MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework
Myanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10301604/ |
| _version_ | 1797357979103657984 |
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| author | Huaqiao Xing Linye Zhu Yuqing Zhang Dongyang Hou Cansong Li |
| author_facet | Huaqiao Xing Linye Zhu Yuqing Zhang Dongyang Hou Cansong Li |
| author_sort | Huaqiao Xing |
| collection | DOAJ |
| description | Myanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few existing studies on long time series land cover datasets in Myanmar, and the acquisition of training samples within different time series is a big challenge. Therefore, this study used Google Earth Engine and Landsat imagery to produce a land cover dataset for every two years from 1990 to 2020 using a training sample migration framework. First, the differences in index change, spectral value change, and spectral shape change were used to determine whether the sample points had changed between the base year and the previous year, and then a small number of samples were manually selected. Second, the spectral features, index information, and texture information of the remote sensing images and the object-oriented segmentation method were used to obtain object-oriented multidimensional features. Finally, the random forest method was employed to train the samples of the previous year to obtain the land cover data of the previous year. The results of the study show that the average overall precision of the land cover classification results for Myanmar for 1990–2020 is 0.83 and Kappa is 0.79. In addition, the land cover classification results for Myanmar of 1990–2020 are significantly better than those of Globeland30-2020, FROM-GLC, and Dynamic World land cover, and comparing with these products showed good agreement. |
| first_indexed | 2024-03-08T14:53:35Z |
| format | Article |
| id | doaj.art-ad84624030824fb284a18afad1e54c8f |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T14:53:35Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-ad84624030824fb284a18afad1e54c8f2024-01-11T00:01:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-011724426010.1109/JSTARS.2023.332830910301604MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration FrameworkHuaqiao Xing0https://orcid.org/0000-0002-8748-1729Linye Zhu1https://orcid.org/0000-0002-2125-6860Yuqing Zhang2https://orcid.org/0009-0008-0109-9846Dongyang Hou3https://orcid.org/0000-0002-1156-9353Cansong Li4https://orcid.org/0009-0007-1674-5081School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, ChinaSchool of Geosciences and Info Physics, Central South University, Changsha, ChinaFaculty of Geography, Yunnan Normal University, Kunming, ChinaMyanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few existing studies on long time series land cover datasets in Myanmar, and the acquisition of training samples within different time series is a big challenge. Therefore, this study used Google Earth Engine and Landsat imagery to produce a land cover dataset for every two years from 1990 to 2020 using a training sample migration framework. First, the differences in index change, spectral value change, and spectral shape change were used to determine whether the sample points had changed between the base year and the previous year, and then a small number of samples were manually selected. Second, the spectral features, index information, and texture information of the remote sensing images and the object-oriented segmentation method were used to obtain object-oriented multidimensional features. Finally, the random forest method was employed to train the samples of the previous year to obtain the land cover data of the previous year. The results of the study show that the average overall precision of the land cover classification results for Myanmar for 1990–2020 is 0.83 and Kappa is 0.79. In addition, the land cover classification results for Myanmar of 1990–2020 are significantly better than those of Globeland30-2020, FROM-GLC, and Dynamic World land cover, and comparing with these products showed good agreement.https://ieeexplore.ieee.org/document/10301604/Land cover classificationMyanmarrandom forestsample migrationtime series |
| spellingShingle | Huaqiao Xing Linye Zhu Yuqing Zhang Dongyang Hou Cansong Li MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Land cover classification Myanmar random forest sample migration time series |
| title | MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework |
| title_full | MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework |
| title_fullStr | MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework |
| title_full_unstemmed | MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework |
| title_short | MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework |
| title_sort | mlc30 a new 30 m land cover dataset for myanmar from 1990 to 2020 using training sample migration framework |
| topic | Land cover classification Myanmar random forest sample migration time series |
| url | https://ieeexplore.ieee.org/document/10301604/ |
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