  International stakeholders increasingly invest in offsetting carbon
emissions, for example, via issuing Payments for Ecosystem Services (PES) to
forest conservation projects. Issuing trusted payments requires a transparent
monitoring, reporting, and verification (MRV) process of the ecosystem services
(e.g., carbon stored in forests). The current MRV process, however, is either
too expensive (on-ground inspection of forest) or inaccurate (satellite).
Recent works propose low-cost and accurate MRV via automatically determining
forest carbon from drone imagery, collected by the landowners. The automation
of MRV, however, opens up the possibility that landowners report untruthful
drone imagery. To be robust against untruthful reporting, we propose
TrueBranch, a metric learning-based algorithm that verifies the truthfulness of
drone imagery from forest conservation projects. TrueBranch aims to detect
untruthfully reported drone imagery by matching it with public satellite
imagery. Preliminary results suggest that nominal distance metrics are not
sufficient to reliably detect untruthfully reported imagery. TrueBranch
leverages metric learning to create a feature embedding in which truthfully and
untruthfully collected imagery is easily distinguishable by distance
thresholding.
