identification of active fires and the estimation of burned areas. Among the many orbital sensors suitable for mapping burned areas on global and regional scales, the moderate resolution imaging spectroradiometer (MODIS), on board the Terra and Aqua platforms, has been the most widely utilized. In this study, the performance of the MODIS MCD45A1 burned area product was thoroughly evaluated in the Brazilian savanna, the second largest biome in South America and a global biodiversity hotspot, characterized by a conspicuous climatic seasonality and the systematic occurrence of natural and anthropogenic fires. Overall, September MCD45A1 polygons (2000–2012) compared well to the Landsat-based reference mapping (r² = 0.92) and were closely accompanied, on a monthly basis, by MOD14 and MYD14 hotspots (r² = 0.89), although large omissions errors, linked to landscape patterns, structures, and overall conditions depicted in each reference image, were observed. In spite of its spatial and temporal limitations, the MCD45A1 product proved instrumental for mapping and understanding fire behavior and impacts on the Cerrado landscapes.     

Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis

Traditional methods of recording fire burned areas and fire severity involve expensive and time-consuming field surveys. Available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper focuses on multiscale characterization of fire severity using multisensor satellite data. To this aim, both MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been processed using geo-statistic analyses to capture pattern features of burned areas.Even if in last decades different authors tried to integrate geo-statistics and remote sensing image processing, methods used since now are only variograms, semivariograms and kriging. In this paper, we propose an approach based on the use of spatial indicators of global and local autocorrelation. Spatial autocorrelation statistics, such as Moran's I and Getis–Ord Local Gi index, were used to measure and analyze dependency degree among spectral features of burned areas. This approach enables the characterization of pattern features of a burned area and improves the estimation of fire severity.     

Fire danger assessment in Iran based on geospatial information

Fire danger assessment is a vital issue to alleviate the impacts of wildland fires. In this study, a fire danger assessment system is proposed, which extensively uses geographical databases to characterize the spatial variations of fire danger conditions in Iran. This assessment requires three steps: (i) generation of the required input variables, (ii) methods to integrate those variables for creating synthetic indices and (iii) validation of those indices versus fire occurrence data. This fire danger model is based on previous works but adapted to Iranian conditions. It includes an estimation of the fire ignition potential (both considering human and climatic factors) and fire propagation potential. The former was generated from a logistic regression approach based on a wide range of input variables. The fire propagation probability was estimated from the Flammap fire behavior model. A first stage for validation of our fire danger system was based on comparing the estimated danger values to actual fire occurrence, based on satellite detected active fires and burned areas. The logistic regression model for fire ignition probability estimated 72.7% of true ignitions. Detected hotspots occurred more frequently in areas with higher fire ignition probability (average value: 0.65) than non hotspots (average value: 0.4). Propagation probability showed higher values for areas with higher proportion of burned area (r = 0.68, p < 0.001).     

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R² = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.     

GIS-based spatiotemporal analysis of forest fires in Turkey from 2010 to 2020

Forests are essential in contributing to the continuity of the natural balance. Therefore, their protection and sustainability are vital. However, all over the world, forest fires occur, and forests are destroyed due to both human factors and unknown causes. It is necessary to carry out studies to prevent this destruction. At this point, GIS-based location–time relationship-based hot spot clustering analysis can provide significant advantages in detecting risky spots of forest fires. In this study, GIS-based emerging hot spot clustering analysis was carried out to determine the risky areas where forest fires will occur and to carry out preventive studies in the relevant areas. Turkey was chosen as the pilot region, and analyses were carried out using the data obtained from the official statistics of the Ministry of Agriculture and Forestry General Directorate of Forestry according to the causes of the fires (negligence, intentional, accidental, unknown cause and natural) between the years 2010 and 2020. Spatial autocorrelation analysis was conducted for each fire type, and threshold distances were determined {with a number of distance bands = 20,000, distant increment = 10,000}. Emerging hot spot analyses were then conducted, and the results were presented as maps and statistical outputs. According to all fire types, 15 new hot spots, 14 persistent hot spots, 33 sporadic hot spots, 9 consecutive hot spots, 15 intensifying, and 2 diminishing hot spot regions were obtained throughout the country.     

