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This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on...
Search relevance: 1.00 | Views last month: 3 | Catalog Last Checked: August 01, 2026 at 04:43 AM -
This raster depicts the distribution of broader community-level vegetation classes across Lāna‘i. To generate this map, the species-specific class probabilities were summed to get total...
Search relevance: 1.00 | Views last month: 1 | Catalog Last Checked: August 01, 2026 at 05:08 AM -
This data set consists of ground control points used for independent pixel-level model validation (ground_control_points.gpkg): This dataset consists of 295 points distributed across the 15...
Search relevance: 1.00 | Views last month: 1 | Catalog Last Checked: August 01, 2026 at 05:51 AM -
This raster integrates the species-specific and community classifications using a hierarchical approach based on classification certainty. A 0.66 probability threshold was applied, with pixels...
Search relevance: 1.00 | Views last month: 0 | Catalog Last Checked: August 01, 2026 at 06:01 AM -
This data set consists of ground control polygons used for model training and evaluation (ground_control_polygons.gpkg): This dataset consists of refined vegetation polygons digitized across the...
Search relevance: 1.00 | Views last month: 0 | Catalog Last Checked: August 01, 2026 at 06:14 AM -
This raster depicts the distribution of 15 species-specific vegetation classes across the island of Lāna‘i at 2m resolution. It represents the final selected neural network model predictions with...
Search relevance: 1.00 | Views last month: 0 | Catalog Last Checked: August 01, 2026 at 03:52 AM -
This raster stack contains 15 probability layers representing the pixel-level predicted probability of membership in each species-specific vegetation class from 0 to 1. These probability layers...
Search relevance: 1.00 | Views last month: 0 | Catalog Last Checked: August 01, 2026 at 04:02 AM
