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canopy heights in forest environments. However, there have been limited studies evaluating their performance for terrain and canopy height retrievals in short-stature vegetation. This study utilizes airborne LiDAR data to validate and compare the accuracies of terrain and canopy height retrievals for short-stature vegetation using the latest versions of ICESat-2 (Version 5) and GEDI (Version 2). Furthermore, this study also analyzes the influence of various factors, such as vegetation type, terrain slope, canopy height, and canopy cover, on terrain and canopy height retrievals. The results indicate that ICESat-2 (bias = \u22120.05 m, RMSE = 0.67 m) outperforms GEDI (bias = 0.39 m, RMSE = 1.40 m) in terrain height extraction, with similar results observed for canopy height retrievals from both missions. Additionally, the findings reveal significant differences in terrain and canopy height retrieval accuracies between ICESat-2 and GEDI data under different data acquisition scenarios. Error analysis results demonstrate that terrain slope plays a pivotal role in influencing the accuracy of terrain height extraction for both missions, particularly for GEDI data, where the terrain height accuracy decreases significantly with increasing terrain slope. However, canopy height has the most substantial impact on the estimation accuracies of GEDI and ICESat-2 canopy heights. Overall, these findings confirm the strong potential of ICESat-2 data for terrain and canopy height retrievals in short-stature vegetation areas, and also provide valuable insights for future applications of space-borne LiDAR data in short-stature vegetation-dominated ecosystems.<\/jats:p>","DOI":"10.3390\/rs15204969","type":"journal-article","created":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T10:47:32Z","timestamp":1697366852000},"page":"4969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoxiao","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing 100029, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-5619","authenticated-orcid":false,"given":"Sheng","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Yamin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang Academy of Surveying and Mapping, Hangzhou 311100, China"}]},{"given":"Yiming","family":"Chen","sequence":"additional","affiliation":[{"name":"China Forestry Group Corporation, Beijing 100036, China"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"China Forestry Group Corporation, Beijing 100036, China"}]},{"given":"Wang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2014.01.025","article-title":"Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data","volume":"151","author":"Zhang","year":"2014","journal-title":"Remote Sens. 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