Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program
The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual lan...
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description | The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance inter |
doi_str_mv | 10.1016/j.rse.2019.111261 |
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•A subset of pixels with duplicate interpretations quantifies consistency.•Interpreter agreement was 88% overall ranging from 46% (Disturbed) to 94% (Water).•Regional variation in class-specific agreement was observed.•Agreement stayed about the same as interpretations were finished over time.•Agreement was greater for data after 2000 coinciding with increased data density.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2019.111261</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Agreements ; Agricultural land ; Alaska ; Archives & records ; Barren lands ; Consistency ; Critical components ; cropland ; Data interpretation ; Disturbance ; Geological surveys ; grasses ; Hawaii ; humans ; Image quality ; Internet ; Interpreters ; Labels ; Land cover ; Land use ; Landsat ; Landsat satellites ; LCMAP ; Monitoring ; Pixels ; Quality assessment ; Quality assurance ; Quality control ; Remote sensing ; Reproduction (copying) ; Satellite imagery ; shrubs ; Time series ; time series analysis ; TimeSync ; Validation ; wetlands</subject><ispartof>Remote sensing of environment, 2020-03, Vol.238, p.111261, Article 111261</ispartof><rights>2019 Elsevier Inc.</rights><rights>Copyright Elsevier BV Mar 1, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c467t-9d0e345ab7975381402f547ae5810548fbca59175f58ade3bb76bad5ed2c0cb83</citedby><cites>FETCH-LOGICAL-c467t-9d0e345ab7975381402f547ae5810548fbca59175f58ade3bb76bad5ed2c0cb83</cites><orcidid>0000-0001-8436-4095 ; 0000-0003-3144-9532 ; 0000-0001-5234-2027 ; 0000-0003-4544-7095 ; 0000-0003-2497-8284 ; 0000-0003-1914-8657 ; 0000-0003-3114-6646 ; 0000-0001-5072-2472</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0034425719302809$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Pengra, Bruce W.</creatorcontrib><creatorcontrib>Stehman, Stephen V.</creatorcontrib><creatorcontrib>Horton, Josephine A.</creatorcontrib><creatorcontrib>Dockter, Daryn J.</creatorcontrib><creatorcontrib>Schroeder, Todd A.</creatorcontrib><creatorcontrib>Yang, Zhiqiang</creatorcontrib><creatorcontrib>Cohen, Warren B.</creatorcontrib><creatorcontrib>Healey, Sean P.</creatorcontrib><creatorcontrib>Loveland, Thomas R.</creatorcontrib><title>Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program</title><title>Remote sensing of environment</title><description>The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance interpreter consistency are critical elements to document and ensure the quality of these reference data.
•A subset of pixels with duplicate interpretations quantifies consistency.•Interpreter agreement was 88% overall ranging from 46% (Disturbed) to 94% (Water).•Regional variation in class-specific agreement was observed.•Agreement stayed about the same as interpretations were finished over time.•Agreement was greater for data after 2000 coinciding with increased data density.</description><subject>Agreements</subject><subject>Agricultural land</subject><subject>Alaska</subject><subject>Archives & records</subject><subject>Barren lands</subject><subject>Consistency</subject><subject>Critical components</subject><subject>cropland</subject><subject>Data interpretation</subject><subject>Disturbance</subject><subject>Geological surveys</subject><subject>grasses</subject><subject>Hawaii</subject><subject>humans</subject><subject>Image quality</subject><subject>Internet</subject><subject>Interpreters</subject><subject>Labels</subject><subject>Land cover</subject><subject>Land use</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>LCMAP</subject><subject>Monitoring</subject><subject>Pixels</subject><subject>Quality assessment</subject><subject>Quality assurance</subject><subject>Quality control</subject><subject>Remote sensing</subject><subject>Reproduction (copying)</subject><subject>Satellite imagery</subject><subject>shrubs</subject><subject>Time series</subject><subject>time series analysis</subject><subject>TimeSync</subject><subject>Validation</subject><subject>wetlands</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kcGKFDEQhoMoOK77AN4CXrz0mEp3Omk8yeLuCgsiuOeQTlcvGbqTMckszDP40lYzevHgJQnU96VS-Rl7B2IPAvqPh30uuJcChj0AyB5esB0YPTRCi-4l2wnRdk0nlX7N3pRyEAKU0bBjv76f3BLqmfsUa04Ld3HirhQsZcVYeZp5iBXzMSOtG1VCqRj9eSu5GEnnyyb59ExAxhkzlZFPrjpyieHpiNnVkCKx8e9hTTHUlEN84secnrJb37JXs1sKXv_Zr9jj7ZcfN_fNw7e7rzefHxrf9bo2wySw7ZQb9aBVa6ATcladdqgMCNWZefRODaDVrIybsB1H3Y9uUjhJL_xo2iv24XIv9f15wlLtGorHhcbAdCpWStMbBabvCH3_D3pIp0zPJ6qlXlK2LRAFF8rnVAp9gT3msLp8tiDsFo89WIrHbvHYSzzkfLo4SJM-B8y2-LB93BQy-mqnFP5j_waLsZrz</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Pengra, Bruce W.