Active fire detection using Landsat-8/OLI data

Schroeder, Wilfrid; Oliva, Patricia; Giglio, L.; Quayle, Brad; Lorenz, Eckehard; Morelli, Fabiano

doi:10.1016/j.rse.2015.08.032

Cited by 266 publications

(201 citation statements)

References 44 publications

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“…In addition to land management data, remote sensing platforms can provide spatially explicit data on historical disturbances, including wildfire (Davies, Ilavajhala, Wong, & Justice, ). In particular, Landsat satellite imagery has been successfully used to estimate a range of proxies for fire attributes at a relatively fine‐scale, including fire severity (Eidenshink et al, ; Escuin, Navarro, & Fernández, ), active fire detection (Schroeder et al, ), and assessment of fire‐affected areas (Chen, Moriya, Sakai, Koyama, & Cao, ). Altogether, these novel data sources provide a wealth of opportunities to quantify how anthropogenic factors influence communities and populations over regional scales.…”

Section: Discussionmentioning

confidence: 99%

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Requena‐Mullor

Maguire

Shinneman

et al. 2019

Global Change Biology

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Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: −6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: −47.9%, −41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

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Section: Discussionmentioning

confidence: 99%

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Requena‐Mullor

Maguire

Shinneman

et al. 2019

Global Change Biology

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“…Large area fire product validation is complicated by the ephemeral nature of fire and difficulties in obtaining timely independent reference data (Cardoso et al 2005;Morisette et al 2005;Schroeder et al 2015;Roy and Boschetti 2009). In this study, qualitative assessment of some of the classified MODIS active fire types were made by visual comparison with higher resolution Landsat 5 scenes.…”

Section: Discussionmentioning

confidence: 99%

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Roy

Kumar

2016

International Journal of Digital Earth

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The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km 2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user's and producer's classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature. ARTICLE HISTORY

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“…Vegetation fires are major sources of air pollutants and climate-forcing agents that degrade air quality and perturb regional and global climate (Al-Saadi et al, 2008;Bond et al, 2013;Brey et al, 2018;Dwyer et al, 2000;Kaulfus et al, 2017;Randerson et al, 2012;Schroeder et al, 2016;Schultz et al, 2008;Soja et al, 2006Soja et al, , 2011Tosca et al, 2010;van der Werf et al, 2010). Satellite observations are critical for quantifying fires and these impacts over most of the globe because ground-based fire reporting is uncommon and may be incomplete Hawbaker et al, 2017b;Soja et al, 2006;van der Werf et al, 2017;Wiedinmyer et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A New Picture of Fire Extent, Variability, and Drought Interaction in Prescribed Fire Landscapes: Insights From Florida Government Records

Nowell

Holmes

Robertson

et al. 2018

Geophysical Research Letters

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Florida, United States, government records provide a new resource for studying fire in landscapes managed with prescribed fire. In Florida, most fire area (92%) is prescribed. Current satellite fire products, which underpin most air pollution emission inventories, detect only 25% of burned area, which alters airborne emissions and environmental impacts. Moreover, these satellite products can misdiagnose spatiotemporal variability of fires. Overall fire area in Florida decreases during drought conditions as prescribed fires are avoided, but satellite data do not reflect this pattern. This pattern is consistent with prescribed fire successfully reducing overall fire risk and damages. Human management of prescribed fires and fuels can, therefore, break the conventional link between drought and wildfire and play an important role in mitigating rising fire risk in a changing climate. These results likely apply in other regions of the world with similar fire regimes.

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Active fire detection using Landsat-8/OLI data

Cited by 266 publications

References 44 publications

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

A New Picture of Fire Extent, Variability, and Drought Interaction in Prescribed Fire Landscapes: Insights From Florida Government Records

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