Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models
Over the last several decades, thanks to improvements in and the diversification of open-access satellite imagery, land cover mapping techniques have evolved significantly. Notable changes in these techniques involve the automation of different steps, yielding promising results in terms of accuracy,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/12/2237 |
| _version_ | 1797530930560106496 |
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| author | Laura Alonso Juan Picos Julia Armesto |
| author_facet | Laura Alonso Juan Picos Julia Armesto |
| author_sort | Laura Alonso |
| collection | DOAJ |
| description | Over the last several decades, thanks to improvements in and the diversification of open-access satellite imagery, land cover mapping techniques have evolved significantly. Notable changes in these techniques involve the automation of different steps, yielding promising results in terms of accuracy, class detection and efficiency. The most successful methodologies that have arisen rely on the use of multi-temporal data. Several different approaches have proven successful. In this study, one of the most recently developed methodologies is tested in the region of Galicia (in Northwestern Spain), with the aim of filling gaps in the mapping needs of the Galician forestry sector. The methodology mainly consists of performing a supervised classification of individual images from a selected time series and then combining them through aggregation using decision criteria. Several of the steps of the methodology can be addressed in multiple ways: pixel resolution selection, classification model building and aggregation methods. The effectiveness of these three tasks as well as some others are tested and evaluated and the most accurate and efficient parameters for the case study area are highlighted. The final land cover map that is obtained for Galicia has high accuracy metrics (an overall accuracy of 91.6%), which is in line with previous studies that have followed this methodology in other regions. This study has led to the development of an efficient open-access solution to support the mapping needs of the forestry sector. |
| first_indexed | 2024-03-10T10:37:01Z |
| format | Article |
| id | doaj.art-4fab5b886f0d4c9a83be530cd52df2fe |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:37:01Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4fab5b886f0d4c9a83be530cd52df2fe2023-11-21T23:13:47ZengMDPI AGRemote Sensing2072-42922021-06-011312223710.3390/rs13122237Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF ModelsLaura Alonso0Juan Picos1Julia Armesto2Forestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainForestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainForestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainOver the last several decades, thanks to improvements in and the diversification of open-access satellite imagery, land cover mapping techniques have evolved significantly. Notable changes in these techniques involve the automation of different steps, yielding promising results in terms of accuracy, class detection and efficiency. The most successful methodologies that have arisen rely on the use of multi-temporal data. Several different approaches have proven successful. In this study, one of the most recently developed methodologies is tested in the region of Galicia (in Northwestern Spain), with the aim of filling gaps in the mapping needs of the Galician forestry sector. The methodology mainly consists of performing a supervised classification of individual images from a selected time series and then combining them through aggregation using decision criteria. Several of the steps of the methodology can be addressed in multiple ways: pixel resolution selection, classification model building and aggregation methods. The effectiveness of these three tasks as well as some others are tested and evaluated and the most accurate and efficient parameters for the case study area are highlighted. The final land cover map that is obtained for Galicia has high accuracy metrics (an overall accuracy of 91.6%), which is in line with previous studies that have followed this methodology in other regions. This study has led to the development of an efficient open-access solution to support the mapping needs of the forestry sector.https://www.mdpi.com/2072-4292/13/12/2237Sentinel-2multi-temporalforestryland coverRandom Forests |
| spellingShingle | Laura Alonso Juan Picos Julia Armesto Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models Remote Sensing Sentinel-2 multi-temporal forestry land cover Random Forests |
| title | Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models |
| title_full | Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models |
| title_fullStr | Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models |
| title_full_unstemmed | Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models |
| title_short | Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models |
| title_sort | forest land cover mapping at a regional scale using multi temporal sentinel 2 imagery and rf models |
| topic | Sentinel-2 multi-temporal forestry land cover Random Forests |
| url | https://www.mdpi.com/2072-4292/13/12/2237 |
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