Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019

Stroppiana, Daniela; Sali, Matteo; Busetto, Lorenzo; Boschetti, Mirco; Ranghetti, Luigi; Franquesa, Magí; Pettinari, M. Lucrecia; Chuvieco, Emilio

doi:10.1016/j.isprsjprs.2022.07.015

Cited by 11 publications

(43 citation statements)

References 30 publications

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“…To each tile subdivision, we added a sequential numeric index to the tile identifier (e.g., 38JMT_1). Then, following Stroppiana et al (2022), all sampling units located in different Sentinel-2 orbits and UTM zones were discarded, obtaining a total population of 242 sampling units (Fig. 2).…”

Section: Spatial Validation Analysesmentioning

confidence: 99%

Madagascar’s burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors

Fernández-García,

Franquesa,

Kull

2024

Preprint

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Madagascar is one of the most burned regions in the world, to the point that it has been called the ‘Isle of fire’ or the ‘Burning Island’. An accurate characterization of the burned area (BA) is crucial for understanding the true situation and impacts of fires on this island, where there is an active scientific debate on how fire affects multiple environmental and socioeconomic aspects, and how fire regimes should be in a complex context with differing interests. Despite this, recent advances have revealed that BA in Madagascar is poorly characterised by the currently available global BA products. In this work, we present, validate, and explore a BA database at 20 m spatial resolution for Madagascar covering the period 2016-2022. The database was built based on 75,010 Sentinel-2 images using a two-phase BA detection algorithm. The validation with independent long-term reference units showed Dice coefficients ≥79%, omission errors ≤24%, commission errors ≤18%, and a relative bias ≥-8%. An intercomparison with other available global BA products (GABAM, FireCCI51, C3SBA11, or MCD64) demonstrated that our product (i) exhibits temporal consistency, (ii) represents a significant accuracy improvement, as it reduces BA underestimations by about eightfold, (iii) yields BA estimates four times higher, and (iv) shows enhanced capability in detecting fires of all sizes. The observed BA spatial patterns were heterogeneous across the island, with 32% of the grasslands burning annually, in contrast to other land cover types such as the dense tropical forest where less than 2% burned every year. We conclude that the BA characterization in Madagascar must be addressed using imagery at spatial resolution higher than MODIS or Sentinel-3 (≥250 m), and temporal resolution higher than Landsat (16 days) to deal with cloudiness, the rapid attenuation of burn scars signals, and small fire patches.

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Section: Spatial Validation Analysesmentioning

confidence: 99%

Madagascar’s burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors

Fernández-García,

Franquesa,

Kull

2024

Preprint

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“…The commission error (CE), omission error (OE), overall accuracy (OA), Dice coefficient (DC) [80], and Relative bias (RelB) were computed employing the assessment tool based on the confusion matrix [81]. These accuracy metrics are widely used to assess the accuracy of satellite-derived BA products [45,79,82]. We considered additional metrics to measure the total area correctly detected as burned (SurfBA) or unburned (SurfUB).…”

Section: Spatial Validationmentioning

confidence: 99%

“…To highlight the differences in terms of burned patch delineation between NEALGEBA and existing BA products, we compared the accuracy metrics (CE, OE, DC, RelB) obtained for all the BA products. To this end, we used the same Sentinel-2 reference data at the same previously selected validation sites to validate the NEALGEBA map for 2017 to assess GABAM, C3SBA11, FireCCI51, MCD64A1 and EFFIS because this is the common year between all these BA products [45,46]. The 2017 BA maps from FireCCI51 and MCD64A1 were already available as image collections in the GEE's data catalogue, and were directly evaluated against S2RD in the assessment tool.…”

Section: Spatial Accuracymentioning

confidence: 99%

“…In recent years, several BA-mapping approaches have been developed for different study regions using medium-resolution data [39][40][41], including the BA Mapping Tools (BAMTs) [42]. By leveraging the powerful capabilities of the GEE's cloud computing platform [33], the BAMTs constitute not only a significant stride as innovative, time-efficient, and resource-conserving tools for accurate multi-year BA mapping [10,43], but also the creation of independent reference data for validation exercises [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

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40-Year Fire History Reconstruction from Landsat Data in a Mediterranean Area of North Africa Following International Standards

Kouachi,

Khairoun,

Moghli

et al. 2024

Preprint

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Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30-m spatial resolution in the typical Mediterranean Ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10-m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10 %; commission and omission errors < 8.20 %; Dice coefficients > 91.90 %, and relative biases < 3.44 %. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most extensive and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution for further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

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“…Since the systematic mapping of BA in situ at large scales is impractical, EO based on satellite data with higher spatial resolution than the product has been demonstrated as a feasible method to create reference fire perimeters [12]. Due to the growing demand of BA validation datasets and the public availability of the Sentinel and Landsat satellites imagery in recent years, various authors have created reference fire perimeters [11,[13][14][15][16]. Additionally, a Burned Area Reference Database (BARD) has been designed to compile several global and regional BA datasets created to facilitate validation and calibration activities [17].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 Reference Fire Perimeters for the Assessment of Burned Area Products over Latin America and the Caribbean for the Year 2019

Gonzalez-Ibarzabal,

Franquesa,

Rodriguez-Montellano

et al. 2024

Remote Sensing

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The increasing availability of products generating burned area (BA) maps in recent years necessitates the creation of more accurate reference perimeters to validate these products and provide users with information about their accuracy. For this purpose, reference perimeters were created using Sentinel-2 images in Latin America and the Caribbean (LAC) for the year 2019. The sampling was adapted to the peculiarities of the Sentinel-2 tiling grid system, and statistically representative sample units were selected for biomes and fire activity through stratified random sampling. Fire perimeters were extracted using a Random Forest supervised classification and results were manually supervised and refined. Efforts were made to maximize the temporal length covered by the reference perimeters for each sample, aiming to minimize temporal errors when using the perimeters for validation. The dataset covers 569,214.2 (3.5% burned, 88.7% unburned, and 7.8% unobserved). These perimeters were compared with higher spatial resolution PlanetScope-derived perimeters, resulting in 8.4% commission errors and 3.8% omission errors. As a validation exercise, MCD64A1 and FireCCI51 global burned area products were validated using the Sentinel-2 reference dataset created, confirming that the temporal extent of the reference perimeters significantly affects the validation of such products. The reference fire perimeters are publicly available in the Burned Area Reference Database (BARD).

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Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019

Cited by 11 publications

References 30 publications

Madagascar’s burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors

Madagascar’s burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors

40-Year Fire History Reconstruction from Landsat Data in a Mediterranean Area of North Africa Following International Standards

Sentinel-2 Reference Fire Perimeters for the Assessment of Burned Area Products over Latin America and the Caribbean for the Year 2019

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