Global burned area mapping from ENVISAT-MERIS and MODIS active fire data

Alonso-Canas, Itziar; Chuvieco, Emilio

doi:10.1016/j.rse.2015.03.011

Cited by 153 publications

(105 citation statements)

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“…GFED version 4 provides monthly burned area time series at a 0.25 • spatial resolution for the period 1995-2015 based on a combination of the MODIS burned area product (from 2000 onwards) with active fire observations from VIRS (Visible and Infrared Scanner) and ATSR (Along-Track Scanning Radiometer) (before 2000) (Giglio et al, 2013). Fire CCI version 4.1 provides burned area time series at 0.25 • spatial resolution for the period 2005-2011 based on a combination of MERIS data and MODIS thermal anomalies (Alonso-Canas and Chuvieco, 2015;. Because of the longer temporal coverage, the GFED dataset was used as the response variable in model development and for model evaluation.…”

Section: Datasets and Predictor Variables For Model Developmentmentioning

confidence: 99%

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

et al. 2017

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Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within processoriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and processoriented global fire models, we introduce a new flexible datadriven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

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Section: Datasets and Predictor Variables For Model Developmentmentioning

confidence: 99%

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

et al. 2017

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“…This dataset is organized as annual files with four layers in the HDF5 format, from which we used the first layer providing the fraction of each grid cell burned in that month. The MERIS FIRE_CCI v4.1 is a recently BA grid dataset [60] delivered from the ESA Fire Cci project [61] and generated by an algorithm based on processing in two-phase [62]. The MERIS FIRE_CCI v4.1 provides monthly burned area (m 2 ) in NetCDF format files from 2002 to 2011 and is a dataset validated from multi-temporal pairs of Landsat images sites with stratified random sample [63].…”

Section: Burned Area Datasetsmentioning

confidence: 99%

Spatial Pattern of the Seasonal Drought/Burned Area Relationship across Brazilian Biomes: Sensitivity to Drought Metrics and Global Remote-Sensing Fire Products

Nogueira

Rambal

Barbosa

et al. 2017

Climate

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Fires are complex processes having important impacts on biosphere/atmosphere interactions. The spatial and temporal pattern of fire activity is determined by complex feedbacks between climate and plant functioning through and biomass desiccation, usually estimated by fire danger indices (FDI) in official fire risk prevention services. Contrasted vegetation types from fire-prone Brazilian biomes may respond differently to soil water deficit during the fire season. Then, we propose to evaluate the burned area (BA)/FDI relationship across Brazil using most common FDIs and the main BA products from global remote sensing. We computed 12 standard FDIs-at 0.5 • resolution from 2002 to 2011 and used the monthly BA from four BA datasets-from the MODIS sensor (MCD45A1), the MERIS sensor (MERIS FIRE_CCI), the Global Fire Emission Database version 4 (GFED4) and version 4s including small fires (GFED4s). We performed a Principal Component Analysis (PCA) on the coefficients of determination (R 2 ) of the FDI/BA relationship to investigate the biome specificities of Brazilian biomes and the sensitivity to BA datasets. Good relationships (R 2 > 0.8) were observed for all BA datasets, except SPEI (R 2 < 0.2). We showed that FDIs computed from empirical water balances considering a lower soil capacity are more correlated to the seasonal pattern of fire occurrence in the Cerrado biome with contrasted adjustments between the western (early drying) and eastern part (late drying), while the fine fuel moisture index is more correlated to the fire seasonal pattern in Amazonia. The biome specificities of the FDI/BA relationship was evaluated with a general linear model. High accuracies in the biome distribution according to the FDI/BA relationship (>50%, p < 0.001) was observed in Amazonia and Cerrado, with lower accuracy (<32%, p < 0.001) in the Atlantic Forest and Caatinga. These results suggest that the FDI/BA relationship are biome-specific to explain the seasonal course of burned in Brazilian biomes, independently of the global BA product used. Selected FDIs should be used for fire danger forecast in each Brazilian biome.

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“…Several studies relied on the use of remote sensing to map burned areas at a global/regional scale [5][6][7][8][9][10][11]. However, the variable persistence of burn scars within different vegetation types, and the spectral confusion with other phenomena (e.g., cloud shadowing) are some of the problems that still hamper accurate burned area mapping [12].…”

Section: Introductionmentioning

confidence: 99%

“…It is possible to improve the detection of small burned areas using existing fire products. Alonso-Canas [5], using 300-m MEdium Resolution Imaging Spectrometer (MERIS) global satellite data, developed an algorithm based on the time series of surface reflectance to identify abrupt changes in near-infrared reflectance (NIR), and regional growing techniques using MODIS active fire as spatial seeds, in a two-phase algorithm. The use of this sensor has improved small fire detection due to better spatial resolution, however, the results were not considered competitive in comparison with MODIS burned area products [17].…”

Section: Introductionmentioning

confidence: 99%

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

Pereira

Libonati

et al. 2017

Remote Sensing

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Abstract:We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.

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Global burned area mapping from ENVISAT-MERIS and MODIS active fire data

Cited by 153 publications

References 61 publications

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Spatial Pattern of the Seasonal Drought/Burned Area Relationship across Brazilian Biomes: Sensitivity to Drought Metrics and Global Remote-Sensing Fire Products

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

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