Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land...
Saved in:
Published in | Remote sensing (Basel, Switzerland) Vol. 14; no. 8; p. 1865 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.04.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map. |
---|---|
AbstractList | Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map. |
Author | Moreira, Francisco D. Caetano, Mário Costa, Hugo Moraes, Daniel Benevides, Pedro |
Author_xml | – sequence: 1 givenname: Hugo orcidid: 0000-0001-6207-8223 surname: Costa fullname: Costa, Hugo – sequence: 2 givenname: Pedro surname: Benevides fullname: Benevides, Pedro – sequence: 3 givenname: Francisco D. surname: Moreira fullname: Moreira, Francisco D. – sequence: 4 givenname: Daniel orcidid: 0000-0002-4568-8182 surname: Moraes fullname: Moraes, Daniel – sequence: 5 givenname: Mário orcidid: 0000-0001-8913-7342 surname: Caetano fullname: Caetano, Mário |
BookMark | eNpNUV1vFDEMjFCRKKUv_IJIvCEtJHH2I4_lKKXiCg8Hz5Ev6xx7WpIlyQEVf75pDwGWLFuj8ViaecpOQgzE2HMpXgEY8TplqcUgh659xE6V6FWjlVEn_-1P2HnOe1ELQBqhT9nvzYJlwnm-5ZuS6uonGjmGkd8c5jI1m4I74hfLkiK6r9zHxD9WVgw48_U9bRV_UOI3uCxT2PE3mOt5DHxDoUyB5kbxt1jwQfHy10Kp8A8h_pxp3NEz9tjjnOn8zzxjX95dfl69b9afrq5XF-vGQSdLY7SBzshWSe0ECNf1YhxQC0NjT7W9F1ptEQZS6AFAO9huyVes18q1As7Y9VF3jLi3S5q-Ybq1ESf7AMS0s5jK5Gayo_PUDkL5bmu0kM545yT2xom-dxJk1Xpx1KqGfD9QLnYfD6m6ka3qWhBK9S1U1ssjy6WYcyL_96sU9j4r-y8ruAMXNodG |
CitedBy_id | crossref_primary_10_1080_22797254_2024_2341414 crossref_primary_10_3390_rs16091504 crossref_primary_10_3390_f14020254 crossref_primary_10_3390_agronomy13112741 crossref_primary_10_1080_15481603_2023_2211881 crossref_primary_10_1016_j_jag_2024_103913 crossref_primary_10_3390_geosciences12100352 crossref_primary_10_3390_land12020490 crossref_primary_10_3390_su142315540 crossref_primary_10_3390_rs14184585 crossref_primary_10_3390_fire6070254 |
Cites_doi | 10.1080/01431161.2017.1280635 10.3390/rs9070754 10.1002/rse2.15 10.1080/01431161.2018.1433343 10.1016/j.rse.2014.02.015 10.1080/07038992.2018.1437719 10.3390/ijgi6080230 10.1080/10106049.2020.1790672 10.1038/s41597-020-0371-4 10.3390/rs12213523 10.1002/rse2.188 10.3390/rs11192249 10.1016/j.rse.2021.112364 10.1080/01431160304987 10.3390/rs10081213 10.1117/12.2599740 10.1016/j.rse.2019.111356 10.3390/rs12020282 10.3390/rs12183062 10.1080/01431160310001657795 10.1080/01431161.2018.1452075 10.1175/BAMS-D-11-00254.1 10.3390/rs9010095 10.1080/01431161.2014.930207 10.1109/IGARSS47720.2021.9553780 10.1080/01431161.2015.1093195 10.3390/rs12152411 10.3390/rs11040433 10.1016/j.rse.2018.12.011 10.1023/A:1010933404324 10.1016/j.rse.2018.09.002 10.1016/j.rse.2019.111288 10.1080/01431160701442120 10.1016/S0034-4257(98)00010-8 10.1002/rse2.248 10.1080/01431169608948714 10.1016/j.rse.2018.10.031 10.3390/rs10030460 10.1016/j.rse.2020.112148 10.5194/isprs-archives-XLIII-B3-2020-83-2020 10.1109/TGRS.2006.864370 10.3390/rs12060954 10.3390/rs13122301 10.1080/014311600210191 10.1016/j.rse.2019.05.018 10.1016/j.rse.2018.11.007 10.3390/rs12203428 10.1109/IGARSS47720.2021.9553252 |
ContentType | Journal Article |
Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PIMPY PQEST PQQKQ PQUKI PRINS PTHSS DOA |
DOI | 10.3390/rs14081865 |
DatabaseName | CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts |
DatabaseTitleList | CrossRef Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Geography Forestry |
EISSN | 2072-4292 |
ExternalDocumentID | oai_doaj_org_article_dcfe5802f6b9401c9fcc1a79c077c131 10_3390_rs14081865 |
GeographicLocations | Greece Portugal Europe Spain |
GeographicLocations_xml | – name: Spain – name: Portugal – name: Greece – name: Europe |
