Recent wildfires in Central Chile: Detecting links between burned areas and population exposure in the wildland urban interface

Sarricolea, Pablo; Serrano‐Notivoli, Roberto; Fuentealba, Magdalena; Hernández-Mora, Marina; Barrera, Francisco de la; Smith, Pamela; Meseguer-Ruiz, Óliver

doi:10.1016/j.scitotenv.2019.135894

Cited by 51 publications

(41 citation statements)

References 54 publications

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“…Long-term and NRT publicly available RS datasets within GEE along with its high-performance computing promote this cloud-based platform for monitoring, forecasting, prevention, vulnerability, and resilience studies of natural disasters. In particular, GEE was utilized for drought monitoring [129], [130], flood mapping and flood risk assessment [131], [132], wildfire severity mapping [133], [134], landslides analyses [135], hurricane studies [136], and tsunami studies [137]. For instance, MODIS and meteorological datasets were employed within GEE to study the temporal and spatial variations of drought events in Potohar Plateau of Punjab, Pakistan between 2000 and 2015 [129].…”

Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

651

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

651

216

View full text Add to dashboard Cite

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“…The associated emissions and atmospheric transport of O 3 and PM 10 affected air quality over most of the region and as far North as Santiago, the capital city, with significantly increased concentrations recorded throughout the air quality monitoring networks [3]. A wide range of social, economic, and environmental impacts result from these fires [17,18], in addition to an estimated 76 premature human deaths and 209 additional hospital admissions for respiratory conditions in the region [13]. To date, studies investigating the atmospheric impact of the 2017 fires rely on local measurements and satellite data analysis rather than modeling, which does not provide a comprehensive, spatially continuous analysis nor permits a scenario approach regarding atmospheric composition modifications induced by the 2017 mega-fires.…”

Section: Introductionmentioning

confidence: 99%

The 2017 Mega-Fires in Central Chile: Impacts on Regional Atmospheric Composition and Meteorology Assessed from Satellite Data and Chemistry-Transport Modeling

2021

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In January 2017, historic forest fires occurred in south-central Chile. Although their causes and consequences on health and ecosystems were studied, little is known about their atmospheric effects. Based on chemistry-transport modeling with WRF-CHIMERE, the impact of the 2017 Chilean mega-fires on regional atmospheric composition, and the associated meteorological feedback, are investigated. Fire emissions are found to increase pollutants surface concentration in the capital city, Santiago, by +150% (+30 µg/m3) for PM2.5 and +50% (+200 ppb) for CO on average during the event. Satellite observations show an intense plume extending over 2000 km, well reproduced by the simulations, with Aerosol Optical Depth at 550 nm as high as 4 on average during the days of fire activity, as well as dense columns of CO and O3. In addition to affecting atmospheric composition, meteorology is also modified through aerosol direct and indirect effects, with a decrease in surface radiation by up to 100 W/m2 on average, leading to reductions in surface temperatures by 1 K and mixing layer heights over land by 100 m, and a significant increase in cloud optical depth along the plume. Large deposition fluxes of pollutants over land, the Pacific ocean and the Andes cordillera are found, signaling potential damages to remote ecosystems.

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“…In hydro-climatological studies, oceanic conditions and oscillations have been presented in the form of large-scale climate indices that capture patterns of heat and mass distribution in the atmosphere and oceans [20,21]. Among climate indices, ENSO (El Niño-Southern Oscillation) has been recognized as one of the leading modes of global climate variability, and other climatic modes, such as the Pacific Decadal Oscillation (PDO) or the Atlantic Multidecadal Oscillation (AMO) interact with ENSO to modulate phenological patterns of vegetation at a global scale [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, an ENSO-driven drought now extending for over a decade (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) in central Chile has imposed different trends on terrestrial productivity depending on latitude [37]. Similarly, the coupling of positive ENSO and SAM anomalies seem to underpin a pattern of increased forest fires across central and southern Chile over the same period during the last decade [21,[38][39][40]. Together, these studies suggest that long-term climatic variability is impacting seems to modulate vegetation dynamics along western south America and that their effects are not homogeneous in space neither in space nor in time.…”

Section: Introductionmentioning

confidence: 99%

Climatic Regulation of Vegetation Phenology in Protected Areas along Western South America

et al. 2021

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Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S–51°S). The relationship between satellite phenology and climate indices, namely MEI (Multivariate ENSO Index), PDO (Pacific Decadal Oscillation) and SAM (Southern Annular Mode) were established using statistical analyses for non-stationary patterns. The annual mode of phenological activity fluctuated in strength through time from the semiarid region to the border of southern Patagonia. Concomitantly, enhanced synchrony between EVI and climatic oscillations appeared over interannual cycles. Cross correlations revealed that variability in MEI was the lead predictor of EVI fluctuations over scales shorter than 4 months at lower latitudes and for the most poleward study site. The PDO was correlated with EVI over lags longer than 4 months at low latitude sites, while the SAM showed relationships with EVI only for sites located around 40°S. Our results indicate that the long-term phenological variability of the vegetation within protected areas along southwestern South America is controlled by processes linked to climate indices and that their influence varies latitudinally. Further studies over longer time scales will be needed to improve our understanding the impacts of climate change on vegetation condition and its effect over phenological variability.

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Recent wildfires in Central Chile: Detecting links between burned areas and population exposure in the wildland urban interface

Cited by 51 publications

References 54 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

The 2017 Mega-Fires in Central Chile: Impacts on Regional Atmospheric Composition and Meteorology Assessed from Satellite Data and Chemistry-Transport Modeling

Climatic Regulation of Vegetation Phenology in Protected Areas along Western South America

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