By T. Trevor Caughlin, Publications Editor.

Vergara-Asenjo, G., D. Sharma, and C. Potvin. In press. Engaging Stakeholders: Assessing Accuracy of Participatory Mapping of Land Cover in Panama. Conservation Letters. DOI: 10.1111/conl.12161.

Large-scale forest management, including REDD+ and the Bonn Challenge, will require accurate maps of land cover. In many tropical landscapes, shifting agriculture has led to a mosaic of land in different successional stages, from intense cultivation to young fallow to secondary forest. Differences between these stages have great ecological importance, yet can be subtle in satellite imagery used to produce land cover maps. A new paper by Gerardo Vergara-Asenjo, a PhD candidate at McGill University, describes a novel solution to the long-standing problem of classifying secondary forest in tropical landscapes: ask the people that live there.

The study compared accuracy of participatory mapping by indigenous stakeholders to digital image classification in eastern Panama. The study took place in several indigenous communities where livelihood activities include cattle ranching, subsistence cultivation and handicraft production. Remote sensing data came from the multispectral Rapideye satellite with 5-meter resolution. In a series of meetings and workshops, >150 landowners collectively decided on key land cover classes and interpreted the satellite imagery, including drawing boundaries around the land cover classes. This participatory mapping exercise was compared to decision tree classification, a machine learning algorithm for land cover classification. The participatory mapping had greater overall accuracy (83.7%) compared to the decision tree classification (79.9%).

The study illuminates the benefits of participatory mapping for land use cover. First, participatory mapping could be a useful supplement to machine learning techniques. The errors between the machine learning algorithm and participatory mapping were different; for example, participatory mapping was better able to detect grassland, while the decision tree approach was better able to detect short fallow. These differences suggest that one method could help validate the other. Perhaps the greatest advantage of participatory mapping is the ability to engage stakeholders with academic research and stimulate conversations on natural resource management. Participatory mapping seems a win-win strategy for land use change research on tropical secondary forests with value both for primary research and for broader impacts.

Photo credit: An agricultural landscape of western Panama. Black lines represent outlines of landowner properties. Image courtesy Google Earth.

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