Forests worldwide are increasingly threatened by climate change and disturbances such as fire, pests, and pathogens, creating a need for scalable monitoring of tree cover and tree mortality. Aerial imagery from drones and aircraft enables detailed mapping of tree crowns and mortality, but progress is limited by the lack of globally distributed, multi-biome, harmonized datasets for joint segmentation of tree cover and mortality. To address this, we introduce two open, machine-learning-ready datasets for joint segmentation of tree cover and tree mortality from centimeter-scale aerial imagery across globally distributed sites. Our training dataset, DTE-aerial-train, comprises 346K image patches of $1024 \times 1024$ pixels at spatial resolutions from 2.5 to 20\,cm, with audited pseudo-labels for tree cover and mortality and expert annotations for rare mortality sites. Our evaluation dataset, DTE-aerial-bench, is a multi-biome, multi-resolution benchmark of 25 globally distributed orthoimages (525 patches) with expert annotations for both tree cover and mortality. Both datasets span tropical, temperate, boreal, and dryland biomes and cover a wide range of forest structures and mortality patterns. Using the benchmark, we establish reference baselines for mortality and tree cover segmentation across biomes and spatial resolutions. Improvements over prior models vary by biome: they are largest in boreal and montane forests (F1 from 0.40 to 0.58) and smaller in tropical forests and drylands. For data, model and code, please visit our \href{https://ayushi-3536.github.io/deadtrees_aerial.github.io/}{project page}. An interactive visualization of the benchmark dataset is also available at \href{https://deadtrees.earth/releases/dte-aerial-bench}{our release page}.

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