Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru)

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.

Bibliographic Details

Main Authors:	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
Format:	info:eu-repo/semantics/article biblioteca
Language:	spa
Published:	MDPI
Subjects:	Grassland dynamics, Google Earth Engine (GEE), Sustainable livestock, Remote sensing, Random forest (RF), Landsat, https://purl.org/pe-repo/ocde/ford#4.05.00,
Online Access:	https://hdl.handle.net/20.500.12955/1691
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