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Deadtrees.earth: Crowdsourced drone data for global tree mortality maps

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    • Date
    4 March 2026
    Timeframe
    16:00 - 17:00 CET
    Duration
    60 minutes
    • Days
      Hours
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    Elevated forest disturbances and excess tree mortality are increasingly reported worldwide. Yet existing assessments are either based on patchy terrestrial observations or on large-scale satellite products, which are limited in resolution to pixel-level, binary tree loss detection. This leaves a blind spot on fine-scale disturbances where only a few trees are declining in an otherwise intact canopy.

    This talk provides an overview of the deadtrees.earth initiative and how the initiative leveraged crowdsourced drone data to build globally generalizing models for mapping tree mortality and disturbances from drones, airplanes, and Sentinel-2. Specifically, the talk goes into details of the upscaling approach where centimeter-scale drone data is leveraged to calibrate a model that processes multi-year Sentinel-2 time series around the globe.

     

    Session Objectives:

    By the end of this session, participants will be able to:

    • Analyze the limitations of existing terrestrial and satellite-based approaches for detecting fine-scale tree mortality.
    • Explain how crowdsourced centimeter-scale drone data can be used to calibrate global models for disturbance mapping.
    • Compare the spatial and temporal resolution trade-offs between drone, airborne, and Sentinel-2 data for tree mortality detection.
    • Apply the principles of multi-scale upscaling to integrate high-resolution drone observations with multi-year Sentinel-2 time series.
    • Evaluate the potential and challenges of crowdsourced remote sensing data for producing globally generalizable tree mortality maps within the deadtrees.earth initiative.

     

    Recommended Mastery Level/Prerequisites:

    • Graduate-level (MSc/PhD) understanding of remote sensing, basic machine learning concepts, and familiarity with time-series satellite data analysis.
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