Spatial variations in vegetation fires and emissions in South and Southeast Asia during COVID-19 and pre-pandemic

Abstract

Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, very few studies focused on vegetation fires and the changes during the COVID as compared to pre-pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations in SA/SEA during COVID-2020 and pre-pandemic (2012–2019). Results from the short-term 2020-COVID versus 2019-non-COVID year showed a decline in fire counts varying from − 2.88 to 79.43% in S/SEA. The exceptions in South Asia include Afghanistan and Sri Lanka, with a 152% and 4.9% increase, and Cambodia and Myanmar in Southeast Asia, with an 11.1% and 8.5% increase in fire counts in the 2020-COVID year. The burnt area decline for 2020 compared to 2019 varied from − 0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012–2020 pre-pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from − 0.24 to − 1, although most countries experienced a decrease in fire counts. The associated mean TPM emissions declined from ~ 2.31 Tg (0.73stdev) during 2012–2019 to 2.0 (0.65stdev)Tg in 2020 in South Asia and 6.83 (0.70stdev)Tg during 2012–2019 to 5.71 (0.69 stdev)Tg in 2020 for South East Asian countries. The study highlights variations in fires and emissions useful for fire management and mitigation.

Automated Method for Artificial Impervious Surface Area Mapping in Temperate, Tropical, and Arid Environments Using Hyperlocal Training Data With Sentinel-2 Imagery

“…For example, GMIS was generated on imagery from 2010 and GISA was generated on 2016 imagery, and while accurate for their respective years, they have not been updated to a more recent year. The primary objectives of this study are (1) to create a framework based on logical decision functions and two existing AIS datasets from prior years (2010 and 2016) with supervised classification for automated mapping of AIS from recent moderate resolution satellite imagery in order to obtain accurate, timely, and current spatial extent of AIS; (2) evaluate different stepwise thresholds on the logical decision functions (NDVI thresholds, Euclidean distance thresholds) to map AIS and compare the accuracy of thresholds across climate-based groups via adaptive thresholding; (4) create a novel beach bare ground sampling method to reduce commission errors on bare ground adjacent to water, create and apply an NDVI texture metric, and include an annual NDVI standard deviation composite and evaluate the impact that these inputs have on accuracy; (5) compare the accuracy of this framework to a more advanced and data intensive deep learning model maintained by Esri.…”

“…Accurate mapping of AIS is critical for urban demographics and population growth estimates, impacts on land cover and land use changes, and more. AIS and associated urban land uses represent a small proportion of the global land area; however, their effects are wide-reaching with impacts on hydrology due to increased run-off, air pollution emissions and associated health impacts due to focused commerce and more [2][3][4][5]. Accurate and up-todate data on the extent and change of AIS is key for gaining a better understanding of the above impacts and broader issues such as climate change mitigation and adaptation.…”

Multi-Source Satellite and WRF-Chem Analyses of Atmospheric Pollution from Fires in Peninsular Southeast Asia

“…South and Southeast Asia is one of the world's top three biomass burning sites. Every spring, wildfires in PSEA (PSEA: Cambodia, Laos, Myanmar, Thailand, Vietnam; PSEA is also referred as Indochina in some cultures) are exceptionally intense [2,3]. The entirety of Southeast Asia belongs to the tropical and subtropical climate zones, with most of the annual precipitation being considerable.…”

Covid-19 Pandemic and Its Environmental Implications: An Investigation of Forest Fires in Northern Iran

“…The data from satellites has shown that the pandemic in Nepal has contributed to a decrease of 4.54% in the number of forest fire incidents and a decrease of 11.36% in the radiation power of fire, and the cause may be due to the restrictions on the movement of people [16]. A study conducted by Vadrevu et al [17] in South and Southeast Asian countries indicated that the number of fire events in 2020 decreased between 2.88 and 79.43% compared to 2019.…”

“…Colombia experienced an increase in forest fires as a result of the quarantine, which was driven by a decline in the supervision provided by the state-level environmental protection agencies [6]. Furthermore, it was reported that the number of forest fires in South Asian countries such as Afghanistan, Sri Lanka, Cambodia, and Myanmar increased by 152, 4.9, 11.1, and 8.5%, respectively, in 2020 compared to 2019 [17]. As a result of Covid-19 quarantine, the management of protected areas has been suspended in some countries [19].…”