Ms. Stephanie Redl Profile

Stephanie Redl

at GAF AG

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 October 2019 Presentation + Paper

Grassland monitoring based on Sentinel-1

R. Siegmund, S. Redl, M. Wagner, S. Hartmann

Proceedings Volume 11149, 1114902 (2019) https://doi.org/10.1117/12.2532801

KEYWORDS: Synthetic aperture radar, Data modeling, Machine learning, Statistical analysis, Vegetation, Data acquisition, Radar, Agriculture, Coherence (optics), Satellites

Read Abstract +

Grassland occupies a large proportion of utilised agricultural area, especially in mountainous regions. Despite its importance current and reliable data on grassland yields and cutting frequencies with a sufficient spatial coverage are lacking. Both are essential for optimizing the use of grassland, nature conservation and policy consultation. Model approaches for the assessment of grassland yields take cutting dates and frequency into account despite environmental and cultivation factors. The European Earth Observation programme Copernicus provides large quantities of spatial and temporal high resolution data collected by a set of Sentinel satellites. The freely and openly accessible Sentinel-1 radar data form a valid basis for automated satellite and ground data processing methods to detect cutting events. These cutting frequencies are a fundamental information source for further analysis – the computation of grassland yields with different model approaches. In this study we like to present our overall approach integrating and analyzing data of different sources. A comparison between two different automated data processing methods to detect cutting frequencies from radar satellite data in three different regions in Bavaria is included. The common statistical detection represents a robust and reliable way by analysing time series of Sentinel-1 radar images of the same acquisition geometry with time intervals of 6 days. In contrast, machine learning techniques offer the opportunity to increase the accuracy and limit cutting dates to more precise time intervals.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years