A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine
Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient arc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/20/4169 |
| _version_ | 1827678632159477760 |
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| author | Erfan Fekri Hooman Latifi Meisam Amani Abdolkarim Zobeidinezhad |
| author_facet | Erfan Fekri Hooman Latifi Meisam Amani Abdolkarim Zobeidinezhad |
| author_sort | Erfan Fekri |
| collection | DOAJ |
| description | Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. |
| first_indexed | 2024-03-10T06:13:47Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:13:47Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d6caf6c1a9444e0385f1ea1f5a5c3d522023-11-22T19:55:14ZengMDPI AGRemote Sensing2072-42922021-10-011320416910.3390/rs13204169A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth EngineErfan Fekri0Hooman Latifi1Meisam Amani2Abdolkarim Zobeidinezhad3Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology (KNTU), Tehran 19967-15433, IranDepartment of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology (KNTU), Tehran 19967-15433, IranWood Environment & Infrastructure Solutions, Ottawa, ON K2E 7L5, CanadaKhuzestan Provincial Department of Environment, Ahvaz 61347-88417, IranWetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.https://www.mdpi.com/2072-4292/13/20/4169wetlandGoogle Earth Engine (GEE)training sample migrationsentinel |
| spellingShingle | Erfan Fekri Hooman Latifi Meisam Amani Abdolkarim Zobeidinezhad A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine Remote Sensing wetland Google Earth Engine (GEE) training sample migration sentinel |
| title | A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine |
| title_full | A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine |
| title_fullStr | A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine |
| title_full_unstemmed | A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine |
| title_short | A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine |
| title_sort | training sample migration method for wetland mapping and monitoring using sentinel data in google earth engine |
| topic | wetland Google Earth Engine (GEE) training sample migration sentinel |
| url | https://www.mdpi.com/2072-4292/13/20/4169 |
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