A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE
Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines fr...
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Online Access: | https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 https://doaj.org/article/145d6f93a6d64f5aae9797ec49d5c37b |
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ftdoajarticles:oai:doaj.org/article:145d6f93a6d64f5aae9797ec49d5c37b 2023-05-15T17:22:23+02:00 A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE A. Jamali M. Mahdianpari İ. R. Karaş 2021-12-01T00:00:00Z https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 https://doaj.org/article/145d6f93a6d64f5aae9797ec49d5c37b EN eng Copernicus Publications https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W5-2021/313/2021/isprs-archives-XLVI-4-W5-2021-313-2021.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 1682-1750 2194-9034 https://doaj.org/article/145d6f93a6d64f5aae9797ec49d5c37b The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W5-2021, Pp 313-319 (2021) Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 article 2021 ftdoajarticles https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 2022-12-31T16:26:08Z Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers. Article in Journal/Newspaper Newfoundland Directory of Open Access Journals: DOAJ Articles Canada The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 313 319 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
spellingShingle |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 A. Jamali M. Mahdianpari İ. R. Karaş A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
topic_facet |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
description |
Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers. |
format |
Article in Journal/Newspaper |
author |
A. Jamali M. Mahdianpari İ. R. Karaş |
author_facet |
A. Jamali M. Mahdianpari İ. R. Karaş |
author_sort |
A. Jamali |
title |
A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
title_short |
A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
title_full |
A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
title_fullStr |
A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
title_full_unstemmed |
A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE |
title_sort |
comparison of tree-based algorithms for complex wetland classification using the google earth engine |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 https://doaj.org/article/145d6f93a6d64f5aae9797ec49d5c37b |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W5-2021, Pp 313-319 (2021) |
op_relation |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W5-2021/313/2021/isprs-archives-XLVI-4-W5-2021-313-2021.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 1682-1750 2194-9034 https://doaj.org/article/145d6f93a6d64f5aae9797ec49d5c37b |
op_doi |
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021 |
container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
XLVI-4/W5-2021 |
container_start_page |
313 |
op_container_end_page |
319 |
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1766108997614567424 |