Examinando por Materia "Forest"

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Crecimiento del “shihuahuaco” Dipteryx ferrea (Ducke) Ducke en plantaciones bajo diferentes espaciamientos y tipos de suelo en Ucayali, Amazonía peruana
(Universidad Nacional Agraria La Molina, Facultad de Ciencias Forestales, 2025-01-10) Flores, Ymber
Se presentan los resultados de los ensayos experimentales con Dipteryx ferrea (Ducke) Ducke, especie forestal conocida como “shihuahuaco”, establecidas bajo cuatro sistemas forestales y agroforestales diferentes en el Centro Experimental Alexander von Humboldt, Ucayali, a una altitud de 225 m.s.n.m, diferenciados por el arreglo espacial implementado, el espaciamiento inicial y tipo de suelo, y con 11 años de edad. Se obtuvieron mediciones de la altura total, el diámetro a la altura del pecho (DAP), el área basal y el volumen maderable de las plantaciones. Los resultados muestran el potencial de D. ferrea en términos de crecimiento, supervivencia y productividad en las plantaciones a campo abierto puras y mixtas. El sistema 1, en suelo Plinthic acrisol, fisiografía plana y distanciamiento de 5 × 5 m, presenta el más alto valor de DAP promedio, altura total promedio y altura dominante, con 20.23 cm, 13.9 m y 16.8 m respectivamente. Estos resultados son estadísticamente superiores (p < 0.05) a los de otras plantaciones analizadas y muestran un rendimiento prometedor. En productividad, el sistema 3, en suelo Plinthic gleysol, fisiografía plana y distanciamiento de 2 × 2 m, presenta el más alto valor de área basal y volumen de madera rolliza con 38.40 m2/ha y 243.3 m3/ha respectivamente, muy superior estadísticamente a los otros sistemas, pero muy influido también por la alta densidad de la plantación. Estos hallazgos sugieren perspectivas alentadoras para el establecimiento de plantaciones de D. ferrea en plantaciones forestales.
Dominance of large trees in carbon storage of Peruvian Amazon forest
(Frontiers Media S.A., 2026-01-26) Lozano, Andi; Gaona Jimenez, Nery; Alvarado, Jaime W.; García Gonzáles, Patricia; Alva Arévalo, Alberto; Ordoñez, Luis; Saavedra Ramírez, Jorge; Tuesta Hidalgo, Juan C.; Vilela, Luis; Tuesta Hidalgo, Oscar A.; Baselly Villanueva, Juan Rodrigo; Chuchon Remon, Rodolfo Juan; Rengifo Del Águila, Sofía; Marin , César; Vallejos Torres, Geomar
Introduction: Forest carbon accumulation is crucial to mitigate ongoing climate change, as large individual trees store a substantial portion of the total carbon in biomass. In this study, large trees and carbon storage were estimated in five forests in the Peruvian Amazon. Methods: For the study, 100 plots were selected (twenty 500 m2 plots per forest site), distributed between 382 to 2086 meters above sea level. Various relationships were explored between the diameter at breast height (DBH) of the most abundant tree species and above- and below-ground carbon. The average carbon content in the tree was calculated based on 50% of the total tree volume at five sites of the Peruvian Amazon. Results: The site with most tree species (Alto Mayo Forest), had 59 tree species. The species Brosimum alicastrum, Ficus insipida, Manilkara bidentata, Inga sp., and Pourouma cecropiifolia showed an average aboveground carbon of 2.31, 3.09, 2.52, 2.78, 2.93 t ha-1, respectively, and values of 0.35, 0.48, 0.38, 0.42 and 0.43 t ha-1 of belowground carbon in trees with ≥ 46 cm DBH. Nectandra sp. showed an above and belowground carbon of 2.50 and 0.38 t ha-1 in trees with ≥ 46 cm DBH, while Cedrelinga catenaeformis showed averages of 5.21 and 0.74 t ha-1 of above and belowground carbon in trees with ≥ 61 cm DBH. Discussion: It was concluded that given the urgency of keeping carbon reserves out of the atmosphere, it is necessary to conserve trees larger than 41 cm, this also allows conserving forest biodiversity and microfauna by buffering the microclimate in the face of future climate changes.
Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation
(Elsevier, 2024-07-28) Gómez Fernández, Darwin; Salas López, Rolando; Zabaleta Santisteban, Jhon Antony; Medina Medina, Angel J.; Goñas Goñas, Malluri; Silva López, Jhonsy O.; Oliva Cruz, Manuel; Rojas Briceño, Nilton B.
Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal combination significantly affects the accuracy and reliability of the final results. This research aimed to analyze landscape change and fragmentation in northwestern Peru. We utilized accurate land cover and land use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) and ArcGIS software. For this, we identified the best dataset based on its highest overall accuracy, and kappa index; then we performed an analysis of variance (ANOVA) to assess the differences in accuracies among the datasets, finally, we obtained the LULC and fragmentation maps and analyzed them. We generated 31 datasets resulting from the combination of spectral bands, indices of vegetation, water, soil and clusters. Our analysis revealed that dataset 19, incorporating spectral bands along with water and soil indices, emerged as the optimal choice. Regarding the number of trees utilized in classification, we determined that using between 10 and 400 decision trees in Random Forest classification doesn't significantly affect overall accuracy or the Kappa index, but we observed a slight cumulative increase in accuracy metrics when using 100 decision trees. Additionally, between 1989 and 2023, the categories Artificial surfaces, Agricultural areas, and Scrub/ Herbaceous vegetation exhibit a positive rate of change, while the categories Forest and Open spaces with little or no vegetation display a decreasing trend. Consequently, the areas of patches and perforated have expanded in terms of area units, contributing to a reduction in forested areas (Core 3) due to fragmentation. As a result, forested areas smaller than 500 acres (Core 1 and 2) have increased. Finally, our research provides a methodological framework for image classification and assessment of landscape change and fragmentation, crucial information for decision makers in a current agricultural zone of northwestern Peru.