Performance of Global Land Use Land Cover Products for Southwest China Karst.
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| Title: | Performance of Global Land Use Land Cover Products for Southwest China Karst. |
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| Authors: | Zhang, Chunhua1 (AUTHOR), Qi, Xiangkun2 (AUTHOR), Cheung, Hoi Shan3 (AUTHOR), Zhang, Mingyang1,2 (AUTHOR), Yue, Yuemin2 (AUTHOR), Wang, Kelin2,3 (AUTHOR) kelin@isa.ac.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1573. 26p. |
| Subjects: | Karst, Land use mapping, Multisensor data fusion, Vegetation classification, Environmental monitoring, European Space Agency, Geomorphology |
| Geographic Terms: | Southwest China |
| Abstract: | Highlights: What are the main findings? The ESA WorldCover 2021 product outperformed ESRI Land Cover and Dynamic World (annual mode composite) in mapping accuracy for the karst region of Southwest China, better preserving fragmented features (e.g., roads and small fields) than its competitors; All three products showed major limitations in separating spectrally similar vegetation (shrub, grass, and crops) and were strongly affected by topographic shadows and haze. Accuracy dropped sharply at patch boundaries (where over half of errors occurred). What are the implications of the main findings? ESA WorldCover 2021 offers the best balance of spatial detail and accuracy among 10 m global products. Regional validation remains essential because global products can underperform in complex terrain; The poor distinction between shrub, crops, and grass classes constrains these datasets' value for monitoring vegetation recovery. Future improvements in karst areas should integrate multi-source (optical + synthetic aperture radar) and multi-temporal data with topographic correction. Accurate land use and land cover (LULC) data are essential for effective environmental management and reliable ecological modeling within complex landscapes such as the karst region of Southwest China. While new 10 m resolution global LULC products (i.e., ESA WorldCover, ESRI Land Cover, and annual mode composite of Dynamic World (DW)) offer unprecedented spatial detail, their reliability in heterogeneous karst remains poorly understood. We evaluated the accuracy and spatial consistency of these products for 2021 in the karst regions across five provinces in Southwest China using 1416 reference points collected through stratified random sampling. The ESA WorldCover dataset outperformed the others, achieving the highest overall accuracy (79.39 ± 2.19%). ESRI's shrub metrics, however, reflect the structural absence of this class from its 2021 product rather than classification error. ESA's superior performance in preserving fine-scale features is consistent with independent global assessments of both the 2020 and 2021 versions. This superior performance is attributed to its integration of Sentinel-1 SAR with optical data, a finer minimum mapping unit (100 m2), and expert-driven post-classification corrections. While all products successfully identified dominant classes like trees, substantial confusion emerged among spectrally similar classes such as shrubs, grass, and crops. A key finding was the strong effect of landscape heterogeneity on accuracy. Classification accuracy was 19.37% lower at patch edges (67.38%) compared to patch interiors (86.75%). Furthermore, edge reference points contribute disproportionately to total errors. Critically, none of the three products currently provide a sufficient basis for shrub-focused ecological monitoring in this region: ESA rarely detected shrub cover, DW mapped extensive but largely inaccurate shrub areas, and ESRI eliminated the shrub class from its 2021 product. These results show that while global 10 m products provide valuable information, careful product selection and regional validation remain essential for heterogeneous karst environments. Future improvements should integrate multi-source data (optical + synthetic aperture radar), apply topographic compensation for shadow effects, and develop region-specific approaches for mapping vegetation transitions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The ESA WorldCover 2021 product outperformed ESRI Land Cover and Dynamic World (annual mode composite) in mapping accuracy for the karst region of Southwest China, better preserving fragmented features (e.g., roads and small fields) than its competitors; All three products showed major limitations in separating spectrally similar vegetation (shrub, grass, and crops) and were strongly affected by topographic shadows and haze. Accuracy dropped sharply at patch boundaries (where over half of errors occurred). What are the implications of the main findings? ESA WorldCover 2021 offers the best balance of spatial detail and accuracy among 10 m global products. Regional validation remains essential because global products can underperform in complex terrain; The poor distinction between shrub, crops, and grass classes constrains these datasets' value for monitoring vegetation recovery. Future improvements in karst areas should integrate multi-source (optical + synthetic aperture radar) and multi-temporal data with topographic correction. Accurate land use and land cover (LULC) data are essential for effective environmental management and reliable ecological modeling within complex landscapes such as the karst region of Southwest China. While new 10 m resolution global LULC products (i.e., ESA WorldCover, ESRI Land Cover, and annual mode composite of Dynamic World (DW)) offer unprecedented spatial detail, their reliability in heterogeneous karst remains poorly understood. We evaluated the accuracy and spatial consistency of these products for 2021 in the karst regions across five provinces in Southwest China using 1416 reference points collected through stratified random sampling. The ESA WorldCover dataset outperformed the others, achieving the highest overall accuracy (79.39 ± 2.19%). ESRI's shrub metrics, however, reflect the structural absence of this class from its 2021 product rather than classification error. ESA's superior performance in preserving fine-scale features is consistent with independent global assessments of both the 2020 and 2021 versions. This superior performance is attributed to its integration of Sentinel-1 SAR with optical data, a finer minimum mapping unit (100 m2), and expert-driven post-classification corrections. While all products successfully identified dominant classes like trees, substantial confusion emerged among spectrally similar classes such as shrubs, grass, and crops. A key finding was the strong effect of landscape heterogeneity on accuracy. Classification accuracy was 19.37% lower at patch edges (67.38%) compared to patch interiors (86.75%). Furthermore, edge reference points contribute disproportionately to total errors. Critically, none of the three products currently provide a sufficient basis for shrub-focused ecological monitoring in this region: ESA rarely detected shrub cover, DW mapped extensive but largely inaccurate shrub areas, and ESRI eliminated the shrub class from its 2021 product. These results show that while global 10 m products provide valuable information, careful product selection and regional validation remain essential for heterogeneous karst environments. Future improvements should integrate multi-source data (optical + synthetic aperture radar), apply topographic compensation for shadow effects, and develop region-specific approaches for mapping vegetation transitions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101573 |
