Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data
Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study cla...
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2022
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Online Access: | https://doi.org/10.3390/land11020240 |
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ftmdpi:oai:mdpi.com:/2073-445X/11/2/240/ 2023-08-20T04:08:51+02:00 Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data Changda Liu Jie Li Qiuhua Tang Jiawei Qi Xinghua Zhou agris 2022-02-05 application/pdf https://doi.org/10.3390/land11020240 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/land11020240 https://creativecommons.org/licenses/by/4.0/ Land; Volume 11; Issue 2; Pages: 240 shore zone classification ICESat-2 Sentinel-2 Google Earth Engine random forest Text 2022 ftmdpi https://doi.org/10.3390/land11020240 2023-08-01T04:04:29Z Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81. Text Nunivak Nunivak Island MDPI Open Access Publishing The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Land 11 2 240 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
shore zone classification ICESat-2 Sentinel-2 Google Earth Engine random forest |
spellingShingle |
shore zone classification ICESat-2 Sentinel-2 Google Earth Engine random forest Changda Liu Jie Li Qiuhua Tang Jiawei Qi Xinghua Zhou Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
topic_facet |
shore zone classification ICESat-2 Sentinel-2 Google Earth Engine random forest |
description |
Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81. |
format |
Text |
author |
Changda Liu Jie Li Qiuhua Tang Jiawei Qi Xinghua Zhou |
author_facet |
Changda Liu Jie Li Qiuhua Tang Jiawei Qi Xinghua Zhou |
author_sort |
Changda Liu |
title |
Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
title_short |
Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
title_full |
Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
title_fullStr |
Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
title_full_unstemmed |
Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data |
title_sort |
classifying the nunivak island coastline using the random forest integration of the sentinel-2 and icesat-2 data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/land11020240 |
op_coverage |
agris |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
The Sentinel |
geographic_facet |
The Sentinel |
genre |
Nunivak Nunivak Island |
genre_facet |
Nunivak Nunivak Island |
op_source |
Land; Volume 11; Issue 2; Pages: 240 |
op_relation |
https://dx.doi.org/10.3390/land11020240 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/land11020240 |
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Land |
container_volume |
11 |
container_issue |
2 |
container_start_page |
240 |
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1774721397446148096 |