Sensitivity of the MODIS fire detection algorithm (MOD14) in the savanna region of the Northern Territory,Australia

Fire detection using satellites is an important source of information for fire management, ecological studies and emission estimates. However, little is known about the minimum sizes of fires that are being detected. This paper presents an approach using fire radiative power estimated from MODIS satellite data to determine the detection threshold for fire-prone savannas in Northern Australia. The results indicate that fires with an active flaming area 100–300 m² can be detected in the study region. It is also shown that the algorithm is slightly more sensitive at night. As expected the detection threshold shows strong view angle dependence. While this study has been undertaken in the savannas of Northern Australia, the results should be transferable to other savanna regions worldwide and other areas where fires are not obscured by a dense tree canopy.     

基于MODIS的重庆森林火灾监测与应用

利用MODIS近红外、中红外及热红外的4个波段监测森林火灾,并提出了以MODIS 7波段为主的高温火点直接判别法和非高温火点综合阈值判别法。2006年重庆市森林火灾监测实践证明,该方法在城市森林火灾监测中是可用的。     

Remote sensing-based assessment of veld fire trends in multiple interwoven land tenure systems in Zimbabwe

The study used Landsat imagery, MODIS fire data and in situ meteorological data to determine emerging fire trends in interwoven multiple tenure systems in Zimbabwe. Remote sensing enabled fire trends to be determined across terrain and official records barriers. The number of fires and area burnt increased from 2001 up to 2009 then fluctuated across tenure systems. Fire events rose from 9 to 80 per year in some of the tenure systems. Complex relationships among number of fires, area burnt and weather variables within and across tenure systems were identified. The fire situation was responsive to intervention; the positive fire trends were reversed from 2009 onwards. Projected trends show that fire events could be reduced to negative values in three systems, while in two they could double by 2026. The veld fire problem could be eliminated if a holistic approach is adopted to tackle it across sectoral and land tenure divides.     

Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products

ABSTRACT

The AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) is a recently developed algorithm designed to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. At present, the AHI-FSA algorithm has only been tested on a number of case study fires in Western Australia. Initial results demonstrate potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes), multi-resolution fire-line detections. This paper intercompares AHI-FSA across the Northern Territory of Australia (1.4 million km²) over a ten-day period with the well-established fire products from LEO (Low Earth Orbiting) satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses the difficulties and solutions when comparing high temporal frequency fire products with existing low temporal resolution LEO satellite products. The results indicate that the multi-resolution approach developed for AHI-FSA is successful in mapping fire activity at 500?m. When compared to the MODIS, daily AHI-FSA omission error was only 7%. High temporal frequency data also results in AHI-FSA observing fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement.     

An Agent-Based Approach for Regional Forest Fire Detection Using MODIS Data: A Preliminary Study in Iran

Detecting fires, which are at their early stages is the first component of effective fire fighting. To date, several algorithms have been proposed to detect fire spots using remote sensing data. Nevertheless, in order to be able to accurately detect small and cool fires, which are very important at the regional scale, most of these algorithms need to be adjusted and improved. In this paper, an agent-based algorithm is presented for regional forest fire detection using bi-temporal MODIS data. The algorithm is designed to be so self-adaptive and consistent that it could be applied to the different pairs of consecutive images taken by the same satellite platform and at the same daytime. The results clearly show that compared with the MODIS contextual algorithm (version 4), the proposed method is more sensitive to small and cool forest fires in Iran.     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.     

利用时空立方体挖掘小微地震时空演变模式

挖掘分析小微地震的时空演变模式,可为地震灾害的分析与预报提供辅助决策参考。本文以四川地区的地震监测数据为基础,利用时空立方体融合地震点的空间、时间与属性数据,基于时空热点统计分析方法挖掘小微地震的时空冷热点分布模式。试验结果表明：在试验数据的时域内,四川地区小微地震的热点模式主要表现为连续热点、逐渐减少热点和振荡热点。冷点模式主要是连续冷点,且冷点覆盖范围比热点覆盖范围广。基于时空立方体的时空热点分析方法能够发挥时空统计学的优势,可有效挖掘分析小微地震的时空演变趋势。     

