A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier
Mangroves are in coastal zones where mass-energy exchange is most active. Their functions in high productivity, strong carbon sequestration capacity, and rich ecosystem services are crucial for achieving the sustainable development goals. Although various classification methods have been extensively...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10750253/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841546115462201344 |
|---|---|
| author | Zhuokai Jian Bin Ai Jiali Zeng Yuchao Sun |
| author_facet | Zhuokai Jian Bin Ai Jiali Zeng Yuchao Sun |
| author_sort | Zhuokai Jian |
| collection | DOAJ |
| description | Mangroves are in coastal zones where mass-energy exchange is most active. Their functions in high productivity, strong carbon sequestration capacity, and rich ecosystem services are crucial for achieving the sustainable development goals. Although various classification methods have been extensively applied in large-scale mangrove observations, they necessitate considerable sample collection and postprocessing, which hampers the long-term identification of mangroves. This study proposes a hybrid identification method that combines the time-frequency threshold of the mangrove index with a random forest binary classifier. It efficiently identifies mangroves over large-scale areas with low postprocessing and high accuracy. The hybrid method was applied to Landsat 8 OLI data to derive the mangrove distribution in China using Google Earth Engine. It demonstrated an overall accuracy rate of 92.86% and an F1 score of 0.92, a significant improvement over using either method alone. Specifically, the method utilized a small sample size to successfully obtain pure and accurate mangrove classification. The feature selection indicates that the short-wave infrared band and its associated remote sensing indices play a pivotal role in differentiating mangroves from other confusable land classes. Additionally, this method was successfully applied to Landsat 5 data, achieving an overall accuracy of 93.49%, with substantial agreement between the classification results of the two sensors. This indicates that the method has the potential for continuous monitoring of mangroves over large areas and extended periods. |
| format | Article |
| id | doaj-art-1f844548a6fc454a9eefe167a4723a20 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-1f844548a6fc454a9eefe167a4723a202025-01-11T00:00:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01182077209210.1109/JSTARS.2024.349405810750253A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary ClassifierZhuokai Jian0Bin Ai1https://orcid.org/0000-0002-7157-8614Jiali Zeng2Yuchao Sun3School of Marine Sciences, Sun Yat-sen University, Zhuhai, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai, ChinaSouth China Sea Development Research Institute, Ministry of Natural Resources, Guangzhou, ChinaMangroves are in coastal zones where mass-energy exchange is most active. Their functions in high productivity, strong carbon sequestration capacity, and rich ecosystem services are crucial for achieving the sustainable development goals. Although various classification methods have been extensively applied in large-scale mangrove observations, they necessitate considerable sample collection and postprocessing, which hampers the long-term identification of mangroves. This study proposes a hybrid identification method that combines the time-frequency threshold of the mangrove index with a random forest binary classifier. It efficiently identifies mangroves over large-scale areas with low postprocessing and high accuracy. The hybrid method was applied to Landsat 8 OLI data to derive the mangrove distribution in China using Google Earth Engine. It demonstrated an overall accuracy rate of 92.86% and an F1 score of 0.92, a significant improvement over using either method alone. Specifically, the method utilized a small sample size to successfully obtain pure and accurate mangrove classification. The feature selection indicates that the short-wave infrared band and its associated remote sensing indices play a pivotal role in differentiating mangroves from other confusable land classes. Additionally, this method was successfully applied to Landsat 5 data, achieving an overall accuracy of 93.49%, with substantial agreement between the classification results of the two sensors. This indicates that the method has the potential for continuous monitoring of mangroves over large areas and extended periods.https://ieeexplore.ieee.org/document/10750253/Hybrid methodLandsatmangrove identificationmangrove vegetation index (MVI)random forest (RF) |
| spellingShingle | Zhuokai Jian Bin Ai Jiali Zeng Yuchao Sun A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hybrid method Landsat mangrove identification mangrove vegetation index (MVI) random forest (RF) |
| title | A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier |
| title_full | A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier |
| title_fullStr | A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier |
| title_full_unstemmed | A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier |
| title_short | A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier |
| title_sort | hybrid mangrove identification method by combining the time frequency threshold of the mangrove index with a random forest binary classifier |
| topic | Hybrid method Landsat mangrove identification mangrove vegetation index (MVI) random forest (RF) |
| url | https://ieeexplore.ieee.org/document/10750253/ |
| work_keys_str_mv | AT zhuokaijian ahybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT binai ahybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT jializeng ahybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT yuchaosun ahybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT zhuokaijian hybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT binai hybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT jializeng hybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier AT yuchaosun hybridmangroveidentificationmethodbycombiningthetimefrequencythresholdofthemangroveindexwitharandomforestbinaryclassifier |