{"id":"https://openalex.org/W2771674764","doi":"https://doi.org/10.1109/igarss.2017.8127101","title":"Forest structure modeling of a coniferous forest using TanDEM-X InSAR and simulated GEDI lidar data","display_name":"Forest structure modeling of a coniferous forest using TanDEM-X InSAR and simulated GEDI lidar data","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2771674764","doi":"https://doi.org/10.1109/igarss.2017.8127101","mag":"2771674764"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127101","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5079152809","display_name":"Wenlu Qi","orcid":"https://orcid.org/0000-0002-5792-8577"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenlu Qi","raw_affiliation_strings":["Department of Geographical Sciences, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geographical Sciences, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077118042","display_name":"Ralph Dubayah","orcid":"https://orcid.org/0000-0003-1440-6346"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ralph O. Dubayah","raw_affiliation_strings":["Department of Geographical Sciences, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geographical Sciences, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1542,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.54779772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"914","last_page":"917"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9979000091552734,"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":0.9979000091552734,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9966999888420105,"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/T10535","display_name":"Landslides and related hazards","score":0.9948999881744385,"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/lidar","display_name":"Lidar","score":0.7015852928161621},{"id":"https://openalex.org/keywords/canopy","display_name":"Canopy","score":0.658135175704956},{"id":"https://openalex.org/keywords/interferometric-synthetic-aperture-radar","display_name":"Interferometric synthetic aperture radar","score":0.6492774486541748},{"id":"https://openalex.org/keywords/forest-structure","display_name":"Forest structure","score":0.5965098142623901},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5901080965995789},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5625489950180054},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5595636367797852},{"id":"https://openalex.org/keywords/tree-canopy","display_name":"Tree canopy","score":0.505695104598999},{"id":"https://openalex.org/keywords/quartile","display_name":"Quartile","score":0.505261242389679},{"id":"https://openalex.org/keywords/backscatter","display_name":"Backscatter (email)","score":0.465934693813324},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4553436040878296},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4159150719642639},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2660921812057495},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26015132665634155},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2584691047668457},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.19736269116401672},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.09868735074996948}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7015852928161621},{"id":"https://openalex.org/C101000010","wikidata":"https://www.wikidata.org/wiki/Q5033434","display_name":"Canopy","level":2,"score":0.658135175704956},{"id":"https://openalex.org/C22286887","wikidata":"https://www.wikidata.org/wiki/Q1666056","display_name":"Interferometric synthetic aperture radar","level":3,"score":0.6492774486541748},{"id":"https://openalex.org/C2983469836","wikidata":"https://www.wikidata.org/wiki/Q4421","display_name":"Forest structure","level":3,"score":0.5965098142623901},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5901080965995789},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5625489950180054},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5595636367797852},{"id":"https://openalex.org/C39807119","wikidata":"https://www.wikidata.org/wiki/Q1134228","display_name":"Tree canopy","level":3,"score":0.505695104598999},{"id":"https://openalex.org/C68443243","wikidata":"https://www.wikidata.org/wiki/Q2786686","display_name":"Quartile","level":3,"score":0.505261242389679},{"id":"https://openalex.org/C30354325","wikidata":"https://www.wikidata.org/wiki/Q204667","display_name":"Backscatter (email)","level":3,"score":0.465934693813324},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4553436040878296},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4159150719642639},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2660921812057495},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26015132665634155},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2584691047668457},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.19736269116401672},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.09868735074996948},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"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/igarss.2017.8127101","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127101","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320321602","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt","ror":"https://ror.org/04bwf3e34"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W809979079","https://openalex.org/W1977338552","https://openalex.org/W1982355028","https://openalex.org/W1992053024","https://openalex.org/W2001470990","https://openalex.org/W2024566288","https://openalex.org/W2045249129","https://openalex.org/W2045505889","https://openalex.org/W2108008160","https://openalex.org/W2111650592","https://openalex.org/W2140175581","https://openalex.org/W2150699620","https://openalex.org/W2153601600","https://openalex.org/W2266079392"],"related_works":["https://openalex.org/W4297998612","https://openalex.org/W2790032735","https://openalex.org/W2373310320","https://openalex.org/W2781934594","https://openalex.org/W3129277342","https://openalex.org/W2146611305","https://openalex.org/W2129610852","https://openalex.org/W2152096514","https://openalex.org/W3183563243","https://openalex.org/W4384828078"],"abstract_inverted_index":{"This":[0],"study":[1],"investigated":[2],"the":[3,22,44,113,117],"efficacy":[4],"of":[5,24,47,84,123],"fusing":[6],"simulated":[7,105],"Global":[8],"Ecosystem":[9],"Dynamics":[10],"Investigation":[11],"(GEDI)":[12],"lidar":[13],"data":[14,20,115,127],"with":[15],"TanDEM-X":[16],"(TDX)":[17],"single-polarized":[18],"InSAR":[19],"for":[21,93,135],"estimation":[23,137],"forest":[25,133],"structure":[26,134],"over":[27,56,138],"a":[28,48],"coniferous":[29],"Forest":[30,34],"-":[31,53],"Teakettle":[32],"Experimental":[33],"(TEF).":[35],"Simulated":[36],"GEDI":[37,106,126],"canopy":[38,64],"heights":[39],"were":[40,110],"used":[41,111],"to":[42,61,131],"constrain":[43],"unknown":[45],"parameter":[46],"widely-used":[49],"SAR":[50],"scattering":[51],"model":[52,59,132],"Random":[54,101],"Volume":[55],"Ground":[57],"(RVoG)":[58],"and":[60,77,125],"help":[62],"derive":[63],"height":[65,73,87,108],"from":[66],"three":[67],"TDX":[68,75,124],"acquisitions.":[69],"Using":[70],"these":[71],"RVoG":[72],"maps,":[74,80],"coherences":[76],"SLC":[78],"magnitude":[79],"we":[81],"predicted":[82],"maps":[83],"quartile":[85,107],"relative":[86],"metrics":[88,109],"(RH100,":[89],"RH75,":[90],"RH50,":[91],"RH25)":[92],"TEF":[94],"using":[95],"relationship":[96],"models":[97],"developed":[98],"based":[99],"on":[100],"Forests":[102],"regression.":[103,118],"The":[104],"as":[112],"training":[114],"in":[116],"Results":[119],"suggest":[120],"that":[121],"fusion":[122],"has":[128],"great":[129],"potential":[130],"biomass":[136],"large-area.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}