Examinando por Materia "Land cover"

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Dinámica de la cobertura y uso de la tierra en Chalamarca, Chota, Cajamarca
(Universidad Nacional Autónoma de Chota, 2023-12-26) Lumba Huamán, Eduar Nann; Tarrillo Cieza, Deyner; Mírez Rubio, Yolmer; Chávez Collantes, Azucena; Chávez Juanito, Yuli Anabel; Sánchez Rojas, Alfonso; Elera Gonzales, Duberli Geomar
Las actividades naturales y antrópicas causan cambios continuos en la cobertura terrestre, que impactan en la sociedad, clima, biodiversidad, ciclos hidrológicos y ecosistemas. Los datos de sensoramiento remoto representan un componente clave para identificar la variación de los diferentes tipos de coberturas. El objetivo en este trabajo fue analizar el cambio de cobertura y uso de la tierra en el distrito de Chalamarca, durante el período 2000-2020. Se utilizó la metodología Corine Land Cover en los niveles I, II y III mediante el uso de imágenes Landsat en la plataforma Google Earth Engine. Se identificó cambios de cobertura y uso de la tierra en 9 571,58 ha, que representa el 53,82% del área de estudio. Se clasificaron ocho tipos de cobertura vegetal: tejido urbano continuo, cultivos transitorios, pastos, bosque plantado, vegetación herbácea, vegetación arbustiva, afloramientos rocosos y tierra desnuda. Las categorías que mostraron mayor incremento fueron: bosques plantados 2 642,82 ha (14,85%), cultivos transitorios 848,05 ha (4,77%) y pastos 322,20 ha (1,81%). Por el contrario, las clases de cobertura que disminuyeron fueron vegetación herbácea 2 943,94 ha (-16,50%) y arbustal 909,55 ha (-5,55%). La expansión de la frontera agrícola, el sobrepastoreo y el establecimiento de nuevas viviendas son los factores que influyeron en el cambio de cobertura y uso de la tierra en el distrito de Chalamarca en el periodo 2000-2020. En conclusión, se generó un cambio del 53,82% de cambio de cobertura y uso de la tierra en el distrito de Chalamarca durante el periodo 2000-2020.
Effectiveness of protected areas in containing the loss of Peruvian Amazonian forests
(Elsevier, 2025-01-11) Rojas Briceño, Nilton B.; Cajas Bravo, Verónica; Pasquel Cajas, Alexander; Guzman Valqui, Betty Karina; Silva López, Jhonsy O.; Veneros, Jaris; García, Ligia
Peruvian Amazonian Forests (PAF), vital for biodiversity, climate, and human well-being, lost 2.92M ha during 2001-2022, mainly due to anthropogenic activities. This prompted strategies to conserve and protect the PAF, such as land use and natural resource restrictions, with natural protected areas (NPAs) being the main strategy. This study evaluated the effectiveness of 41 NPAs in containing deforestation in the PAF by analyzing national spatial data. An Effectiveness Index (EI) was constructed by adding five standardized parameters: (1) the percentage of deforested area (DA%) and (2) its annual rate of change (DAr) between 2000 and 2022 inside each NPA, (3) the difference in DAr between NPAs and their surrounding areas, (4) their corresponding ecoregions, and (5) the entire PAF. In 2000, the DA% was 7.15% of the PAF, increasing to 10.88% in 2022. NPAs showed lower DAr than their surrounding areas and ecoregions, except for five NPAs. Of the 41 NPAs, nine were non-effective (EI≤3), 31 moderately effective (3
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.