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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ftdoajarticles:oai:doaj.org/article:587d2c34b16440fbadc56c00ced74c80 2024-09-15T18:20:20+00:00 Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine Hamid Jafarzadeh Masoud Mahdianpari Eric W. Gill Fariba Mohammadimanesh 2024-03-01T00:00:00Z https://doi.org/10.3390/s24051651 https://doaj.org/article/587d2c34b16440fbadc56c00ced74c80 EN eng MDPI AG https://www.mdpi.com/1424-8220/24/5/1651 https://doaj.org/toc/1424-8220 doi:10.3390/s24051651 1424-8220 https://doaj.org/article/587d2c34b16440fbadc56c00ced74c80 Sensors, Vol 24, Iss 5, p 1651 (2024) classification GEDI Google Earth Engine LiDAR Sentinel wetland Chemical technology TP1-1185 article 2024 ftdoajarticles https://doi.org/10.3390/s24051651 2024-08-05T17:49:49Z 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 ( ... Article in Journal/Newspaper Newfoundland Directory of Open Access Journals: DOAJ Articles Sensors 24 5 1651 |
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English |
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classification GEDI Google Earth Engine LiDAR Sentinel wetland Chemical technology TP1-1185 |
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classification GEDI Google Earth Engine LiDAR Sentinel wetland Chemical technology TP1-1185 Hamid Jafarzadeh Masoud Mahdianpari Eric W. Gill Fariba Mohammadimanesh Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine |
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classification GEDI Google Earth Engine LiDAR Sentinel wetland Chemical technology TP1-1185 |
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 ( ... |
format |
Article in Journal/Newspaper |
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 |
title |
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_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_sort |
enhancing wetland mapping: integrating sentinel-1/2, gedi data, and google earth engine |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/s24051651 https://doaj.org/article/587d2c34b16440fbadc56c00ced74c80 |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Sensors, Vol 24, Iss 5, p 1651 (2024) |
op_relation |
https://www.mdpi.com/1424-8220/24/5/1651 https://doaj.org/toc/1424-8220 doi:10.3390/s24051651 1424-8220 https://doaj.org/article/587d2c34b16440fbadc56c00ced74c80 |
op_doi |
https://doi.org/10.3390/s24051651 |
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Sensors |
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24 |
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5 |
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1651 |
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1810458704425779200 |