</creator><creator>Stehman, Stephen V.</creator><creator>Horton, Josephine A.</creator><creator>Dockter, Daryn J.</creator><creator>Schroeder, Todd A.</creator><creator>Yang, Zhiqiang</creator><creator>Cohen, Warren B.</creator><creator>Healey, Sean P.</creator><creator>Loveland, Thomas R.</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0001-8436-4095</orcidid><orcidid>https://orcid.org/0000-0003-3144-9532</orcidid><orcidid>https://orcid.org/0000-0001-5234-2027</orcidid><orcidid>https://orcid.org/0000-0003-4544-7095</orcidid><orcidid>https://orcid.org/0000-0003-2497-8284</orcidid><orcidid>https://orcid.org/0000-0003-1914-8657</orcidid><orcidid>https://orcid.org/0000-0003-3114-6646</orcidid><orcidid>https://orcid.org/0000-0001-5072-2472</orcidid></search><sort><creationdate>20200301</creationdate><title>Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program</title><author>Pengra, Bruce W. ; Stehman, Stephen V. ; Horton, Josephine A. ; Dockter, Daryn J. ; Schroeder, Todd A. ; Yang, Zhiqiang ; Cohen, Warren B. ; Healey, Sean P. ; Loveland, Thomas R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c467t-9d0e345ab7975381402f547ae5810548fbca59175f58ade3bb76bad5ed2c0cb83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agreements</topic><topic>Agricultural land</topic><topic>Alaska</topic><topic>Archives & records</topic><topic>Barren lands</topic><topic>Consistency</topic><topic>Critical components</topic><topic>cropland</topic><topic>Data interpretation</topic><topic>Disturbance</topic><topic>Geological surveys</topic><topic>grasses</topic><topic>Hawaii</topic><topic>humans</topic><topic>Image quality</topic><topic>Internet</topic><topic>Interpreters</topic><topic>Labels</topic><topic>Land cover</topic><topic>Land use</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>LCMAP</topic><topic>Monitoring</topic><topic>Pixels</topic><topic>Quality assessment</topic><topic>Quality assurance</topic><topic>Quality control</topic><topic>Remote sensing</topic><topic>Reproduction (copying)</topic><topic>Satellite imagery</topic><topic>shrubs</topic><topic>Time series</topic><topic>time series analysis</topic><topic>TimeSync</topic><topic>Validation</topic><topic>wetlands</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pengra, Bruce W.</creatorcontrib><creatorcontrib>Stehman, Stephen V.</creatorcontrib><creatorcontrib>Horton, Josephine A.</creatorcontrib><creatorcontrib>Dockter, Daryn J.</creatorcontrib><creatorcontrib>Schroeder, Todd A.</creatorcontrib><creatorcontrib>Yang, Zhiqiang</creatorcontrib><creatorcontrib>Cohen, Warren B.</creatorcontrib><creatorcontrib>Healey, Sean P.</creatorcontrib><creatorcontrib>Loveland, Thomas R.</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pengra, Bruce W.</au><au>Stehman, Stephen V.</au><au>Horton, Josephine A.</au><au>Dockter, Daryn J.</au><au>Schroeder, Todd A.</au><au>Yang, Zhiqiang</au><au>Cohen, Warren B.</au><au>Healey, Sean P.</au><au>Loveland, Thomas R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program</atitle><jtitle>Remote sensing of environment</jtitle><date>2020-03-01</date><risdate>2020</risdate><volume>238</volume><spage>111261</spage><pages>111261-</pages><artnum>111261</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time? 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance interpreter consistency are critical elements to document and ensure the quality of these reference data.
•A subset of pixels with duplicate interpretations quantifies consistency.•Interpreter agreement was 88% overall ranging from 46% (Disturbed) to 94% (Water).•Regional variation in class-specific agreement was observed.•Agreement stayed about the same as interpretations were finished over time.•Agreement was greater for data after 2000 coinciding with increased data density.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2019.111261</doi><orcidid>https://orcid.org/0000-0001-8436-4095</orcidid><orcidid>https://orcid.org/0000-0003-3144-9532</orcidid><orcidid>https://orcid.org/0000-0001-5234-2027</orcidid><orcidid>https://orcid.org/0000-0003-4544-7095</orcidid><orcidid>https://orcid.org/0000-0003-2497-8284</orcidid><orcidid>https://orcid.org/0000-0003-1914-8657</orcidid><orcidid>https://orcid.org/0000-0003-3114-6646</orcidid><orcidid>https://orcid.org/0000-0001-5072-2472</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agreements Agricultural land Alaska Archives & records Barren lands Consistency Critical components cropland Data interpretation Disturbance Geological surveys grasses Hawaii humans Image quality Internet Interpreters Labels Land cover Land use Landsat Landsat satellites LCMAP Monitoring Pixels Quality assessment Quality assurance Quality control Remote sensing Reproduction (copying) Satellite imagery shrubs Time series time series analysis TimeSync Validation wetlands |
title | Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program |
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