UPDF AI

Satellite-based deforestation alerts with training and incentives for patrolling facilitate community monitoring in the Peruvian Amazon

Tara Slough,Jacob Kopas,Johannes Urpelainen

2021 · DOI: 10.1073/pnas.2015171118
Proceedings of the National Academy of Sciences of the United States of America · 52 Citations

TLDR

The results suggest that externally facilitated community-based monitoring protocols that combine remote-sensed early deforestation alerts with training and incentives for monitors could contribute to sustainable forest management.

Abstract

Significance Remote-sensed deforestation alerts provide high-frequency information on tree cover loss in the Amazon. However, these alerts often do not reach immediately impacted populations. We conduct a randomized controlled trial to assess the effects of an externally facilitated community monitoring program in the Peruvian Amazon. The program selected, trained, and incentivized monitors to patrol communal forests while providing access to early deforestation alerts. This monitoring yielded imprecisely estimated reductions in average tree cover loss over 2 y of monitoring. Survey evidence suggests that community members perceived the new monitors as authorities with influence over forest management and that the monitors’ incentivized patrols substitute for traditional citizen patrols that suffer from free riding and inhibit timely detection of and responses to deforestation. Despite substantial investments in high-frequency, remote-sensed forest monitoring in the Amazon, early deforestation alerts generated by these systems rarely reach the most directly affected populations in time to deter deforestation. We study a community monitoring program that facilitated transfer of early deforestation alerts from the Global Forest Watch network to indigenous communities in the Peruvian Amazon and trained and incentivized community members to patrol forests in response to those alerts. The program was randomly assigned to 39 of 76 communities. The results from our analysis suggest that the program reduced tree cover loss, but the estimated effects from the experiment are imprecise: We estimate a reduction of 8.4 ha per community in the first year (95% CI [−19.4, 2.6]) and 3.3 ha in the second year (95% CI: [−13.6, 7.0]) of monitoring. The estimated reductions were largest in communities facing the largest threats. Data from monitoring records and community surveys provide evidence about how the program may affect forest outcomes. Community members perceived that the program’s monitors were new authorities with influence over forest management and that the monitors’ incentivized patrols were substitutes for traditional, unincentivized citizen patrols that suffer from free riding and inhibit timely community detection of and responses to deforestation. Should our findings be replicated elsewhere, they imply that externally facilitated community-based monitoring protocols that combine remote-sensed early deforestation alerts with training and incentives for monitors could contribute to sustainable forest management.