Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 880, P. 163314 - 163314
Published: April 6, 2023
Language: Английский
Citations
34Agriculture Ecosystems & Environment, Journal Year: 2023, Volume and Issue: 354, P. 108438 - 108438
Published: May 16, 2023
Language: Английский
Citations
20The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 867, P. 161520 - 161520
Published: Jan. 13, 2023
Language: Английский
Citations
18The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 946, P. 174188 - 174188
Published: June 24, 2024
Language: Английский
Citations
5Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38373 - e38373
Published: Sept. 24, 2024
Language: Английский
Citations
5Agriculture Ecosystems & Environment, Journal Year: 2025, Volume and Issue: 383, P. 109519 - 109519
Published: Feb. 1, 2025
Language: Английский
Citations
0Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 377, P. 134247 - 134247
Published: Sept. 23, 2022
Language: Английский
Citations
11Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(10), P. 16028 - 16047
Published: Feb. 3, 2024
Language: Английский
Citations
2Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 139968 - 139968
Published: Nov. 29, 2023
Language: Английский
Citations
5Remote Sensing, Journal Year: 2023, Volume and Issue: 15(3), P. 658 - 658
Published: Jan. 22, 2023
Rice-crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of local crop cultivation structure and agricultural development. The single-temporal images two phenological periods in year were classified separately, then areas where water disappeared identified as RCFs previous studies. However, due to differences segmentation lakes rivers between images, incorrect extraction unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed order map Sihong County. We mapped by segmenting bi-temporal simultaneously based on method selecting appropriate feature classification features. evaluate applicability, results years obtained using single-temporal- (STOB) compared with BTFDOB method. suggested that spectral showed high importance, which could effectively distinguish from non-RCFs. Our worked well, an overall accuracy (OA) 96.77%. Compared STOB method, OA improved up 2.18% across three data. concentrated low-lying eastern southern regions, scale expanded Sihong. These findings indicate can accurately identify RCFs, providing scientific support dynamic monitoring rational management pattern.
Language: Английский
Citations
4