GroupedDBID | 29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ADBBV AENEX AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PIMPY PROAC PTHSS RIG TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PQEST PQQKQ PQUKI PRINS |
ID | FETCH-LOGICAL-c361t-94936915214c030c670d8a409ed7eed7ff042ba38e2af3334c3bbef42b742c503 |
IEDL.DBID | 8FG |
ISSN | 2072-4292 |
IngestDate | Fri Oct 04 13:16:05 EDT 2024 Thu Oct 10 16:36:37 EDT 2024 Thu Sep 26 20:57:57 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 8 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c361t-94936915214c030c670d8a409ed7eed7ff042ba38e2af3334c3bbef42b742c503 |
ORCID | 0000-0001-6207-8223 0000-0002-4568-8182 0000-0001-8913-7342 |
OpenAccessLink | https://www.proquest.com/docview/2653022753?pq-origsite=%requestingapplication% |
PQID | 2653022753 |
PQPubID | 2032338 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_dcfe5802f6b9401c9fcc1a79c077c131 proquest_journals_2653022753 crossref_primary_10_3390_rs14081865 |
PublicationCentury | 2000 |
PublicationDate | 2022-04-01 |
PublicationDateYYYYMMDD | 2022-04-01 |
PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Basel |
PublicationPlace_xml | – name: Basel |
PublicationTitle | Remote sensing (Basel, Switzerland) |
PublicationYear | 2022 |
Publisher | MDPI AG |
Publisher_xml | – name: MDPI AG |
References | Stehman (ref_51) 1998; 64 Defourny (ref_31) 2019; 221 Liaw (ref_46) 2002; 2 Foody (ref_54) 2008; 29 ref_14 ref_58 ref_57 ref_12 ref_11 ref_10 ref_19 ref_18 ref_16 ref_15 Brown (ref_21) 2020; 238 Olofsson (ref_52) 2014; 148 Hollmann (ref_3) 2013; 94 Hermosilla (ref_59) 2018; 44 ref_25 Campagnolo (ref_50) 2019; 232 Pettorelli (ref_4) 2016; 2 ref_22 Costa (ref_24) 2020; 42 ref_20 Zha (ref_35) 2003; 24 Stehman (ref_56) 2014; 35 Simoes (ref_2) 2020; 7 Grekousis (ref_5) 2015; 1161 ref_27 Biging (ref_42) 1997; 14 Stehman (ref_55) 2019; 231 Hadjikakou (ref_30) 2021; 252 ref_33 ref_32 Mayaux (ref_7) 2006; 44 Mcfeeters (ref_34) 1996; 17 Cano (ref_39) 2017; 38 ref_38 Loveland (ref_6) 2000; 21 ref_37 Griffiths (ref_28) 2019; 220 Comber (ref_23) 2004; 25 Carver (ref_13) 2020; 55 Liu (ref_9) 2021; 258 Feilhauer (ref_49) 2020; 7 ref_45 Wulder (ref_17) 2018; 39 ref_43 Breiman (ref_44) 2001; 45 ref_41 ref_40 ref_1 Hernandez (ref_47) 2020; 43 Weigand (ref_29) 2020; 88 Maxwell (ref_26) 2018; 39 ref_48 ref_8 Roteta (ref_36) 2019; 222 Claverie (ref_53) 2018; 219 |
References_xml | – volume: 38 start-page: 1865 year: 2017 ident: ref_39 article-title: Improved forest-cover mapping based on MODIS time series and landscape stratification publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2017.1280635 contributor: fullname: Cano – ident: ref_32 – ident: ref_10 doi: 10.3390/rs9070754 – volume: 2 start-page: 122 year: 2016 ident: ref_4 article-title: Framing the concept of satellite remote sensing essential biodiversity variables: Challenges and future directions publication-title: Remote Sens. Ecol. Conserv. doi: 10.1002/rse2.15 contributor: fullname: Pettorelli – volume: 39 start-page: 2784 year: 2018 ident: ref_26 article-title: Implementation of machine-learning classification in remote sensing: An applied review publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1433343 contributor: fullname: Maxwell – volume: 148 start-page: 42 year: 2014 ident: ref_52 article-title: Good practices for estimating area and assessing accuracy of land change publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2014.02.015 contributor: fullname: Olofsson – volume: 44 start-page: 67 year: 2018 ident: ref_59 article-title: Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2018.1437719 contributor: fullname: Hermosilla – ident: ref_8 doi: 10.3390/ijgi6080230 – ident: ref_12 doi: 10.1080/10106049.2020.1790672 – ident: ref_16 – volume: 7 start-page: 34 year: 2020 ident: ref_2 article-title: Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017 publication-title: Sci. Data doi: 10.1038/s41597-020-0371-4 contributor: fullname: Simoes – ident: ref_19 doi: 10.3390/rs12213523 – volume: 7 start-page: 292 year: 2020 ident: ref_49 article-title: Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation publication-title: Remote Sens. Ecol. Conserv. doi: 10.1002/rse2.188 contributor: fullname: Feilhauer – ident: ref_37 doi: 10.3390/rs11192249 – volume: 42 start-page: 29 year: 2020 ident: ref_24 article-title: Introducing automatic satellite image processing into land cover mapping by photo-interpretation of airborne data publication-title: ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. contributor: fullname: Costa – volume: 258 start-page: 112364 year: 2021 ident: ref_9 article-title: Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020—iMap World 1.0 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112364 contributor: fullname: Liu – volume: 55 start-page: 25 year: 2020 ident: ref_13 article-title: Assessment of the classification accuracy of the Globeland30 Forest class for the temperate and tropical forests of Mexico publication-title: Appl. Geomat. contributor: fullname: Carver – volume: 24 start-page: 583 year: 2003 ident: ref_35 article-title: Use of normalized difference built-up index in automatically mapping urban areas from TM imagery publication-title: Int. J. Remote Sens. doi: 10.1080/01431160304987 contributor: fullname: Zha – ident: ref_11 doi: 10.3390/rs10081213 – ident: ref_40 doi: 10.1117/12.2599740 – volume: 238 start-page: 111356 year: 2020 ident: ref_21 article-title: Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111356 contributor: fullname: Brown – volume: 88 start-page: 102065 year: 2020 ident: ref_29 article-title: Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data publication-title: Int. J. Appl. Earth Obs. Geoinf. contributor: fullname: Weigand – ident: ref_1 doi: 10.3390/rs12020282 – ident: ref_38 doi: 10.3390/rs12183062 – volume: 25 start-page: 3177 year: 2004 ident: ref_23 article-title: Application of knowledge for automated land cover change monitoring publication-title: Int. J. Remote Sens. doi: 10.1080/01431160310001657795 contributor: fullname: Comber – volume: 39 start-page: 4254 year: 2018 ident: ref_17 article-title: Land cover 2.0 publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1452075 contributor: fullname: Wulder – volume: 94 start-page: 1541 year: 2013 ident: ref_3 article-title: The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables publication-title: Bull. Am. Meteorol. Soc. doi: 10.1175/BAMS-D-11-00254.1 contributor: fullname: Hollmann – ident: ref_45 – ident: ref_41 doi: 10.3390/rs9010095 – volume: 35 start-page: 4923 year: 2014 ident: ref_56 article-title: Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2014.930207 contributor: fullname: Stehman – ident: ref_48 doi: 10.1109/IGARSS47720.2021.9553780 – volume: 1161 start-page: 5309 year: 2015 ident: ref_5 article-title: An overview of 21 global and 43 regional land-cover mapping products publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2015.1093195 contributor: fullname: Grekousis – ident: ref_57 doi: 10.3390/rs12152411 – ident: ref_27 doi: 10.3390/rs11040433 – volume: 222 start-page: 1 year: 2019 ident: ref_36 article-title: Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.12.011 contributor: fullname: Roteta – volume: 45 start-page: 5 year: 2001 ident: ref_44 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 contributor: fullname: