Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?

Nogueira, Joana; Ruffault, Julien; Chuvieco, Emilio; Mouillot, Florent

doi:10.3390/rs9010007

Cited by 45 publications

(36 citation statements)

References 62 publications

(65 reference statements)

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“…The detection of small burned areas is one of the main limitations in burned area mapping that uses low-resolution sensors, as previously reported [7,11,[15][16][17]. It is possible to improve the detection of small burned areas using existing fire products.…”

Section: Introductionmentioning

confidence: 92%

“…There are limitations to using this ideal approach in the proposed study, due to the very broad extent of the study area (the whole of Cerrado), the limited accessibility of many regions, and the ephemeral nature of the signal, which starts to fade out a few days after the fire occurrence. Currently, this problem is circumvented by using higher spatial resolution satellite imagery as reference data for evaluating the lower resolution derived maps, being a well-established procedure [7,15,16,18,48,[50][51][52]. Accordingly, the higher spatial resolution satellite imagery used in this study as reference data for accuracy assessment came from the Landsat-8 (L8) Operational Land Imager (OLI), which has a spatial resolution (30 m), more than 2-3 orders of magnitude higher than that of the evaluated PROBA-V and MODIS instruments.…”

Section: Study Area and Datamentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 92%

Section: Study Area and Datamentioning

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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“…This peculiar result then points out a potential bias in model developments based on BA observation from global remote sensing, and DGVMs/fire module benchmarking for this region when using the large fire dataset only. Recent studies actually pointed out the BA underestimation from these global products when compared to finer resolution Landsat-based remote sensing analysis [63,66,67].…”

Section: Sensitivity Of the Seasonal Fdi/ba Relationship To Ba 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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“…The variables investigated were (i) the ability of the medium spatio-temporal resolution to identify individual fire patches and capture their size distribution following the self-criticality hypothesis , and (ii) how the resulting fire patch shape improves with finer resolution to better capture spreading processes and impacts on post-fire vegetation dynamic as proposed by Nogueira et al (2017) and Chuvieco et al (2016) for pixel-level global remote sensing product assessment. MODIS Fire_cci v5.0 (approx.…”

Section: Product Assessment 30mentioning

confidence: 99%

“…In conjunction with other human and physical variables, BA 20 datasets are required to understand factors controlling fire activity (Forkel et al, 2017) and particularly those affecting changes in fire regimes Andela et al, 2017), with an increasing concern on fire patch identification derived from higher resolution pixel-level information (Nogueira et al, 2017). Atmospheric emission models require precise information on spatio-temporal patterns of fire occurrence, as well as combustion characteristics (van der Werf et al, 2017;Knorr et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

Chuvieco¹,

Lizundia-Loiola²,

Pettinari³

et al. 2018

Preprint

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Abstract. This paper presents a new global burned area (BA) product, generated from the MODIS red (R) and near infrared (NIR) reflectances and thermal anomalies data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) of the MODIS archive. The BA detection 20 algorithm was based on temporal composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one aiming to reduce omission errors by applying contextual analysis around the seed pixels. The product was developed within the European Space Agency's (ESA) Climate Change Initiative programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA products: monthly full-resolution continental tiles 25 (http://doi.org/cpk7) and biweekly global grid files at a degraded resolution of 0.25 degrees (http://doi.org/gcx9gf). Each one includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and emission models. The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The estimated commission and omission error rates of the pixel product was 0.512 (0.020) and 0.708 (0.030), respectively, lower than previous ESA products but higher than the latest 30 NASA MCD64A1 BA dataset. Examples of potential applications of this product to fire modelling based on burned patches analysis are included in this paper. They show greater sensitivity of our product to small burn patch detection than existing BA products.Earth Syst. Sci. Data Discuss., https://doi

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Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?

Cited by 45 publications

References 62 publications

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

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

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

Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

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