{"id":"https://openalex.org/W2987973318","doi":"https://doi.org/10.1109/igarss.2019.8899886","title":"Using the Two-Level Model with Tandem-X for Large-Scale Forest Mapping","display_name":"Using the Two-Level Model with Tandem-X for Large-Scale Forest Mapping","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2987973318","doi":"https://doi.org/10.1109/igarss.2019.8899886","mag":"2987973318"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 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/A5031343326","display_name":"Henrik Persson","orcid":"https://orcid.org/0000-0002-3403-057X"},"institutions":[{"id":"https://openalex.org/I298625061","display_name":"Swedish University of Agricultural Sciences","ror":"https://ror.org/02yy8x990","country_code":"SE","type":"education","lineage":["https://openalex.org/I298625061"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Henrik J. Persson","raw_affiliation_strings":["Swedish University of Agricultural Sciences, Ume\u00e2, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swedish University of Agricultural Sciences, Ume\u00e2, Sweden","institution_ids":["https://openalex.org/I298625061"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056803638","display_name":"Maciej J. Soja","orcid":"https://orcid.org/0000-0002-4683-3142"},"institutions":[{"id":"https://openalex.org/I92070644","display_name":"Forestry Tasmania","ror":"https://ror.org/056j33p55","country_code":"AU","type":"government","lineage":["https://openalex.org/I92070644"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Maciej J. Soja","raw_affiliation_strings":["MJ Soja Consulting, West Hobart, Tasmania, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MJ Soja Consulting, West Hobart, Tasmania, Australia","institution_ids":["https://openalex.org/I92070644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009216756","display_name":"Johan E. S. Fransson","orcid":"https://orcid.org/0000-0002-7913-8592"},"institutions":[{"id":"https://openalex.org/I298625061","display_name":"Swedish University of Agricultural Sciences","ror":"https://ror.org/02yy8x990","country_code":"SE","type":"education","lineage":["https://openalex.org/I298625061"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Johan E.S. Fransson","raw_affiliation_strings":["Swedish University of Agricultural Sciences, Ume\u00e2, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swedish University of Agricultural Sciences, Ume\u00e2, Sweden","institution_ids":["https://openalex.org/I298625061"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030673696","display_name":"Lars M. H. Ulander","orcid":"https://orcid.org/0000-0001-5757-9517"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Lars M. H. Ulander","raw_affiliation_strings":["Chalmers University of Technology, Gothenburg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0343,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75636499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4484","last_page":"4487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.996399998664856,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5140622854232788},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4665125906467438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4548720121383667},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4401242733001709},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4297511577606201},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41677671670913696},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4121151268482208},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.378264844417572},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3503783941268921},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09815824031829834},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07803574204444885}],"concepts":[{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5140622854232788},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4665125906467438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4548720121383667},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4401242733001709},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4297511577606201},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41677671670913696},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4121151268482208},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.378264844417572},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3503783941268921},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09815824031829834},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07803574204444885}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2019.8899886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:research.chalmers.se:516361","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/516361","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1480800708","https://openalex.org/W1992053024","https://openalex.org/W2033489424","https://openalex.org/W2057327074","https://openalex.org/W2059394037","https://openalex.org/W2070716741","https://openalex.org/W2092911007","https://openalex.org/W2100348820","https://openalex.org/W2111449514","https://openalex.org/W2143718706","https://openalex.org/W2148080629","https://openalex.org/W2175899267","https://openalex.org/W2216605777","https://openalex.org/W2515294851","https://openalex.org/W2515583119","https://openalex.org/W2537499819","https://openalex.org/W2615774114","https://openalex.org/W2617211699","https://openalex.org/W2775250907","https://openalex.org/W2897781142","https://openalex.org/W4233606946","https://openalex.org/W4238514242","https://openalex.org/W6628760605"],"related_works":["https://openalex.org/W2575795810","https://openalex.org/W2610868774","https://openalex.org/W4400591661","https://openalex.org/W4399767649","https://openalex.org/W2092994918","https://openalex.org/W3216594821","https://openalex.org/W2390006526","https://openalex.org/W31220157","https://openalex.org/W3215700490","https://openalex.org/W1915333409"],"abstract_inverted_index":{"This":[0],"study":[1,122],"applies":[2],"the":[3,21,28,45,59,85,100,104,147,153,171],"two-level":[4],"model":[5,37,46,131,143,174],"to":[6,42,119,151],"predict":[7],"stem":[8],"volume":[9],"(VOL),":[10],"presented":[11],"as":[12],"wall-to-wall":[13],"rasters.":[14],"The":[15,55,82,113],"SAR":[16],"data":[17],"were":[18,88,107],"acquired":[19],"with":[20,90],"TanDEM-X":[22],"system":[23],"and":[24,75,96,103,110],"518":[25],"scenes":[26],"covered":[27],"entire":[29],"Sweden.":[30],"For":[31],"comparison,":[32],"a":[33,78,120,126],"multiple":[34,128,176],"linear":[35,129,177],"regression":[36,130],"is":[38,61,115],"also":[39],"evaluated.":[40],"Compared":[41],"earlier":[43],"studies,":[44],"parameters":[47,144],"are":[48],"fitted":[49,145],"separately":[50],"for":[51,134],"each":[52],"satellite":[53],"scene.":[54],"prediction":[56,154],"accuracy":[57],"at":[58,99,146],"stand-level":[60],"evaluated":[62,179],"using":[63,142],"field":[64],"inventoried":[65],"reference":[66],"stands":[67],"within":[68],"one":[69],"scene,":[70],"located":[71],"in":[72,156,180],"Northern":[73],"Sweden":[74],"provided":[76],"by":[77],"Swedish":[79],"forest":[80],"company.":[81],"results":[83],"from":[84],"two":[86],"models":[87],"similar,":[89],"an":[91],"RMSE":[92],"of":[93,158],"34.8":[94],"m3/ha":[95,98,109],"32.9":[97],"stand-level,":[101],"respectively,":[102],"corresponding":[105],"biases":[106],"14.3":[108],"12.1":[111],"m3/ha.":[112],"error":[114],"significantly":[116],"lower,":[117],"compared":[118],"previous":[121],"(52-65":[123],"m3/ha)":[124],"where":[125],"universal":[127],"was":[132],"used":[133],"all":[135],"scenes.":[136],"It":[137],"can":[138],"be":[139,165],"concluded,":[140],"that":[141],"local":[148],"scene":[149],"appears":[150],"improve":[152],"performance":[155],"terms":[157],"RMSE,":[159],"but":[160],"no":[161],"significant":[162],"difference":[163],"could":[164],"determined":[166],"between":[167],"predictions":[168],"based":[169],"on":[170],"two-":[172],"level":[173],"or":[175],"regression,":[178],"this":[181],"study.":[182]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}