Breiman – volume: 219 start-page: 145 year: 2018 ident: ref_53 article-title: The Harmonized Landsat and Sentinel-2 surface reflectance data set publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.09.002 contributor: fullname: Claverie – volume: 14 start-page: 92 year: 1997 ident: ref_42 article-title: Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data publication-title: Remote Sens. Environ. contributor: fullname: Biging – volume: 232 start-page: 111288 year: 2019 ident: ref_50 article-title: A patch-based algorithm for global and daily burned area mapping publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111288 contributor: fullname: Campagnolo – volume: 29 start-page: 3137 year: 2008 ident: ref_54 article-title: Harshness in image classification accuracy assessment publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701442120 contributor: fullname: Foody – volume: 64 start-page: 331 year: 1998 ident: ref_51 article-title: Design and analysis for thematic map accuracy assessment publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(98)00010-8 contributor: fullname: Stehman – ident: ref_14 doi: 10.1002/rse2.248 – volume: 17 start-page: 1425 year: 1996 ident: ref_34 article-title: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features publication-title: Int. J. Remote Sens. doi: 10.1080/01431169608948714 contributor: fullname: Mcfeeters – volume: 2 start-page: 18 year: 2002 ident: ref_46 article-title: Classification and Regression by randomForest publication-title: R News contributor: fullname: Liaw – volume: 220 start-page: 135 year: 2019 ident: ref_28 article-title: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.10.031 contributor: fullname: Griffiths – ident: ref_33 doi: 10.3390/rs10030460 – volume: 252 start-page: 112148 year: 2021 ident: ref_30 article-title: High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112148 contributor: fullname: Hadjikakou – volume: 43 start-page: 83 year: 2020 ident: ref_47 article-title: Exploring Sentinel-2 for land cover and crop mapping in Portugal publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. doi: 10.5194/isprs-archives-XLIII-B3-2020-83-2020 contributor: fullname: Hernandez – volume: 44 start-page: 1728 year: 2006 ident: ref_7 article-title: Validation of the Global Land Cover 2000 Map publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.864370 contributor: fullname: Mayaux – ident: ref_15 – ident: ref_25 doi: 10.3390/rs12060954 – ident: ref_20 doi: 10.3390/rs13122301 – volume: 21 start-page: 1303 year: 2000 ident: ref_6 article-title: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data publication-title: Int. J. Remote Sens. doi: 10.1080/014311600210191 contributor: fullname: Loveland – ident: ref_43 – ident: ref_22 – volume: 231 start-page: 111199 year: 2019 ident: ref_55 article-title: Key issues in rigorous accuracy assessment of land cover products publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.05.018 contributor: fullname: Stehman – volume: 221 start-page: 551 year: 2019 ident: ref_31 article-title: Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.11.007 contributor: fullname: Defourny – ident: ref_58 doi: 10.3390/rs12203428 – ident: ref_18 doi: 10.1109/IGARSS47720.2021.9553252 |
SSID | ssj0000331904 |
Score | 2.4580595 |
Snippet | Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology... |
SourceID | doaj proquest crossref |
SourceType | Open Website Aggregation Database |
StartPage | 1865 |
