Inter-Comparison of Fire Severity Indices from Moderate (Modis) and Moderate-To-High Spatial Resolution (Landsat 8 &amp; Sentinel-2A) Satellite Sensors

Rahman, Shahriar; Chang, Hsing-Chung; Hehir, Warwick; Magill, Christina; Tomkins, Kerrie M.

doi:10.1109/igarss.2018.8518449

Cited by 11 publications

(9 citation statements)

References 8 publications

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“…The W-UI is difficult and potentially dangerous for combating fire because burners are usually trained to fight structural or wildfires, but rarely both [8]. Climate change, construction and land management are increasing the risks associated with wildfires [2, 9,10]. The W-UI will become increasingly important as more homes are built in W-UI areas and as fire activity increases due to climate change.…”

Section: Introductionmentioning

confidence: 99%

Defensible Spaces and Home Ignition Zones of Wildland-Urban Interfaces in the Fire-prone Areas of the World

Rahman¹,

Rahman²

2020

Preprint

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Wildland-Urban Interfaces (W-UI) are at high risk of wildfires. Defensible spaces and home ignition zones are the two main aspects to protect lives and livelihoods of W-UI in the United States, Canada and Australia. The different part of the world has different rules and regulations for W-UI land management. We have discussed the defensible spaces in fire-prone areas, current ignition zone distances from structures, building materials, architectural design, the fire resistance trees, ground cover, landscaping and some other precautions to save lives and assets in the prominent fire-prone zones for three different countries (United States, Canada and Australia) of the world.

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Section: Introductionmentioning

confidence: 99%

Defensible Spaces and Home Ignition Zones of Wildland-Urban Interfaces in the Fire-prone Areas of the World

Rahman¹,

Rahman²

2020

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“…Traditionally, burn severity has been quantified from Landsat sensors through different methods, including those based on radiative transfer models (Chuvieco et al, 2006, De Santis et al, 2009, spectral unmixing (Fernández-Manso et al, 2009, Quintano et al, 2017, or spectral indices (Chu andGuo, 2014, Fernández-García et al, 2018a). Among them, the standard method to quantify burn severity is through the delta Normalized Burn Ratio (dNBR) (Key and Benson, 2006) spectral index, and its relativized version (RdNBR) (Miller and Thode, 2007), which is less dependent on the pre-fire vegetation, and potentially more suitable than dNBR for comparisons among zones with different environmental conditions (Miller and Thode, 2007;Rahman et al, 2018). Both spectral indices are based on the change caused by fire in near infrared (NIR) and shortwave infrared (SWIR) reflectance, which are highly sensitive to canopy density and moisture content respectively (Chuvieco, 2010).…”

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confidence: 99%

“…dNBR and RdNBR indices have shown a high capacity (R 2 about 0.75) to correlate field measurements of biomass consumption and plant mortality in mediterranean (Fernández-García et al., 2018a), temperate (Parks et al, 2014), boreal (Soverel et al, 2011) and tropical ecosystems (Rozario et al, 2018). Despite the possibility of calculating burn severity indices with satellites allowing planetary coverage such as MODIS (Veraverbeke et al, 2011;Rahman et al, 2018) coverage since 2000 at 500 m spatial resolution. Additionally, this work describes the algorithm to develop the database and we compared the MOSEV burn severity data with their Landsat-8 equivalents.…”

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confidence: 99%

MOSEV: a global burn severity database from MODIS (2000–2020)

Alonso‐González

Fernández-García²

2021

Earth Syst. Sci. Data

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Abstract. To make advances in the fire discipline, as well as in the study of CO2 emissions, it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products from November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the normalized burn ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR) and its relativized version (RdNBR), as well as the post-burn NBR, which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR, and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6904 km2 (n=32 163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R=0.88; P<0.001), and dNBR (R=0.74; P<0.001) showed stronger relationships than RdNBR (R=0.42; P<0.001). Differences between MOSEV and Landsat-8 indices are attributable to variability in reflectance values and to the different temporal resolution of both satellites (MODIS: 1–2 d; Landsat: 16 d). The database is structured according to the MODIS tiling system and is freely downloadable at https://doi.org/10.5281/zenodo.4265209 (Alonso-González and Fernández-García, 2020).

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MOSEV: a global burn severity database from MODIS (2000–2020)

Alonso‐González¹,

Fernández-García²

2020

Preprint

View full text Add to dashboard Cite

Abstract. To advance in the fire discipline as well as in the study of CO2 emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes, which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6,904 km2 (n = 32,163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R = 0.88; P

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Inter-Comparison of Fire Severity Indices from Moderate (Modis) and Moderate-To-High Spatial Resolution (Landsat 8 & Sentinel-2A) Satellite Sensors

Cited by 11 publications

References 8 publications

Defensible Spaces and Home Ignition Zones of Wildland-Urban Interfaces in the Fire-prone Areas of the World

Defensible Spaces and Home Ignition Zones of Wildland-Urban Interfaces in the Fire-prone Areas of the World

MOSEV: a global burn severity database from MODIS (2000–2020)

MOSEV: a global burn severity database from MODIS (2000–2020)

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