News
Effective Integration of drone technology for mapping and managing palm species in the Peruvian Amazon
An article of Ximena Tagle Casapia, Rodolfo Cardenas-Vigo, Diego Marcos, Ernesto Fernández Gamarra, Harm Bartholomeus, Eurídice N. Honorio Coronado, Silvana Di Liberto Porles, Lourdes Falen, Susan Palacios, Nandin-Erdene Tsenbazar, Gordon Mitchell, Ander Dávila Díaz, Freddie C. Draper, Gerardo Flores Llampazo, Pedro Pérez-Peña, Giovanna Chipana, Dennis Del Castillo Torres, Martin Herold & Timothy R. Baker: Effective integration of drone technology for mapping and managing palm species in the Peruvian Amazon, has been published in Nature Communications volume 16, Article number: 3764 (2025).
Some key findings
- We developed and implemented precise, landscape-scale methods for assessing the distribution and abundance of economically important Amazonian palms.
- The Protected Areas Authority of Peru (SERNANP) achieved a 99% reduction in inventory costs per hectare for Mauritia flexuosa, decreasing from USD 411 per hectare to USD 5.
- SERNANP also reduced total operational costs for developing management plans by 23% and the time spent by personnel by 36%.
- We demonstrate that tailoring technology to the scale and precision required for management, and involvement of stakeholders at all stages, can help expand sustainable management in tropical forests.
Abstract
Remote sensing data could increase the value of tropical forest resources by helping to map economically important species. However, current tools lack precision over large areas, and remain inaccessible to stakeholders. Here, we work with the Protected Areas Authority of Peru to develop and implement precise, landscape-scale, species-level methods to assess the distribution and abundance of economically important arborescent Amazonian palms using field data, visible-spectrum drone imagery and deep learning. We compare the costs and time needed to inventory and develop sustainable fruit harvesting plans in two communities using traditional plot-based and our drone-based methods. Our approach detects individual palms of three species, even when densely clustered (average overall score, 74%), with high accuracy and completeness for Mauritia flexuosa (precision; 99% and recall; 81%). Compared to plot-based methods, our drone-based approach reduces costs per hectare of an inventory of Mauritia flexuosa for a management plan by 99% (USD 5 ha-1 versus USD 411 ha-1), and reduces total operational costs and personnel time to develop a management plan by 23% and 36%, respectively. These findings demonstrate how tailoring technology to the scale and precision required for management, and involvement of stakeholders at all stages, can help expand sustainable management in the tropics.