SubjectTerms | Accuracy Classification Confidence intervals COSsim Data analysis Forestry Land cover land cover land use Land use machine learning Mapping multi-temporal Plant species Product development random forest Regions Remote sensing satellite image Task complexity |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELVQF1gQn6JQkCVYrSZx7DhjW6gQUBao1C3yZ0GqUtSGoeLPc3ZSKGJgYbUsO7o7-92Lzu8QutKAYrE1iihANwJ8QxKR5AmxTsfUMJFJ54ni6JHfjtO7CZtstPryNWG1PHBtuK7RzjIRJY6rHLiAzp3WscxyHWUZLFcTn5htkKlwB1MIrSit9Ugp8PruYglUwsu3sR8IFIT6f93DAVyGe2i3yQpxr_6afbRlywO03TQof1kdog_fOhhCZbbCQU_21UHqiGVpcHhBSyBnnFrcawTCMWSiuFG8nuEHP23gSzXxSHo5hinuA3YZPC_xky8WKu2MJPhaVjKsGNSPK3y__tt2hMbDm-fBLWn6JhBNeVyRPPVd-jwwpxrOsOZZZIQEImdNBpCYOQcnVUkqbCIdpTTVVCnrYAx4smYRPUatcl7aE4S1dIKbiAtmaMqEUpw6JWQsIVEzzpk2ulzbsnir5TEKoBXe4sW3xduo7838NcNLWocBcHTROLr4y9Ft1Fk7qWjO2bJIuO96lADnOv2PPc7QTuKfN4TKnA5qVYt3ew5JR6UuQnx9Ao5B1dA priority: 102 providerName: Directory of Open Access Journals |
Title | Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge |
URI | https://www.proquest.com/docview/2653022753 https://doaj.org/article/dcfe5802f6b9401c9fcc1a79c077c131 |
Volume | 14 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LSxxBEC6iguYiiQ-yUZcGvTbOTM-j5ySuupFERRIFb0M_V2GZ3eyOB8mfT1VvryKBXGeaOVRXddVXU_19AEcGs1jqrOYasxtHvKG4zOqMO29SYQtZKU9A8fqmvLzPvz8UD7HhNo9jlcszMRzUdmKoR36claRvk2F1fTL9zUk1iv6uRgmNFVhLiQmPbooPv732WBKBDpbkC1ZSgej-eDZHQEEkbsW7PBTo-v85jUOKGX6CzVgbstPFZn6GD67dgnUSzyRFti3YiIrljy_b8Ie0hNF3xi8sEMw-eawlmWotC1dqORaRI8dOI2M4w9KURQrsMbuiZWc0u8muFfEzjNgAk5llk5b9oumh1o15xs5Vp8IXAx1yx34s2287cD-8uDu75FFIgRtRph2vc5Lto0ydGwxqU1aJlQqRnbMV5sjKewxdrYR0mfJCiNwIrZ3HZwicTZGIXVhtJ637AswoL0ublLKwIi-k1qXwWqpUYeVmvbc9OFyatZku-DIaxBlk_ObN-D0YkMVfVxDHdXgwmY2aGDKNNd4VMsl8qWtEgab2xqSqqk1SVehIaQ_2l_vVxMCbN29u8vX_r_fgY0Y3GcIQzj6sdrNnd4D1Raf7wYn6sDa4uLn92Q8o_S8LXNLX |
link.rule.ids | 315,786,790,870,2115,12792,21416,27955,27956,33406,33777,43633,43838,74390,74657 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NT9wwEB21VCq9VBRasRRaS-3VIokTxzlVQLtdYJcLIHGL_LkgrbJ0NxxQ_3xnvF5QVanXxMphPOOZNxm_B_DVYhbLvTPcYHbjiDc0V0VTcB9sLlylah0IKE4u5Oi6PLupblLDbZnGKtdnYjyo3dxSj_ywkKRvU2B1_e3-FyfVKPq7miQ0XsKrUkhBfq6GP596LJlAB8vKFSupQHR_uFgioCASt-qvPBTp-v85jWOKGW7B21QbsqPVZr6DF77bhtcknkmKbNuwmRTLbx934DdpCaPvzB5ZJJi9C1hLMt05Fq_Uciwip54dJcZwhqUpSxTYMzamZSc0u8kmmvgZpuwYk5lj845d0vRQ52e8YN91r-MXIx1yz87X7bf3cD38cXUy4klIgVsh8543Jcn2UaYuLQa1lXXmlEZk512NObIOAUPXaKF8oYMQorTCGB_wGQJnW2XiA2x0887vArM6KOkyqSonykoZI0UwSucaKzcXghvAl7VZ2_sVX0aLOIOM3z4bfwDHZPGnFcRxHR_MF9M2hUzrbPCVyoogTYMo0DbB2lzXjc3qGh0pH8D-er_aFHjL9tlN9v7_-jNsjq4m43Z8enH-Ed4UdKshDuTsw0a_ePAHWGv05lN0qD922NMw |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LTxsxEB5RkCgXxKsiFIoluFrZXe_De6p4NKXloUoFidvKz4AUbUKyPaD-eWYcB4Qq9WpbexjPeL7xjr8P4NhgFkud1VxjduNYbyguszrjzptU2EJWylOheH1TXtzlP--L-9j_NIttlYszMRzUdmzojryflaRvkyG67vvYFvHrfPB18sRJQYr-tEY5jQ-wQiCbZBzk4PvrfUsi0NmSfM5QKnC-P51hcUGEbsW7nBSo-_85mUO6GWzAesSJ7GS-sZuw5NotWCUhTVJn24KPUb384Xkb_pKuMPrR6JkFstlHj7iSqday8LyWI6AcOnYS2cMZwlQW6bBH7IqWnVEfJ7tWxNUwZKeY2Cwbt-w3dRK1bsQzdq46Fb4YqJE7drm4ituBu8G327MLHkUVuBFl2vE6Jwk_ytq5wQA3ZZVYqbDKc7bCfFl5j2GslZAuU14IkRuhtfM4hkW0KRLxCZbbcet2gRnlZWmTUhZW5IXUuhReS5UqRHHWe9uDo4VZm8mcO6PBmoOM37wZvwenZPHXFcR3HQbG02ETw6exxrtCJpkvdY0Voam9MamqapNUFTpV2oP9xX41MQhnzZvL7P1_-hBW0Zeaqx83l59hLaMHDqE3Zx-Wu-kfd4Cwo9Nfgj-9AC0h12U |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatially+Stratified+and+Multi-Stage+Approach+for+National+Land+Cover+Mapping+Based+on+Sentinel-2+Data+and+Expert+Knowledge&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Costa%2C+Hugo&rft.au=Benevides%2C+Pedro&rft.au=Moreira%2C+Francisco+D&rft.au=Moraes%2C+Daniel&rft.date=2022-04-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=14&rft.issue=8&rft.spage=1865&rft_id=info:doi/10.3390%2Frs14081865&rft.externalDBID=HAS_PDF_LINK |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon |