{"id":"https://openalex.org/W2944707852","doi":"https://doi.org/10.3390/rs11091050","title":"Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach","display_name":"Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach","publication_year":2019,"publication_date":"2019-05-03","ids":{"openalex":"https://openalex.org/W2944707852","doi":"https://doi.org/10.3390/rs11091050","mag":"2944707852"},"language":"en","primary_location":{"id":"doi:10.3390/rs11091050","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11091050","pdf_url":"https://www.mdpi.com/2072-4292/11/9/1050/pdf?version=1556865700","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/2072-4292/11/9/1050/pdf?version=1556865700","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101941338","display_name":"Mengxi Wang","orcid":"https://orcid.org/0000-0002-1215-4723"},"institutions":[{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210132245","display_name":"Research Institute of Forestry","ror":"https://ror.org/02nmvgz47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210132245","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengxi Wang","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]},{"raw_affiliation_string":"Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210132245","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069044551","display_name":"Qingwang Liu","orcid":"https://orcid.org/0000-0003-2339-6223"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingwang Liu","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087729203","display_name":"Liyong Fu","orcid":"https://orcid.org/0000-0002-5794-9458"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyong Fu","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086878627","display_name":"Guangxing Wang","orcid":"https://orcid.org/0000-0002-5419-4547"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guangxing Wang","raw_affiliation_strings":["Department of Geography and Environmental Resources, Southern Illinois University Carbondale, Carbondale, IL 62901, USA"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Resources, Southern Illinois University Carbondale, Carbondale, IL 62901, USA","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030204661","display_name":"Xiongqing Zhang","orcid":"https://orcid.org/0000-0001-5592-6730"},"institutions":[{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210132245","display_name":"Research Institute of Forestry","ror":"https://ror.org/02nmvgz47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210132245","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiongqing Zhang","raw_affiliation_strings":["Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210132245","https://openalex.org/I4210128615"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5030204661"],"corresponding_institution_ids":["https://openalex.org/I4210128615","https://openalex.org/I4210132245"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.3423,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.58355053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"11","issue":"9","first_page":"1050","last_page":"1050"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"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":1.0,"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/T11880","display_name":"Forest ecology and management","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"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/T11753","display_name":"Forest Management and Policy","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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":0.7174803018569946},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6030386090278625},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45579230785369873},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4441543519496918},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4216650724411011},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4143046736717224},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4051857590675354},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3947850465774536},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36783382296562195},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27089571952819824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16229236125946045},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09601202607154846}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7174803018569946},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6030386090278625},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45579230785369873},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4441543519496918},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4216650724411011},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4143046736717224},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4051857590675354},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3947850465774536},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36783382296562195},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27089571952819824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16229236125946045},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09601202607154846}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11091050","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11091050","pdf_url":"https://www.mdpi.com/2072-4292/11/9/1050/pdf?version=1556865700","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:46ae6847369945b8a0f5d86af9d7c4b0","is_oa":true,"landing_page_url":"https://doaj.org/article/46ae6847369945b8a0f5d86af9d7c4b0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 11, Iss 9, p 1050 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/9/1050/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11091050","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 11; Issue 9; Pages: 1050","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11091050","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11091050","pdf_url":"https://www.mdpi.com/2072-4292/11/9/1050/pdf?version=1556865700","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":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2944707852.pdf","grobid_xml":"https://content.openalex.org/works/W2944707852.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1503956561","https://openalex.org/W1603903339","https://openalex.org/W1626119784","https://openalex.org/W1787669200","https://openalex.org/W1930492459","https://openalex.org/W1974671049","https://openalex.org/W1987794512","https://openalex.org/W1990305456","https://openalex.org/W1991228755","https://openalex.org/W2002054594","https://openalex.org/W2004363051","https://openalex.org/W2037718066","https://openalex.org/W2051316645","https://openalex.org/W2055314812","https://openalex.org/W2061407689","https://openalex.org/W2076239013","https://openalex.org/W2087198979","https://openalex.org/W2089871738","https://openalex.org/W2090269208","https://openalex.org/W2102650396","https://openalex.org/W2119615870","https://openalex.org/W2120914348","https://openalex.org/W2138710913","https://openalex.org/W2152429819","https://openalex.org/W2157286661","https://openalex.org/W2194590177","https://openalex.org/W2261165592","https://openalex.org/W2519147460","https://openalex.org/W2583866283","https://openalex.org/W2615790269","https://openalex.org/W2738339366","https://openalex.org/W2787413061","https://openalex.org/W2794437207","https://openalex.org/W3114978541","https://openalex.org/W3124961047","https://openalex.org/W6630205696","https://openalex.org/W6648233216","https://openalex.org/W6665642519","https://openalex.org/W6673076098","https://openalex.org/W6726612338","https://openalex.org/W6732546393","https://openalex.org/W6999445181","https://openalex.org/W7043771933"],"related_works":["https://openalex.org/W2562263695","https://openalex.org/W2135187896","https://openalex.org/W2095430756","https://openalex.org/W2015518264","https://openalex.org/W2147201983","https://openalex.org/W2795035211","https://openalex.org/W2160108762","https://openalex.org/W2164129707","https://openalex.org/W4292122269","https://openalex.org/W2949366006"],"abstract_inverted_index":{"Conventional":[0],"ground":[1],"survey":[2],"data":[3,11,76],"are":[4],"very":[5],"accurate,":[6],"but":[7],"expensive.":[8],"Airborne":[9],"lidar":[10,38,132],"can":[12],"reduce":[13,48],"the":[14,49,62,79,94,115,120,138,150,156,161,168,175,182,189,199,203,207],"costs":[15,219],"and":[16,31,53,77,124,212],"effort":[17],"required":[18],"to":[19,27,136,205],"conduct":[20],"large-scale":[21],"forest":[22],"surveys.":[23],"It":[24],"is":[25],"critical":[26],"improve":[28,54,206],"biomass":[29,66],"estimation":[30,51],"evaluate":[32],"carbon":[33],"stock":[34],"when":[35,173],"we":[36,92,113],"use":[37],"data.":[39],"Bayesian":[40,71,81,110,144,158,183,200],"methods":[41,85,170],"integrate":[42],"prior":[43],"information":[44],"about":[45],"unknown":[46],"parameters,":[47],"parameter":[50,147],"uncertainty,":[52],"model":[55,72,82,101,140],"performance.":[56],"This":[57],"study":[58,196],"focused":[59],"on":[60],"predicting":[61],"independent":[63,134],"tree":[64,121],"aboveground":[65],"(AGB)":[67],"with":[68,83,119,146,160,221],"a":[69,108,142],"hierarchical":[70,80,109,143,157],"using":[73,214],"airborne":[74,131],"LIDAR":[75,215],"comparing":[78],"classical":[84,190],"(nonlinear":[86],"mixed":[87],"effect":[88],"model,":[89],"NLME).":[90],"Firstly,":[91],"chose":[93],"best":[95],"diameter":[96],"at":[97],"breast":[98],"height":[99,122],"(DBH)":[100],"from":[102,149],"several":[103],"widely":[104],"used":[105,114],"models":[106],"through":[107,141],"method.":[111,152,163,191],"Secondly,":[112],"DBH":[116,211],"predictions":[117],"together":[118],"(LH)":[123],"canopy":[125],"projection":[126],"area":[127],"(CPA)":[128],"derived":[129],"by":[130],"as":[133],"variables":[135],"develop":[137],"AGB":[139,213],"method":[145,159,184,201],"priors":[148],"NLME":[151,162],"We":[153],"then":[154],"compared":[155,220],"The":[164,192],"results":[165,193],"showed":[166],"that":[167,198],"two":[169],"performed":[171],"similarly":[172],"pooling":[174],"data,":[176,216],"while":[177],"for":[178],"small":[179],"sample":[180],"sizes,":[181],"was":[185],"much":[186],"better":[187],"than":[188],"of":[194,209],"this":[195],"imply":[197],"has":[202],"potential":[204],"estimations":[208],"both":[210],"which":[217],"reduces":[218],"conventional":[222],"measurements.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
