Comparison of Various Annual Land Cover Datasets in the Yellow River Basin
Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2539 |
| _version_ | 1797598446552612864 |
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| author | Bo Liu Zemin Zhang Libo Pan Yibo Sun Shengnan Ji Xiao Guan Junsheng Li Mingzhu Xu |
| author_facet | Bo Liu Zemin Zhang Libo Pan Yibo Sun Shengnan Ji Xiao Guan Junsheng Li Mingzhu Xu |
| author_sort | Bo Liu |
| collection | DOAJ |
| description | Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB. |
| first_indexed | 2024-03-11T03:21:20Z |
| format | Article |
| id | doaj.art-51f073a0545b422d843c2c533d5442f7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-51f073a0545b422d843c2c533d5442f72023-11-18T03:06:33ZengMDPI AGRemote Sensing2072-42922023-05-011510253910.3390/rs15102539Comparison of Various Annual Land Cover Datasets in the Yellow River BasinBo Liu0Zemin Zhang1Libo Pan2Yibo Sun3Shengnan Ji4Xiao Guan5Junsheng Li6Mingzhu Xu7State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaCommand Center of Natural Resources Comprehensive Survey, CGS, Beijing 100055, ChinaZhejiang Environmental Technology Co., Ltd., Hangzhou 311100, ChinaAccurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.https://www.mdpi.com/2072-4292/15/10/2539annual land cover datasetsYellow River Basinspatial and temporal distributionerror decomposition analysislandscape heterogeneity |
| spellingShingle | Bo Liu Zemin Zhang Libo Pan Yibo Sun Shengnan Ji Xiao Guan Junsheng Li Mingzhu Xu Comparison of Various Annual Land Cover Datasets in the Yellow River Basin Remote Sensing annual land cover datasets Yellow River Basin spatial and temporal distribution error decomposition analysis landscape heterogeneity |
| title | Comparison of Various Annual Land Cover Datasets in the Yellow River Basin |
| title_full | Comparison of Various Annual Land Cover Datasets in the Yellow River Basin |
| title_fullStr | Comparison of Various Annual Land Cover Datasets in the Yellow River Basin |
| title_full_unstemmed | Comparison of Various Annual Land Cover Datasets in the Yellow River Basin |
| title_short | Comparison of Various Annual Land Cover Datasets in the Yellow River Basin |
| title_sort | comparison of various annual land cover datasets in the yellow river basin |
| topic | annual land cover datasets Yellow River Basin spatial and temporal distribution error decomposition analysis landscape heterogeneity |
| url | https://www.mdpi.com/2072-4292/15/10/2539 |
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