Examinando por Materia "Google Earth Engine (GEE)"

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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.
Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru)
(MDPI, 2022-05-01) Atalaya Marin, Nilton; Barboza Castillo, Elgar; Salas López, Rolando; Vásquez Pérez, Héctor Vladimir; Gómez Fernández, Darwin; Terrones Murga, Renzo E.; Rojas Briceño, Nilton B.; Oliva Cruz, Manuel; Gamarra Torres, Oscar Ándres; Silva López, Jhonsy Omar; Turpo Cayo, Efrain
In Peru, grasslands monitoring is essential to support public policies related to the identification, recovery and management of livestock systems. In this study, therefore, we evaluated the spatial dynamics of grasslands in Pomacochas and Ventilla micro-watersheds (Amazonas, NW Peru). To do this, we used Landsat 5, 7 and 8 images and vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI). The data were processed in Google Earth Engine (GEE) platform for 1990, 2000, 2010 and 2020 through random forest (RF) classification reaching accuracies above 85%. The application of RF in GEE allowed surface mapping of grasslands with pressures higher than 85%. Interestingly, our results reported the increase of grasslands in both Pomacochas (from 2457.03 ha to 3659.37 ha) and Ventilla (from 1932.38 ha to 4056.26 ha) micro-watersheds during 1990–2020. Effectively, this study aims to provide useful information for territorial planning with potential replicability for other cattle-raising regions of the country. It could further be used to improve grassland management and promote semi-extensive livestock farming.