Rapid Loss of the Sierra Nevada's Largest Trees Driven by Fire

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 project page. An interactive visualization of the benchmark dataset is also available at our release page.

Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression. A self-supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions. We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.

Counting individual trees is a fundamental task for environmental monitoring, yet remains largely unexplored with satellite imagery. At these resolutions, isolated trees may still be identifiable, but crown boundaries become ambiguous in dense forests, making the notion of an individual tree inherently ill-defined. Moreover, large-scale manual annotations of individual trees are prohibitively expensive. While scalable supervision can be derived from airborne LiDAR, the resulting annotations are noisy and difficult to exploit effectively. We address these challenges by formulating tree counting as a spatial density matching problem supervised through Unbalanced Optimal Transport. This formulation naturally accommodates both precise localization of isolate trees and robust density estimation in dense forests. We further introduce a self-correction mechanism that leverages transport residuals to progressively refine noisy supervision during training. We evaluate our approach on TinyTrees, a new benchmark spanning three continents and three satellite sensors, comprising over 216 million tree annotations (including 639k manually verified instances) across $25\,890$ km$^2$. Our method consistently outperforms detection-based, regression-based, and transport-based distribution-matching baselines, demonstrating the effectiveness of unbalanced transport and reliability-aware supervision for large-scale tree counting from satellite imagery. Code, data and models are available at https://github.com/dgominski/treematch.