BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

Bastarrika, Aitor; Alvarado, Maite; Artano, Karmele; Martínez, María Pilar; Mesanza, Amaia; Torre, Leyre; Ramo, Rubén; Chuvieco, Emilio

doi:10.3390/rs61212360

Cited by 101 publications

(91 citation statements)

References 37 publications

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“…Figure 4. Flowchart of the fire data processing from (i) the raw data on burn date (BD) and Quality Assessment (QA) for the MODIS MCD45A1 and ESA FIRE_CCI pixel level fire products, the LANDSAT images and their processing into fire patches with the ABAMS software [30] as previously performed for this study site [20], (ii) the flood fill algorithm allowing pixel aggregation into patches identification (ID) according to their burn date, (iii) the crosstabulation of fire patches between fire products, the selection of patches overlapping over more than 30% of their surface, (iv) the calculation of patch morphological metrics and (v) the final correlation analysis using a linear regression.…”

Section: Discussionmentioning

confidence: 99%

“…The fire perimeters from LANDSAT were derived from a semi-automatic algorithm (ABAMS) [30], following a standard protocol defined for the ESA FIRE_CCI project [31], based on the CEOS-Land Product Validation guidelines [32] and used for previous global burned area products' validation [33]. Burned area perimeters were verified by a systematic quality control through visual Remote Sens.…”

Section: Burned Area Datasetmentioning

confidence: 99%

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

et al. 2016

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Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an additional way of providing informative features about fire regimes through the analysis of patch size distribution. We evaluated the ability of global BA products to accurately represent morphological features of fire patches, in the fire-prone Brazilian savannas. We used the pixel-level burned area from LANDSAT images, as well as two global products: MODIS MCD45A1 and the European Space Agency (ESA) fire Climate Change Initiative (FIRE_CCI) product for the 2002-2009 time period. Individual fire patches were compared by linear regressions to test the consistency of global products as a source of burned patch shape information. Despite commission and omission errors respectively reaching 0.74 and 0.81 for ESA FIRE_CCI and 0.64 and 0.62 for MCD45A1 when compared to LANDSAT due to missing small fires, correlations between patch areas showed R 2 > 0.6 for all comparisons, with a slope of 0.99 between ESA FIRE_CCI and MCD45A1 but a lower slope (0.6-0.8) when compared to the LANDSAT data. Shape complexity between global products was less correlated (R 2 = 0.5) with lower values (R 2 = 0.2) between global products and LANDSAT data, due to their coarser resolution. For the morphological features of the ellipse fitted over fire patches, R 2 reached 0.6 for the ellipse's eccentricity and varied from 0.4 to 0.8 for its azimuthal directional angle. We conclude that global BA products underestimate total BA as they miss small fires, but they also underestimate burned patch areas. Patch complexity is the least correlated variable, but ellipse features appear to provide information to be further used for quality product assessment, global pyrogeography or DGVM benchmarking.

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Section: Discussionmentioning

confidence: 99%

Section: Burned Area Datasetmentioning

confidence: 99%

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

et al. 2016

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“…In this validation effort, however, errors of commission exceeded errors of omission in all four ecoregions. The difference in error distribution can be potentially attributed to: (1) region-growing functions included in generating both the BAECV [10] and Landsat reference dataset (using the program Burned Area Mapping Software (BAMS)) [73]; and (2) an intrinsic ability to map more fire heterogeneity as the spatial resolution of the input imagery becomes finer. A potential source of this increased patchiness or fire heterogeneity mapped by the high-resolution imagery (e.g., Figure 4) could have been live crowns mapped incorrectly as unburned in areas with surface burns, which would have incorrectly elevated errors of commission for the BAECV; however, much of the increased heterogeneity can be attributed to the high-resolution images correctly mapping bare soil, rock and unburned patches of vegetation within the burn perimeter as unburned.…”

Section: Discussionmentioning

confidence: 99%

“…When burned area algorithms are optimized for site level performance, the accuracy can exceed 95% [29,72]. As the target area expands, accuracy often begins to decrease (e.g., 15% to 30% error for burned area) due to variance imposed by local factors [33,73,74]. Disturbances (fires, clearcut and insect mortality) have been mapped with Landsat imagery across forested portions of CONUS.…”

Section: Discussionmentioning

confidence: 99%

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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“…The multispectral characteristics, spatial resolution and temporal range and frequency of the Landsat sensors make the imagery highly suitable for mapping fire scars at a regional scale [25,26]. The Global Visualization Viewer (http://glovis.usgs.gov) was used to preview the Landsat imagery and to select the best cloud-free scenes.…”

Section: Landsat Imagery Acquisition and Pre-processingmentioning

confidence: 99%

The Implications of Fire Management in the Andean Paramo: A Preliminary Assessment Using Satellite Remote Sensing

et al. 2015

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Abstract:The upper ranges of the northern Andes are characterized by unique Neotropical, high altitude ecosystems known as paramos. These tundra-like grasslands are widely recognized by the scientific community for their biodiversity and their important ecosystem services for the local human population. Despite their remoteness, limited accessibility for humans and waterlogged soils, paramos are highly flammable ecosystems. They are constantly under the influence of seasonal biomass burning mostly caused by humans. Nevertheless, little is known about the spatial extent of these fires, their regime and the resulting ecological impacts. This paper presents a thorough mapping and analysis of the fires in one of the world's largest paramo, namely the "Complejo logistic regression model analysis was undertaken to test the hypothesis that the dynamics of the paramo fires can be related to human pressures. The resulting map shows that the burned paramo areas account for 57,179.8 hectares, of which 50% (28,604.3 hectares) are located within the Sumapaz National Park. The findings show that the fire season mainly occurs from January to March. The accuracy assessment carried out using a confusion matrix based on 20 reference burned areas shows values of 90.1% (producer accuracy) for the mapped burned areas with a Kappa Index of Agreement (KIA) of 0.746. The results of the logistic regression model suggest a significant predictive relevance of the variables road distance (0.55 ROC (receiver operating characteristic)) and slope gradient (0.53 ROC), indicating that the higher the probability of fire occurrence, the smaller the distance to the road and the higher the probability of more gentle slopes. The paper sheds light on fires in the Colombian paramos and provides a solid basis for further investigation of the impacts on the natural ecosystem functions and biodiversity.

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BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

Cited by 101 publications

References 37 publications

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

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

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

The Implications of Fire Management in the Andean Paramo: A Preliminary Assessment Using Satellite Remote Sensing

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