Satellite remote sensing of active fires: History and current status, applications and future requirements

Wooster, Martin J.; Roberts, G.; Giglio, L.; Roy, David P.; Freeborn, Patrick H.; Boschetti, Luigi; Justice, C. O.; Ichoku, Charles; Schroeder, Wilfrid; Davies, David K.; Smith, A. J.; Setzer, Alberto; Csiszar, Ivan; Strydom, Terica; Frost, Philip; Zhang, Tianran; Xu, Weidong; Jong, Mark de; Johnston, Joshua M.; Ellison, Luke; Vadrevu, Krishna Prasad; McCarty, J. L.; Tanpipat, Veerachai; Schmidt, C. C.; San-Miguel, Jesús F.

doi:10.1016/j.rse.2021.112694

Cited by 137 publications

(76 citation statements)

References 193 publications

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“…One of the most critical advances in fire science in recent decades has been the development of operational satellite products that detect burned area (BA) and active fires, particularly since the late 2000s (Chuvieco et al, 2020;Giglio & Roy, 2020;Roy et al, 2019;Wooster et al, 2021). Satellite observations of fire commenced in the 1970s and, since the 1980s, the NOAA advanced very-highresolution radiometer (AVHRR) has been used to generate fire products with variable success (Giglio and Roy, 2020).…”

Section: Expansion Of Earth Observationsmentioning

confidence: 99%

Global and Regional Trends and Drivers of Fire Under Climate Change

Jones

Abatzoglou

Veraverbeke

et al. 2022

Reviews of Geophysics

359

159

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Recent wildfire outbreaks around the world have prompted concern that climate change is increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating climate change. Here we review current understanding of the impacts of climate change on fire weather (weather conditions conducive to the ignition and spread of wildfires) and the consequences for regional fire activity as mediated by a range of other bioclimatic factors (including vegetation biogeography, productivity and lightning) and human factors (including ignition, suppression, and land use). Through supplemental analyses, we present a stocktake of regional trends in fire weather and burned area (BA) during recent decades, and we examine how fire activity relates to its bioclimatic and human drivers.Fire weather controls the annual timing of fires in most world regions and also drives inter-annual variability in BA in the Mediterranean, the Pacific US and high latitude forests. Increases in the frequency and extremity of fire weather have been globally pervasive due to climate change during 1979-2019, meaning that landscapes are primed to burn more frequently. Correspondingly, increases in BA of ~50% or higher have been seen in some extratropical forest ecoregions including in the Pacific US and highlatitude forests during 2001-2019, though interannual variability remains large in these regions.Nonetheless, other bioclimatic and human factors can override the relationship between BA and fire weather. For example, BA in savannahs relates more strongly to patterns of fuel production or to the fragmentation of naturally fire-prone landscapes by agriculture. Similarly, BA trends in tropical forests relate more strongly to deforestation rates and forest degradation than to changing fire weather. Overall, BA has reduced by ~30% globally in the past two decades, due in large part to a ~40% decline in BA in African savannahs.According to climate models, the prevalence and extremity of fire weather has already emerged beyond its pre-industrial variability in the Mediterranean due to climate change, and emergence will become increasingly widespread at additional levels of warming. Moreover, several of the major wildfires experienced in recent years, including the Australian bushfires of 2019/2020, have occurred amidst fire weather conditions that were considerably more likely due to climate change.Current fire models incompletely reproduce the observed spatial patterns of BA based on their existing representations of the relationships between fire and its bioclimatic and human controls, and historical trends in BA also vary considerably across models. Advances in the observation of fire and understanding of its controlling factors are supporting the addition or optimisation of a range of processes in models.Overall, climate change is exerting a pervasive upwards pressure on fire globally by increasing the frequency and intensity of fire weather, and this upwards pressure will escalate with each increment of global warming. Improvements to ...

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Section: Expansion Of Earth Observationsmentioning

confidence: 99%

Global and Regional Trends and Drivers of Fire Under Climate Change

Jones

Abatzoglou

Veraverbeke

et al. 2022

Reviews of Geophysics

359

159

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“…Thermal infrared imagery represents outgoing longwave radiation and reflects variability in the land surface energy Journal of Remote Sensing balance due to loss of vegetation cover caused by fire [126,127]. Satellite observations in the middle and shortwave infrared region are generally used to detect active fires and subsequent estimation of fire radiative power [128]. The estimation of fire radiative power is based on the notion that heat produced by a fixed quantity of biomass is invariant to the type of vegetation (e.g., woody or herbaceous) consumed by the fire [129], which causes savanna fires to have relatively high fire radiative power [130] due to their spatial and structural heterogeneity [131].…”

Section: Estimation Of Abovegroundmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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“…However, due to these systems' lack of global coverage, satellite datasets covering almost the entire Earth to monitor active fires are one of the best alternative options. Various spaceborne sensors have been used for AFD [12]. In the past decade, the earth observations (EO) from two multispectral imaging sensors, namely a moderate resolution imaging spectroradiometer (MODIS) and a visible infrared imaging radiometer suite (VIIRS), have been frequently employed for AFD [13].…”

Section: Introductionmentioning

confidence: 99%

“…However, only a few studies have focused on using these methods for AFD in satellite imagery [31,32]. Most previous AFD algorithms in satellite imagery have been based on fixed thresholds, contextual methods, multi-temporal approaches, and non-thermal IR methods using multi-sensor data [12]. These methods' main problem is their low generalizability in complex terrain and illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

et al. 2022

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Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

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Satellite remote sensing of active fires: History and current status, applications and future requirements

Cited by 137 publications

References 193 publications

Global and Regional Trends and Drivers of Fire Under Climate Change

Global and Regional Trends and Drivers of Fire Under Climate Change

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

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