Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.

The capacity of the United States' National Airspace System (NAS) must double to handle the passenger demands that are projected over the next 25 years. NASA initiated the virtual airspace modeling and simulation (VAMS) project in 2002 with participants, including members from industry, government, and academia to develop and share ideas on revolutionary concepts to meet the future. The constraints in the terminal area domain are the focus of Raytheon's VAMS concept, terminal area capacity enhancement concept (TACEC). TACEC envisions a high level of automation and synchronization, generating optimized 4D flight profiles to land/depart multiple aircraft "simultaneously" on closely spaced parallel runways. Implementation requires infrastructure improvements such as high-speed data link, improved surveillance, integrated terminal area network, and highly automated guidance and scheduling systems. This paper focuses on the issues associated with the TACEC high-speed data link required to send flight profiles to the arrival and departing aircraft upon entering the terminal airspace. The minimum essential uplink/downlink parameters needed to support the optimized 4D flight profiles are defined. Issues such as data rate, traffic load, and feasibility are also examined.

Background: Although soil burn severity (SBS) is traditionally mapped post-fire, pre-fire predictive approaches remain limited despite their potential to identify areas at risk of soil degradation, runoff and erosion. Aim: To develop a large-scale model for predicting post-fire SBS from pre-fire conditions at ~30 m spatial resolution. Methods: A harmonized dataset of 62 wildfires (2018–2024) was used to build machine learning (ML) frameworks based on Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using pre-fire vegetation, climate, soil–terrain and anthropogenic predictors. Performance was evaluated using five-fold cross-validation (CV), leave-one-fire-out (LOFO) validation and independent testing on 2025 fires. Key Results: All models showed similar CV overall accuracy (OA ~0.58), but LOFO performance varied. LightGBM maintained more consistent OA (0.51–0.58) across fire size classes, whereas XGBoost and Random Forest exhibited comparable performance. Key predictors included vegetation biomass (Normalized Difference Moisture Index, Enhanced Vegetation Index), fuel stress (Evaporative Stress Index, Evapotranspiration), elevation and soil moisture. High SBS was associated with dry conditions and high biomass availability, with region-specific controls modulating these patterns. Conclusion: ML models using pre-fire environmental data enable proactive SBS prediction. Implications: This framework supports fuel management planning, potential post-fire hydrological impact assessment and the prediction of SBS from pre-fire conditions.