Examinando por Materia "forest monitoring"

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Cloud computing application for the analysis of land use and land cover changes in dry forests of Peru
(International Information and Engineering Technology Association (IIETA), 2024-09-30) Barboza, Elgar; Salazar Coronel, Wilian; Gálvez Paucar, David; Valqui Valqui, Lamberto; Valqui, Leandro; Zagaceta, Luis H.; Gonzales, Jhony; Vásquez, Héctor V.; Arbizu, Carlos I.
Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.
UAV Flight Orientation and Height Influence on Tree Crown Segmentation in Agroforestry Systems
(Forests, 2026-01-09) Baselly Villanueva, Juan Rodrigo; Fernández Sandoval, Andrés; Pinedo Freyre, Sergio Fernando; Salazar Hinostroza, Evelin Judith; Cárdenas Rengifo, Gloria Patricia; Puerta, Ronald; Huanca Diaz, José Ricardo; Tuesta Cometivos, Gino Anthony; Vallejos Torres, Geomar; Goycochea Casas, Gianmarco; Álvarez Álvarez, Pedro; Ismail, Zool Hilmi
Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems.