Examinando por Autor "Sánchez Fuentes, Teiser"

Mostrando 1 - 3 de 3

Indirect monitoring of heterogeneous tropical agroforestry systems using active and passive remote sensing
(Elsevier B.V., 2026-03-11) 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
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.
Mathematical Models for Studying Growth of Retrophyllum rospigliosii in Agroforestry Systems with Coffee: A Case Study in Northern Peru
(MDPI, 2026-02-14) Oblitas Troyes, Jhon Franklin; Ocaña Zúñiga, Candy Lisbeth; Quiñones Huatangari, Lenin; Sánchez Fuentes, Teiser; Atalaya Marin, Nilton; Gómez Fernández, Darwin; Taboada Mitma, Víctor Hugo; Tineo Flores, Daniel; Goñas Goñas, Malluri
Romerillo (Retrophyllum rospigliosii), a vulnerable conifer native to the cloud forests of Cajamarca, Peru, persists in small remnants at high altitudes in San Ignacio province, where its integration into agroforestry systems may support both conservation and sustainable production. This study aimed to model the growth of R. rospigliosii associated with coffee (Coffea arabica L.) using diameter and height as indicators. Field data were collected over 18 months in two experimental plots and the study analyzed 329 individuals selected from 600 initially planted, with monthly monitoring to evaluate early growth and survival dynamics. The data were analyzed with nonlinear mathematical models, including Schumacher, Chapman–Richards, and Weibull, with model selection based on goodness-of-fit and prediction statistics such as R², AIC, and BIC. Results showed that Schumacher provided the best performance for height (R² = 0.98, AIC = 27,978.54), while Weibull (R² = 0.80, AIC = 27,204.63) and Chapman–Richards (R² = 0.80, AIC = 27,207.97) also yielded consistent estimates. For diameter, Schumacher was the most accurate (R² = 0.92, AIC = 2627.87). Survival analysis revealed significant differences between plots (p = 0.011), with higher survival at 1820 m (87.8% at 18 months) compared to 1540 m (77.3%). These findings indicate that the Schumacher model is most suitable for growth estimation, while altitude plays a critical role in survival, underscoring its importance in establishing R. rospigliosii within coffee-based agroforestry systems.
Prediction of biomass and nutritional quality of tropical pastures using multispectral analysis and machine learning models
(Elsevier B.V., 2026-05-17) Tafur Culqui, Josué; Atalaya Marin, Nilton; Gómez Fernandez, Darwin; Taboada Mitma, Víctor Hugo; Cruz Luis, Juancarlos Alejandro; Neyra, Henri; Anchayhua Torres, Janella Jelin; Quichua Baldeon, Rosalía; Sánchez Fuentes, Teiser; Olano Camán, Yadhira Milagros; Barrazueta Campos, Mauro Adel; Tineo Flores, Daniel; Goñas Goñas, Malluri
Determining pasture productivity and nutritional value through non-destructive approaches aimed at optimizing forage resource management and improving efficiency in livestock systems has become an urgent priority. In this context, the objective of this study was to evaluate the performance of machine learning models in predicting biomass production and the nutritional contribution of different pasture species, as well as to assess the role of vegetation indices (VIs) in these predictions. To this end, a multispectral sensor mounted on a DJI Matrice 350 RTK platform was used, together with agronomic, yield, and nutritional variables. The curated dataset was subsequently analyzed using linear and polynomial models, as well as tree-based algorithms and support vector machines. Model validation was performed using a group-constrained random partitioning scheme (Group Shuffle Split), with species considered as the grouping variable. Model interpretability was addressed through the SHAP (SHapley Additive Explanations) framework. The results indicated better predictive performance for yield-related variables compared to nutritional attributes. In particular, the Extra Trees model achieved the highest coefficients of determination (R²). SHAP analysis revealed that the Visible Atmospherically Resistant Index (VARI) contributed more strongly to yield-related predictions, whereas the Normalized Difference Red Edge (NDRE) showed a more consistent contribution to nutritional variables. In conclusion, these findings highlight the potential of integrating vegetation indices and machine learning models as effective tools for forage management, supporting informed decision-making in livestock production systems.