{"id":"https://openalex.org/W4309736075","doi":"https://doi.org/10.3390/rs14225849","title":"A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR","display_name":"A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR","publication_year":2022,"publication_date":"2022-11-18","ids":{"openalex":"https://openalex.org/W4309736075","doi":"https://doi.org/10.3390/rs14225849"},"language":"en","primary_location":{"id":"doi:10.3390/rs14225849","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14225849","pdf_url":"https://www.mdpi.com/2072-4292/14/22/5849/pdf?version=1668937732","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/2072-4292/14/22/5849/pdf?version=1668937732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088009928","display_name":"Hongbin Luo","orcid":"https://orcid.org/0000-0002-9885-3014"},"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":"Hongbin Luo","raw_affiliation_strings":["College of Forestry, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":"https://orcid.org/0000-0002-9885-3014","affiliations":[{"raw_affiliation_string":"College of Forestry, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056280551","display_name":"Cairong Yue","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":true,"raw_author_name":"Cairong Yue","raw_affiliation_strings":["College of Forestry, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Forestry, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068046582","display_name":"Fuming Xie","orcid":"https://orcid.org/0000-0002-1935-121X"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuming Xie","raw_affiliation_strings":["Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"],"raw_orcid":"https://orcid.org/0000-0002-1935-121X","affiliations":[{"raw_affiliation_string":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002068378","display_name":"Bodong Zhu","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"]},{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bodong Zhu","raw_affiliation_strings":["College of Forestry, Northeastern Forestry University, Harbin 150040, China","College of Forestry, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Forestry, Northeastern Forestry University, Harbin 150040, China","institution_ids":["https://openalex.org/I47689461"]},{"raw_affiliation_string":"College of Forestry, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077880715","display_name":"Si Chen","orcid":"https://orcid.org/0000-0002-0305-8641"},"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":"Si Chen","raw_affiliation_strings":["College of Forestry, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Forestry, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056280551"],"corresponding_institution_ids":["https://openalex.org/I25399270"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":0.5209,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.55970897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"14","issue":"22","first_page":"5849","last_page":"5849"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/computer-science","display_name":"Computer science","score":0.6627126932144165},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.562574565410614},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5348120927810669},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4252203702926636},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4141075313091278},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38146594166755676},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3759932816028595},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15183809399604797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6627126932144165},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.562574565410614},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5348120927810669},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4252203702926636},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4141075313091278},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38146594166755676},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3759932816028595},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15183809399604797},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14225849","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14225849","pdf_url":"https://www.mdpi.com/2072-4292/14/22/5849/pdf?version=1668937732","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:b5de0261c18e4791a7bd8430987e57cc","is_oa":true,"landing_page_url":"https://doaj.org/article/b5de0261c18e4791a7bd8430987e57cc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 22, p 5849 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/22/5849/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14225849","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 22; Pages: 5849","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14225849","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14225849","pdf_url":"https://www.mdpi.com/2072-4292/14/22/5849/pdf?version=1668937732","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":[{"score":0.75,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1805763117","display_name":null,"funder_award_id":"42061072","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3793913872","display_name":null,"funder_award_id":"202002AA100007","funder_id":"https://openalex.org/F4320325440","funder_display_name":"Yunnan Provincial Science and Technology Department"},{"id":"https://openalex.org/G5896984104","display_name":null,"funder_award_id":"202002AA100007-015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8573200209","display_name":null,"funder_award_id":"2022Y579","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G987194482","display_name":null,"funder_award_id":"202002AA100007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325440","display_name":"Yunnan Provincial Science and Technology Department","ror":"https://ror.org/03whqdf70"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4309736075.pdf","grobid_xml":"https://content.openalex.org/works/W4309736075.