Indirect monitoring of heterogeneous tropical agroforestry systems using active and passive remote sensing

• Autores: Sánchez Fuentes, Teiser, Gómez Fernández, Darwin, Fernandez Jibaja, Jorge Antonio, Oblitas Troyes, Jhon Franklin, Chuquibala Checan, Beimer, Tafur Culqui, Josué, Quichua Baldeon, Rosalia, Taboada Mitma, Víctor Hugo, Tineo Flores, Daniel, Goñas Goñas, Malluri, Atalaya Marin, Nilton
• Formato: artículo
• Fecha de Publicación: 2026
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/3125
• Enlace del recurso: http://hdl.handle.net/20.500.12955/3125; https://doi.org/10.1016/j.rsase.2026.101966
• Nivel de acceso: acceso abierto
• Materia: LiDAR; Multispectral Mapping; Mapeo multiespectral; Canopy; Dosel; Terra; https://purl.org/pe-repo/ocde/ford#4.01.00; Agroforestry; Agroforestería; Remote sensing; Teledetección; Forest; Bosque; Tropical zones; Zona tropical; Monitoring; Vigilancia; Vegetation; Vegetación

Monitoring agroforestry systems remains challenging due to canopy heterogeneity and the coexistence of species with contrasting dynamics. While field-based methods offer high accuracy, they are inefficient for rapid and multitemporal structural assessments. This study integrated LiDAR and multispectral data collected using a Matrice 350 RTK equipped with a Zenmuse L2 sensor and a RedEdge-P camera. Raw LiDAR data were processed in DJI Terra v4.1 and subsequently pre-processed and corrected in TerraSolid v23.011, whereas multispectral products were generated in Agisoft Metashape Professional v2.2.1. The derived metrics indicated greater growth in System A, driven by fast-growing species, whereas System B showed an overall reduction with slight increases in the upper percentiles. In addition, MSAVI and MTVI2 were sensitive to canopy structure, while GNDVI and NDRE responded to foliage content. The agreement analysis revealed a slight bias (0.09 m) toward height overestimation by LiDAR compared to the hypsometer, with no apparent proportional error. This approach provides a replicable framework for multitemporal monitoring of structural and physiological changes in tropical vegetation, with potential for regional scaling and application in sustainable forest system management.