Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series
Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that S...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1298 |
| _version_ | 1827718324890370048 |
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| author | Ewa Grabska Paweł Hawryło Jarosław Socha |
| author_facet | Ewa Grabska Paweł Hawryło Jarosław Socha |
| author_sort | Ewa Grabska |
| collection | DOAJ |
| description | Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring. |
| first_indexed | 2024-03-10T20:21:35Z |
| format | Article |
| id | doaj.art-b8bccdc65ab04bd2b990fffd235ade15 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:21:35Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b8bccdc65ab04bd2b990fffd235ade152023-11-19T22:10:39ZengMDPI AGRemote Sensing2072-42922020-04-01128129810.3390/rs12081298Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time SeriesEwa Grabska0Paweł Hawryło1Jarosław Socha2Department of Forest Resources Management, Faculty of Forestry, University of Agriculture, Al. 29 Listopada 46, 31-425 Kraków, PolandDepartment of Forest Resources Management, Faculty of Forestry, University of Agriculture, Al. 29 Listopada 46, 31-425 Kraków, PolandDepartment of Forest Resources Management, Faculty of Forestry, University of Agriculture, Al. 29 Listopada 46, 31-425 Kraków, PolandClimate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring.https://www.mdpi.com/2072-4292/12/8/1298vegetationchange detectionScots pinetime series analysisdisturbances |
| spellingShingle | Ewa Grabska Paweł Hawryło Jarosław Socha Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series Remote Sensing vegetation change detection Scots pine time series analysis disturbances |
| title | Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series |
| title_full | Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series |
| title_fullStr | Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series |
| title_full_unstemmed | Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series |
| title_short | Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series |
| title_sort | continuous detection of small scale changes in scots pine dominated stands using dense sentinel 2 time series |
| topic | vegetation change detection Scots pine time series analysis disturbances |
| url | https://www.mdpi.com/2072-4292/12/8/1298 |
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