The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/13/1537 |
| _version_ | 1828139019747196928 |
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| author | Aaron Judah Baoxin Hu |
| author_facet | Aaron Judah Baoxin Hu |
| author_sort | Aaron Judah |
| collection | DOAJ |
| description | Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality. |
| first_indexed | 2024-04-11T18:45:36Z |
| format | Article |
| id | doaj.art-c557508caf304df19b4d01bceba5ba1a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:45:36Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c557508caf304df19b4d01bceba5ba1a2022-12-22T04:08:50ZengMDPI AGRemote Sensing2072-42922019-06-011113153710.3390/rs11131537rs11131537The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of WetlandsAaron Judah0Baoxin Hu1Department of Earth and Space Science and Engineering, York University, 4700 Keele st., Toronto, ON M3J1P3, CanadaDepartment of Earth and Space Science and Engineering, York University, 4700 Keele st., Toronto, ON M3J1P3, CanadaWetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality.https://www.mdpi.com/2072-4292/11/13/1537wetlandslandsatradarrandom forestsupport vector machineclassification |
| spellingShingle | Aaron Judah Baoxin Hu The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands Remote Sensing wetlands landsat radar random forest support vector machine classification |
| title | The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands |
| title_full | The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands |
| title_fullStr | The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands |
| title_full_unstemmed | The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands |
| title_short | The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands |
| title_sort | integration of multi source remotely sensed data in support of the classification of wetlands |
| topic | wetlands landsat radar random forest support vector machine classification |
| url | https://www.mdpi.com/2072-4292/11/13/1537 |
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