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Journal Article

Detection of subpixel tree fall gaps with landsat imagery in Central Amazon forests


Marra,  Daniel M.
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Negrón-Juárez, R. I., Chambers, J. Q., Marra, D. M., Ribeiro, G. H., Rifai, S. W., Higuchi, N., et al. (2011). Detection of subpixel tree fall gaps with landsat imagery in Central Amazon forests. Remote Sensing of Environment, 115(12), 3322-3328. doi:10.1016/j.rse.2011.07.015.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0018-53E3-4
Treefall gaps play important roles in both forest dynamics and species diversity, but variability across the full
range of gap sizes has not been reported at a regional scale due to the lack of a consistent methodology for
their detection. Here we demonstrate the sensitivity of Landsat data for detecting gaps at the subpixel level in
the Manaus region, Central Amazon. Spectral mixture analysis (SMA) on treefall gaps was used to map their
occurrence across a 3.4×104 km2 landscape using the annual change in non-photosynthetic vegetation
(ΔNPV) as the change metric. Thirty randomly selected pixels with a spectral signature of a treefall event (i.e.
high ΔNPV) were surveyed in the field. The most frequent single-pixel gap size detected using Landsat was
~360 m2, and the severity of the disturbance (ΔNPV) exhibited a significant (r2=0.32, p=0.001) correlation
with the number of dead trees (N10 cm diameter at breast height), enabling quantification of the number of
downed trees in each gap. To place the importance of these single-pixel disturbances into a broader context,
the cumulative disturbance of these gaps was equivalent to 40% of the calculated deforestation across the
Manaus region in 2008. Most detected single-pixel gaps consisted of six to eight downed trees covering an
estimated area of 250–900 m2. These results highlight the quantitative importance of small blowdowns that
have been overlooked in previous satellite remote sensing studies.