A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests
Moso bamboo forests (MBFs) represented vital ecological and economic resources, necessitating dynamic monitoring of their on-year and off-year cycles to ensure sustainable management and effective carbon sequestration. To address classification challenges arising from regional phenological variation...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25006223 |
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| author | Jing Ma Nan Li Yong Yang Xiang Li Mengyi Hu Shijun Zhang Linjia Wei Longwei Li |
| author_facet | Jing Ma Nan Li Yong Yang Xiang Li Mengyi Hu Shijun Zhang Linjia Wei Longwei Li |
| author_sort | Jing Ma |
| collection | DOAJ |
| description | Moso bamboo forests (MBFs) represented vital ecological and economic resources, necessitating dynamic monitoring of their on-year and off-year cycles to ensure sustainable management and effective carbon sequestration. To address classification challenges arising from regional phenological variations and the absence of key-period imagery, this study developed a knowledge-based on-year and off-year Moso bamboo forests (on/off-MBFs) classification algorithm (KB-OFBC) utilizing spectral feature analysis of Sentinel-2 imagery. First, by systematically analyzing the annual variation characteristics of NDVI and LSWI in MBFs, the Moso bamboo Time Series Index (MTSI) was constructed to extract MBFs. Second, by leveraging periodic spectral differences between on/off-MBFs in the NIR and red-edge bands, the Phenological Difference Index for bamboo (PDIbamboo) was established to distinguish these phenological phases. Finally, accurate spatial mapping of on/off-MBFs was achieved though the integration of MTSI and PDIbamboo. Results demonstrated that compared with existing indices, MTSI and PDIbamboo exhibited superior interclass separability, achieving an overall accuracy (OA) of 0.92 and a Kappa coefficient of 0.88. Application of KB-OFBC revealed that MBFs in Deqing County covered 18,036.56 ha in 2022, predominantly distributed in western mountainous regions, with off-MBFs accounting for 74.13%—significantly exceeding on-year coverage. In the validation areas of Anji County (Zhejiang Province) and Taojiang County, the algorithm achieved producer’s accuracy (PA) and user’s accuracy (UA) exceeding 0.85 and 0.80 respectively, demonstrating robust classification stability and spatial transferability. This algorithm provides a robust method for enhancing phenological monitoring, sustainable management, and large-scale mapping of MBFs. |
| format | Article |
| id | doaj-art-c642f6ed49b84ba9b276b3549e4e98ce |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-c642f6ed49b84ba9b276b3549e4e98ce2025-08-20T03:31:10ZengElsevierEcological Indicators1470-160X2025-07-0117611369210.1016/j.ecolind.2025.113692A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forestsJing Ma0Nan Li1Yong Yang2Xiang Li3Mengyi Hu4Shijun Zhang5Linjia Wei6Longwei Li7School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China; School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China; Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, ChinaSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China; Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, China; School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China; Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239000, China; Corresponding author at: School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China.Moso bamboo forests (MBFs) represented vital ecological and economic resources, necessitating dynamic monitoring of their on-year and off-year cycles to ensure sustainable management and effective carbon sequestration. To address classification challenges arising from regional phenological variations and the absence of key-period imagery, this study developed a knowledge-based on-year and off-year Moso bamboo forests (on/off-MBFs) classification algorithm (KB-OFBC) utilizing spectral feature analysis of Sentinel-2 imagery. First, by systematically analyzing the annual variation characteristics of NDVI and LSWI in MBFs, the Moso bamboo Time Series Index (MTSI) was constructed to extract MBFs. Second, by leveraging periodic spectral differences between on/off-MBFs in the NIR and red-edge bands, the Phenological Difference Index for bamboo (PDIbamboo) was established to distinguish these phenological phases. Finally, accurate spatial mapping of on/off-MBFs was achieved though the integration of MTSI and PDIbamboo. Results demonstrated that compared with existing indices, MTSI and PDIbamboo exhibited superior interclass separability, achieving an overall accuracy (OA) of 0.92 and a Kappa coefficient of 0.88. Application of KB-OFBC revealed that MBFs in Deqing County covered 18,036.56 ha in 2022, predominantly distributed in western mountainous regions, with off-MBFs accounting for 74.13%—significantly exceeding on-year coverage. In the validation areas of Anji County (Zhejiang Province) and Taojiang County, the algorithm achieved producer’s accuracy (PA) and user’s accuracy (UA) exceeding 0.85 and 0.80 respectively, demonstrating robust classification stability and spatial transferability. This algorithm provides a robust method for enhancing phenological monitoring, sustainable management, and large-scale mapping of MBFs.http://www.sciencedirect.com/science/article/pii/S1470160X25006223Moso bamboo forestsOn-year and off-yearSentinel-2Google Earth EngineTime series |
| spellingShingle | Jing Ma Nan Li Yong Yang Xiang Li Mengyi Hu Shijun Zhang Linjia Wei Longwei Li A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests Ecological Indicators Moso bamboo forests On-year and off-year Sentinel-2 Google Earth Engine Time series |
| title | A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests |
| title_full | A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests |
| title_fullStr | A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests |
| title_full_unstemmed | A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests |
| title_short | A knowledge-based time series algorithm for robust mapping of on- and off-year Moso bamboo forests |
| title_sort | knowledge based time series algorithm for robust mapping of on and off year moso bamboo forests |
| topic | Moso bamboo forests On-year and off-year Sentinel-2 Google Earth Engine Time series |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25006223 |
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