{"id":"https://openalex.org/W4402316405","doi":"https://doi.org/10.3390/rs16173313","title":"Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery","display_name":"Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery","publication_year":2024,"publication_date":"2024-09-06","ids":{"openalex":"https://openalex.org/W4402316405","doi":"https://doi.org/10.3390/rs16173313"},"language":"en","primary_location":{"id":"doi:10.3390/rs16173313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16173313","pdf_url":"https://www.mdpi.com/2072-4292/16/17/3313/pdf","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/16/17/3313/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074531415","display_name":"Wenge Ni\u2010Meister","orcid":"https://orcid.org/0000-0001-9723-2075"},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]},{"id":"https://openalex.org/I39694355","display_name":"Hunter College","ror":"https://ror.org/00g2xk477","country_code":"US","type":"education","lineage":["https://openalex.org/I39694355"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenge Ni-Meister","raw_affiliation_strings":["Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA","Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA","institution_ids":["https://openalex.org/I39694355","https://openalex.org/I174216632"]},{"raw_affiliation_string":"Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA","institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105533459","display_name":"Anthony Albanese","orcid":null},"institutions":[{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]},{"id":"https://openalex.org/I39694355","display_name":"Hunter College","ror":"https://ror.org/00g2xk477","country_code":"US","type":"education","lineage":["https://openalex.org/I39694355"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Albanese","raw_affiliation_strings":["Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA","institution_ids":["https://openalex.org/I39694355","https://openalex.org/I174216632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107042017","display_name":"Francesca Lingo","orcid":"https://orcid.org/0000-0002-7484-0788"},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Francesca Lingo","raw_affiliation_strings":["Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA"],"affiliations":[{"raw_affiliation_string":"Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA","institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074531415"],"corresponding_institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632","https://openalex.org/I39694355"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.8254,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85193925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"16","issue":"17","first_page":"3313","last_page":"3313"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8381810784339905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5454810261726379},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316054821014404},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5185425877571106},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4205663800239563},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1972651183605194}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8381810784339905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5454810261726379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316054821014404},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5185425877571106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4205663800239563},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1972651183605194}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16173313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16173313","pdf_url":"https://www.mdpi.com/2072-4292/16/17/3313/pdf","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:475cc60450d94888ab44ad5ad0d49cbd","is_oa":true,"landing_page_url":"https://doaj.org/article/475cc60450d94888ab44ad5ad0d49cbd","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 17, p 3313 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16173313","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16173313","pdf_url":"https://www.mdpi.com/2072-4292/16/17/3313/pdf","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1879958704","display_name":null,"funder_award_id":"80NSSC21K0194","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"},{"id":"https://openalex.org/G8370694242","display_name":null,"funder_award_id":"80NSSC24K1077","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"}],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402316405.pdf","grobid_xml":"https://content.openalex.org/works/W4402316405.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W2017917631","https://openalex.org/W2039433676","https://openalex.org/W2079167274","https://openalex.org/W2087556827","https://openalex.org/W2101234009","https://openalex.org/W2112118618","https://openalex.org/W2405365025","https://openalex.org/W2515306179","https://openalex.org/W2592849532","https://openalex.org/W2904486852","https://openalex.org/W2929935266","https://openalex.org/W2998198695","https://openalex.org/W3021060199","https://openalex.org/W3036016333","https://openalex.org/W3036224891","https://openalex.org/W3085784695","https://openalex.org/W3131943250","https://openalex.org/W3132859298","https://openalex.org/W3159278315","https://openalex.org/W3200020327","https://openalex.org/W3210465753","https://openalex.org/W3212752397","https://openalex.org/W4214520160","https://openalex.org/W4283829529","https://openalex.org/W4285792070","https://openalex.org/W4293253007","https://openalex.org/W4297920830","https://openalex.org/W4320494470","https://openalex.org/W4391309293","https://openalex.org/W4391567184","https://openalex.org/W4391880490","https://openalex.org/W4392379071","https://openalex.org/W4394686350","https://openalex.org/W4399232247","https://openalex.org/W6675354045","https://openalex.org/W6739901393","https://openalex.org/W6779997284","https://openalex.org/W6790649662"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190","https://openalex.org/W1991437568","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Tree":[0],"species":[1,25,42,57,87,187],"classification":[2,43,58,88],"using":[3,89,107],"hyperspectral":[4,40,56,90],"imagery":[5,91],"shows":[6],"incredible":[7],"promise":[8],"in":[9,53,167],"developing":[10,180],"a":[11,37,55,145,169],"large-scale,":[12],"high-resolution":[13],"model":[14],"for":[15,35,75,85,126],"identifying":[16],"tree":[17,24,41,86,118,186],"species,":[18],"providing":[19],"unprecedented":[20],"details":[21],"on":[22],"global":[23],"distribution.":[26],"Many":[27],"questions":[28],"remain":[29],"unanswered":[30],"about":[31],"the":[32,62,103,108,122,134,141,163,177,201],"best":[33],"practices":[34],"creating":[36,54,168],"global,":[38],"general":[39,170],"model.":[44,59,148,172],"This":[45,173],"study":[46],"aims":[47],"to":[48,68,132,139,143],"address":[49],"three":[50,65,72],"key":[51],"issues":[52],"We":[60],"assessed":[61],"effectiveness":[63],"of":[64,136,179,204],"data-labeling":[66,105],"methods":[67,74],"create":[69,144],"training":[70,191],"data,":[71],"data-splitting":[73,119],"training/validation/testing,":[76],"and":[77,79,129,158,193],"machine-learning":[78],"deep-learning":[80,160],"(including":[81],"semi-supervised":[82,157,195],"deep-learning)":[83],"models":[84,161,182],"at":[92],"National":[93],"Ecological":[94],"Observatory":[95],"Network":[96],"(NEON)":[97],"Sites.":[98],"Our":[99],"analysis":[100],"revealed":[101],"that":[102,183,194],"existing":[104],"method":[106,125],"field":[109],"vegetation":[110],"structure":[111],"survey":[112],"performed":[113],"reasonably":[114],"well.":[115],"The":[116],"random":[117,153],"technique":[120],"was":[121],"most":[123,164],"efficient":[124],"both":[127,156],"intra-site":[128],"inter-site":[130],"classifications":[131],"overcome":[133],"impact":[135],"spatial":[137],"autocorrelation":[138],"avoid":[140],"potential":[142],"locally":[146],"overfit":[147],"Deep":[149],"learning":[150,197],"consistently":[151],"outperformed":[152],"forest":[154,206],"classification;":[155],"supervised":[159],"displayed":[162],"promising":[165],"results":[166],"taxa-classification":[171],"work":[174],"has":[175],"demonstrated":[176],"possibility":[178],"tree-classification":[181],"can":[184],"identify":[185],"from":[188],"outside":[189],"their":[190],"area":[192],"deep":[196],"may":[198],"potentially":[199],"utilize":[200],"untapped":[202],"terabytes":[203],"unlabeled":[205],"imagery.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}