Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series

Ewa Grabska , Paweł Hawryło , Jarosław Socha


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.
Author Ewa Grabska
Ewa Grabska ,,
, Paweł Hawryło (FoF / IoFRM / DoFDGaEoF)
Paweł Hawryło,,
, Jarosław Socha (FoF / IoFRM / DoBaPoF)
Jarosław Socha,,
Journal seriesRemote Sensing, ISSN 2072-4292, (N/A 100 pkt)
Issue year2020
Publication size in sheets0.95
Article number1298
Keywords in Englishvegetation; change detection; Scots pine; time series analysis; disturbances
ASJC Classification1900 General Earth and Planetary Sciences
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); after publication
Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series of 02-05-2020
9,26 MB
Score (nominal)100
Score sourcejournalList
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.559; WoS Impact Factor: 2018 = 4.118 (2) - 2018=4.74 (5)
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Wkład autorówConceptualization, E.G.; methodology, E.G.; validation, E.G.; formal analysis, E.G.; investigation, E.G.; data curation, E.G.; writing—original draft preparation, E.G., P.H. and J.S.; writing—review and editing, E.G. and P.H.; visualization, E.G.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.
FinansowanieThis research was supported by the project I-MAESTRO. Project Innovative forest MAnagEment STrategies for a Resilient biOeconomy under climate change and disturbances (I-MAESTRO) is supported under the umbrella of ForestValue ERA-NET Cofund by the National Science Centre, Poland and French Ministry of Agriculture, Agrifood, and Forestry; French Ministry of Higher Education, Research and Innovation, German Federal Ministry of Food and Agriculture (BMEL) via Agency for Renewable Resources (FNR), Slovenian Ministry of Education, Science and Sport (MIZS). ForestValue has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N◦ 773324. The APC was funded by the Ministry of Science and Higher Education of the Republic of Poland for the University of Agriculture in Krakow for 2020.
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