Global and Regional Trends and Drivers of Fire Under Climate Change

Reviews of Geophysics, Год журнала: 2022, Номер 60(3)

Опубликована: Апрель 11, 2022

Abstract Recent wildfire outbreaks around the world have prompted concern that climate change is increasing fire incidence, threatening human livelihood and biodiversity, perpetuating change. Here, we review current understanding of impacts on weather (weather conditions conducive to ignition spread wildfires) consequences for regional activity as mediated by a range other bioclimatic factors (including vegetation biogeography, productivity lightning) ignition, suppression, land use). Through supplemental analyses, present stocktake trends in burned area (BA) during recent decades, examine how relates its drivers. Fire controls annual timing fires most regions also drives inter‐annual variability BA Mediterranean, Pacific US high latitude forests. Increases frequency extremity been globally pervasive due 1979–2019, meaning landscapes are primed burn more frequently. Correspondingly, increases ∼50% or higher seen some extratropical forest ecoregions including high‐latitude forests 2001–2019, though interannual remains large these regions. Nonetheless, can override relationship between weather. For example, savannahs strongly patterns fuel production fragmentation naturally fire‐prone agriculture. Similarly, tropical relate deforestation rates degradation than changing Overall, has reduced 27% past two part decline African savannahs. According models, prevalence already emerged beyond pre‐industrial Mediterranean change, emergence will become increasingly widespread at additional levels warming. Moreover, several major wildfires experienced years, Australian bushfires 2019/2020, occurred amidst were considerably likely Current models incompletely reproduce observed spatial based their existing representations relationships controls, historical vary across models. Advances observation controlling supporting addition optimization processes exerting upwards pressure intensity weather, this escalate with each increment global Improvements better interactions climate, extremes, humans required predict future mitigate against consequences.

Язык: Английский

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Influences of wildfire on the forest ecosystem and climate change: A comprehensive study

Environmental Research, Год журнала: 2023, Номер 240, С. 117537 - 117537

Опубликована: Окт. 30, 2023

Язык: Английский

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Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Remote Sensing of Environment, Год журнала: 2023, Номер 300, С. 113918 - 113918

Опубликована: Ноя. 27, 2023

Язык: Английский

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Pollutant emissions from biomass burning: A review on emission characteristics, environmental impacts, and research perspectives

Particuology, Год журнала: 2023, Номер 85, С. 296 - 309

Опубликована: Июль 28, 2023

Язык: Английский

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Increased Amazon carbon emissions mainly from decline in law enforcement

Nature, Год журнала: 2023, Номер 621(7978), С. 318 - 323

Опубликована: Авг. 23, 2023

Язык: Английский

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Drought triggers and sustains overnight fires in North America

Nature, Год журнала: 2024, Номер 627(8003), С. 321 - 327

Опубликована: Март 13, 2024

Язык: Английский

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Reimagine fire science for the anthropocene

PNAS Nexus, Год журнала: 2022, Номер 1(3)

Опубликована: Июль 1, 2022

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are fundamental cause consequence global change, impacting people the biophysical systems on which they depend. As part newly emerging Anthropocene, marked by human-caused climate change radical changes to ecosystems, danger increasing, fires having increasingly devastating impacts human health, infrastructure, ecosystem services. Increasing vexing problem requires deep transdisciplinary, trans-sector, inclusive partnerships address. Here, we outline barriers opportunities in next generation science provide guidance investment future research. We synthesize insights needed better address long-standing challenges innovation across disciplines (i) promote coordinated research efforts; (ii) embrace different ways knowing knowledge generation; (iii) exploration science; (iv) capitalize "firehose" data societal benefit; (v) integrate natural into models multiple scales. thus at critical transitional moment. need shift from observation modeled representations varying components climate, people, vegetation, more integrative predictive approaches support pathways toward mitigating adapting our flammable world, including utilization safety benefit. Only through overcoming institutional silos accessing diverse communities can effectively undertake improves outcomes fiery future.

Язык: Английский

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

Remote Sensing, Год журнала: 2022, Номер 14(4), С. 992 - 992

Опубликована: Фев. 17, 2022

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

Язык: Английский

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope

Remote Sensing of Environment, Год журнала: 2022, Номер 280, С. 113203 - 113203

Опубликована: Авг. 8, 2022

High spatial resolution commercial satellite data provide new opportunities for terrestrial monitoring. The recent availability of near-daily 3 m observations provided by the PlanetScope constellation enables mapping small and spatially fragmented burns that are not detected at coarser resolution. This study demonstrates, first time, potential automated burned area mapping. sensors have no onboard calibration or short-wave infrared bands, variable overpass times, making them challenging to use large area, automated, To help overcome these issues, a U-Net deep learning algorithm was developed classify areas from two-date Planetscope image pairs acquired same location. approach, unlike conventional algorithms, is applied subsets single pixels so incorporates as well spectral information. Deep requires amounts training data. Consequently, transfer undertaken using pre-existing Landsat-8 derived reference train then refined with smaller set Results across Africa considering 659 radiometrically normalized sensed one day apart in 2019 presented. trained different numbers randomly selected 256 × 30 pixel patches extracted 92 sets defined 2014 2015. 300,000 Landsat about 13% burn omission commission errors respect 65,000 independent evaluation patches. on 5,000 independently interpreted Qualitatively, able more precisely delineate boundaries, including interiors unburned areas, better “faint” indicative low combustion completeness and/or sparse burns. classification accuracy assessed 20 sets, composed 339.4 million pixels, 12.29% 12.09% errors. dependency proportion within also examined, <6.5% were less accurately classified. A regression analysis between grid cells classified against labelled maps showed high agreement (r2 = 0.91, slope 0.93, intercept <0.001), indicating largely compensate

Язык: Английский

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What Is Polluting Delhi’s Air? A Review from 1990 to 2022

Sustainability, Год журнала: 2023, Номер 15(5), С. 4209 - 4209

Опубликована: Фев. 26, 2023

Delhi’s annual average PM2.5 concentration in 2021–22 was 100 μg/m3—20 times more than the WHO guideline of 5 μg/m3. This is an improvement compared to limited information available for pre-CNG-conversion era (~30%), immediately before and after 2010 CWG (~28%), mid-2010s (~20%). These changes are a result continuous technical economic interventions interlaced with judicial engagement various sectors. Still, Delhi ranked most polluted capital city world. air quality major social political concern India, often questions regarding its severity primary sources, despite several studies on topic, there consensus source contributions. paper offers insight by reviewing influence urban growth since 1990 pollution levels sources evolution technical, institutional, legal measures control emissions National Capital Region Delhi.

Язык: Английский

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