Просторово-часовий розподіл пожеж у природних ландшафтах Рівненської області

Integrated fire management during the epoch of climate and land use change need to be organized on landscape level on the basis of strong vertical and horizontal co-operation between forestry and emergency agencies and local communities and requires clear understanding of fire regime and factors that it determined both in temporal and spatial aspects. For this purpose, fire regimes of natural and cultural landscapes of Rivne Oblast, Ukraine, for the period of 2001–2018 were studied based on remote sensing data. For getting better understanding of long-term pathways of wildfire regimes of natural landscapes of Rivne Oblast of Ukraine, we used data about hotspots from MOD14/MYD14 for number of fires, data about contours and areas of fires from MCD64A1and Landsat 8 OLI with classification of all fires on different levels of intensity and landcover type and data from three local meteorological stations on the fire weather danger level. The study showed that in Rivne Oblast for the period 7 fire maximums occurred, in particularly in 2002, 2009, 2012, 2015 and 2018. Official statistical data presents essentially underestimated area of fires: 257 ha in 2002 (while this study shows 750 ha based on RS data or 143 ha in 2015 (563 ha based on RS data). On average during the period, 190 wildfires with a total area of 12.6 thousand hectares occurred annually in landscapes of Rivne Oblast. The highest amount of fires occurred in the north part of the region – Rokytnivskii (62 000 ha burned), Dubrovitskii (26 000 ha), Kostopilskii (19 400 ha), Zarichnenskii (14 900 ha), Volodymyretskii (10 300 ha) districts. The main driver of fires is drought periods: 54.3 % of days in fire season determined as highest emergency level fire weather class – V, 15.3 % days – IV class, 18.5 % – III class. High level of fire weather danger in the region and permanent presence of ignition sources of human origin determine seasonal patterns of burning. Seasonal fire peaks occur in March (80 000 ha burned)-April (87 300 ha burned or 29.6 % of the average number of wildfires), August (32.2 %) and September (19.9 %) – totally 127 000 ha burned, when the maximum level of preparedness of fire-fighting brigades should be provided. Long-term dynamic showed increase of the average annual fire area for the period from 7.4 to 17.8 thousand hectares. The increase of annual amount of days with high and emergency fire whether levels from 24 to 62 during last 13 years is detected. Shortcomings of the existing system of statistical data collection on wildfires as an important component of fire management are discussed.Аналіз історичної динаміки пожеж та встановлення їх просторово-часових особливостей є основою для розроблення плану управління пожежами та стратегічного розміщення протипожежних сил і засобів на місцевості, а також здійснення попереджувальних заходів. Використовуючи дані дистанційного зондування Землі, отримано основні показники горючості природних територій Рівненської області за період з 2001 по 2018 рр. та виконано їх аналіз. Щорічно на території області відбувається в середньому 190 випадків пожеж на загальній площі 12,6 тис. га. Усереднені дані свідчать про тенденцію до зростання показників горючості території, зокрема середньорічна площа пожеж за період з 2001 по 2018 рр. зросла від 7,4 до 17,8 тис. га. За результатами дослідження встановлено, що найбільше пожеж стається у березні-квітні (29,6 %), серпні (32,2 %) та вересні (19,9 %), що повинно бути основою для максимальної готовності протипожежних служб у ці періоди. Виконано аналіз погодно-кліматичних показників за багаторічний період та досліджено їх зв'язок з основними показникам горючості. Зроблено висновки щодо проблеми пожеж у природних ландшафтах Рівненської області. Наведено недоліки наявної системи моніторингу пожеж як важливої складової системи охорони природних територій від пожеж

Wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire

Key message We propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss. Context The Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ. Aims The aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war. Methods The aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors. Results Random forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha−1 or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine (Pinus sylvestris L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020. Conclusion The change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible

Distance learning: opportunities and challenges in quarantine.

The urgent introduction of distance education into the learning process due to the COVID-19 pandemic has become a spontaneous experiment, requiring urgent decisions, effective mechanisms to implement into the educational process and analysis of ongoing educational process. The possibilities of modern digital technologies made it possible to quickly set up the information component of education and to controle student’s knowledge. A detailed analysis of the communication between the faculty members of the department among themselves and analysis of the features of teaching and self-education of students in the field of medical radiology and oncology based on answers and comments with using Google classroom, Socrative, Messenger Telegram, etc. was made. Issues of communicative interaction between faculty members and students, psychological aspects of distance communication, language of instruction, logistics of distance education and methodological problems are described in details. It has been suggested that distance education may be equally effective for theoretical subjects in full-time study form, but when studying clinical disciplines, the priority should be given to traditional methods of teaching, involving student's bedside training. Taking into account technical possibilities and high risk of development of extreme and emergency situations, it is suggested to introduce permanent readiness for distance education, to create all – Ukrainian distance imitation service of patients’ follow-up. The importance of using the state language in training as the basis for mastering the professional Ukrainian and English language as the basis for mastering international medical experience is indicated

Pandemic Boredom: Little Evidence That Lockdown-Related Boredom Affects Risky Public Health Behaviors Across 116 Countries

