Spatial distribution of young forests and carbon fluxes within recent disturbances in Russia.

Forest stand age plays a major role in regulating carbon fluxes in boreal and temperate ecosystems. Young boreal forests represent a relatively small but persistent source of carbon to the atmosphere over 30 years after disturbance, while temperate forests switch from a substantial source over the first 10 years to a notable sink until they reach maturity. Russian forests are the largest contiguous forest belt in the world that accounts for 17% of the global forest cover; however, despite its critical role in controlling global carbon cycle, little is known about spatial patterns of young forest distribution across Russia as a whole, particularly before the year 2000. Here, we present a map of young (0-27 years of age) forests, where 12- to 27-year-old forests were modeled from the single-date 500 m satellite record and augmented with the 0- to 11-year-old forest map aggregated from the 30 m resolution contemporary record between 2001 and 2012. The map captures the distribution of forests with the overall accuracy exceeding 85% within three largest bioclimatic vegetation zones (northern, middle, and southern taiga), although mapping accuracy for disturbed classes was generally low (the highest of 31% for user's and producer's accuracy for the 12-27 age class and the maximum of 74% for user's and 32% for producer's accuracy for the 0-11 age class). The results show that 75.5 ± 17.6 Mha (roughly 9%) of Russian forests were younger than 30 years of age at the end of 2012. The majority of these 47 ± 4.7 Mha (62%) were distributed across the middle taiga bioclimatic zone. Based on the published estimates of net ecosystem production (NEP) and the produced map of young forests, this study estimates that young Russian forests represent a total sink of carbon at the rate of 1.26 Tg C yr-1 .

Can carbon emissions from tropical deforestation drop by 50% in 5 years?

Halving carbon emissions from tropical deforestation by 2020 could help bring the international community closer to the agreed goal of <2 degree increase in global average temperature change and is consistent with a target set last year by the governments, corporations, indigenous peoples' organizations and non-governmental organizations that signed the New York Declaration on Forests (NYDF). We assemble and refine a robust dataset to establish a 2001-2013 benchmark for average annual carbon emissions from gross tropical deforestation at 2.270 Gt CO2 yr(-1). Brazil did not sign the NYDF, yet from 2001 to 2013, Brazil ranks first for both carbon emissions from gross tropical deforestation and reductions in those emissions - its share of the total declined from a peak of 69% in 2003 to a low of 20% in 2012. Indonesia, an NYDF signatory, is the second highest emitter, peaking in 2012 at 0.362 Gt CO2 yr(-1) before declining to 0.205 Gt CO2 yr(-1) in 2013. The other 14 NYDF tropical country signatories were responsible for a combined average of 0.317 Gt CO2 yr(-1) , while the other 86 tropical country non-signatories were responsible for a combined average of 0.688 Gt CO2 yr(-1). We outline two scenarios for achieving the 50% emission reduction target by 2020, both emphasizing the critical role of Brazil and the need to reverse the trends of increasing carbon emissions from gross tropical deforestation in many other tropical countries that, from 2001 to 2013, have largely offset Brazil's reductions. Achieving the target will therefore be challenging, even though it is in the self-interest of the international community. Conserving rather than cutting down tropical forests requires shifting economic development away from a dependence on natural resource depletion toward recognition of the dependence of human societies on the natural capital that tropical forests represent and the goods and services they provide.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires.

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Comparison of deforestation and forest land use factors for malaria elimination in Myanmar.

Objectives: 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. Methods: 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. Results: 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. Conclusion: Malaria prevention methods in Myanmar should focus on anyone who lives near forests or engages in land use activities that bring them within proximity of forested landscapes, whether through occupation or chores.

Satellite Observations and Malaria: New Opportunities for Research and Applications.

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate-malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

Home and Neighborhood Physical Activity Location Availability among African American Adolescent Girls Living in Low-Income, Urban Communities: Associations with Objectively Measured Physical Activity.

Based on the ecological model of active living, the neighborhood environment may relate to individual physical activity (PA) behaviors. The purposes of this study were to (1) generate a replicable neighborhood-level physical activity location availability score (PALAS) from data variables associated with physical activity among adolescents and adults, and apply this score to Baltimore City, Maryland, and (2) determine if relationships exist between PA and PA location availability. Geographic information systems (GISs) were used to create the PALAS. Using linear regression models, we examined relations between objectively measured PA among low-income, urban, predominantly African American adolescent girls (n = 555, 2009-2012 data collection), and the PALAS rating of their neighborhood environment (neighborhood PALAS) and their home neighborhood area (PALAS variables/subcomponents within 0.25 miles of the home). A PALAS map of the study area was created, illustrating neighborhoods varying in availability and variety of PA locations. After adjusting for confounders, a higher neighborhood PALAS (ß = 0.10, p = 0.041) and the presence of a recreation center in the home neighborhood area (ß = 0.46, p = 0.011) were associated with more minutes per day spent in moderate to vigorous PA. Policy makers and stakeholders should consider increasing access to PA locations as a strategy to promote PA among adolescent girls.

