Explore the vast range of titles available.

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003–2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012–2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.

A better understanding of land cover/land use changes (LCLUC) and their interactions with the atmospheric environment is essential for the sustainable management of natural resources, environmental protection, air quality, agricultural planning and food security. The 15 papers published in this focus issue showcase a variety of studies relating to drivers and impacts of LCLUC and air pollution in different South/Southeast Asian (S/SEA) countries. This synthesis article, in addition to giving context to the articles in this focus issue, also reviews the broad linkages between population, LCLUC and air pollution. Additionally, we identify knowledge gaps and research priorities that are essential in addressing air pollution issues in the region. We conclude that for effective pollution mitigation in S/SEA countries, quantifying drivers, sources and impacts of pollution need a thorough data analysis through ground-based instrumentation, models and integrated research approaches. We also stress the need for the development of sustainable technologies and strengthening the scientific and resource management communities through capacity building and training activities to address air pollution issues in S/SEA countries.

In this study, we characterize the impacts of COVID-19 on air pollution using NO 2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO 2 reduction during the lockdown (March 25–May 3rd, 2020) compared to the pre-lockdown (January 1st–March 24th, 2020) period. Also, a 19% reduction in NO 2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO 2 reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO 2 for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO 2 levels decreased exponentially. In contrast, to the decreased NO 2 observed for most of the cities, we observed an increase in NO 2 for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO 2 temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO 2 , and the results can inform pollution mitigation efforts across different cities of India.

Phoebe bournei is nationally conserved in China due to its high economic value and positive effect on the ecological environment. P. bournei has an excellent wood structure, making it useful for industrial and domestic applications. Despite its importance, there are only a few studies on the lateral organ boundary domain (LBD) genes in P. bournei. The LBD gene family contributes to prompting rooting in multiple plant species and therefore supports their survival directly. To understand the LBD family in P. bournei, we verified its characteristics in this article. By comparing the sequences of Arabidopsis and identifying conserved domains and motifs, we found that there were 38 members of the LBD family in P. bournei, which were named PbLBD1 to PbLBD38. Through evolutionary analysis, we found that they were divided into two different populations and five subfamilies in total. The LBD gene family in P. bournei (Hemsl.) Yang species had two subfamilies, including 32 genes in Class I and 6 genes in Class II. It mainly consists of a Lateral Organ Boundary (LOB) conservative domain, and the protein structure is mostly “Y”-shaped. The gene expression pattern of the LBD gene family showed that the LBD genes were mainly expressed in lateral organs of plants, such as flowers and fruits. The response of LBD transcription factors to red and blue light was summarized, and several models of optogenetic expression regulation were proposed. The effect of regulatory mechanisms on plant rooting was also predicted. Moreover, quantitative real-time PCR (qRT-PCR) revealed that most PbLBDs were differentially expressed under cold, heat, drought, and salt stresses, indicating that PbLBDs might play different functions depending on the type of abiotic stress. This study provides the foundation for further research on the function of LBD in this tree species in the future.

In this study, we explored the relationships between the satellite-retrieved fire counts (FC), fire radiative power (FRP) and aerosol indices using multi-satellite datasets at a daily time-step covering ten different biomass burning regions in Asia. We first assessed the variations in MODIS-retrieved aerosol optical depths (AOD's) in agriculture, forests, plantation and peat land burning regions and then used MODIS FC and FRP (hereafter FC FRP) to explain the variations in AOD characteristics. Results suggest that tropical broadleaf forests in Laos burn more intensively than the other vegetation fires. FC FRP-AOD correlations in different agricultural residue burning regions did not exceed 20% whereas in forest regions they reached 40%. To specifically account for absorbing aerosols, we used Ozone Monitoring Instrument-derived aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI). Results suggest relatively high AAOD and UVAI values in forest fires compared with peat and agriculture fires. Further, FC FRP could explain a maximum of 29% and 53% of AAOD variations, whereas FC FRP could explain at most 33% and 51% of the variation in agricultural and forest biomass burning regions, respectively. Relatively, UVAI was found to be a better indicator than AOD and AAOD in both agriculture and forest biomass burning plumes. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations data showed vertically elevated aerosol profiles greater than 3.2-5.3 km altitude in the forest fire plumes compared to 2.2-3.9 km and less than 1 km in agriculture and peat-land fires, respectively. We infer the need to assimilate smoke plume height information for effective characterization of pollutants from different sources.

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.

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring.

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.

Two experiments demonstrate that post‐event information, when delivered by another person, can affect people's memory reports. In the first experiment participants were shown several cars, and later, in pairs, given an ‘old’/‘new’ recognition test on these cars plus several lures. There was a small but reliable effect of memory conformity. When the person was given misinformation this lowered accuracy, while presenting accurate information increased accuracy. In the second experiment participants, in pairs, viewed an identical crime except that half saw an accomplice with the thief and half did not. Initial memories were very accurate, but after discussing the crime with the other person in the pair (who saw a slightly different sequence), most pairs conformed. Confidence ratings strongly predicted which person in the pair persuaded the other. Parallels with eyewitness testimony in the Oklahoma bombing case and implications for police interviewing more generally are discussed.

This study addresses the effect of political transition and subsequent timber bans on forest loss in Myanmar, in the context of identified drivers. Cook’s Distance (CD) was applied to remotely sensed time-series forest loss dataset to measure the effect of the events. Forest loss derived fragmentation metrics were linked to drivers at a landscape scale. Results show that at the national level, the political transition in 2011 had maximum effect (CD 0.935) on forest loss while the timber bans decreased forest loss by 612.04 km² and 213.15 km² in 2015 and 2017 (CD 0.146 and 0.035), respectively. The effect of the events varied for different States/Regions. The dominant drivers of change shifted from plantations in 2011 to infrastructure development in 2015. This study demonstrates the effects of policy on forest loss at various scales and can inform decision-makers for forest conservation, planning and development of mitigation measures.