Global validation of the collection 6 MODIS burned area product

Boschetti, Luigi; Roy, David P.; Giglio, L.; Huang, Haiyan; Zubkova, Maria; Humber, M. L.

doi:10.1016/j.rse.2019.111490

Cited by 167 publications

(129 citation statements)

References 69 publications

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“…This low imbalance between omission and commission errors (along with the sharp drop in OE) translates into a better BA estimate. These results are comparable to those obtained by other authors using other reference sets [ 51 , 58 ]. In a recent validation of Fire_CCI 5.1 using 1200 global samples over the 2003–2014 period, Lizundia-Loiola et al [ 58 ] obtained values of CE = 54.4% and OE = 67.1%.…”

Section: Discussionsupporting

confidence: 91%

“…It is necessary to understand the uncertainty of these products before incorporating them as input data into global carbon, vegetation or climate models, as well as for the management of all the wildfire phases. Numerous prior studies have evaluated the behavior of the MODIS sensor-derived products analyzed in this study, both as part of ESA’s Fire Climate Change Initiative Project and in the different versions [ 13 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ] of the MODIS Direct Broadcast Monthly Burned Area Product. However, many of these works construct reference fire perimeters using images from better spatial resolution sensors such as Landsat TM/ETM or Sentinel-2, and which are limited to short time periods, usually one or several years, due to the difficulty in creating larger reference sets.…”

Section: Discussionmentioning

confidence: 99%

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MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

Moreno-Ruiz

García-Lázaro

Arbeló

et al. 2020

Sensors

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This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

Moreno-Ruiz

García-Lázaro

Arbeló

et al. 2020

Sensors

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“…A comparison analysis done with reference data from 2008 found that NASA's MCD64A1 was the most accurate global BA product of the existing global BA products [11] with commission (CE) and omission errors (OE) reaching 42% and 68%, respectively. More recent analyses, carried out with reference data from 2003 to 2014 [9] and 2014 to 2015 [12], found average commission errors of 35% and 40.2%, respectively, while omission errors reached 62% and 72.6%, respectively. With these values, global BA products are far from achieving the accuracy requirements needed for climate modelling, i.e., omission and commission errors below 20%, [13], in part due to the low spatial resolution, which results in small fires being overlooked [14].…”

Section: Introductionmentioning

confidence: 97%

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

et al. 2020

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This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different time points during the 2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection.

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“…The most recent version (Collection 6-C6 in short) of the MODIS MCD64A1 Global Burned Area product (Giglio et al 2018b;Boschetti et al 2019) was employed in this study and to update the FAOSTAT emissions estimates. This MODIS product supersedes the previous version of the MODIS burned area product (MCD64A1 Collection 5.1, C5.1 in short).…”

Section: Introductionmentioning

confidence: 99%

New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas

et al. 2020

Self Cite

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The Paris Agreement calls on parties to undertake ambitious efforts to combat climate change by engaging in appropriate policies and measures as put forward through Nationally Determined Contributions (NDCs), to strengthen transparency when reporting their greenhouse gas (GHG) emissions and to increase their mitigation contributions to climate action from 2020. It also calls for regular and transparent monitoring and reporting of the GHG emissions and on the NDCs implementation efforts. Biomass fires significantly affect the GHG atmospheric balance, with fire emissions representing more than 5% of total emissions from agriculture, forestry, and other land use (AFOLU), according to recent estimates produced by the Food and Agriculture Organization (FAO). We update previously published Tier 1 estimates of GHG emissions in FAOSTAT-which had been used in the IPCC AR5 analysis-by using new burned area activity data from the Moderate Resolution Imaging Spectroradiometer (MODIS) known as MCD64A1, Collection 6. The previous FAOSTAT estimates had used as input the Global Fire Emission Database v.4 (GFED4) burned area product, based on older MODIS Collection 5.1 burned area product. In line with differences between the input data used, the new FAOSTAT estimates indicate roughly 30% higher fire emissions globally than previously published. Our analysis also confirms that the FAOSTAT Tier 1 approach produces fire emissions estimates that are comparable to those computed at Tier 3 by GFED, and thus represent a useful complementary tool in support of country GHG reporting.

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Global validation of the collection 6 MODIS burned area product

Cited by 167 publications

References 69 publications

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas

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