Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons
Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent ye...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/23/3862 |
| _version_ | 1827701450188259328 |
|---|---|
| author | Joana Borges Thomas P. Higginbottom Elias Symeonakis Martin Jones |
| author_facet | Joana Borges Thomas P. Higginbottom Elias Symeonakis Martin Jones |
| author_sort | Joana Borges |
| collection | DOAJ |
| description | Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration. |
| first_indexed | 2024-03-10T14:34:38Z |
| format | Article |
| id | doaj.art-1633c0d821fd45feb65a70cf3f8a8e49 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:34:38Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1633c0d821fd45feb65a70cf3f8a8e492023-11-20T22:18:20ZengMDPI AGRemote Sensing2072-42922020-11-011223386210.3390/rs12233862Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and SeasonsJoana Borges0Thomas P. Higginbottom1Elias Symeonakis2Martin Jones3Department of Natural Sciences, Manchester Metropolitan University, Manchester M15 6BH, UKSchool of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UKDepartment of Natural Sciences, Manchester Metropolitan University, Manchester M15 6BH, UKDepartment of Natural Sciences, Manchester Metropolitan University, Manchester M15 6BH, UKSavannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.https://www.mdpi.com/2072-4292/12/23/3862Sentinel-2Sentinel-1radarseasonalitysavannah landscapes |
| spellingShingle | Joana Borges Thomas P. Higginbottom Elias Symeonakis Martin Jones Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons Remote Sensing Sentinel-2 Sentinel-1 radar seasonality savannah landscapes |
| title | Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons |
| title_full | Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons |
| title_fullStr | Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons |
| title_full_unstemmed | Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons |
| title_short | Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons |
| title_sort | sentinel 1 and sentinel 2 data for savannah land cover mapping optimising the combination of sensors and seasons |
| topic | Sentinel-2 Sentinel-1 radar seasonality savannah landscapes |
| url | https://www.mdpi.com/2072-4292/12/23/3862 |
| work_keys_str_mv | AT joanaborges sentinel1andsentinel2dataforsavannahlandcovermappingoptimisingthecombinationofsensorsandseasons AT thomasphigginbottom sentinel1andsentinel2dataforsavannahlandcovermappingoptimisingthecombinationofsensorsandseasons AT eliassymeonakis sentinel1andsentinel2dataforsavannahlandcovermappingoptimisingthecombinationofsensorsandseasons AT martinjones sentinel1andsentinel2dataforsavannahlandcovermappingoptimisingthecombinationofsensorsandseasons |