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Integrating remotely sensed fires for predicting deforestation for REDD.

Dolors Armenteras1, Cerian Gibbes2, Jesús A Anaya3

  • 1Grupo 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.

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Summary

Fire management is crucial for tropical forests and carbon budgets. Integrating near-real-time fire monitoring into Reducing Emissions from Deforestation and Forest Degradation (REDD+) programs improves deforestation prediction and forest management.

Keywords:
Moderate Resolution Imaging Spectroradiometeredgefireforest lossmodelingmonitoring

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Area of Science:

  • Environmental Science
  • Forestry
  • Remote Sensing

Background:

  • Fire significantly impacts tropical forest ecosystems, influencing composition, structure, and carbon budgets.
  • The United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD+) program aims for sustainable forest management and carbon stock enhancement.
  • Current REDD+ decision-making and monitoring, reporting, and verification (MRV) systems often overlook the critical role of fire.

Purpose of the Study:

  • To assess the integration of fire into REDD+ MRV systems.
  • To model the relationship between fire and forest dynamics for deforestation prediction.
  • To evaluate the utility of near-real-time (NRT) fire monitoring data for early warning systems.

Main Methods:

  • A literature review of REDD+ projects and programs to examine fire inclusion in MRV.
  • Spatially explicit modeling using Moderate Resolution Imaging Spectroradiometer (MODIS) NRT fire data in a Colombian pilot site.
  • Comparison of model-based deforestation predictions against an existing REDD early-warning system.

Main Results:

  • The literature review confirmed that fire is inadequately incorporated into MRV systems.
  • The developed model demonstrated a strong inverse relationship between proximity to fire and deforestation probability (AUC 0.82).
  • NRT fire monitoring-based predictions significantly outperformed the official REDD early-warning system (AUC 0.81 vs. 0.52-0.68).

Conclusions:

  • NRT fire monitoring is a powerful tool for predicting deforestation hotspots.
  • Integrating open-access NRT fire data into early warning systems is essential for effective deforestation detection and prevention.
  • The study provides actionable tools to enhance both REDD+ MRV systems and deforestation early warning in Colombia.