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Ecol Appl. 2017 Jun;27(4):1294-1304. doi: 10.1002/eap.1522. Epub 2017 Apr 17.

Integrating remotely sensed fires for predicting deforestation for REDD.

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Grupo de Ecología del Paisaje y Modelación de Ecosistemas ECOLMOD, Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Edificio 421, Cra 30 # 45-03, Bogotá, 111321, Colombia.
Department of Geography & Environmental Studies, University of Colorado, Colorado Springs, Colorado, 80918, USA.
Facultad de Ingenierías, Universidad de Medellín, Carrera 87 Nro. 30-65, Medellín, 050026, Colombia.
Department of Ecology and Evolution and Consortium for Interdisciplinary Environmental Research, Stony Brook University, Stony Brook, New York, 11794, USA.


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 to 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.


Moderate Resolution Imaging Spectroradiometer; edge; fire; forest loss; modeling; monitoring


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