Data from: Integrating remotely sensed fires for predicting deforestation for REDD+

Fire is an important tool in tropical forest management, as it alters
forest composition, structure, and the carbon budget. The United Nations
program on Reducing Emissions from Deforestation and Forest Degradation
(REDD+) aims to sustainably manage forests, as well as conserve and
enhance their carbon stocks. Despite the crucial role of fire management,
decision-making on REDD+ interventions fails to systematically include
fires. Here, we address this critical knowledge gap in two ways. First, we
review REDD+ projects and programs to assess the inclusion of fires in
monitoring, reporting and verification (MRV) systems. Second, we model the
relationship between fire and forest for a pilot site in Colombia using
near-real-time (NRT) fire monitoring data derived from the Moderate
Resolution Imaging Spectroradiometer (MODIS). The literature review
revealed fire remains to be incorporated as a key component of MRV
systems. Spatially-explicit modeling of land use change showed the
probability of deforestation declined sharply with increasing distance to
the nearest fire the preceding year (multi-year model area under the curve
[AUC] 0.82). Deforestation predictions based on the model performed better
than the official REDD early-warning system. The model AUC for 2013 and
2014 was 0.81 compared to 0.52 for the early warning system in 2013 and
0.68 in 2014. This demonstrates NRT fire monitoring is a powerful tool to
predict sites of forest deforestation. Applying new, publicly available,
and open-access NRT fire data should be an essential element of
early-warning systems to detect and prevent deforestation. Our results
provide tools for improving both the current MRV systems, and the
deforestation early-warning system in Colombia.