Integrating global land cover datasets for deriving user-specific maps
Global scale land cover (LC) mapping has interested many researchers over the last two decades as it is an input data source for various applications. Current global land cover (GLC) maps often do not meet the accuracy and thematic requirements of specific users. This study aimed to create an improv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-03-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2016.1217942 |
| _version_ | 1827811242650107904 |
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| author | Nandin-Erdene Tsendbazar Sytze de Bruin Martin Herold |
| author_facet | Nandin-Erdene Tsendbazar Sytze de Bruin Martin Herold |
| author_sort | Nandin-Erdene Tsendbazar |
| collection | DOAJ |
| description | Global scale land cover (LC) mapping has interested many researchers over the last two decades as it is an input data source for various applications. Current global land cover (GLC) maps often do not meet the accuracy and thematic requirements of specific users. This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets. We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends. We used a regression kriging method to integrate Globcover-2009, LC-CCI-2010, MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets. Overall correspondence of the integrated GLC map with reference LC was 80% based on 10-fold cross-validation using 24,681 sample sites. This is globally 10% and regionally 6–13% higher than the input map correspondences. Based on LC class presence probability maps, expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments. Since more reference datasets are becoming freely accessible, GLC mapping can be further improved by using the pool of all available reference datasets. LC proportion information allow tuning LC products to specific user needs. |
| first_indexed | 2024-03-11T23:03:05Z |
| format | Article |
| id | doaj.art-e912630622ec4e1399dd68de1c68c753 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:03:05Z |
| publishDate | 2017-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-e912630622ec4e1399dd68de1c68c7532023-09-21T14:38:04ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552017-03-0110321923710.1080/17538947.2016.12179421217942Integrating global land cover datasets for deriving user-specific mapsNandin-Erdene Tsendbazar0Sytze de Bruin1Martin Herold2Wageningen UniversityWageningen UniversityWageningen UniversityGlobal scale land cover (LC) mapping has interested many researchers over the last two decades as it is an input data source for various applications. Current global land cover (GLC) maps often do not meet the accuracy and thematic requirements of specific users. This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets. We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends. We used a regression kriging method to integrate Globcover-2009, LC-CCI-2010, MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets. Overall correspondence of the integrated GLC map with reference LC was 80% based on 10-fold cross-validation using 24,681 sample sites. This is globally 10% and regionally 6–13% higher than the input map correspondences. Based on LC class presence probability maps, expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments. Since more reference datasets are becoming freely accessible, GLC mapping can be further improved by using the pool of all available reference datasets. LC proportion information allow tuning LC products to specific user needs.http://dx.doi.org/10.1080/17538947.2016.1217942global land coverdata integrationuser-specific legendlc proportion |
| spellingShingle | Nandin-Erdene Tsendbazar Sytze de Bruin Martin Herold Integrating global land cover datasets for deriving user-specific maps International Journal of Digital Earth global land cover data integration user-specific legend lc proportion |
| title | Integrating global land cover datasets for deriving user-specific maps |
| title_full | Integrating global land cover datasets for deriving user-specific maps |
| title_fullStr | Integrating global land cover datasets for deriving user-specific maps |
| title_full_unstemmed | Integrating global land cover datasets for deriving user-specific maps |
| title_short | Integrating global land cover datasets for deriving user-specific maps |
| title_sort | integrating global land cover datasets for deriving user specific maps |
| topic | global land cover data integration user-specific legend lc proportion |
| url | http://dx.doi.org/10.1080/17538947.2016.1217942 |
| work_keys_str_mv | AT nandinerdenetsendbazar integratinggloballandcoverdatasetsforderivinguserspecificmaps AT sytzedebruin integratinggloballandcoverdatasetsforderivinguserspecificmaps AT martinherold integratinggloballandcoverdatasetsforderivinguserspecificmaps |