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The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model’s accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management.

Fire management has proven successful in reducing deforestation, preserving biodiversity and mitigating greenhouse gas (GHG) emissions. After years of zero burning policies in fire-adapted ecosystems, and resulting increases in fire hazards and risks, countries are moving towards integrated fire management (IFM) including prescribed burning (PB). With a primary focus on biodiversity, Brazilian governmental organizations endorsed this paradigm shift in 2014, with the introduction of IFM in a number of protected areas (PA) of the Cerrado. Reducing high intensity mid/late dry season (M/LDS) fires through PB in the early dry season (EDS) has proven successful in other savanna ecosystems, with demonstrated mitigation potential as EDS fires are associated with lower GHG emissions. In the present study, Earth observation data were used to analyze the seasonality of active fires, burned areas and fuel loads. A dynamic performance benchmark (control-treatment paired sample test) was applied to assess the effectiveness of existing IFM activities in promoting emission abatement over the pre-covid period 2014–2019. Compared against the responses of PAs without IFM-PB, the PAs with IFM-PB showed significant increases in EDS fires (+137% hotspots) and EDS burned areas (from a share of 11.2% to 29.5% of the total yearly burned area). Fuel fragmentation through EDS-PB, tracked through calibrated fuel load maps, also led to a 62% reduction in burned areas in the IFM period 2014–2019. Combined M/LDS burned areas decreased from 85.1% of the total yearly burned area to a share of 67.7%. When applying the observed shift in fire seasonality and the effect of burned area reduction to all the PA of the Cerrado for the same period, we estimate an emission abatement potential of 1085 764 tCO2 e / y . Given the fact that IFM followed a biodiversity-centred approach in the Cerrado, an emission abatement-centered approach could result in even higher abatement potentials.

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

Increasing conflicts between farmers and pastoralists continue to be a major challenge in the Sahel. Political and social factors are in tandem important underlying determinants for conflicts in the region, which are amplified by the variability and scarcity of natural resources, often as a result of climate variability and climate change. This study aimed at holistically assessing the main environmental parameters that influence the patterns of seasonal migratory movements (transhumance) in a transboundary area in the southern Republic of Chad and northern Central African Republic through a broad set of Earth observation (EO) data and data from the Transhumance Tracking Tool. A spatial model was applied to the datasets to determine the spatiotemporal dynamics of environmental suitability that reflects suitable areas and corridors for pastoralists. A clear difference in environmental suitability between the origin and destination areas of herders was found in the dry season, proving the main reason for pastoralists’ movements, i.e., the search for grazing areas and water. Potential conflict risk areas could be identified, especially along an agricultural belt, which was proven by conflict location data. The results demonstrate the potential and innovation of EO-derived environmental information to support the planning of transhumance corridors and conflict prevention in the Sahel. In the future, a combination of real-time tracking of herders and EO-derived information can eventually lead to the development of an early warning system for conflicts along transhumance corridors in the Sahel.

Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.

Effective monitoring of the soil organic carbon (SOC) content at the field scale is crucial for supporting sustainable agricultural practices. This study evaluates the utility of multispectral data acquired by an unmanned aerial vehicles (UAV) during bare soil conditions for predicting the SOC content of a long-term experimental field site (LTE) in Saxony-Anhalt, Germany. Our methodology involves constructing predictive models using multiple algorithms (CUBIST, MARS, linear regression) and applying image correction techniques to enhance prediction accuracy by mitigating the influence of confounding factors such as crop residuals. Among the tested models, the CUBIST algorithm, combined with a pixel selection strategy employing a 2 m radius and stratified image correction, demonstrates the most promising results, achieving an R-squared value of 0.54 and an RMSE of 1.9 g kg −1 . Spatial distribution maps generated by this optimized model effectively depict the impact of organic fertilization on the SOC content, although the clarity of these patterns varies depending on the image processing method and algorithm used. Our findings highlight the potential of utilizing UAV-derived multispectral data for SOC monitoring at the LTE scale. However, further research is warranted to assess the generalizability of this approach to agricultural fields with lower SOC variability.

Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R² between 0.4 and 0.54 (adj. R² between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change.

Rising temperatures in Africa present an increasing threat to agricultural productivity and public health, particularly among subsistence farming communities reliant on rain-fed agriculture. Heat exposure can impair farmers' work capacity, disrupt harvests, and heighten health risks, especially for young children vulnerable to undernutrition. The Heat to Harvest (H2H) study investigates how environmental heat exposure influences farmers' physiological and behavioral responses, and how these in turn affect harvest yields and child nutrition. It also examines differences in labor performance and recovery between households with and without cool roof coatings, although this intervention is not the central focus. H2H is designed as a prospective cohort study nested within two Health and Demographic Surveillance Systems (HDSS) in Nouna, Burkina Faso, and Siaya, Kenya. The study integrates environmental monitoring (temperature and humidity sensors used to compute Wet Bulb Globe Temperature), biometric data (via wearables tracking heart rate, temperature, physical activity, energy expenditure, and sleep), and GPS tracking (capturing spatial mobility and labor duration). The study is embedded within a larger cluster-randomized controlled trial, facilitating comparative analysis under varying thermal conditions. Findings will provide evidence-based insights into how climate-related heat stress affects health and agricultural outcomes, supporting the development of targeted adaptation strategies to enhance resilience, health, and food security in vulnerable farming communities.

Due to high spatiotemporal variability of aquatic ecosystems, relationships between microplastic sources and sinks are highly complex and transportation pathways yet to be understood. Field data acquisitions are a necessary component for monitoring of microplastic contamination but alone cannot capture such complex relationships. Remote sensing is a key technology for environmental monitoring through which extrapolation of spatially limited field data to larger areas can be obtained. We tested whether microplastic distribution follows the same movement pattern as water constituents depictable from Landsat-8, Sentinel-2, and RapidEye satellite images, namely chlorophyll-a, suspended particulate matter, and colored dissolved organic matter, and if they can be utilized as proxies. As rivers are a major source for marine microplastic contamination, we sampled three example river systems: the lower courses and river mouths of the Trave and Elbe estuary in Germany and the Po delta in Italy. For a full quantitative analysis of microplastics (>300 µm), ATR- and FPA-based µFTIR spectroscopy and NIR imaging spectroscopy were utilized. A comparison of water constituents with in-situ data using regression analysis showed no consistent pattern. Only for the Trave river, a positive relationship between microplastics and water constituents was present. Differences in hydrodynamic conditions and spatiotemporal dynamics of water constituents and microplastic emissions among the rivers are possible explanations for the contrary results. The lower detection limit of 300 µm for microplastics could also have influenced relationships as microplastic abundance exponentially increases with decreasing size class. Further studies with improved sampling methods are necessary to assess our proposed method.

Vapor phase infiltration (VPI) has emerged as a promising tool for fabrication of novel hybrid materials. In the field of polymeric gas separation membranes, a beneficial impact on stability and membrane performance is known for several polymers with differing functional groups. This study for the first time investigates VPI of trimethylaluminum (TMA) into poly(1‐trimethylsilyl‐1‐propyne) (PTMSP), featuring a carbon–carbon double bond as functional group. Saturation of the precursor inside the polymer is already attained after 60 s infiltration time leading to significant densification of the material. Depth profiling proves accumulation of aluminum in the polymer itself, but a significantly increased accumulation is visible in the gradient layer between polymer and SiO2 substrate. A reaction pathway is proposed and supplemented by density‐functional theory (DFT) calculations. Infrared spectra derived from both experiments and simulation support the presented reaction pathway. In terms of permeance, a favorable impact on selectivity is observed for infiltration times up to 1 s. Longer infiltration times yield greatly reduced permeance values close or even below the detection limit of the measurement device. The present results of this study set a strong basis for the application of VPI on polymers for gas‐barrier and membrane applications in the future. The vapor phase infiltration of trimethylaluminum into poly(1‐trimethylsilyl‐1‐propyne) for gas separation applications is reported: Infiltration leads to a reduction of the water contact angle on the surface and a densification of the morphological structure in the bulk. Depending on the infiltration time, either gas separation with increased selectivity or gas barrier functionality with greatly reduced permeation is achieved.