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

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.

[1]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[2]  Urbano Nunes,et al.  Novel Maximum-Margin Training Algorithms for Supervised Neural Networks , 2010, IEEE Transactions on Neural Networks.

[3]  Louisa J. M. Jansen,et al.  Land-cover harmonisation and semantic similarity: some methodological issues , 2008 .

[4]  Armel Thibaut Kaptué Tchuenté,et al.  Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Ryutaro Tateishi,et al.  Use of phenological features to identify cultivated areas in Asia , 2011 .

[6]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[7]  C. Woodcock,et al.  The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from landsat TM☆ , 1996 .

[8]  Steffen Fritz,et al.  Cropland for sub‐Saharan Africa: A synergistic approach using five land cover data sets , 2011 .

[9]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[11]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[12]  Siamak Khorram,et al.  Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach , 2006 .

[13]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[14]  A. Huete,et al.  MODIS VEGETATION INDEX ( MOD 13 ) ALGORITHM THEORETICAL BASIS DOCUMENT Version 3 . 1 Principal Investigators , 1999 .

[15]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[16]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[17]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Alan H. Strahler,et al.  Validation of the global land cover 2000 map , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[21]  Jianwen Ma,et al.  Land cover classification from MODIS EVI times-series data using SOM neural network , 2005 .

[22]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[23]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.