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.

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.

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.

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.

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

Forest disturbances pose an increasing threat to the ecosystem services provided by tropical forests in the Congo Basin, yet their distribution remains poorly understood. To address this, we create a high‐confidence forest disturbance data set for the Congo Basin from 2000 to 2022 by harmonizing the Global Forest Change (GFC) and Tropical Moist Forest (TMF) data sets. Though the Democratic Republic of Congo (DRC) accounted for the most disturbance observed (61,174 km2), we identify Cameroon as an emerging deforestation frontier. Among all six Congo Basin countries, only Cameroon saw a significant increasing annual contribution to the total forest disturbance observed in the Congo Basin over the past 20 years (slope = 0.49% yr−1, p = 0.004) and the second‐highest extent of forest disturbance (7,013 km2). Across the Congo Basin, disturbances mainly occurred near roads (26,737 km2) and outside formal land allocations (19,217 km2). In Cameroon, the extent of forest disturbance in community forests (5 ± 1%) was higher than in agro‐industrial plantations (3 ± 2%) and logging concessions (3 ± 1%). We observe a basin‐wide increase in the extent and frequency of forest disturbances, suggesting a shift toward commercial land‐use practices associated with larger clearing. Our findings reveal changes in forest disturbance patterns in the Congo Basin over the past 20 years that warrant continued monitoring and improved understanding of their socioeconomic drivers to prevent large‐scale deforestation as observed in the Amazon and Southeast Asia. Plain Language Summary Though forest disturbances threaten the essential ecosystem services provided by tropical forests in the Congo Basin, the location and spatial patterns of disturbance in the region are not well known. To fill this gap, we created a high‐confidence forest disturbance data set for the Congo Basin from 2000 to 2022 by combining two global forest disturbance data sets, GFC and TMF. Using this data set, we found that though the majority of the forest disturbances occur in DRC, Cameroon is an emerging deforestation frontier due to its second‐highest extent of forest disturbance, increasing forest disturbance, and significant annual contribution to the total forest disturbance observed in the Congo Basin over the past two decades. Most disturbances occurred along roads and outside formal land allocations across the Congo Basin. The extent of forest disturbance in community forests in Cameroon was higher than in agro‐industrial plantations and logging concessions. We observed an increase in the size and number of forest disturbances across the basin, implying a shift toward land‐use practices associated with larger clearings. Our findings demonstrate changes in forest disturbance patterns in the Congo Basin over the past two decades that need to be monitored and understood, along with their drivers, to prevent large‐scale deforestation as observed in the Amazon and Southeast Asia. Key Points Cameroon is an emerging forest disturbance frontier in the Congo Basin with a significant increasing annual contribution regionally from 2000 to 2022 (p = 0.004) Over 61% (45,954 km2) of the forest disturbances in the region occurred near roads or outside formal land allocations The frequency of forest disturbances greater than 10 km2 in the Congo Basin increased from 2000 to 2022, especially in the latter part of the decade

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.

The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as Araucaria angustifolia. A. angustifolia is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. A. angustifolia’s distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping Araucaria angustifolia across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect A. angustifolia at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of A. angustifolia trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of A. angustifolia.

Understanding forest cover dynamics is important for a nation’s environmental, social and political commitments. In the past decade, Myanmar had the highest deforestation rate, in mainland South East Asia (Hansen et al., 2013). Further, in 2009, Myanmar embarked on a landmark political change from military regime to democratic transition which significantly impacted its forest cover. Myanmar also ranks first with respect to forest fires in South/Southeast Asia. In Myanmar, forest cover loss and fire are intrinsically linked through the traditional taungya system of slash and burn. Thus, quantifying factors controlling forest fires in Myanmar is an important topic that needs attention. Although the Myanmar government established protected areas throughout the country to conserve forests, their effectiveness remains unevaluated. This dissertation aims to understand the current status of forest cover dynamics in Myanmar. The five chapters in this dissertation address the impact of the political transition on forest cover loss and fragmentation, fire disturbance in tropical evergreen and deciduous forests including the factors controlling vegetation fires in the protected and non-protected forests. The dissertation contributes to the existing knowledge in land cover and land use change science (LCLUC), especially the impact of institutional changes on forest cover in the tropics. The analysis of the relationship between forest loss, fire and effectiveness of the protected areas addressed in the study, contributes to regional knowledge on fire and conservation science respectively. The findings of this dissertation depict that in Myanmar, the political transition to democracy significantly influenced its forest cover. Our analysis showed that during 2001-2014, a total loss of 2,030,101 ha of forest occurred at the rate of 145,007.21 ha/year with a linear increase of 15,359 (±1793) ha/year. The observed increase in variance in between 2008-2011 coincides with political transition period which started with the formation of the new Constitution in 2008 and ended with the military government handing over power to the democratic government in 2011. Analysis of trend and variance patterns of two landscape fragmentation metrics (Number of Patches and Mean Patch Area) at the provincial level show the influence of the political transition on landscape fragmentation. The impact of political transition was more pronounced in provinces associated with plantations and urban areas. Among the rubber producing States, the border States, Shan, Kayah, and Kayin were more impacted compared to inland Mon. Tanintharyi and Bago Regions showed higher variance in residuals of both metrics before the transition occurred due to the military government supported oil palm and teak plantations. Fragmentation and the variance in fragmentation metrics in Kachin increased post 2008. Apart from plantation areas, urban areas like Yangon and Mandalay showed high fragmentation post 2009 period after the new government was formed. We attribute the forest loss and fragmentation to the economic and structural reforms of the democratic government, specifically to the increased granting of agricultural concessions and logging for plantations. A study of the fire regime from 2003 to 2012 using MODIS satellite data suggested March as the peak of the fire season with 12900 km2 of Burned Area (BA) and 95000 fire counts. Forests accounted for majority (41.3%) of the total BA and most fires (89.7%) resulted in medium or high vegetation disturbance. A higher negative correlation between BA and Gross Primary Productivity (GPP) was reported for deciduous forests than for evergreen forests (r=0.49 vs r = 0.36, p ∼ 0). A maximum decrease in 29% of original GPP (2007-2012) was observed in the evergreen forest patches. The scale-dependent correlation analysis suggested significant BA-GPP correlation at 1 × 1 degree, as compared to finer resolutions. These results highlight the significance of fires impacting carbon cycle. An in-depth analysis of fire causative factors in Myanmar was studied. The mean fire density in non-protected areas was found to be two times more than in protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order were found to be population density, land cover, tree cover percent, travel time from nearest city and temperature. The causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot region of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. These findings provide information to policy makers about the current forest loss, forest fragmentation and forest fire hotspots, status of forest conservation and can be used to inform, update or evaluate policies. These findings are timely and can guide policy makers to arrive at best management strategies as the new government is formulating policies and laws and amending old ones to aid forest conservation.