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Open AccessArticle

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

Earth Observation and Modelling, Dept. of Geography, Kiel University, 24098 Kiel, Germany
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Remote Sens. 2019, 11(6), 711; https://doi.org/10.3390/rs11060711
Received: 18 February 2019 / Revised: 14 March 2019 / Accepted: 19 March 2019 / Published: 25 March 2019
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set. View Full-Text
Keywords: machine learning; Synthetic Aperture Radar (SAR); grassland; time series; cutting status machine learning; Synthetic Aperture Radar (SAR); grassland; time series; cutting status
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Taravat, A.; Wagner, M.P.; Oppelt, N. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sens. 2019, 11, 711.

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