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A considerable fraction of low-quality photons exists in ICESAT-2\/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height.<\/jats:p>","DOI":"10.3390\/rs15235436","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T01:48:45Z","timestamp":1700531325000},"page":"5436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Mengting","family":"Sang","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Hai","family":"Xiao","sequence":"additional","affiliation":[{"name":"The Second Surveying and Mapping Institute of Hunan Province, Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410009, China"}]},{"given":"Zhili","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2801-7176","authenticated-orcid":false,"given":"Junchen","family":"He","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7930-9147","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6059","DOI":"10.1080\/01431161.2019.1587201","article-title":"Aboveground forest biomass based on OLSR and an ANN model integrating LiDAR and optical data in a mountainous region of China","volume":"40","author":"Dong","year":"2019","journal-title":"Int. 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