A Disease Control-Oriented Land Cover Land Use Map for Myanmar.

Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.

Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys.

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.

Oil palm plantations in Peninsular Malaysia: Determinants and constraints on expansion.

Agricultural expansion is one of the leading causes of deforestation in the tropics and in Southeast Asia it is predominantly driven by large-scale production for international trade. Peninsular Malaysia has a long history of plantation agriculture and has been a predominantly resource-based economy where expanding plantations like those of oil palm continue to replace natural forests. Habitat loss from deforestation and expanding plantations threatens Malaysian biodiversity. Expanding industrial plantations have also been responsible for drainage and conversions of peatland forests resulting in release of large amounts of carbon dioxide. The demand for palm oil is expected to increase further and result in greater pressures on tropical forests. Given Malaysia's high biophysical suitability for oil palm cultivation, it is important to understand patterns of oil palm expansion to better predict forest areas that are vulnerable to future expansion. We study natural forest conversion to industrial oil palm in Peninsular Malaysia between 1988 and 2012 to identify determinants of recent oil palm expansion using logistic regression and hierarchical partitioning. Using maps of recent conversions and remaining forests, we characterize agro-environmental suitability and accessibility for the past and future conversions. We find that accessibility to previously existing plantations is the strongest determinant of oil palm expansion and is significant throughout the study period. Almost all (> 99%) of the forest loss between 1988 and 2012 that has been converted to industrial oil palm plantations is within 1 km from oil palm plantations that have been established earlier. Although most forest conversions to industrial oil palm have been in areas of high biophysical suitability, there has been an increase in converted area in regions with low oil palm suitability since 2006. We find that reduced suitability does not necessarily restrict conversions to industrial oil palm in the region; however, lack of access to established plantations does.

Strong cooling induced by stand-replacing fires through albedo in Siberian larch forests.

The Siberian larch forests, taking up about a fifth of the global boreal biome, are different from the North American boreal forests in that they generally do not undergo a secondary succession. While wildfires in the boreal forests in North America have been shown to exert a cooling effect on the climate system through a sharp increase in surface albedo associated with canopy removal and species composition change during succession, the magnitude of the surface forcing resulting from fire-induced albedo change and its longevity in Siberia have not been previously quantified. Here we show that in contrast to previous expectations, stand-replacing fires exert a strong cooling effect similar in magnitude to that in North America. This cooling effect is attributable to the increase in surface albedo during snow-on periods. However, the observed earlier snowmelt in the region, and subsequently a longer snow-free season, has resulted in a warming effect which has the potential to offset the fire-induced cooling. The net albedo-induced forcing of the Siberian larch forests in the future would hinge on the interaction between the fire-induced cooling effect and the climate-induced warming effect, both of which will be impacted by the expected further warming in the region.

Assessing the risk of ignition in the Russian far east within a modeling framework of fire threat.

The forests of high biological importance in the Russian Far East (RFE) have been experiencing increasing pressure from growing demands for natural resources under the changing economy of post-Soviet Russia. This pressure is further amplified by the rising threat of large and catastrophic fire occurrence, which threatens both the resources and the economic potential of the region. In this paper we introduce a conceptual Fire Threat Model (FTM) and use it to provide quantitative assessment of the risk of ignition in the Russian Far East. The remotely sensed data driven FTM is aimed at evaluating potential wildland fire occurrence and its impact and recovery potential for a given resource. This model is intended for use by resource managers to assist in assessing current levels of fire threat to a given resource, projecting the changes in fire threat under changing climate and land use, and evaluating the efficiency of various management approaches aimed at minimizing the fire impact. Risk of ignition (one of the major uncertainties within fire threat modeling) was analyzed using the MODIS active fire product. The risk of ignition in the RFE is shown to be highly variable in spatial and temporal domains. However, the number of ignition points is not directly proportional to the amount of fire occurrence in the area. Fire ignitions in the RFE are strongly linked to anthropogenic activity (transportation routes, settlements, and land use). An increase in the number of fire ignitions during summer months could be attributed to (1) disruption of the summer monsoons and subsequent changes in fire weather and (2) an increase in natural sources of fire ignitions.