{"id":"https://openalex.org/W3207810165","doi":"https://doi.org/10.1109/igarss47720.2021.9554083","title":"Forest Type Mapping at a Regional Scale Based Using Multitemporal Sentinel-2 Imagery","display_name":"Forest Type Mapping at a Regional Scale Based Using Multitemporal Sentinel-2 Imagery","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207810165","doi":"https://doi.org/10.1109/igarss47720.2021.9554083","mag":"3207810165"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100364771","display_name":"Jin Li","orcid":"https://orcid.org/0000-0001-9339-3349"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jin Li","raw_affiliation_strings":["Faculty of Forestry, Southwest Forestry University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Forestry, Southwest Forestry University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025539231","display_name":"Leiguang Wang","orcid":"https://orcid.org/0000-0003-2962-1508"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leiguang Wang","raw_affiliation_strings":["Institutes of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Institutes of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063624234","display_name":"Panfei Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Panfei Fang","raw_affiliation_strings":["Faculty of Forestry, Southwest Forestry University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Forestry, Southwest Forestry University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009671995","display_name":"Weiheng Xu","orcid":"https://orcid.org/0000-0002-9588-4931"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiheng Xu","raw_affiliation_strings":["Key Laboratory for Forestry and Ecological Big Data of National Forestry and Grassland Administration, Southwest Forestry University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Forestry and Ecological Big Data of National Forestry and Grassland Administration, Southwest Forestry University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114137440","display_name":"Qinling Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinling Dai","raw_affiliation_strings":["Art and Design College, Southwest Forestry University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Art and Design College, Southwest Forestry University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I25399270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100364771"],"corresponding_institution_ids":["https://openalex.org/I25399270"],"apc_list":null,"apc_paid":null,"fwci":0.5049,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.68985672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4228","last_page":"4231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9973000288009644,"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":0.9965000152587891,"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/forest-inventory","display_name":"Forest inventory","score":0.5628146529197693},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.5168219208717346},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5152450203895569},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.4433598816394806},{"id":"https://openalex.org/keywords/pinus-roxburghii","display_name":"Pinus roxburghii","score":0.43151751160621643},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.42441245913505554},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.41592758893966675},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.33564576506614685},{"id":"https://openalex.org/keywords/forest-management","display_name":"Forest management","score":0.32019156217575073},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.07090142369270325}],"concepts":[{"id":"https://openalex.org/C147103442","wikidata":"https://www.wikidata.org/wiki/Q1423188","display_name":"Forest inventory","level":3,"score":0.5628146529197693},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.5168219208717346},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5152450203895569},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.4433598816394806},{"id":"https://openalex.org/C2780274944","wikidata":"https://www.wikidata.org/wiki/Q3239892","display_name":"Pinus roxburghii","level":2,"score":0.43151751160621643},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.42441245913505554},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41592758893966675},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.33564576506614685},{"id":"https://openalex.org/C28631016","wikidata":"https://www.wikidata.org/wiki/Q372561","display_name":"Forest management","level":2,"score":0.32019156217575073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.07090142369270325},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1999635212","https://openalex.org/W2056435747","https://openalex.org/W2898152330","https://openalex.org/W2935706473"],"related_works":["https://openalex.org/W3095602406","https://openalex.org/W315238707","https://openalex.org/W2883563055","https://openalex.org/W2765876372","https://openalex.org/W2149267348","https://openalex.org/W2995370404","https://openalex.org/W2208843201","https://openalex.org/W1983585394","https://openalex.org/W3088563418","https://openalex.org/W2166137953"],"abstract_inverted_index":{"This":[0],"study":[1],"used":[2,40],"multispectral":[3],"satellite":[4,37],"imagery":[5,209],"(Sentinel-2":[6],"MSI)":[7],"to":[8,41,59,199,213],"evaluate":[9],"forest":[10,55,63,71,77,83,133,146,154,158,181,203,216],"type":[11,204,217],"mapping":[12],"capabilities":[13],"over":[14],"a":[15],"mountainous":[16],"area":[17,67],"(Shangri-La,":[18],"Yunnan":[19],"Province,":[20],"China)":[21],"at":[22,218],"regional":[23,219],"level.":[24,220],"Coupled":[25],"with":[26,50],"the":[27,35,61,66,76,88,119,126,129,139,145,148,152,156,162,169,172,176,200],"cloud":[28],"computing":[29],"platform":[30],"of":[31,92,121,132,151,175,179,202],"Google":[32],"Earth":[33],"Engine,":[34],"sentinel-2":[36],"images":[38],"were":[39,116],"extract":[42],"multi-temporal":[43,207],"and":[44,47,72,84,134,138,155,161,184,206],"spectral":[45],"information,":[46,205],"then":[48],"combined":[49],"terrain":[51],"information.":[52],"The":[53,190],"random":[54],"algorithm":[56],"was":[57,79,136,142,159,165,182,188],"adopted":[58],"identify":[60,215],"typical":[62],"types.":[64,74],"Firstly,":[65],"is":[68,197],"classified":[69],"into":[70,81],"non-forest":[73,135],"Secondly,":[75],"cover":[78,120],"sub-classified":[80],"coniferous":[82,93,122,153,170,180],"broad-leaved":[85],"forest.":[86,123],"In":[87],"end,":[89],"eight":[90,177],"types":[91,178],"forests":[94],"(Cupressus":[95],"funebris":[96],"forest,":[97,99,102,104,107,109,112,171],"Abies":[98],"Pinus":[100,105,110],"densata":[101],"Picea":[103],"yunnanensis":[106],"Larix":[108],"armandi":[111],"Tsuga":[113],"dumosa":[114],"forest)":[115],"identified":[117],"within":[118,168],"As":[124],"for":[125],"whole":[127],"area,":[128],"overall":[130,149,173],"accuracy":[131,150,174],"95.76%,":[137],"Kappa":[140,163,186],"coefficient":[141,164,187],"91.34%.":[143],"Within":[144],"coverage,":[147],"broadleaf":[157],"89.74%,":[160],"79.26%.":[166],"And":[167],"91.59%,":[183],"its":[185],"90.33%.":[189],"classification":[191],"results":[192],"indicated":[193],"that":[194],"topographic":[195],"information":[196],"beneficial":[198],"extraction":[201],"Sentinel-2":[208],"has":[210],"great":[211],"potential":[212],"accurately":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
