Evaluation of time-series and phenological indicators for land cover classification based on MODIS data

Francesco Vuolo; Katja Richter; Clement Atzberger

doi:10.1117/12.898389

6 October 2011 Evaluation of time-series and phenological indicators for land cover classification based on MODIS data

Francesco Vuolo, Katja Richter, Clement Atzberger

Proceedings Volume 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII; 81740E (2011) https://doi.org/10.1117/12.898389
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

In the context of defining a procedure for near real time land use/land cover (LULC) mapping with seasonal updated products, this research examines the use of time-series and phenological indicators from MODIS NDVI. 16-day NDVI composites from MODIS (MOD13Q1) covering the period from 2001 to the present were acquired for three test sites located in different parts of Europe. The newly proposed Whittaker smoother was used for filtering purposes. Metrics of vegetation dynamics (such as minimum, maximum and amplitude, etc.) were extracted from the filtered time-series. Subsequently, the capability of three data sets (raw, filtered data and phenological indicators) was evaluated to separate between different LULC classes by calculating the overall classification accuracy for the years 2002 and 2009. Ground truth data for model calibration and testing set was derived combining existing land cover products (GLC2000 and GlobCover 2009). Based on these results, the benefits of using phenological indicators and cleaned data for land cover classification are discussed.

Citation Download Citation

Francesco Vuolo, Katja Richter, and Clement Atzberger "Evaluation of time-series and phenological indicators for land cover classification based on MODIS data", Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81740E (6 October 2011); https://doi.org/10.1117/12.898389

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