Next Article in Journal
Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data
Next Article in Special Issue
Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers
Previous Article in Journal
Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data
Previous Article in Special Issue
Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series

Environmental Geography group, VU University Amsterdam, De Boelelaan 1085, 1081 Amsterdam, The Netherlands
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
Institute of Geography and Spatial Management, Jagiellonian University, Gronostajowa 7, 30-387 Kraków, Poland
Author to whom correspondence should be addressed.
Received: 28 June 2018 / Accepted: 31 July 2018 / Published: 3 August 2018
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
Grassland use intensity is a topic of growing interest worldwide, as grasslands are integral in supporting biodiversity, food production, and regulating of the global carbon cycle. Data available for characterizing grasslands management are largely descriptive and collected from laborious field campaigns or questionnaires. The recent launch of the Sentinel-2 earth monitoring constellation provides new possibilities for high temporal and spatial resolution remote sensing data covering large areas. This study aims to evaluate the potential of a time series of Sentinel-2 data for mapping of mowing frequency in the region of Canton Aargau, Switzerland. We tested two cloud masking processes and three spatial mapping units (pixels, parcel polygons and shrunken parcel polygons), and investigated how missing data influence the ability to accurately detect and map grassland management activity. We found that more than 40% of the study area was mown before 15 June, while the remaining part was either mown later, or was not mown at all. The highest accuracy for detection of mowing events was achieved using additional clouds masking and size reduction of parcels, which allowed correct detection of 77% of mowing events. Additionally, we found that using only standard cloud masking leads to significant overestimation of mowing events, and that the detection based on sparse time series does not fully correspond to key events in the grass growth season. View Full-Text
Keywords: grassland; mowing; Sentinel-2; time series; NDVI grassland; mowing; Sentinel-2; time series; NDVI
Show Figures

Figure 1

MDPI and ACS Style

Kolecka, N.; Ginzler, C.; Pazur, R.; Price, B.; Verburg, P.H. Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sens. 2018, 10, 1221.

AMA Style

Kolecka N, Ginzler C, Pazur R, Price B, Verburg PH. Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sensing. 2018; 10(8):1221.

Chicago/Turabian Style

Kolecka, Natalia, Christian Ginzler, Robert Pazur, Bronwyn Price, and Peter H. Verburg. 2018. "Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series" Remote Sensing 10, no. 8: 1221.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop