Cyrus Samimi

This study investigated perceptions of climate change among residents of 12 villages in Bushbuckridge, Mpumalanga, South Africa, through household surveys and focus group discussions. These perceptions were compared with climatic trends derived from Skukuza meteorological station data (1981–2023), focusing on summer seasonal temperature and rainfall patterns. The analysis of meteorological station data revealed significant increases in seasonal maximum temperatures, a marked decline in the number of wet days, and high interannual variability in total seasonal rainfall. While local perceptions aligned with recorded trends of rising temperatures, delayed onset of the rainy season, and more erratic rainfall patterns, discrepancies emerged regarding total rainfall amounts; participants perceived an overall decline, whereas meteorological data showed no statistically significant long-term trend in seasonal rainfall totals. These findings highlight the value of integrating local knowledge into climate change research. Local perceptions provide critical insights into micro-level climate impacts, such as changes in rainfall frequency and intensity, that may not be evident in observational data. Combining these perspectives with scientific measurements can enhance the design of culturally relevant and effective climate adaptation strategies. This approach fosters stronger community engagement and ensures that policies address both the observed and perceived impacts of climate change, thereby supporting more sustainable and inclusive responses at local and national scales.

In the coming decades, smallholder agriculture in the Global South will experience significant transformations due to environmental challenges, urbanization and demographic shifts. Although current research offers valuable perspectives on agrarian change, few studies have systematically tracked transitions in smallholder agriculture over time. Understanding long-term dynamics and integrating insights from diverse regions and sub-populations are essential for crafting effective and sustainable rural policies. This study employs a mixed-methods approach, combining quantitative longitudinal data from four time points (2010, 2014, 2019, and 2023) with qualitative insights from 10 villages within the Agincourt Health and Demographic Surveillance System site in Bushbuckridge, Mpumalanga Province, South Africa. Through both quantitative modeling and qualitative analysis, we explore agrarian trajectories, revealing trends, determinants, and transitions among various agrarian strategies. The findings emphasize the significant role of migrant and local off-farm employment in influencing the likelihood and level of subsistence farming, highlighting the dynamic nature of agrarian livelihoods. The impact of demographic factors, such as household size and gender, vary across different agrarian strategies. These insights offer valuable guidance for policy and intervention strategies aimed at enhancing rural livelihoods.

Wildfires increasingly threaten European ecosystems and communities, highlighting the necessity for effective predictive metrics to enhance fire risk management strategies. This study aims to compare the effectiveness of Vapor Pressure Deficit (VPD) and the Fire Weather Index (FWI) in forecasting wildfire occurrence and the extent of burned areas across various European forest types. Utilizing the European Forest Fire Information System (EFFIS) for comprehensive fire event data and the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) for meteorological variables, daily VPD and FWI values will be derived for multiple fire seasons spanning from 2000 to 2024.The research will explore how VPD and FWI each predict wildfire occurrence and burned area, with a focus on different forest types are categorized according to the CORINE Land Cover classification into broadleaf, conifer, and mixed forests while encompassing a range of climatic regions across Europe. VPD calculation methods are generally more straightforward and require fewer input parameters. In contrast FWI system is more complex, requiring a broader range of input data to compute its numerous indices.By comparing these two metrics across diverse forest types and biomes, the study seeks to determine the most effective indicators for wildfire prediction in Europe. The findings are intended to inform policymakers and fire management agencies, aiding in the development of targeted early warning systems and adaptive fire management strategies. This comparative assessment will contribute to a deeper understanding of the climatic drivers of wildfires and support efforts to mitigate their impacts under changing environmental conditions.

Trees have a pivotal role in mitigating heat island effects and several studies analyzed temperature differences of various species. However, the potential of trees to decrease temperatures during heat waves, drought and extreme events is inadequately studied, particularly at the city scale. Therefore, we present a remote sensing based approach that evaluates surface temperatures of trees in the urban environment of Forchheim (Germany) during the heatwave 2022. To provide an example of extreme conditions, we conducted measurements on July 20, 2022, the day with the current absolute daily heat record of the region since temperature measurements started in 1949. The three-month period before the survey flight (May-July) was the second warmest and third driest May-July period ever measured, leading to an ideal setting to assess the role of trees in urban regions during projected climate extremes. Analyzing such situations is highly relevant for city planning as existing research showed that sap flow is only reduced after several weeks of drought. We performed a low-altitude flight campaign (350 meters above ground) during the daily maximum temperature period (2-4 pm) with a thermal camera (Optris PI 450) for surface temperatures and a multispectral camera (Micasense RedEdge M) for vegetation parameters and land cover. We compared derived surface temperatures at field mapped locations of more than 3000 trees covering more than 30 species (n ≥ 20) to assess species patterns and the influence of urban parameters such as imperviousness. We show differences between species and interrelationship with vegetation parameters (e.g. NDVI) to provide insights into mitigation effects and patterns of urban trees during extreme events.

The cooling potential of tree species in Central European cities is insufficiently studied during extreme heat and drought, although a stronger surge in heatwaves compared to the global average is observed in this region. Remote sensing-based thermal surveys are an important tool to shed light on the mitigation effects of green infrastructure, but approaches covering extreme events are scarce. In this study, we present a simple, low-cost thermal airborne methodology that covers the current daily heat record in 2022, after the second warmest and third driest spring-to-summer period since 1949, in the medium-sized German city of Forchheim. We found that in spite of record-breaking heat and drought conditions, trees still had a considerable cooling potential with surface temperatures of 2 °C to 6 °C below air temperatures. Tree species were characterized by substantial median differences in tree surface temperatures up to 3.64 °C. Conifers and drought-sensitive broadleaf species showed the highest temperatures during the extreme event, while riparian species with potentially good water provision showed the highest cooling potential. In addition to tree species, imperviousness and tree NDVI were important variables for urban tree surface temperature, showing positive (imperviousness) and negative (NDVI) correlations with tree surface temperatures. Our study provides a methodological remote sensing example for the spontaneous and rapid coverage of extreme events, documenting the benefits of tree species in the urban context.

Above-water spectral measurements are a critical component of ocean remote sensing and provide essential information for validating and improving remote sensing algorithms for water quality analysis. The downwelling irradiance (Es) is an essential component of above-water spectral measurements and it is particularly important for water constituent concentration, sea surface temperature, and atmospheric correction algorithms. The atmosphere particles scatter and absorb the direct and diffusive components of the downwelling irradiance, creating a complex and distorted spectrum. We analyzed 6 years of the above-water hyperspectral measurements collected every 15 minutes at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station (JNS) installed in the Marsdiep tidal inlet of the Dutch Wadden Sea. The minimum level of solar irradiance and spectral shape of Es(λ) were simulated using Radiative Transfer (RT) to identify optimal Es spectra that could be favorable for remote sensing applications. The RT models were adjusted for the study area using in-situ bio-optical measurements. The spectrum of Es(λ) at the 3-optical depths were simulated to identify the minimum level of Es(λ). The red-shifted spectrum of Es(λ), caused by intense atmospheric scattering and the reddish hue of dusk or dawn radiations, was identified by Support Vector Machine applied to the simulated Es(λ) components. The results indicated that the maximum spectral value of Es(λ)_max <= 25 mW m^-2 nm^-1 identifies the minimum level of above-water solar irradiance, and the ratios of Es(480)/Es(680) ≤ 1 mW m^-2 nm^-1 and Es(λ)_max/Es(865) ≤ 1.25 mW m^-2 nm^-1 show the red-shifted spectra.