Amanda Hoffman-Hall

ABSTRACT The Arctic Boreal Burned Area (ABBA) product is a region‐specific burned area product designed for high northern latitudes (HNL), including boreal forests and Arctic tundra. Developed to address limitations of global products like MODIS MCD64A1, ABBA uses MODIS data to calculate the differenced Normalised Burn Ratio (dNBR) and incorporates vegetation cover thresholds to identify burned areas. It includes a spring composite modification for high‐latitude tiles to capture late‐season fires obscured by snow. ABBA achieves higher accuracy (87.2%) compared to MCD64A1 (82.7%), with lower commission and omission errors, enabling more reliable detection of burned areas. Between 2002 and 2022, ABBA mapped over 2 million km2 of burned area, significantly more than MCD64A1. Its applications include improving wildfire emission estimates, studying climate feedbacks like albedo changes and assessing ecological impacts such as permafrost thaw and vegetation regrowth. ABBA is available via the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC).

An intricate relationship exists between land cover and land use change (LCLUC) and the transmission of vector-borne diseases. For the vector-borne disease malaria, the influence of LCLUC on malaria exposure is complex and multifaceted, but understanding these interactions is critical to developing effective public health interventions. In Myanmar, recent advancements in LCLUC mapping using remote sensing technologies have allowed for deeper insights into the connection between specific LCLUC patterns and malaria risk. Specifically, recent studies reveal that malaria is influenced by natural forest cover near residential areas, emphasizing the importance of considering land cover in malaria transmission models. Furthermore, research underlines the impact of human activities, such as forest chores and outdoor jobs, on malaria risk. The insights gained from this research can inform targeted malaria intervention strategies, particularly in recent political turmoil, highlighting earth observations' critical role in providing essential data for public health initiatives amidst challenging circumstances.

•Deforestation in Myanmar is not linked to increased malaria incidence.•Forest land use (plantation work, logging, gathering firewood, etc.) is linked to malaria.•Malaria prevention methods should target those living or working near forests. Within the remote region of Ann Township in Myanmar's Rakhine State, malaria prevalence has remained steady at ∼10% of the population from 2016 – 2019. Previous studies have linked areas of higher malaria prevalence in the region to heavily forested areas, however, little is known about how people live, work, and move through these areas. This work aims to disentangle landscape from land use in regard to malaria exposure. We investigated the roles of forest cover, forest loss, and land-use activities with malaria prevalence through the combined use of land-use surveys, malaria surveillance, and satellite earth observations. Our results confirm previous research that linked areas of high forest cover with high malaria prevalence. However, areas experiencing high levels of deforestation were not associated with malaria prevalence. The land-use factors that contribute most significantly to increased malaria risk remained those which put people in direct contact with forests, including conducting forest chores, having an outdoor job, and having a primary occupation in the logging and/or plantation industry. Malaria prevention methods in Myanmar should focus on anyone who lives near forest or engages in land-use activities that bring them within proximity of forested landscapes, whether through occupation or chores.

As marginalized communities continue to bear disproportionate impacts from environmental hazards, we urgently call for researchers and institutions to elevate the principles of Environmental Justice. The American Geophysical Union (AGU) GeoHealth section supports members' engagement in health-related community-engaged and community-led transdisciplinary research. We highlight intersectional research that provides examples and actions for both individuals and organizations on community science and trust building, removing barriers created by scientific agency priorities and career expectations, and opportunities in education and policy. Justice does not start or end at one meeting; this is ongoing work that is active, evolving, and an ethical responsibility of AGU's membership.
Plain Language Summary GeoHealth was recently defined as a new field of science that combines earth, environmental, and health sciences with a mission to answer scientific questions important to the health of humans and our planet. An incredibly important topic within GeoHealth is how racism affects environmental and human health. This topic is known as Environmental Justice: the right of all people and communities to have equal and fair protection by environmental laws and policies, regardless of race or color. However, many scientists feel underprepared to apply Environmental Justice to their own research due to a lack of training. Using GeoHealth projects from the American Geophysical Union Fall Meeting 2021 as examples, we highlight recent research that provides examples and clear suggested actions for both individuals and organizations on community science and trust building, changing funding methods for science, education, and policy in order to incorporate Environmental Justice across the all of the types of research we conduct.

Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy‐to‐implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30‐m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.
Villages in Ann Township, Myanmar, exhibiting high forest cover are strongly associated with increased risk of malaria
Ann Township, Myanmar, villagers living in villages where croplands are the dominant land cover type experience decreased malaria risk
Remote sensing offers a means to locate LCLU areas associated with high malaria risk to allow for more efficient targeted interventions

As globally important forested areas situated in a context of dramatic socio-economic changes, Siberia and the Russian Far East (RFE) are important regions to monitor for anthropogenic land-use trends. Therefore, we compiled decadal Landsat-derived land-cover and land-use data for eight dominantly rural case study sites in these regions and focused on trends associated with settlements, agriculture, logging, and roads 1975-2010. Several key spatial-temporal trends emerged from the integrated landscape-scale analyses. First, road building increased in all case study sites over the 35-year period, despite widespread socio-economic decline post-1990. Second, increase in settlements area was negligible over all sites. Third, increased road building, largely of minor roads, was especially high in more rugged and remote RFE case study sites not associated with greater agriculture extent or settlement densities. High demands for wood export coupled with the expansion of commercial timber harvest leases starting in the mid-1990s are likely among leading reasons for an increase in roads. Fourth, although fire was the dominant disturbance over all sites and dates combined, logging exerted a strong land-use pattern, serving as a reminder that considering local anthropogenic landscapes is important, especially in Siberia and the RFE, which represent almost 10% of the Earth's terrestrial land surface. The paper concludes by identifying remaining research needs regarding anthropogenic land use in the region: more frequent moderate spatial resolution imagery and greater access to more finely resolved statistical and other spatial data will enable further research. Social media abstract Landsat reveals long-term anthropogenic land-use trends in Siberia and Russian Far East

This dataset is a 30-m land cover land use map for the country of Myanmar for 2016. It was generated by fusing the data layers that we produced using machine learning algorithms with the existing remotely sensed datasets. The map contains ten classes: Perennial water, Impervious surface, Villages, Croplands, Managed forests, Natural forests, Ephemeral water, Shrub and grass, Depressions and Bare surfaces. Version 3 is the most updated version.

Accurate and timely population distribution maps are critical to addressing health epidemics, coordinating natural disaster response, tracking global changes for environmental conservation, confronting human rights issues, and more. Satellite imagery has opened numerous ways for mapping population distribution – previously unattainable without conducting a resource intensive census. While great advances have been made in urban population mapping, large gaps still exist for rural and remote populations which tend to be the most vulnerable to environmental, economic, and socio-political change. Moderate resolution remote sensing data, such as Landsat, has generally been viewed as insufficient for rural mapping given its coarseness relative to the size of a rural dwelling. This paper presents an approach for detecting small rural settlements within Ann Township of Rakhine State, a remote region of Myanmar with a highly mobile population, by combining Landsat data with publicly available auxiliary geospatial data. The key to the success of detecting small settlements at the Landsat resolution was the inclusion of multi-temporal auxiliary data sensitive to human activity patterns in the regions, such as distance to water, change in seasonal vegetation signals, and distance to a recent active fire. The use of active fire in this context is novel and provided a key metric that increased the accuracy of the resultant map. The final dataset has a classification accuracy of 86.5% on a per-pixel basis and 93.1% on a location identification basis. Numerous small settlements (on the order of 2–3 structures in some cases) not previously mapped by other datasets are identified, revealing that the population of Ann Township is far more dispersed and isolated than previously mapped. This study concludes that by incorporating regionally specific characteristics, moderate resolution remotely sensed data can be used to successfully map geographically marginalized communities so that services and aid are better able to reach them.
•Rural populations are vulnerable to environmental and socio-political change.•Isolated settlements present an extreme mapping challenge.•30 m remotely sensed imagery offers a solution but is limited by spatial resolution.•Adding auxiliary geospatial data can overcome these mapping limitations.•Metrics based on local land use practices can enhance settlement mapping algorithms.