Land cover classification for Siberia leveraging diverse global land cover datasets
Abstract Understanding the land cover is crucial to comprehending the functioning of the Earth’s system. The land cover of Siberia is characterized by uncertainty because it is wide-ranging and comprises various classification types. However, comparisons among land cover products reveal substantial...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Progress in Earth and Planetary Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40645-024-00672-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544215188733952 |
|---|---|
| author | Munseon Beak Kazuhito Ichii Yuhei Yamamoto Ruci Wang Beichen Zhang Ram C. Sharma Tetsuya Hiyama |
| author_facet | Munseon Beak Kazuhito Ichii Yuhei Yamamoto Ruci Wang Beichen Zhang Ram C. Sharma Tetsuya Hiyama |
| author_sort | Munseon Beak |
| collection | DOAJ |
| description | Abstract Understanding the land cover is crucial to comprehending the functioning of the Earth’s system. The land cover of Siberia is characterized by uncertainty because it is wide-ranging and comprises various classification types. However, comparisons among land cover products reveal substantial discrepancies and uncertainties. Therefore, a reliable land cover product for Siberia is necessary. In this study, we generated new land cover data for Siberia using random forest (RF) classifiers with global land cover datasets. To assess their accuracy and characteristics, we individually validated global land cover products in Siberia using multi-source sample datasets. We trained the RF classifiers with multiple land cover products to produce a more precise land cover product for Siberia. The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. However, in warm summer climates, precipitation plays a critical role in vegetation distribution. The more accurate and detailed land cover created in this study enhances the reliability of analyses in Siberia and fosters a deeper understanding of the impact of the carbon cycle. |
| format | Article |
| id | doaj-art-d1edc7f1bac64ff9a468c86750f3f913 |
| institution | Kabale University |
| issn | 2197-4284 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Progress in Earth and Planetary Science |
| spelling | doaj-art-d1edc7f1bac64ff9a468c86750f3f9132025-01-12T12:44:22ZengSpringerOpenProgress in Earth and Planetary Science2197-42842025-01-0112111510.1186/s40645-024-00672-5Land cover classification for Siberia leveraging diverse global land cover datasetsMunseon Beak0Kazuhito Ichii1Yuhei Yamamoto2Ruci Wang3Beichen Zhang4Ram C. Sharma5Tetsuya Hiyama6Center for Environmental Remote Sensing (CEReS), Chiba UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityCenter for Environmental Remote Sensing (CEReS), Chiba UniversityInstitute for Space-Earth Environmental Research, Nagoya UniversityAbstract Understanding the land cover is crucial to comprehending the functioning of the Earth’s system. The land cover of Siberia is characterized by uncertainty because it is wide-ranging and comprises various classification types. However, comparisons among land cover products reveal substantial discrepancies and uncertainties. Therefore, a reliable land cover product for Siberia is necessary. In this study, we generated new land cover data for Siberia using random forest (RF) classifiers with global land cover datasets. To assess their accuracy and characteristics, we individually validated global land cover products in Siberia using multi-source sample datasets. We trained the RF classifiers with multiple land cover products to produce a more precise land cover product for Siberia. The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. However, in warm summer climates, precipitation plays a critical role in vegetation distribution. The more accurate and detailed land cover created in this study enhances the reliability of analyses in Siberia and fosters a deeper understanding of the impact of the carbon cycle.https://doi.org/10.1186/s40645-024-00672-5Land coverGlobal land coverSiberiaRandom forest classifiersCross-validation |
| spellingShingle | Munseon Beak Kazuhito Ichii Yuhei Yamamoto Ruci Wang Beichen Zhang Ram C. Sharma Tetsuya Hiyama Land cover classification for Siberia leveraging diverse global land cover datasets Progress in Earth and Planetary Science Land cover Global land cover Siberia Random forest classifiers Cross-validation |
| title | Land cover classification for Siberia leveraging diverse global land cover datasets |
| title_full | Land cover classification for Siberia leveraging diverse global land cover datasets |
| title_fullStr | Land cover classification for Siberia leveraging diverse global land cover datasets |
| title_full_unstemmed | Land cover classification for Siberia leveraging diverse global land cover datasets |
| title_short | Land cover classification for Siberia leveraging diverse global land cover datasets |
| title_sort | land cover classification for siberia leveraging diverse global land cover datasets |
| topic | Land cover Global land cover Siberia Random forest classifiers Cross-validation |
| url | https://doi.org/10.1186/s40645-024-00672-5 |
| work_keys_str_mv | AT munseonbeak landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT kazuhitoichii landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT yuheiyamamoto landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT ruciwang landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT beichenzhang landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT ramcsharma landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets AT tetsuyahiyama landcoverclassificationforsiberialeveragingdiversegloballandcoverdatasets |