Some public officials have expressed concern that policies mandating collective public health behaviors (e.g., national/regional "lockdown ") may result in behavioral fatigue that ultimately renders such policies ineffective. Boredom, specifically, has been singled out as one potential risk factor for noncompliance. We examined whether there was empirical evidence to support this concern during the COVID-19 pandemic in a large cross-national sample of 63,336 community respondents from 116 countries. Although boredom was higher in countries with more COVID-19 cases and in countries that instituted more stringent lockdowns, such boredom did not predict longitudinal within-person decreases in social distancing behavior (or vice versa; n = 8,031) in early spring and summer of 2020. Overall, we found little evidence that changes in boredom predict individual public health behaviors (handwashing, staying home, self-quarantining, and avoiding crowds) over time, or that such behaviors had any reliable longitudinal effects on boredom itself. In summary, contrary to concerns, we found little evidence that boredom posed a public health risk during lockdown and quarantine

Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: A cross-sectional and longitudinal study

Background The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust in government regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government during the pandemic. Methods This study analysed data from the PsyCorona Survey, an international project on COVID-19 that included 23 733 participants from 23 countries (representative in age and gender distributions by country) at baseline survey and 7785 participants who also completed follow-up surveys. Specification curve analysis was used to examine concurrent associations between trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes. Results Higher trust in government regarding COVID-19 control was significantly associated with higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised beta = 0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated with trust in government (standardised beta = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trust at baseline survey was significantly associated with lower rate of decline in health behaviours over time (p for interaction = 0.001). Conclusions These results highlighted the importance of trust in government in the control of COVID-19

Politicization of COVID-19 health-protective behaviors in the United States:Longitudinal and cross-national evidence

During the initial phase of the COVID-19 pandemic, U.S. conservative politicians and the media downplayed the risk of both contracting COVID-19 and the effectiveness of recommended health behaviors. Health behavior theories suggest perceived vulnerability to a health threat and perceived effectiveness of recommended health-protective behaviors determine motivation to follow recommendations. Accordingly, we predicted that—as a result of politicization of the pandemic—politically conservative Americans would be less likely to enact recommended health-protective behaviors. In two longitudinal studies of U.S. residents, political conservatism was inversely associated with perceived health risk and adoption of health-protective behaviors over time. The effects of political orientation on health-protective behaviors were mediated by perceived risk of infection, perceived severity of infection, and perceived effectiveness of the health-protective behaviors. In a global cross-national analysis, effects were stronger in the U.S. (N = 10,923) than in an international sample (total N = 51,986), highlighting the increased and overt politicization of health behaviors in the U.S

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

COVID-19 stressors and health behaviors:A multilevel longitudinal study across 86 countries

Anxiety associated with the COVID-19 pandemic and home confinement has been associated with adverse health behaviors, such as unhealthy eating, smoking, and drinking. However, most studies have been limited by regional sampling, which precludes the examination of behavioral consequences associated with the pandemic at a global level. Further, few studies operationalized pandemic-related stressors to enable the investigation of the impact of different types of stressors on health outcomes. This study examined the association between perceived risk of COVID-19 infection and economic burden of COVID-19 with health-promoting and health-damaging behaviors using data from the PsyCorona Study: an international, longitudinal online study of psychological and behavioral correlates of COVID-19. Analyses utilized data from 7,402 participants from 86 countries across three waves of assessment between May 16 and June 13, 2020. Participants completed self-report measures of COVID-19 infection risk, COVID-19-related economic burden, physical exercise, diet quality, cigarette smoking, sleep quality, and binge drinking. Multilevel structural equation modeling analyses showed that across three time points, perceived economic burden was associated with reduced diet quality and sleep quality, as well as increased smoking. Diet quality and sleep quality were lowest among respondents who perceived high COVID-19 infection risk combined with high economic burden. Neither binge drinking nor exercise were associated with perceived COVID-19 infection risk, economic burden, or their interaction. Findings point to the value of developing interventions to address COVID-related stressors, which have an impact on health behaviors that, in turn, may influence vulnerability to COVID-19 and other health outcomes

Conceptual replication and extension of health behavior theories' predictions in the context of COVID-19: Evidence across countries and over time

Virus mitigation behavior has been and still is a powerful means to fight the COVID-19 pandemic irrespective of the availability of pharmaceutical means (e.g., vaccines). We drew on health behavior theories to predict health-protective (coping-specific) responses and hope (coping non-specific response) from health-related cognitions (vulnerability, severity, self-assessed knowledge, efficacy). In an extension of this model, we proposed orientation to internal (problem-focused coping) and external (country capability) coping resources as antecedents of health protection and hope; health-related cognitions were assumed as mediators of this link. We tested these predictions in a large multi-national multi-wave study with a cross-sectional panel at T1 (Baseline, March-April 2020; N = 57,631 in 113 countries) and a panel subsample at two later time points, T2 (November 2020; N = 3097) and T3 (April 2021; N = 2628). Multilevel models showed that health-related cognitions predicted health-protective responses and hope. Problem-focused coping was mainly linked to health-protective behaviors (T1-T3), whereas country capability was mainly linked to hope (T1-T3). These relationships were partially mediated by health-related cognitions. We conceptually replicated predictions of health behavior theories within a real health threat, further suggesting how different coping resources are associated with qualitatively distinct outcomes. Both patterns were consistent across countries and time

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas