{"id":"https://openalex.org/W2901695786","doi":"https://doi.org/10.1109/igarss.2018.8518803","title":"Forest Stand Extraction: Which Optimal Remote Sensing Data Source(S)?","display_name":"Forest Stand Extraction: Which Optimal Remote Sensing Data Source(S)?","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2901695786","doi":"https://doi.org/10.1109/igarss.2018.8518803","mag":"2901695786"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8518803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5076433052","display_name":"Cl\u00e9ment Dechesne","orcid":null},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Clement Dechesne","raw_affiliation_strings":["Univ. Paris-Est, ENSG, Saint-Mande, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. Paris-Est, ENSG, Saint-Mande, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001898964","display_name":"Cl\u00e9ment Mallet","orcid":"https://orcid.org/0000-0002-2675-165X"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Clement Mallet","raw_affiliation_strings":["Univ. Paris-Est, ENSG, Saint-Mande, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. Paris-Est, ENSG, Saint-Mande, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033550914","display_name":"Arnaud Le Bris","orcid":"https://orcid.org/0000-0002-1450-9161"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Arnaud Le Bris","raw_affiliation_strings":["Univ. Paris-Est, ENSG, Saint-Mande, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. Paris-Est, ENSG, Saint-Mande, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041561153","display_name":"Val\u00e9rie Gouet-Brunet","orcid":"https://orcid.org/0000-0003-3666-5146"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Valerie Gouet-Brunet","raw_affiliation_strings":["Univ. Paris-Est, ENSG, Saint-Mande, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. Paris-Est, ENSG, Saint-Mande, France","institution_ids":["https://openalex.org/I2800365227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800365227"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.2056239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"202","issue":null,"first_page":"7279","last_page":"7282"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9990000128746033,"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/T10895","display_name":"Species Distribution and Climate Change","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.888575553894043},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.7927799224853516},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.753743052482605},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6901776790618896},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.6786074042320251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.638508677482605},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5257152915000916},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.46655648946762085},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.46242088079452515},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4402563273906708},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41155654191970825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36159569025039673},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3237783908843994},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.31266915798187256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08246040344238281}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.888575553894043},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.7927799224853516},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.753743052482605},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6901776790618896},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.6786074042320251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.638508677482605},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5257152915000916},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.46655648946762085},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.46242088079452515},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4402563273906708},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41155654191970825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36159569025039673},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3237783908843994},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.31266915798187256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08246040344238281},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8518803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"},{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320336050","display_name":"Office National d'\u00e9tudes et de Recherches A\u00e9rospatiales","ror":"https://ror.org/005y2ap84"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1583106483","https://openalex.org/W1584663654","https://openalex.org/W1665663874","https://openalex.org/W1970535395","https://openalex.org/W1999478155","https://openalex.org/W2055734610","https://openalex.org/W2549994478","https://openalex.org/W2594474574","https://openalex.org/W2765982424","https://openalex.org/W4231458434","https://openalex.org/W4236261977"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W4318664220","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2022304901","https://openalex.org/W2018850895","https://openalex.org/W1987483041","https://openalex.org/W2988577871"],"abstract_inverted_index":{"It":[0],"has":[1,41],"been":[2,42],"now":[3],"widely":[4],"assessed":[5,112],"in":[6,118],"the":[7,34,62,104],"literature":[8],"that":[9],"both":[10],"multi/hyperspectral":[11],"optical":[12,67],"images":[13,68],"and":[14,54,69,85,93,103],"3D":[15],"lidar":[16,70,96],"point":[17,71,97],"clouds":[18],"are":[19,48,101],"necessary":[20],"inputs":[21],"for":[22],"tree":[23],"species":[24],"based":[25],"forest":[26],"stand":[27],"detection.":[28],"Nevertheless,":[29],"no":[30],"comprehensive":[31],"analysis":[32],"of":[33,37,65,107],"genuine":[35],"relevance":[36],"each":[38],"data":[39],"source":[40],"performed":[43],"so":[44],"far:":[45],"existing":[46],"strategies":[47],"limited":[49],"to":[50],"a":[51,114],"single":[52],"spatial":[53],"spectral":[55],"resolution.":[56],"This":[57],"paper":[58],"investigates":[59],"which":[60],"is":[61,78,111],"optimal":[63],"combination":[64],"geospatial":[66],"clouds.":[72],"A":[73],"supervised":[74],"semantic":[75],"segmentation":[76],"framework":[77],"fed":[79],"with":[80],"various":[81],"sources":[82],"(multispectral":[83],"satellite":[84],"airborne":[86,89],"images,":[87,90],"hyperspectral":[88],"low,":[91],"medium":[92],"high":[94],"density":[95],"clouds),":[98],"ablation":[99],"cases":[100],"defined,":[102],"discrimination":[105],"performance":[106],"several":[108],"fusion":[109],"schemes":[110],"under":[113],"challenging":[115],"mountainous":[116],"area":[117],"France.":[119]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
