{"name": "GLUB", "package_name": "glub", "description": "Seagrass and shallow seabed mapping: Sentinel-2 or Landsat, water-column correction and supervised classification with a depth mask.", "about": "GLUB (GIS Looking Under the Blue) maps shallow benthic habitats (seagrass, sand, rock...) from satellite imagery (Sentinel-2, Landsat and others). It downloads Sentinel-2 imagery from the Copernicus Data Space Ecosystem, prepares masked surface reflectance from Sentinel-2 L2A or Landsat 4-9 Collection 2 Level-2 (or imports ACOLITE), or any multiband reflectance raster (PlanetScope, drones), removes sunglint (Hedley), builds median composites of several dates (with a water-clarity ranking of the scenes), computes the Lyzenga depth-invariant bottom index and classifies the seabed with your training points or polygons (Random Forest or maximum likelihood) or, without samples, into k-means groups that you name afterwards, optionally with bottom texture (local standard deviation) as extra features. A three-state depth mask (bottom visible, bottom not visible, too deep for seagrass) uses a bathymetry such as StarShoal's for the optical limit and an independent bathymetry for the ecological limit. Validation with spatial blocks by class or common to all classes; HTML report with confusion matrix, producer's and user's accuracy, F1, accuracy by depth and Olofsson et al. (2014) error-corrected areas. An optional minimum probability leaves doubtful pixels unclassified. A validation tab draws stratified random validation points by map class and assesses the map with them once labelled (area-weighted accuracy and corrected areas with 95-percent intervals). A change tab compares two class maps of the same place (transition map and matrix, gains and losses, only where both dates see the bottom). Class and change maps can be exported to polygons with class, area and perimeter. With Sentinel-2 the realistic target is seagrass / not seagrass in clear, shallow water. One window with tabs, in Spanish and English; every tool is also in the Processing Toolbox. Dependencies: GDAL and numpy (shipped with QGIS); Random Forest also needs scikit-learn (on Windows, from the OSGeo4W Shell: python -m pip install scikit-learn); without it, maximum likelihood is used. Downloads need a free Copernicus Data Space Ecosystem account.", "homepage": "https://github.com/adanielibarra/glub", "repository": "https://github.com/adanielibarra/glub", "tracker": "https://github.com/adanielibarra/glub/issues", "author": "Daniel Ibarra-Marinas, Alejandro Fenollar-Rueda, Ana M\u00f3nica de Jhes\u00fa Garc\u00eda-Garc\u00eda, \u00c1ngela Bellido-Solano, Dulce Mata-Chac\u00f3n, Marta Serrano-Vicente, Arturo Mora-Olivo", "tags": ["landsat", "change detection", "seagrass", "posidonia", "benthic habitat", "seabed", "classification", "sentinel-2", "remote sensing", "coastal", "ocean", "water column", "accuracy assessment", "validation"], "downloads": 31, "latest_version": "1.0.2", "versions": [{"version": "1.0.2", "experimental": false, "qgis_min": "3.28.0", "qgis_max": "4.99.0", "downloads": 31, "uploaded_by": "adanielibarra", "upload_datetime": "2026-10-07T09:52:24.452496"}]}