dc.creatorBarboza Castillo, Elgar
dc.creatorSalazar Coronel, Wilian
dc.creatorGálvez Paucar, David
dc.creatorValqui Valqui, Lamberto
dc.creatorSaravia Navarro, David
dc.creatorGonzales, Jhony
dc.creatorAldana, Wiliam
dc.creatorVásquez Pérez, Héctor Vladimir
dc.creatorArbizu Berrocal, Carlos Irvin
dc.date.accessioned2023-02-17T16:01:52Z
dc.date.available2023-02-17T16:01:52Z
dc.date.created2023-02-17T16:01:52Z
dc.date.issued2022-10-21
dc.identifierBarboza, E.; Salazar, W.; Gálvez-Paucar, D.; Valqui-Valqui, L.; Saravia, D.; Gonzales, J.; Aldana, W.; Vásquez, H.V.; Arbizuri, C.I (2022). Cover and land use changes in the dry forest of tumbes (Peru) using sentinel-2 and google earth engine data. Environmental.Sciences.Proceeding. 22,2. doi: 10.3390/IECF2022-13095.
dc.identifier2673-4931
dc.identifierhttps://hdl.handle.net/20.500.12955/2076
dc.identifierhttps://doi.org/10.3390/IECF2022-13095
dc.description.abstractDry 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
dc.languagespa
dc.publisherMDPI
dc.publisherPE
dc.relationurn:issn:2673-4931
dc.relationEnvironmental Sciences Proceedings
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceInstituto Nacional de Innovación Agraria
dc.sourceRepositorio Institucional - INIA
dc.subjectForest remote sensing
dc.subjectRandom Forest (RF)
dc.subjectTemporal series
dc.subjectBiodiversity
dc.titleCover and land use changes in the dry forest of Tumbes (Peru) using sentinel-2 and google earth engine data
dc.typeinfo:eu-repo/semantics/conferenceObject


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