{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:34Z","timestamp":1776274534721,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"NASA","doi-asserted-by":"publisher","award":["80NSSC24K1077"],"award-info":[{"award-number":["80NSSC24K1077"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"NASA","doi-asserted-by":"publisher","award":["80NSSC21K0194"],"award-info":[{"award-number":["80NSSC21K0194"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global tree species distribution. Many questions remain unanswered about the best practices for creating a global, general hyperspectral tree species classification model. This study aims to address three key issues in creating a hyperspectral species classification model. We assessed the effectiveness of three data-labeling methods to create training data, three data-splitting methods for training\/validation\/testing, and machine-learning and deep-learning (including semi-supervised deep-learning) models for tree species classification using hyperspectral imagery at National Ecological Observatory Network (NEON) Sites. Our analysis revealed that the existing data-labeling method using the field vegetation structure survey performed reasonably well. The random tree data-splitting technique was the most efficient method for both intra-site and inter-site classifications to overcome the impact of spatial autocorrelation to avoid the potential to create a locally overfit model. Deep learning consistently outperformed random forest classification; both semi-supervised and supervised deep-learning models displayed the most promising results in creating a general taxa-classification model. This work has demonstrated the possibility of developing tree-classification models that can identify tree species from outside their training area and that semi-supervised deep learning may potentially utilize the untapped terabytes of unlabeled forest imagery.<\/jats:p>","DOI":"10.3390\/rs16173313","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T06:18:35Z","timestamp":1725603515000},"page":"3313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9723-2075","authenticated-orcid":false,"given":"Wenge","family":"Ni-Meister","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA"},{"name":"Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA"}]},{"given":"Anthony","family":"Albanese","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7484-0788","authenticated-orcid":false,"given":"Francesca","family":"Lingo","sequence":"additional","affiliation":[{"name":"Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9812624","DOI":"10.34133\/2021\/9812624","article-title":"Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective","volume":"2021","author":"Pu","year":"2021","journal-title":"J. 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