Post-Processing Optimization of the Global 30 m Land Cover Dynamic Monitoring Product
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| Publicado en: | Remote Sensing vol. 17, no. 9 (2025), p. 1558 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17091558 |2 doi | |
| 035 | |a 3203224768 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Li, Zhehua |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 245 | 1 | |a Post-Processing Optimization of the Global 30 m Land Cover Dynamic Monitoring Product | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. Global land cover (GLC) mapping has gained significant attention over the past decade, but current GLC time-series products suffer from considerable inconsistencies in mapping results between different epochs, leading to severe erroneous changes. Here, we aimed to design a novel post-processing approach by combining multi-source data to optimize the GLC_FCS30D product, which represents a groundbreaking improvement in GLC dynamic mapping at a resolution of 30 m. First, spatiotemporal filtering with a window size of 3 × 3 × 3 was applied to reduce the “salt-and-pepper” effect. Second, a temporal consistency optimization algorithm based on LandTrendr was used to identify land cover changes across the entire time series and eliminate excessively frequent erroneous changes. Third, certain land cover transitions between easily misclassified types were optimized using logical rules and multi-source data. Specifically, the illogical wetland-related transitions (wetland–water and wetland–forest) were corrected using a simple replacement rule. To address the noticeable erroneous changes in arid and semi-arid regions, the erroneous land cover transitions involving bare areas, sparse vegetation, grassland, and shrubland were corrected by combining NDVI and precipitation data. Finally, the performance of our post-processing optimization approach was evaluated and quantified. The proposed approach successfully reduced the cumulative change area from 7537.00 million hectares (Mha) in the GLC_FCS30D product without optimization to 1981.00 Mha in the GLC_FCS30D product with optimization, eliminating 5556.00 Mha of erroneous changes across 26 epochs. Furthermore, the overall accuracy of the mapping was also improved from 73.04% to 74.24% for the Land Cover Classification System (LCCS) level-1 validation system. Erroneous changes in GLC_FCS30D were considerably mitigated with the post-processing optimization method, providing more reliable insights into GLC changes from 1985 to 2022 at a 30 m resolution. | |
| 610 | 4 | |a National Aeronautics & Space Administration--NASA | |
| 651 | 4 | |a United States--US | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Algorithms | ||
| 653 | |a Arid zones | ||
| 653 | |a Grasslands | ||
| 653 | |a Wetlands | ||
| 653 | |a Optimization | ||
| 653 | |a Mapping | ||
| 653 | |a Hydrologic data | ||
| 653 | |a Land cover | ||
| 653 | |a Landsat satellites | ||
| 653 | |a Vegetation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Time series | ||
| 653 | |a Arid regions | ||
| 653 | |a Semiarid zones | ||
| 653 | |a Semi arid areas | ||
| 700 | 1 | |a Zhang, Xiao |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 700 | 1 | |a Liu, Wendi |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 700 | 1 | |a Zhao, Tingting |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 700 | 1 | |a Ai Weitao |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 700 | 1 | |a Wang, Jinqing |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 700 | 1 | |a Liu Liangyun |u Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Chinaliuly@radi.ac.cn (L.L.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 9 (2025), p. 1558 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203224768/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3203224768/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203224768/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |