{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:09:59Z","timestamp":1768417799370,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"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>Forest structure is an important variable in ecology, fire behaviour, and carbon management. New spaceborne lidar sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), enable forest structure to be mapped at a global scale. Virtual GEDI-like observations can be derived from airborne laser scanning (ALS) data for given locations using the GEDI simulator, which was a tool initially developed for GEDI\u2019s pre-launch calibration. This study compares the relative height (RH) and ground elevation metrics of real and simulated GEDI observations against ALS-derived benchmarks in southeast Australia. A total of 15,616 footprint locations were examined, covering a large range of forest types and topographic conditions. The impacts of canopy cover and height, terrain slope, and ALS point cloud density were assessed. The results indicate that the simulator produces more accurate canopy height (RH95) metrics (RMSE: 4.2 m, Bias: \u22121.3 m) than the actual GEDI sensor (RMSE: 9.6 m, Bias: \u22121.6 m). Similarly, the simulator outperforms GEDI in ground detection accuracy. In contrast to other studies, which favour the Gaussian algorithm for ground detection, we found that the Maximum algorithm performed better in most settings. Despite the determined differences between real and simulated GEDI observations, this study indicates the compatibility of both data sources, which may enable their combined use in multitemporal forest structure monitoring.<\/jats:p>","DOI":"10.3390\/rs14092096","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T22:20:20Z","timestamp":1651098020000},"page":"2096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Intercomparison of Real and Simulated GEDI Observations across Sclerophyll Forests"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2618-4349","authenticated-orcid":false,"given":"Sven","family":"Huettermann","sequence":"first","affiliation":[{"name":"Remote Sensing Centre, Geospatial Science, School of Science, RMIT University, 402 Swanston Street, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3914-3717","authenticated-orcid":false,"given":"Simon","family":"Jones","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Geospatial Science, School of Science, RMIT University, 402 Swanston Street, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1285-1622","authenticated-orcid":false,"given":"Mariela","family":"Soto-Berelov","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Geospatial Science, School of Science, RMIT University, 402 Swanston Street, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0176-0410","authenticated-orcid":false,"given":"Samuel","family":"Hislop","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, Geospatial Science, School of Science, RMIT University, 402 Swanston Street, Melbourne 3000, Australia"},{"name":"New South Wales Department of Primary Industries, 4 Parramatta Square, Parramatta 2150, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7472","DOI":"10.1073\/pnas.1423147112","article-title":"An estimate of the number of tropical tree species","volume":"112","author":"Slik","year":"2015","journal-title":"Proc. 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