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
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Wageningen, The Netherlands.
Instituto de Investigaciones de la Amazonia Peruana (IIAP), Iquitos, Loreto, Perú.
Nat Commun. 2025 Apr 22;16(1):3764. doi: 10.1038/s41467-025-58358-5.
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 versus USD 411 ha), 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.
遥感数据有助于绘制具有经济价值的物种分布图，从而提高热带森林资源的价值。然而，当前的工具在大面积区域缺乏精度，利益相关者也无法使用。在此，我们与秘鲁保护区管理局合作，利用实地数据、可见光谱无人机图像和深度学习，开发并实施精确的、景观尺度的、物种层面的方法，以评估具有经济价值的亚马逊乔木状棕榈的分布和丰度。我们比较了使用传统样地法和基于无人机的方法在两个社区进行清查并制定可持续水果采摘计划所需的成本和时间。我们的方法能够检测出三种棕榈树的单株个体，即使它们密集丛生（总体平均得分74%），对弯叶毛里求斯棕的检测具有高精度和完整性（精度为99%，召回率为81%）。与基于样地的方法相比，我们基于无人机的方法将为管理计划清查每公顷弯叶毛里求斯棕的成本降低了99%（从每公顷411美元降至5美元），并将制定管理计划的总运营成本和人员时间分别降低了23%和36%。这些发现表明，根据管理所需的规模和精度定制技术，并让利益相关者参与各个阶段，有助于扩大热带地区的可持续管理。
