Taravat, Alireza, Wagner, Matthias and Oppelt, Natascha (2019) Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing, 11 (6). p. 711. DOI 10.3390/rs11060711.

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Abstract

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.

Document Type:	Article
Keywords:	machine learning; Synthetic Aperture Radar (SAR); grassland; time series; cutting status
Research affiliation:	Kiel University > Kiel Marine Science OceanRep > The Future Ocean - Cluster of Excellence Kiel University
Refereed:	Yes
Open Access Journal?:	Yes
Publisher:	MDPI
Date Deposited:	17 Apr 2019 12:14
Last Modified:	31 Jan 2022 09:14
URI:	https://oceanrep.geomar.de/id/eprint/46392

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