{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:22:07Z","timestamp":1767612127947,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A large percentage of the Costa Rican territory is covered with high evergreen forests. In order to compute a 1\u2033 Bare-Earth Digital Terrain Model (DTM) for Costa Rica CRDTM2020, stochastic Vegetation Bias (VB) was reduced from the 1\u2033 NASADEM, Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) data. Several global models such as: canopy heights from the Global Forest Canopy Height 2019 model, canopy heights for the year 2000 from the Forest Canopy Height Map, and canopy density from the Global Forest Change model 2000 to 2019, were used to represent the vegetation in the year of SRTM data collection. Four analytical VB models based on canopy heights and canopy density were evaluated and validated using bare-earth observations and canopy heights from the Laser Vegetation Imaging Sensor (LVIS) surveys from 1998, 2005, and 2019 and a levelling dataset. The results show that differences between CRDTM2020 and bare-earth elevations from LVIS2019 in terms of the mean, median, standard deviation, and median absolute difference (0.9, 0.8, 7.9 and 3.7 m, respectively) are smaller than for any other of the nine evaluated global DEMs.<\/jats:p>","DOI":"10.3390\/rs14102421","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T23:14:26Z","timestamp":1652915666000},"page":"2421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Regional \u201cBare-Earth\u201d Digital Terrain Model for Costa Rica Based on NASADEM Corrected for Vegetation Bias"],"prefix":"10.3390","volume":"14","author":[{"given":"Olga","family":"Pimenova","sequence":"first","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW 2052, Australia"},{"name":"School of Topography, Cadastre and Geodesy, National University of Costa Rica, Heredia 86-3000, Costa Rica"}]},{"given":"Craig","family":"Roberts","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW 2052, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-1488","authenticated-orcid":false,"given":"Chris","family":"Rizos","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW 2052, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1093\/biosci\/biz152","article-title":"World Scientists\u2019 Warning of a Climate Emergency","volume":"70","author":"Ripple","year":"2020","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2016.04.018","article-title":"A multi-sensor approach towards a global vegetation corrected SRTM DEM product","volume":"182","author":"Paiva","year":"2016","journal-title":"Remote Sens. 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