Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation

Padilla, Marc; Stehman, Stephen V.; Ramo, Rubén; Corti, D.; Hantson, Stijn; Oliva, Patricia; Alonso-Canas, Itziar; Bradley, Andrew V.; Tansey, Kevin; Mota, Bernardo; Pereira, José M. C.; Chuvieco, Emilio

doi:10.1016/j.rse.2015.01.005

Cited by 183 publications

(169 citation statements)

References 26 publications

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“…GFED4, L3JRC, MCD45, and Fire_cci: see Table 3). In addition to the fact that all of these products systematically underestimate burnt area because of difficulties in detecting small fires Padilla et al, 2015), they differ from one another. Although all four products show a similar spatial pattern with more burnt area in the tropical savannas and less in temperate and boreal regions, L3JRC and MCD45 have a higher total burnt area than MERIS or GFED4 (Table 3).…”

Section: Carbon Combustibilitymentioning

confidence: 99%

The status and challenge of global fire modelling

et al. 2016

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Abstract. Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.

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Section: Carbon Combustibilitymentioning

confidence: 99%

The status and challenge of global fire modelling

et al. 2016

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“…At the global scale, accuracy of existing global burned area products, derived from coarser resolution imagery, show relatively weak accuracy. Errors of omission for such products have been documented to range from 51% to 93% for omission error and from 36% to 94% for commission error [23,24]. Error is even higher for specific vegetation types.…”

Section: Discussionmentioning

confidence: 99%

“…Error is even higher for specific vegetation types. For example, although the MODIS burned area product (MCD45) performed well relative to other global burned area products, it performed poorly in temperate forest (99% omission, and 95% commission), which is a major biome across CONUS (Figure 1) [23].…”

Section: Discussionmentioning

confidence: 99%

“…The Committee on Earth Observation Satellites (CEOS), Land Product Validation Subgroup (LPVS), which formed in 2000, has specified that validation should follow internationally agreed upon validation best practices to measure accuracy and precision (standard error of accuracy estimates) at comprehensive spatial and temporal scales [21]. Accuracy of existing global burned area products is relatively low with documented errors of omission and commission for burned areas ranging 51% to 93%, and 42% to 94%, respectively [22][23][24]. This is due to the tremendous amount of spectral diversity introduced when attempting to map burned areas across diverse vegetation types, fire combustion levels (e.g., ash, char, soot), and burn severities (e.g., ground vs. crown fires).…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the tremendous amount of spectral diversity introduced when attempting to map burned areas across diverse vegetation types, fire combustion levels (e.g., ash, char, soot), and burn severities (e.g., ground vs. crown fires). Coarse resolution (300 m to 1 km) burned area products are most commonly validated with Landsat imagery [22][23][24][25]. Similarly, efforts to map forest disturbance or burned area using Landsat at a regional scale, also typically rely on Landsat imagery for validation and use manual interpretation of the validation imagery to improve the quality of the reference dataset [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery

et al. 2017

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The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution . Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.

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Biomass Burning Emissions Variation from Satellite-Derived Land Cover, Burned Area, and Emission Factors in Vietnam

Lasko

Vadrevu

2018

Springer Remote Sensing/Photogrammetry

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Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation

Cited by 183 publications

References 26 publications

The status and challenge of global fire modelling

The status and challenge of global fire modelling

Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery

Biomass Burning Emissions Variation from Satellite-Derived Land Cover, Burned Area, and Emission Factors in Vietnam

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