Satellite Remote Sensing Contributions to Wildland Fire Science and Management

Chuvieco, Emilio; Aguado, Inmaculada; Salas, Julián; Garcı́a, Mariano; Yebra, Marta; Oliva, Patricia

doi:10.1007/s40725-020-00116-5

Cited by 146 publications

(97 citation statements)

References 171 publications

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“…Natural disasters are generally divided into two main categories: geophysical (e.g., wildfires, floods, earthquakes, and droughts) and biological (e.g., biotic stresses) [1]. Among natural geophysical disasters, wildfire occurs more than a thousand times annually over different regions of the globe [2][3][4] and can cause considerable economic and ecological damages, as well as human casualties. Additionally, wildfires have significant longterm impacts on both the environment and people, such as changing the structure of the ecosystems, soil erosion, destructing wildlife habitats, and increasing potential of flooding [4].…”

Section: Introductionmentioning

confidence: 99%

“…Among natural geophysical disasters, wildfire occurs more than a thousand times annually over different regions of the globe [2][3][4] and can cause considerable economic and ecological damages, as well as human casualties. Additionally, wildfires have significant longterm impacts on both the environment and people, such as changing the structure of the ecosystems, soil erosion, destructing wildlife habitats, and increasing potential of flooding [4]. Moreover, wildfires emit a considerable amount of greenhouse gases (e.g., carbon dioxide and methane) which results in global warming [5].…”

Section: Introductionmentioning

confidence: 99%

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Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Seydi

Akhoondzadeh

Amani³

et al. 2021

Remote Sensing

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Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Seydi

Akhoondzadeh

Amani³

et al. 2021

Remote Sensing

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“…To identify and remove duplicate MODIS fire detections, we calculated the ground area for each fire pixel and estimated the proportion of overlapped fire pixel area using the geolocation product MYD03 [51]. The fire pixel's ground area was calculated used Equations (2)- (5), following the methods of [22,49]. θ = s * (SID − 676.5) (2) where θ ( o ) is MODIS scan angle, s = 0.0014184 is a specific constant for MODIS; SID is the along-scan pixel index of fire pixel within the MODIS image pixel matrix.…”

Section: Modis 1-km Fire Productmentioning

confidence: 99%

“…Satellite observation with relatively high spatiotemporal resolution and broad spatial coverage is often used to investigate fire impacts on vegetation, atmosphere, climate, air quality, and human society [1][2][3][4][5][6]. Regional to global scale observation of vegetation fires (e.g., fire detection, to fuel type, fire size, and temperature [30,41].…”

Section: Introductionmentioning

confidence: 99%

Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products

Wang²,

Bergeron

et al. 2020

Remote Sensing

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Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.

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“…Debido a que las áreas destruidas por incendios forestales son extensas, la evaluación in situ sería muy compleja. Una alternativa confiable y económica para la evaluación de los daños producidos son los Sistemas de Información Geográfica (SIG) y la teledetección [5].…”

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Algoritmo Semiautomático para Mapear Incendios Forestales y Estimar la Recuperación Vegetal Mediante Imágenes Landsat-8

Cabrera

Guerrero

León-Salto

et al. 2020

rte

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Este estudio propone una metodología reproducible para la identificación semi automática de incendios forestales y la estimación de la recuperación vegetal mediante el uso de imágenes satelitales Landsat 8 (OLI) y el análisis de la información obtenida del cálculo de índices espectrales. Para ello se desarrolló un algoritmo utilizando información de servidores de geo información y paquetes de análisis espacial del software R. Las imágenes satelitales procesadas se obtuvieron de la plataforma EarthExplorer del Servicio Geológico de Estados Unidos (USGS): antes, durante y después del incendio forestal producido en el Cantón Quilanga, Loja-Ecuador en el año 2019. Los resultados obtenidos permitieron delimitar el área afectada con los índices NBRI, GNDVI, EVI2, DVI estimando su severidad y los índices NDVI, SAVI permitieron estimar la recuperación vegetal. De acuerdo a los resultados obtenidos se pudo estimar una recuperación temprana de la vegetación.

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Satellite Remote Sensing Contributions to Wildland Fire Science and Management

Cited by 146 publications

References 171 publications

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products

Algoritmo Semiautomático para Mapear Incendios Forestales y Estimar la Recuperación Vegetal Mediante Imágenes Landsat-8

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