Sub-pixel mapping of disturbance and tree mortality dynamics from Sentinel-2 time series around the globe

Clemens Mosig , Teja Kattenborn, David Montero Loaiza, Janusch Vanja-Jehle, John Brandt, Nathan Jacobs, Subash Khanal, Eric Xing, Martin Schwartz, Helene C. Muller-Landau, Mirela Beloiu, Aurora Bozzini, Yan Cheng, Keenan Ganz , Björn Grüning, Henrik Hartmann, Jan Hempel, Stéphanie Horion, Samuli Junttila, Kirill Korznikov, Guido Kraemer, Milena Mönks, Davide Nardi, Paul Neumeier, Jonathan Schmid, Salim Soltani, Marie Therese-Schmehl, Josh Veitch-Michaelis, Miguel Mahecha

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. Here, we present a methodology for
annually mapping sub-pixel fractional cover of standing deadwood and trees from
rolling four-year windows of Sentinel-2 time series. Fractional cover is the proportion of
each pixel covered by dead or live tree crowns. To obtain globally distributed sub-pixel
reference labels, we leveraged the crowd-sourced archive deadtrees.earth of
centimeter-scale drone orthophotos with two globally calibrated semantic segmentation
models to derive tree and standing deadwood masks, yielding 6.2 million labeled
Sentinel-2 pixels. Spatial block cross-validation shows high performance for tree cover,
Pearson’s r = 0.58–0.64, across biomes, and moderate performance for standing
deadwood cover, Pearson’s r = 0.30–0.56. Viewed as a binary classification task, the
model achieves a mean precision of 91% and recall of 90% for tree cover, and a
precision of 54% and recall of 82% for standing deadwood. Validation against
independently obtained and ground-validated data in all major biomes reproduced
known disturbance patterns and timing at unprecedented spatial detail compared to
state-of-the-art products. Our method provides the critically needed missing link between fine-scale
round observations and low-resolution remote sensing products, allowing more realistic
estimates of global trends in forest disturbance and tree mortality.