联合多源遥感数据监测四川木里县森林火灾

森林火灾既严重影响森林生态系统的稳定,还威胁到人类生命财产安全。传统监测森林火灾方法,覆盖范围小,难以及时监测小面积火灾。遥感卫星能大范围精确监测火情,提高了监测方法的时效性,但使用单一卫星数据源很容易受到云雨等客观环境因素影响,降低监测的时效性。本文以四川木里藏族自治县"330森林火灾"区域为对象,开展多源卫星遥感数据对小范围火灾联合监测的研究。首先,充分挖掘高分四号高时空分辨率和中红外火烧敏感波段优势,联合烟幕、温度和植被指数时序变化确定火烧时间与位置;然后,使用Sentinel-2数据监测不同火烧区域光谱信息;接着,使用Sentinel-2数据提取dNBR(differenced Normalized Burn Ratio),提出了基于最大类间方差算法(OTSU)分步骤确定不同程度火烧迹地与面积的方法;最后,建立Sentinel-1A极化比值PR (Polarization Ratio)和NDVI之间关系,利用微波雷达突破云雨限制。结果表明:(1)高分四号联合IRS(InfraRed Scanner)和PMS(Panchromatic Multispectral Sensor)能够实时监测小范围火灾;(2)根据火点位置,确定火灾蔓延期间NDVI下降(由0.7降低至0.25),确定起火时间(3月30日);(3)火灾区域与未受灾区,以及不同类型火烧迹地之间的光谱在490—2200 nm范围存在差异;(4)基于OTSU算法自动确定阈值,确定林地损失面积41.56公顷(dNBR=0.35),精度达94.67%,提取林地过火未损失面积66.56公顷(dNBR=0.10),精度达90.94%,林地损失区域基本符合实际调查结果;(5)火灾前后极化比值由6.6 dB升高至10.8 dB,NDVI与PR经线性回归,R2=0.58,验证R2=0.50。联合多源卫星监测森林火灾,能提高森林火灾监测的时效性,避免了云雨等复杂环境的影响。研究成果能为小火点的及时识别和灾害评估提供参考,其应用可为林火应急响应提供技术支撑。     

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

ABSTRACT

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.     

FY-3D/MERSI-II全球火点监测产品及其应用

FY-3D/MERSI-II全球火点监测产品主要包括全球范围内的火点位置、亚像元火点面积和火点强度等信息,可用于实时监测全球范围的森林草原火灾、秸秆焚烧等生物质燃烧状况。火点判识算法主要根据中红外通道对高温热源的敏感特性,即含有火点的中红外通道像元辐亮度和亮温较远红外通道的辐亮度和亮温偏高,同时较周边非火点的中红外像元偏高,建立合适的阈值可探测含有火点的像元。亚像元火点面积估算主要使用中红外单通道估算,根据亚像元火点面积估算结果对火点强度进行分级,不同的级别表示不同程度的火点辐射强度。基于全球火点自动判识结果,每日生成0.01°分辨率的卫星遥感日全球火点产品,每月生产0.25°×0.25°格点的全球月火点密度图。在利用FY-3D/MERSI-II火点产品开展的全球火点监测应用中,对多起全球重大野火事件进行了监测,为防灾减灾、全球气候变化研究、生态环境保护等方面提供卫星遥感信息支持。     

Satellite data and geographic information systems for fire and resource management in the Canadian arctic

Abstract

A method of analyzing remotely sensed data, a geographic information system, and an intelligent fire management system have been developed to provide integrated resource data for fire and other resources management. Natural and cultural features were digitized from 1:50,000 topographic maps using a geographic information system (GIS) to cover the 29 communities below the tree line in the western Canadian Arctic. Landsat Thematic Mapper data covering the same area were classified into land cover or fuel types. Detailed information on each fire such as location, area burned, date of discovery, fire number, fire zone, fire class and source of ignition was obtained and added to each map sheet as attribute data. A generalized vegetation cover map using NOAA AVHRR data was also obtained. The Intelligent Fire Management Information System (IFMIS) integrates relational data bases, geographic information display, and expert systems. It also has a spatial analysis procedure for forest fire preparedness planning. Linking the weather to the forest fuels through the Fire Weather Index system (FWI) and the Fire Behaviour Prediction System (FBPS), fire danger and fire behaviour are calculated and displayed, cell‐by‐cell. Values‐at‐risk and fire suppression resources are used in the dispatching and planning component of the system. The planning component allows the user to evaluate the coverage of fire suppression resources under the prevalent forecast fire behaviour conditions. Through the integration of data from the above systems, a set of maps were created which were used to analyze fire behaviour potential, identify fire hazards, and provide a basis for settlement protection strategies within the context of other land use activities such as wildlife harvesting and recreational activities.     

An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.     

The pyrogeography of sub-Saharan Africa: a study of the spatial non-stationarity of fire–environment relationships using GWR

This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well (R ² = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires.