{"id":"https://openalex.org/W1554190159","doi":"https://doi.org/10.3390/rs70809655","title":"An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms","display_name":"An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms","publication_year":2015,"publication_date":"2015-07-29","ids":{"openalex":"https://openalex.org/W1554190159","doi":"https://doi.org/10.3390/rs70809655","mag":"1554190159"},"language":"en","primary_location":{"id":"doi:10.3390/rs70809655","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs70809655","pdf_url":"https://www.mdpi.com/2072-4292/7/8/9655/pdf?version=1438242568","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/2072-4292/7/8/9655/pdf?version=1438242568","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009612605","display_name":"Ren\u00e9 R. Colditz","orcid":"https://orcid.org/0000-0002-6431-3183"},"institutions":[{"id":"https://openalex.org/I4210105983","display_name":"National Commission for the Knowledge and Use of Biodiversity","ror":"https://ror.org/01g53pj53","country_code":"MX","type":"government","lineage":["https://openalex.org/I2891076039","https://openalex.org/I4210105983"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Ren\u00e9 Colditz","raw_affiliation_strings":["National Commission for the Knowledge and Use of Biodiversity (CONABIO),  Av. Liga Perif\u00e9rico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan, 14010 Mexico City, Mexico"],"affiliations":[{"raw_affiliation_string":"National Commission for the Knowledge and Use of Biodiversity (CONABIO),  Av. Liga Perif\u00e9rico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan, 14010 Mexico City, Mexico","institution_ids":["https://openalex.org/I4210105983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5009612605"],"corresponding_institution_ids":["https://openalex.org/I4210105983"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":7.8113,"has_fulltext":false,"cited_by_count":179,"citation_normalized_percentile":{"value":0.97241007,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"7","issue":"8","first_page":"9655","last_page":"9681"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9997000098228455,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9891999959945679,"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/land-cover","display_name":"Land cover","score":0.6257832050323486},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.546029269695282},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5414015054702759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.538296639919281},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.50987708568573},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48616334795951843},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.430156946182251},{"id":"https://openalex.org/keywords/bidirectional-reflectance-distribution-function","display_name":"Bidirectional reflectance distribution function","score":0.42490458488464355},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.41727542877197266},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41706395149230957},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3545472025871277},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35414421558380127},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.302465558052063},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.21472761034965515},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.20227310061454773},{"id":"https://openalex.org/keywords/reflectivity","display_name":"Reflectivity","score":0.18150094151496887},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10550564527511597}],"concepts":[{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6257832050323486},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.546029269695282},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5414015054702759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.538296639919281},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.50987708568573},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48616334795951843},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.430156946182251},{"id":"https://openalex.org/C151596937","wikidata":"https://www.wikidata.org/wiki/Q856980","display_name":"Bidirectional reflectance distribution function","level":3,"score":0.42490458488464355},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41727542877197266},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41706395149230957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3545472025871277},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35414421558380127},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.302465558052063},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.21472761034965515},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.20227310061454773},{"id":"https://openalex.org/C108597893","wikidata":"https://www.wikidata.org/wiki/Q663650","display_name":"Reflectivity","level":2,"score":0.18150094151496887},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10550564527511597},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs70809655","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs70809655","pdf_url":"https://www.mdpi.com/2072-4292/7/8/9655/pdf?version=1438242568","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:5b9e510f45fb485989abd0459389d452","is_oa":true,"landing_page_url":"https://doaj.org/article/5b9e510f45fb485989abd0459389d452","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 7, Iss 8, Pp 9655-9681 (2015)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/7/8/9655/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs70809655","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 7; Issue 8; Pages: 9655-9681","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs70809655","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs70809655","pdf_url":"https://www.mdpi.com/2072-4292/7/8/9655/pdf?version=1438242568","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":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1554190159.pdf","grobid_xml":"https://content.openalex.org/works/W1554190159.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W242465711","https://openalex.org/W273955616","https://openalex.org/W649977621","https://openalex.org/W1481566577","https://openalex.org/W1504694836","https://openalex.org/W1580493526","https://openalex.org/W1920872892","https://openalex.org/W1964206146","https://openalex.org/W1976734386","https://openalex.org/W1981213426","https://openalex.org/W1981377684","https://openalex.org/W1987415163","https://openalex.org/W1996777760","https://openalex.org/W2001510610","https://openalex.org/W2011068521","https://openalex.org/W2023889823","https://openalex.org/W2024968541","https://openalex.org/W2030257787","https://openalex.org/W2032701790","https://openalex.org/W2034443267","https://openalex.org/W2035549557","https://openalex.org/W2036081619","https://openalex.org/W2042038556","https://openalex.org/W2042692910","https://openalex.org/W2043300851","https://openalex.org/W2057479110","https://openalex.org/W2059229514","https://openalex.org/W2063395983","https://openalex.org/W2063545471","https://openalex.org/W2075218710","https://openalex.org/W2077202991","https://openalex.org/W2077519572","https://openalex.org/W2082874195","https://openalex.org/W2094171467","https://openalex.org/W2095410437","https://openalex.org/W2095795470","https://openalex.org/W2100001151","https://openalex.org/W2101786234","https://openalex.org/W2112278810","https://openalex.org/W2117519593","https://openalex.org/W2121025662","https://openalex.org/W2125055259","https://openalex.org/W2131448468","https://openalex.org/W2136783177","https://openalex.org/W2138408852","https://openalex.org/W2138448722","https://openalex.org/W2138577155","https://openalex.org/W2139670955","https://openalex.org/W2148022840","https://openalex.org/W2156419436","https://openalex.org/W2161570034","https://openalex.org/W2168481151","https://openalex.org/W2169526039","https://openalex.org/W2170804038","https://openalex.org/W2199321793","https://openalex.org/W2911964244","https://openalex.org/W2915157699","https://openalex.org/W3113529501","https://openalex.org/W4285719527","https://openalex.org/W6609245954","https://openalex.org/W6610017368","https://openalex.org/W6680121253"],"related_works":["https://openalex.org/W1996671438","https://openalex.org/W4382897192","https://openalex.org/W2611841783","https://openalex.org/W2063805703","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487","https://openalex.org/W2088899772"],"abstract_inverted_index":{"Land":[0],"cover":[1,54,90,147],"mapping":[2,18,55,148],"for":[3,31,49,139,144],"large":[4],"regions":[5],"often":[6],"employs":[7],"satellite":[8],"images":[9],"of":[10,19,28,45,66,75,84,92,95,112,123,134,184],"medium":[11],"to":[12,64,104,130,165],"coarse":[13],"spatial":[14,117],"resolution,":[15],"which":[16,24],"complicates":[17],"discrete":[20,50,67],"classes.":[21],"Class":[22],"memberships,":[23],"estimate":[25,167],"the":[26,72,76,85,96,131,168,172],"proportion":[27],"each":[29,135],"class":[30,136],"every":[32],"pixel,":[33],"have":[34],"been":[35],"suggested":[36],"as":[37,101],"an":[38],"alternative.":[39],"This":[40],"paper":[41],"compares":[42],"different":[43],"strategies":[44],"training":[46],"data":[47],"allocation":[48,128],"and":[51,58,68,126,155,186],"continuous":[52,69,145],"land":[53,89,146],"using":[56],"classification":[57,140,177],"regression":[59],"tree":[60],"algorithms.":[61],"In":[62],"addition":[63],"measures":[65],"map":[70],"accuracy":[71],"correct":[73],"estimation":[74],"area":[77,133],"is":[78,137],"another":[79],"important":[80],"criteria.":[81],"A":[82],"subset":[83],"30":[86],"m":[87,116],"national":[88],"dataset":[91],"2006":[93],"(NLCD2006)":[94],"United":[97],"States":[98],"was":[99],"used":[100],"reference":[102],"set":[103],"classify":[105],"NADIR":[106],"BRDF-adjusted":[107],"surface":[108],"reflectance":[109],"time":[110],"series":[111],"MODIS":[113],"at":[114],"900":[115],"resolution.":[118],"Results":[119],"show":[120],"that":[121],"sampling":[122],"heterogeneous":[124],"pixels":[125],"sample":[127],"according":[129],"expected":[132],"best":[138],"trees.":[141],"Regression":[142],"trees":[143,183],"should":[149,157],"be":[150,158],"trained":[151],"with":[152,160],"random":[153,175,192],"allocation,":[154],"predictions":[156],"normalized":[159],"a":[161],"linear":[162],"scaling":[163],"function":[164],"correctly":[166],"total":[169],"area.":[170],"From":[171],"tested":[173],"algorithms":[174],"forest":[176,193],"yields":[178],"lower":[179],"errors":[180],"than":[181,191],"boosted":[182],"C5.0,":[185],"Cubist":[187],"shows":[188],"higher":[189],"accuracies":[190],"regression.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":29},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-22T08:38:42.863108","created_date":"2025-10-10T00:00:00"}