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W72179029","https://openalex.org/W199755745","https://openalex.org/W329296823","https://openalex.org/W1179254689","https://openalex.org/W1495847551","https://openalex.org/W1968330818","https://openalex.org/W1981365660","https://openalex.org/W1992541517","https://openalex.org/W2018130020","https://openalex.org/W2044769733","https://openalex.org/W2044987471","https://openalex.org/W2045505889","https://openalex.org/W2047323441","https://openalex.org/W2070795492","https://openalex.org/W2076546844","https://openalex.org/W2077809046","https://openalex.org/W2097701306","https://openalex.org/W2105850256","https://openalex.org/W2119184976","https://openalex.org/W2142677925","https://openalex.org/W2160969117","https://openalex.org/W2370346375","https://openalex.org/W2416310637","https://openalex.org/W2515583119","https://openalex.org/W2756193419","https://openalex.org/W2767921252","https://openalex.org/W2772817128","https://openalex.org/W2807913786","https://openalex.org/W2891271911","https://openalex.org/W2897150309","https://openalex.org/W2902644708","https://openalex.org/W2905442578","https://openalex.org/W2908772002","https://openalex.org/W2911799365","https://openalex.org/W2911964244","https://openalex.org/W2962763794","https://openalex.org/W2972753220","https://openalex.org/W2998577031","https://openalex.org/W3026127998","https://openalex.org/W3095923272","https://openalex.org/W3129634141","https://openalex.org/W6772375692"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W1557905920","https://openalex.org/W2043093291","https://openalex.org/W2101155126"],"abstract_inverted_index":{"The":[0,18,138,205,355,372],"mapping":[1],"of":[2,67,110,142,156,185,200,230,240,257,265,276,299,318,338],"tropical":[3],"rainforest":[4],"forest":[5,26,54,83,144,162,176,381],"structure":[6,140],"parameters":[7,141],"plays":[8],"an":[9,59],"important":[10],"role":[11],"in":[12,52,243,380],"biodiversity":[13],"and":[14,37,78,91,106,132,164,220,254,290,310,329,335,349],"carbon":[15],"stock":[16],"estimation.":[17],"current":[19],"mechanism":[20,89,112,122,148,193,387],"models":[21,123,170],"based":[22],"on":[23],"PolInSAR":[24,99],"for":[25,40,48,76,357,367],"height":[27,55,84,382],"inversion":[28,183],"(e.g.,":[29],"the":[30,65,82,88,92,103,128,133,143,147,153,157,175,182,186,192,201,224,227,231,238,244,255,258,266,270,274,277,285,291,297,300,306,311,316,319,324,330,336,339,344,350,358,365,368,385],"RVoG":[31,129,134,278,301],"model)":[32],"are":[33,43,362],"physical":[34],"process":[35],"models,":[36],"realistic":[38],"conditions":[39],"model":[41,68,90,113,149,203,260,292,321,341],"parameterization":[42],"often":[44],"difficult":[45],"to":[46,173,252],"establish":[47],"practical":[49],"applications,":[50],"resulting":[51],"large":[53],"estimation":[56,85,383],"errors.":[57],"As":[58],"alternative,":[60],"machine":[61,116,158,187],"learning":[62,159,188],"approaches":[63],"offer":[64],"benefit":[66],"simplicity,":[69],"but":[70],"these":[71],"tools":[72],"provide":[73],"limited":[74],"capabilities":[75],"interpretation":[77],"generalization.":[79],"To":[80],"explore":[81],"method":[86,131,282,303],"combining":[87],"empirical":[93],"model,":[94,194],"we":[95],"utilized":[96],"UAVSAR":[97],"multi-baseline":[98],"L-band":[100],"data":[101],"from":[102,146,250,384],"AfriSAR":[104],"project":[105],"propose":[107],"a":[108,111,377],"solution":[109],"combined":[114,190,373],"with":[115,191,364],"learning.":[117],"In":[118],"this":[119],"paper,":[120],"two":[121,245],"were":[124,150,171,210],"used":[125,151,172],"as":[126,152],"controls,":[127],"three-phase":[130],"phase-coherence":[135],"amplitude":[136,281],"method.":[137,204,389],"vertical":[139],"obtained":[145],"independent":[154,208,232,241],"variables":[155,209,233,242],"model.":[160,268],"Random":[161],"(RF)":[163],"partial":[165],"least":[166],"squares":[167],"(PLS)":[168],"regression":[169],"invert":[174],"canopy":[177],"height.":[178],"Results":[179],"show":[180],"that":[181,199,226,264],"accuracy":[184,256],"method,":[189],"is":[195,283,287,294,304,308,313,322,326,332,342,346,352],"significantly":[196],"better":[197],"than":[198,236,263],"single-mechanism":[202],"most":[206],"influential":[207],"penetration":[211],"depth,":[212],"volume":[213],"coherence":[214,218,280],"phase":[215,279],"center":[216],"height,":[217],"separation,":[219],"baseline":[221],"selection.":[222],"With":[223],"precondition":[225],"cumulative":[228],"contribution":[229],"was":[234,248,261],"greater":[235],"90%,":[237],"number":[239],"study":[246],"areas":[247],"reduced":[249],"19":[251],"4,":[253],"RF-RVoG-DEP":[259,340],"higher":[262],"PLS-RVoG-DEP":[267,320],"For":[269],"Lope":[271,369],"test":[272,360,370],"area,":[273],"R2":[275,298,317,337],"0.723,":[284],"RMSE":[286,307,325,345],"8.583":[288],"m,":[289,315,328,348],"bias":[293,312,331,351],"\u22122.431":[295],"m;":[296,334],"three-stage":[302],"0.775,":[305],"7.748,":[309],"1.120":[314],"0.850,":[323],"6.320":[327],"0.002":[333],"0.900,":[343],"5.154":[347],"\u22120.061":[353],"m.":[354],"results":[356],"Pongara":[359],"area":[361],"consistent":[363],"pattern":[366],"area.":[371],"\u201cfusion":[374],"model\u201d":[375],"offers":[376],"substantial":[378],"improvement":[379],"traditional":[386],"modeling":[388]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2022-11-29T00:00:00"}
