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Effective integration of drone technology for mapping and managing palm species in the Peruvian Amazon

Urheber*innen

Casapia,  Ximena Tagle
External Organizations;

Cardenas-Vigo,  Rodolfo
External Organizations;

Marcos,  Diego
External Organizations;

Gamarra,  Ernesto Fernández
External Organizations;

Bartholomeus,  Harm
External Organizations;

Coronado,  Eurídice N. Honorio
External Organizations;

Porles,  Silvana Di Liberto
External Organizations;

Falen,  Lourdes
External Organizations;

Palacios,  Susan
External Organizations;

Tsenbazar,  Nandin-Erdene
External Organizations;

Mitchell,  Gordon
External Organizations;

Díaz,  Ander Dávila
External Organizations;

Draper,  Freddie C.
External Organizations;

Llampazo,  Gerardo Flores
External Organizations;

Pérez-Peña,  Pedro
External Organizations;

Chipana,  Giovanna
External Organizations;

Torres,  Dennis Del Castillo
External Organizations;

/persons/resource/herold

Herold,  Martin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Baker,  Timothy R.
External Organizations;

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Volltexte (frei zugänglich)

5035905.pdf
(Verlagsversion), 4MB

Ergänzendes Material (frei zugänglich)
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Zitation

Casapia, X. T., Cardenas-Vigo, R., Marcos, D., Gamarra, E. F., Bartholomeus, H., Coronado, E. N. H., Porles, S. D. L., Falen, L., Palacios, S., Tsenbazar, N.-E., Mitchell, G., Díaz, A. D., Draper, F. C., Llampazo, G. F., Pérez-Peña, P., Chipana, G., Torres, D. D. C., Herold, M., Baker, T. R. (2025): Effective integration of drone technology for mapping and managing palm species in the Peruvian Amazon. - Nature Communications, 16, 3764.
https://doi.org/10.1038/s41467-025-58358-5


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5035905
Zusammenfassung
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.