Cover and land use changes in the dry forest of Tumbes (Peru) using sentinel-2 and google earth engine data

• Autores: Barboza Castillo, Elgar, Salazar Coronel, Wilian, Gálvez Paucar, David, Valqui Valqui, Lamberto, Saravia Navarro, David, Gonzales, Jhony, Aldana, Wiliam, Vásquez Pérez, Héctor Vladimir, Arbizu Berrocal, Carlos Irvin
• Formato: objeto de conferencia
• Fecha de Publicación: 2022
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: español
• OAI Identifier: oai:null:20.500.12955/2076
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2076; https://doi.org/10.3390/IECF2022-13095
• Nivel de acceso: acceso abierto
• Materia: Forest remote sensing; Random Forest (RF); Temporal series; Biodiversity; https://purl.org/pe-repo/ocde/ford#4.04.00; forest biodiversity; biodiversity

Dry forests are home to large amounts of biodiversity, are providers of ecosystem services, and control the advance of deserts. However, globally, these ecosystems are being threatened by various factors such as climate change, deforestation, and land use and land cover (LULC). The objective of this study was to identify the dynamics of LULC changes and the factors associated with the transformations of the dry forest in the Tumbes region (Peru) using Google Earth Engine (GEE). For this, the annual collection of Sentinel 2 (S2) satellite images of 2017 and 2021 was analyzed. Six types of LULC were identified, namely urban area (AU), agricultural land (AL), land without or with little vegetation (LW), water body (WB), dense dry forest (DDF), and open dry forest (ODF). Subsequently, we applied the Random Forest (RF) method for the classification. LULC maps reported accuracies greater than 89%. In turn, the rates of DDF and ODF between 2017 and 2021 remained unchanged at around 82%. Likewise, the largest net change occurred in the areas of WB, AL, and UA, at 51, 22, and 21%, respectively. Meanwhile, forest cover reported a loss of 4% (165.09 km2 ) of the total area in the analyzed period (2017–2021). The application of GEE allowed for an evaluation of the changes in forest cover and land use in the dry forest, and from this, it provided important information for the sustainable management of this ecosystem