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

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.

Contemporary forest loss in Myanmar: Effect of democratic transition and subsequent timber bans on landscape structure and composition.

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.

Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period.

In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO2 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 NO2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO2 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 NO2 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 NO2 levels decreased exponentially. In contrast, to the decreased NO2 observed for most of the cities, we observed an increase in NO2 for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO2 temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO2, and the results can inform pollution mitigation efforts across different cities of India.

Trends in Vegetation fires in South and Southeast Asian Countries.

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 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield 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.

Land cover, land use changes and air pollution in Asia: a synthesis.

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.

Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.

The surface reflectance, i.e., satellite derived top of atmosphere (TOA) reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land surface reliably. For this reason, the surface reflectance, and not TOA reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the Visible and Near Infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433-0.450µm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM+ blue bands. A cloud and cloud shadow mask has also been developed using the "cirrus" band (1.360-1.390 µm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the surface reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted surface reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements.

Mapping global cropland and field size.

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

NASA Land Cover and Land Use Change (LCLUC): an interdisciplinary research program.

Understanding Land Cover/Land Use Change (LCLUC) in diverse regions of the world and at varied spatial scales is one of the important challenges in global change research. In this article, we provide a brief overview of the NASA LCLUC program, its focus areas, and the importance of satellite remote sensing observations in LCLUC research including future directions. The LCLUC Program was designed to be a cross-cutting theme within NASA's Earth Science program. The program aims to develop and use remote sensing technologies to improve understanding of human interactions with the environment. Since 1997, the NASA LCLUC program has supported nearly 280 research projects on diverse topics such as forest loss and carbon, urban expansion, land abandonment, wetland loss, agricultural land use change and land use change in mountain systems. The NASA LCLUC program emphasizes studies where land-use changes are rapid or where there are significant regional or global LCLUC implications. Over a period of years, the LCLUC program has contributed to large regional science programs such as Land Biosphere-Atmosphere (LBA), the Northern Eurasia Earth Science Partnership Initiative (NEESPI), and the Monsoon Area Integrated Regional Study (MAIRS). The primary emphasis of the program will remain on using remote sensing datasets for LCLUC research. The program will continue to emphasize integration of physical and social sciences to address regional to global scale issues of LCLUC for the benefit of society.

Vegetation fires and air pollution in Vietnam.

Forest fires are a significant source of air pollution in Asia. In this study, we integrate satellite remote sensing data and ground-based measurements to infer fire-air pollution relationships in selected regions of Vietnam. We first characterized the active fires and burnt areas at a regional scale from MODIS satellite data. We then used satellite-derived active fire data to correlate the resulting atmospheric pollution. Further, we analyzed the relationship between satellite atmospheric variables and ground-based air pollutant parameters. Our results show peak fire activity during March in Vietnam, with hotspots in the Northwest and Central Highlands. Active fires were significantly correlated with UV Aerosol Index (UVAI), aerosol extinction absorption optical depth (AAOD), and Carbon Monoxide. The use of satellite aerosol optical thickness improved the prediction of Particulate Matter (PM) concentration significantly.

Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets.

In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.