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Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map","award":["32071677"],"award-info":[{"award-number":["32071677"]}]},{"name":"Korea Environment Industry &amp; Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map","award":["2019FY101602-1"],"award-info":[{"award-number":["2019FY101602-1"]}]},{"name":"Korea Environment Industry &amp; Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map","award":["2005DKA32200-OH"],"award-info":[{"award-number":["2005DKA32200-OH"]}]},{"name":"Korea Environment Industry &amp; Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map","award":["31870530"],"award-info":[{"award-number":["31870530"]}]},{"name":"Korea Environment Industry &amp; Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map","award":["RS-2023-00232066"],"award-info":[{"award-number":["RS-2023-00232066"]}]},{"name":"Korea Ministry of Environment (MOE)","award":["32071677"],"award-info":[{"award-number":["32071677"]}]},{"name":"Korea Ministry of Environment (MOE)","award":["2019FY101602-1"],"award-info":[{"award-number":["2019FY101602-1"]}]},{"name":"Korea Ministry of Environment (MOE)","award":["2005DKA32200-OH"],"award-info":[{"award-number":["2005DKA32200-OH"]}]},{"name":"Korea Ministry of Environment (MOE)","award":["31870530"],"award-info":[{"award-number":["31870530"]}]},{"name":"Korea Ministry of Environment (MOE)","award":["RS-2023-00232066"],"award-info":[{"award-number":["RS-2023-00232066"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R2 = 0.80, RMSE = 9.58 Mg\/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R2 = 0.65, RMSE = 18.55 Mg\/ha, rRMSE = 0.17) but also the best machine learning model (RF: R2 = 0.50, RMSE = 19.42 Mg\/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs.<\/jats:p>","DOI":"10.3390\/rs15184364","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T10:26:43Z","timestamp":1693909603000},"page":"4364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests"],"prefix":"10.3390","volume":"15","author":[{"given":"Ye","family":"Ma","sequence":"first","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}]},{"given":"Lianjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44610, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1933-8357","authenticated-orcid":false,"given":"Yinghui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9281-4260","authenticated-orcid":false,"given":"Zhen","family":"Zhen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00468-015-1196-1","article-title":"Developing additive systems of biomass equations for nine hardwood species in Northeast China","volume":"29","author":"Dong","year":"2015","journal-title":"Trees"},{"key":"ref_2","first-page":"967","article-title":"Dynamic forest biomass carbon pools in China and their significance","volume":"9","author":"Fang","year":"2001","journal-title":"Chin. 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