Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models
Mapping and quantification of biomass changes is critical to understanding mangrove carbon sequestration, conservation, and restoration. Few previous studies have focused on mangrove biomass changes based on high spatial resolution images, particularly for disturbed and recovering areas. This study...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9089293/ |
| _version_ | 1818620564720320512 |
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| author | Yuanhui Zhu Kai Liu Lin Liu Soe W. Myint Shugong Wang Jingjing Cao Zhifeng Wu |
| author_facet | Yuanhui Zhu Kai Liu Lin Liu Soe W. Myint Shugong Wang Jingjing Cao Zhifeng Wu |
| author_sort | Yuanhui Zhu |
| collection | DOAJ |
| description | Mapping and quantification of biomass changes is critical to understanding mangrove carbon sequestration, conservation, and restoration. Few previous studies have focused on mangrove biomass changes based on high spatial resolution images, particularly for disturbed and recovering areas. This study developed an effective model to estimate and map mangrove aboveground biomass dynamic change between 2010 and 2016 on Qi'ao Island in South China. The study area includes native Kandelia candel (K. candel) and planted Sonneratia apetala (S. apetala) mangrove species within the largest planted area in China. Models were developed using WorldView-2 images, digital surface models (DSMs), and the random forest algorithm. Accuracies of the model were assessed using multiyear field samples. DSMs were identified as the most important variable for model accuracy, reducing relative error by up to 3.14%. Three models were developed: a model for 2010, another model for 2016, and a combined model for 2010 and 2016. Compared with the 2010 (RMSE = 41.03 t/ha, RMSEr = 24.31%) and 2016 (RMSE = 39.92 t/ha, RMSEr = 23.40%) models, the combined model (RMSE = 50.99 t/ha, RMSEr = 30.48%) only increased the relative error by 6.17% and 7.08%, respectively. Mangrove biomass maps generated from the most accurate models showed total biomass increased from 23270.43 to 39819.03 tons by up to 71.11% over the study period. K. candel total biomass decreased by 36.5% due to Derris trifoliata challenge. S. apetala total biomass increased by 74.79% due to reforestation programs, achieving aboveground biomass accumulation of 4.17 t/ha for stands that existed in 2010. This study provides insights into biomass dynamic change in disturbed and recovering mangrove areas. Future studies should consider using LiDAR techniques to obtain actual tree height applied for biomass estimation instead of DSM. |
| first_indexed | 2024-12-16T17:55:23Z |
| format | Article |
| id | doaj.art-a1779c07275e41f98db44eb0e48bec4b |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-16T17:55:23Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a1779c07275e41f98db44eb0e48bec4b2022-12-21T22:22:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01132123213410.1109/JSTARS.2020.29895009089293Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface ModelsYuanhui Zhu0https://orcid.org/0000-0002-0474-5945Kai Liu1https://orcid.org/0000-0002-1829-7557Lin Liu2Soe W. Myint3Shugong Wang4Jingjing Cao5Zhifeng Wu6Center of GeoInformatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou, ChinaGuangdong Provincial Engineering Research Center for Public Security and Disaster, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaCenter of GeoInformatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou, ChinaSchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USAGuangdong Key Laboratory of Geological Processes and Mineral Resources Survey, School of Earth Science and Geological Engineering, Sun Yat-sen University, Guangzhou, ChinaGuangdong Provincial Engineering Research Center for Public Security and Disaster, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaGuangdong Province Engineering Technology Research for Geographical Conditions Monitoring and Comprehensive Analysis, School of Geographical Sciences, Guangzhou University, Guangzhou, ChinaMapping and quantification of biomass changes is critical to understanding mangrove carbon sequestration, conservation, and restoration. Few previous studies have focused on mangrove biomass changes based on high spatial resolution images, particularly for disturbed and recovering areas. This study developed an effective model to estimate and map mangrove aboveground biomass dynamic change between 2010 and 2016 on Qi'ao Island in South China. The study area includes native Kandelia candel (K. candel) and planted Sonneratia apetala (S. apetala) mangrove species within the largest planted area in China. Models were developed using WorldView-2 images, digital surface models (DSMs), and the random forest algorithm. Accuracies of the model were assessed using multiyear field samples. DSMs were identified as the most important variable for model accuracy, reducing relative error by up to 3.14%. Three models were developed: a model for 2010, another model for 2016, and a combined model for 2010 and 2016. Compared with the 2010 (RMSE = 41.03 t/ha, RMSEr = 24.31%) and 2016 (RMSE = 39.92 t/ha, RMSEr = 23.40%) models, the combined model (RMSE = 50.99 t/ha, RMSEr = 30.48%) only increased the relative error by 6.17% and 7.08%, respectively. Mangrove biomass maps generated from the most accurate models showed total biomass increased from 23270.43 to 39819.03 tons by up to 71.11% over the study period. K. candel total biomass decreased by 36.5% due to Derris trifoliata challenge. S. apetala total biomass increased by 74.79% due to reforestation programs, achieving aboveground biomass accumulation of 4.17 t/ha for stands that existed in 2010. This study provides insights into biomass dynamic change in disturbed and recovering mangrove areas. Future studies should consider using LiDAR techniques to obtain actual tree height applied for biomass estimation instead of DSM.https://ieeexplore.ieee.org/document/9089293/Biomass changedigital surface models (DSMs)mangrove speciesWorldView-2 images |
| spellingShingle | Yuanhui Zhu Kai Liu Lin Liu Soe W. Myint Shugong Wang Jingjing Cao Zhifeng Wu Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Biomass change digital surface models (DSMs) mangrove species WorldView-2 images |
| title | Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models |
| title_full | Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models |
| title_fullStr | Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models |
| title_full_unstemmed | Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models |
| title_short | Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models |
| title_sort | estimating and mapping mangrove biomass dynamic change using worldview 2 images and digital surface models |
| topic | Biomass change digital surface models (DSMs) mangrove species WorldView-2 images |
| url | https://ieeexplore.ieee.org/document/9089293/ |
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