Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

Automatic detection of human settlements in rural Sub-Saharan Africa from satellite imagery with convolutional neural networks and OpenStreetMap

Spatial data play a critical role in both long-term planning and sustainable development as well as immediate emergency response. In Sub-Saharan Africa, a region struggling with extreme poverty, they are essential for effective humanitarian action. The advancements of deep neural networks in the past decade have opened great possibilities for generating this data in an automated way. However, neural networks require large amounts of training data. In regions where the data is not provided by national agencies, crowd-sourced mapping platforms such as OpenStreetMap have been proposed as an alternative. In this thesis, convolutional neural networks were trained on satellite imagery and OpenStreetMap data to detect buildings in rural Sub-Saharan Africa. Multiple models were trained on different data sets and with different hyperparameters. Performance of all models was assessed and compared. Additionally, a method for fine-tuning trained models to new geographic areas that requires only a small amount of additional data was proposed and tested in multiple settings. Using the presented methodology, buildings were detected in 16 test areas across Tanzania, Zambia and Malawi with average f1 score over 0.7. Small buildings and densely populated areas presented a challenge to all models. The fine-tuning method was successfully used to adapt a model to a region in Cameroon more than 2600 km away from where it was trained. Additionally, fine-tuning a model improves its performance in other areas as it benefits from learning from new data. The findings of this thesis can be used to assist in humanitarian mapping, especially in identifying areas where human settlements are missing in maps and estimating with high accuracy the amount of missing buildings

Willingness to Vaccinate Against COVID-19: The Role of Health Locus of Control and Conspiracy Theories

Pochopení prediktorů ochoty nechat se očkovat proti COVID-19 může pomoci při řešení současných i budoucích pandemií. Zkoumáme, jak ochota věřit konspiračním teoriím a tři dimenze místa kontroly zdraví (HLOC) ovlivňují postoj k očkování. Průřezová studie byla provedena na základě dat z online průzkumu na výzkumném souboru českých vysokoškoláků (n = 866) sesbíraných v lednu 2021 za použití vícerozměrných lineárních regresních modelů a moderace. Výsledky ukázaly, že 60 % českých studentů se chtělo nechat očkovat proti COVID-19. 40 % rozptylu ochoty nechat se očkovat bylo vysvětleno vírou v konspirační teorie související s COVID-19 a HLOC dimenzí - vliv jiných mocných (lékaři, policie apod.). Jedna šestina rozptylu ochoty nechat se očkovat byla vysvětlena HLOC, kognitivní reflexí a digitální zdravotní gramotností [eHealth Literacy Scale (EHEALS)]. HLOC a konspirační mentalita a její prediktory jsou platnými prediktory nerozhodnosti se očkovat proti COVID-19. Kampaně propagující očkování by se měly zaměřit na skupiny specificky náchylné k víře v konspirační teorie a postrádající HLOC související s dimenzí - vliv jiných mocných.Understanding the predictors of the willingness to get vaccinated against COVID-19 may aid in the resolution of current and future pandemics. We investigate how the readiness to believe conspiracy theories and the three dimensions of health locus of control (HLOC) affect the attitude toward vaccination. A cross-sectional study was conducted based on the data from an online survey of a sample of Czech university students (n = 866) collected in January 2021, using the multivariate linear regression models and moderation analysis. The results found that 60% of Czech students wanted to get vaccinated against COVID-19. In addition, 40% of the variance of willingness to get vaccinated was explained by the belief in the COVID-19-related conspiracy theories and the powerful others dimension of HLOC. One-sixth of the variance of the willingness to get vaccinated was explained by HLOC, cognitive reflection, and digital health literacy [eHealth Literacy Scale (EHEALS)]. HLOC and conspiracy mentality (CM) and its predictors are valid predictors of a hesitancy to get vaccinated against COVID-19. The campaigns promoting vaccination should target the groups specifically vulnerable to the conspiracy theories and lacking HLOC related to powerful others

The effect of acute stress response on conspiracy beliefs

Conspiracy theories wield significant influence across diverse domains encompassing health, politics, and the workplace (Douglas et al., 2015). For example, believing in COVID-19-related conspiracy theories is associated with diminished apprehension of the pandemic's severity, reduced adoption of preventive measures like mask-wearing, and decreased intent to undergo vaccination within the United States (Romer & Jamieson, 2020). In their analysis across 17 European countries, Syropoulos and Gkinopoulos (2023) found that institutional trust and conspiracy theory beliefs predict vaccine hesitancy even after accounting for demographic variables. Further, allegations of election rigging conspiracy theories served as a catalyst for the tumultuous storming of the United States Capitol in 2021. During periods of heightened stress, individuals may exhibit a proclivity for embracing conspiracy theories (Douglas et al., 2019; Lantian et al., 2017; J. W. Van Prooijen, 2019). Numerous conspiracy theories emerge during times of crisis (Nefes, 2014). In parallel, encountering significant life stressors and experiencing elevated perceived stress levels correlates with the belief in conspiracy theories among adults in the United States (Swami et al., 2016). An investigation involving Italian adults established a linkage between perceived stress, COVID-19 conspiracy beliefs and mistrust to medical authorities (Simione et al., 2021). The belief in conspiracy theories may be influenced by stress, and this effect could arise from the impact of biological stress on the brain. For instance, the influence of cortisol on hippocampal activity may affect memory processes related to conspiracy explanations (Duch, 2021; Hermans et al., 2014) or the propensity to less complex learning and reasoning mechanisms under stress may be involved (Moravec et al., 2019; Schwabe, 2017). Nevertheless, the academic literature on conspiracy theories did not examine the relationship between acute stress and inclinations to believe in conspiracy theories. Indeed, perceived psychological stress exhibits only a modest association with biological stress and cortisol levels (Halford et al., 2012), and the heightened inclination toward conspiracy theories may augment perceived stress levels, rather than the reverse (Liekefett et al., 2023). Also, the correlation between perceived stress and conspiracy beliefs may be spurious, caused by the same situational factors affecting survey measures of both variables (Podsakoff et al., 2003). Accordingly, the scholarship could benefit from empirical studies that examine the relationship between stress and conspiracy theory beliefs. Our research addresses this gap in the scholarship by exploring how acute physiological stress influences conspiracy beliefs. In our experimental study, we will utilize the Maastricht Acute Stress Procedure (MAST) to induce biological stress, characterized by elevated cortisol levels. Aligned with prior academic literature on conspiracy theories (Duch, 2021; Swami et al., 2016; J.-W. van Prooijen et al., 2018), we expect that inducing stress will heighten reported agreement with conspiracy theory statements. Additionally, we aim to differentiate whether physiological stress levels impact the expression of agreement with conspiracy theories or the adoption of new conspiracy theories. This distinction mirrors Swami et al.’s (2011) categorization of real-world and fictitious conspiracy theories, where the former pertains to circulated conspiracy accounts and the latter involves fictional theories created by researchers. For assessing agreement with conspiracy theories, we will present items addressing agreement with straightforward statements summarizing conspiracy theories, such as those related to the Russia-Ukraine War. To examine the adoption of novel conspiracy theories, we will introduce brief fictional narratives describing an event with two alternative explanations, one of which is conspiratorial. By gauging agreement with these explanations, we aim to investigate acute stress as a factor that increases the likelihood of adopting a novel conspiracy theory when encountering a new situation (Duch, 2021)