Alert-Driven Community-Based Forest Monitoring: A Case of the Peruvian Amazon

Christina Cappello*, Arun Kumar Pratihast, Alonso Pérez Ojeda Del Arco, Johannes Reiche, Veronique De Sy, Martin Herold, Rolando Eduardo Vivanco Vicencio, Daniel Castillo Soto

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems. View Full-Text
Original languageEnglish
Article number4284
JournalRemote Sensing
Volume14
Issue number17
DOIs
Publication statusPublished - 30 Aug 2022

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