Examinando por Materia "Amazon"

Mostrando 1 - 4 de 4

Distribution Models of Timber Species for Forest Conservation and Restoration in the Andean-Amazonian Landscape, North of Peru
(MDPI, 2020-09-25) Cotrina Sánchez, Dany A.; Barboza Castillo, Elgar; Rojas Briceño, Nilton B.; Oliva Cruz, Manuel; Torres Guzmán, Cristóbal; Amasifuen Guerra, Carlos Alberto; Bandopadhyay, Subhajit
The Andean-Amazonian landscape has been universally recognized for its wide biodiversity, and is considered as global repository of ecosystem services. However, the severe loss of forest cover and rapid reduction of the timber species seriously threaten this ecosystem and biodiversity. In this study, we have modeled the distribution of the ten most exploited timber forest species in Amazonas (Peru) to identify priority areas for forest conservation and restoration. Statistical and cartographic protocols were applied with 4454 species records and 26 environmental variables using a Maximum Entropy model (MaxEnt). The result showed that the altitudinal variable was the main regulatory factor that significantly controls the distribution of the species. We found that nine species are distributed below 1000 m above sea level (a.s.l.), except Cedrela montana, which was distributed above 1500 m a.s.l., covering 40.68%. Eight of 10 species can coexist, and the species with the highest percentage of potential restoration area is Cedrela montana (14.57% from Amazonas). However, less than 1.33% of the Amazon has a potential distribution of some species and is protected under some category of conservation. Our study will contribute as a tool for the sustainable management of forests and will provide geographic information to complement forest restoration and conservation plans.
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.
Methodology for the phenotypic evaluation in Guazuma crinita trees in Ucayali, Peru
(Universidade Federal de Santa Maria, 2024-02-20) Revilla Chávez, Jorge Manuel; López Galán, Edinson Eduardo; Gonzales Alvarado, Antony Cristhian; Sáenz Ramírez, Lyanna Hellen; Mori Vásquez, Jorge Arturo; Rojas Mego, Krystel Clarissa; Abanto Rodríguez, Carlos; Sebbenn, Alexandre Magno
The objective of this study was to present a methodological tool for the phenotypic evaluation in progeny tests of Guazuma crinita in producer plots of the Aguaytía river basin, Ucayali, Peru, which allows field technicians to standardize the morphological evaluation criteria of trees in forest plantations. Therefore, the phenotypic traits were evaluated for plant height (m), diameter at the height of the base (cm), number of branches, number of rings, stem form, branch orientation, presence and quantity of leaves. The heritability and genetic and phenotypic correlations between traits were also estimated. Therefore, 32 morphological categories were plotted based on the significant correlations (p≤ 0.05) shown between the place of planting, the stem form, the orientation of the branches and the presence of leaves. For the same reason, the progeny showed low morphological patterns, being a low factor of phenotypic variability. It is concluded that the correlations between the biometric and morphological traits evaluated, allowed to validate the phenotypic evaluation procedures of Guazuma crinita progeny tests at 36 months of age.
Spatial patterns of diversity and genetic erosion of traditional cassava (Manihot esculenta Crantz) in the Peruvian Amazon: An evaluation of socio-economic and environmental indicators
(Springer Nature, 2007-02-23) Willemen, Louise; Scheldeman, Xavier; Soto Cabellos, Víctor; Rafael Salazar, Simón; Guarino, Luigi
This study evaluates quantitatively the suitability of the use of site-specific socio-economic and environmental data as indicators to rapidly assess patterns of diversity and genetic erosion risk in cassava. Socio-economic data as well as farmers’ estimation of genetic erosion were collected in the study area, the Ucayali region of the Peruvian Amazon, through interviews with 285 cassava farmers in 50 communities, while diversity was assessed based on agromorphological characterization of 295 cassava accessions. Using multivariate regression analyses, 50 and 45% of the variation in respectively diversity and genetic erosion estimation could be explained by a selected set of socio-economic and environmental indicators. In both regression models four out of the total of 38 variables proved to contribute significantly (at p < 0.10 level). Additionally, the study revealed that farmers are a good direct source of information on the diversity present at community level, which can contribute to the development of methodologies to assess diversity more rapidly. The results of this study are valuable for the development of models to rapidly assess diversity dynamics in large areas.