Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine
Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1651 |
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| author | Hamid Jafarzadeh Masoud Mahdianpari Eric W. Gill Fariba Mohammadimanesh |
| author_facet | Hamid Jafarzadeh Masoud Mahdianpari Eric W. Gill Fariba Mohammadimanesh |
| author_sort | Hamid Jafarzadeh |
| collection | DOAJ |
| description | Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. |
| first_indexed | 2024-04-25T00:18:47Z |
| format | Article |
| id | doaj.art-587d2c34b16440fbadc56c00ced74c80 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:18:47Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-587d2c34b16440fbadc56c00ced74c802024-03-12T16:55:31ZengMDPI AGSensors1424-82202024-03-01245165110.3390/s24051651Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth EngineHamid Jafarzadeh0Masoud Mahdianpari1Eric W. Gill2Fariba Mohammadimanesh3Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaDepartment of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaDepartment of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaC-CORE, St. John’s, NL A1B 3X5, CanadaWetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.https://www.mdpi.com/1424-8220/24/5/1651classificationGEDIGoogle Earth EngineLiDARSentinelwetland |
| spellingShingle | Hamid Jafarzadeh Masoud Mahdianpari Eric W. Gill Fariba Mohammadimanesh Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine Sensors classification GEDI Google Earth Engine LiDAR Sentinel wetland |
| title | Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
| title_full | Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
| title_fullStr | Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
| title_full_unstemmed | Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
| title_short | Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
| title_sort | enhancing wetland mapping integrating sentinel 1 2 gedi data and google earth engine |
| topic | classification GEDI Google Earth Engine LiDAR Sentinel wetland |
| url | https://www.mdpi.com/1424-8220/24/5/1651 |
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