Effective integration of drone technology for mapping and managing palm species in the Peruvian Amazon
Tagle Casapia, Ximena | Cardenas-Vigo, Rodolfo | Marcos, Diego | Fernández Gamarra, Ernesto | Bartholomeus, Harm | Honorio Coronado, Eurídice, N | Di Liberto Porles, Silvana | Falen, Lourdes | Palacios, Susan | Tsenbazar, Nandin-Erdene | Mitchell, Gordon | Dávila Díaz, Ander | Draper, Freddie, C | Flores Llampazo, Gerardo | Pérez-Peña, Pedro | Chipana, Giovanna | del Castillo Torres, Dennis | Herold, Martin | Baker, Timothy, R | Wageningen University and Research [Wageningen] (WUR) | Instituto de Investigaciones de la Amazonía Peruana (IIAP) | Observation de la terre et apprentissage machine pour les défis agro-environnementaux (EVERGREEN) ; Centre Inria d'Université Côte d'Azur ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) | University of St Andrews [Scotland] | Department of Information Engineering [Brescia] ; Università degli Studi di Brescia = University of Brescia (UniBs) | University of Florida [Gainesville] (UF) | University of Leeds
International audience
Показать больше [+] Меньше [-]Английский. 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 plotbased 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.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил Institut national de la recherche agronomique