Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a p...

Full description

Saved in:
Bibliographic Details
Published in2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) pp. 1 - 4
Main Authors Garioud, Anatol, Giordano, Sebastien, Valero, Silvia, Mallet, Clement
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2019
Subjects
Biomedical optical imaging
Coherence
Monitoring
Mowing Events
NDVI
Optical imaging
Optical sensors
Permanent Grasslands
Recurrent Neural Networks
Regression
Sentinel
Synthetic aperture radar
Time Series
Time series analysis
Online AccessGet full text

Cover

More Information
Summary:Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection.
DOI:10.1109/Multi-Temp.2019.8866914