mlsanityData to 14 Oct 2026

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

arXiv 2609.17925 ↗First posted 15 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Large trees disproportionately contribute to biomass storage, habitat structure, and ecosystem functioning. However, their distribution and health dynamics remain poorly quantified at a regional scale. Here, a deep learning model (U-Net-ID) and canopy height models derived from sub-meter aerial imagery from 2020 were used to delineate all individual trees with crown area ≥ 100 m2 across the Sierra Nevada Floristic Province. The model was trained using more than 3.3 million synthetic tree crowns and achieved a median Intersection over Union (IoU) of 0.602 when validated against an independent dataset of 20,273 crowns. A total of 6,515,705 large trees were mapped, occurring across approximately 78.7% of the Sierra Nevada Floristic Province. The spatial distribution of large trees showed associations with elevation, temperature, and precipitation. Using Sentinel-2 time series from 2020 to 2025, tree health dynamics were characterized by extracting spectral trajectories for each crown and applying BFAST breakpoint detection algorithm combined with a disturbance classification framework to identify mortality, disturbance, and recovery trajectories of individual trees. Wildfires, estimated from CAL FIRE fire perimeters, were identified as the dominant driver of large-tree mortality, killing 10% of all large trees in the Sierra Nevada, with mortality strongly concentrated during the extreme 2020-2021 fire seasons.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 72 references that are in the map. First posted on arXiv 15 Sep 2026.

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3 of 72 references.

References · 3

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2023Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Modelcited by 10
  3. 2022Satellite Image Time Series Analysis for Big Earth Observation Datacited by 5

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