Vegetation monitoring with satellite time series: an integrated approach for user-oriented knowledge extraction

Gohar Ghazaryan; Olena Dubovyk; Valerie Graw; Jürgen Schellberg

doi:10.1117/12.2325762

10 October 2018 Vegetation monitoring with satellite time series: an integrated approach for user-oriented knowledge extraction

Gohar Ghazaryan, Olena Dubovyk, Valerie Graw, Jürgen Schellberg

Author Affiliations +

Proceedings Volume 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX; 107830W (2018) https://doi.org/10.1117/12.2325762
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

Climate change, food insecurity and limited land and water resources strengthen the need for operational and spatially explicit information on vegetation condition and dynamics. The detection of vegetation condition as well as multiannual and seasonal changes using satellite remote sensing, however, depends on the choice of data including length and frequency of time series. Thus, this contribution focuses on the derivation of the optimal remotely sensed data for vegetation monitoring and extraction of relevant metrics. Time series of satellite data from Landsat-8, Sentinel-1/2, and MODIS were used to identify characteristics of vegetation at different spatiotemporal scales. We derived parameters, such as: maximum and amplitude based on vegetation index time series, as well as Land Surface Temperature (LST). Along with optical data, we used backscattering intensity over consecutive vegetation growing seasons. The analysis was carried out using Google Earth Engine, a cloud computing platform which allows to access various data archives and conduct data-intensive analysis. Taking advantage of this platform, we developed a web-based application named GreenLeaf. The application is computing metrics and plotting time series, based on parameters defined by the user. The derived vegetation condition parameters provide sufficient information to detect vegetation change. In addition, the images acquired from near-coincident dates provide similar information over continuous surfaces. The developed application contributes to the use of satellite data and the simplification of data access for users with limited remote sensing experience and/or restricted processing power. Aiming at providing this knowledge to stakeholders can further support decision making on multiple scales.

Conference Presentation

Citation Download Citation

Gohar Ghazaryan, Olena Dubovyk, Valerie Graw, and Jürgen Schellberg "Vegetation monitoring with satellite time series: an integrated approach for user-oriented knowledge extraction", Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107830W (10 October 2018); https://doi.org/10.1117/12.2325762

ACCESS THE FULL ARTICLE

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