4d30e697-6a97-45ed-95e6-ac4d66247284 English ISO/IEC 8859-1 (also known as Latin 1) dataset dataset NERC EDS Environmental Information Data Centre Lancaster Environment Centre, Library Avenue, Bailrigg Lancaster LA1 4AP UK info@eidc.ac.uk https://eidc.ac.uk/ EIDC website The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences. information pointOfContact 2025-11-13T16:16:59 UK GEMINI 2.3 WGS 84 / Pseudo-Mercator Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018 2024-06-12 publication https://catalogue.ceh.ac.uk/id/4d30e697-6a97-45ed-95e6-ac4d66247284 10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284 doi: Powell, P.A., Gutierrez-Angonese, J., Burslem, D.F.R.P., Travis, J.M.J., Jiménez, Y.G., Montti, L. (2024). Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018. NERC EDS Environmental Information Data Centre 10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284 The data resource consists of a series of land cover maps built using raster and shapefiles to evaluate the expansion of the invasive Ligustrum lucidum forest cover in Sierra de San Javier (Yungas forest ecoregion). The classification was conducted to investigate the expansion of the invasion of non-native species Ligustrum at the landscape scale and to model future management strategies using RangeShifter software. The data includes 4 maps with 8 classes of land used: Ligustrum forest; Subtropical montane forest; Dry forest; Montane grasslands; Anthropogenic grasslands and shrubland used for livestock and temporary agriculture, a mixed class including also herbaceous agriculture and low-density urban areas; Sugar cane; Citrus plantations, mostly lemon; high/medium-density urban and build up areas. The work was carried out as part of NERC grant NE/S011641/1 Optimising the long-term management of invasive species affecting biodiversity and the rural economy using adaptive management Full details about this dataset can be found at https://doi.org/10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284 Powell, P.A. Universidad Nacional de Tucumán & CONICET priscilaapowell@gmail.com https://orcid.org/0000-0003-3911-0912 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Gutierrez-Angonese, J. Freelance GIS consultant jor_gutierrez@yahoo.com.ar author Burslem, D.F.R.P. University of Aberdeen d.burslem@abdn.ac.uk https://orcid.org/0000-0001-6033-0990 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Travis, J.M.J. University of Aberdeen justin.travis@abdn.ac.uk https://orcid.org/0000-0002-5785-4272 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Jiménez, Y.G. Universidad Nacional de Tucumán & CONICET yohanajimenez2406@gmail.com author Montti, L. Instituto de Investigaciones Marinas y Costeras Mar del Plata (CONICET) liamontti@gmail.com author Montti, L. Instituto de Investigaciones Marinas y Costeras Mar del Plata (CONICET) liamontti@gmail.com pointOfContact NERC EDS Environmental Information Data Centre info@eidc.ac.uk custodian NERC EDS Environmental Information Data Centre info@eidc.ac.uk publisher Instituto de Ecología Regional (Universidad Nacional de Tucumán & CONICET), Argentina EMAIL NOT PROVIDED owner Environmental Monitoring Facilities theme GEMET - INSPIRE themes, version 1.0 2008-06-01 publication Ligustrum lucidum Wikidata 2012-10-29 creation Yungas Subtropical secondary forest invaded forest land cover maps montane forest invasive alien species otherRestrictions no limitations otherRestrictions This resource is available under the terms of the Open Government Licence otherRestrictions If you reuse this data, you should cite: Powell, P.A., Gutierrez-Angonese, J., Burslem, D.F.R.P., Travis, J.M.J., Jiménez, Y.G., Montti, L. (2024). Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284 grid vector 1000 English utf8 biota environment 1986-01-01 2018-12-31 -67.742 -64.71 -27.637 -24.713 TIFF Shapefile NERC EDS Environmental Information Data Centre info@eidc.ac.uk distributor https://data-package.ceh.ac.uk/data/4d30e697-6a97-45ed-95e6-ac4d66247284 Download the data Download a copy of this data download https://data-package.ceh.ac.uk/sd/4d30e697-6a97-45ed-95e6-ac4d66247284.zip Supporting information Supporting information available to assist in re-use of this dataset information dataset dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 Land cover maps (LCM) were constructed by applying the machine learning algorithm Random Forest (RF), based on an ensemble of classification trees, fully run on the Google Earth Engine platform. For the classification of tree model runs, 315 points from Thematic Mapper (TM) were chosen by visual interpretation of satellite data. Training areas previously obtained and tested were used to build LCM series of the same study area. All data collected in training areas is recorded onto GPS in the field. For the Ligustrum class, 52 training points were collected during fieldwork in 2010 and spectrally verified using vegetation indices. A total of 367 points for the 8 categories were used and then were transferred into spreadsheets and exported as comma-separated value (CSV) files for deposit into the EIDC. To classify 1986 LCM a subset of training points of L. lucidum was used, including only 28 areas that were identified as source points of dispersion. The land cover maps obtained were downloaded in raster (tif) format. All training points were presented as shape files.