An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data

Abstract In this study we compared the Savitzky–Golay, asymmetric Gaussian, double-logistic, Whittaker smoother, and discrete Fourier transformation smoothing algorithms (noise reduction) applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data, to provide continuous phenology data used for land-cover (LC) classifications across the Laurentian Great Lakes Basin (GLB). MODIS 16-day 250 m NDVI imagery for the GLB was used in conjunction with National Land Cover Database (NLCD) from 2001, 2006 and 2011, and the Cropland Data Layers (CDL) from 2011 to 2014 to conduct classification evaluations. Inter-class separability was measured by Jeffries–Matusita (JM) distances between selected cover type pairs (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within cover types. For the GLB, we found that the application of a smoothing algorithm significantly reduced image noise compared to the raw data. However, the Jeffries–Matusita (JM) measures for smoothed NDVI temporal profiles resulted in large inconsistencies. Of the five algorithms tested, only the Fourier transformation algorithm and Whittaker smoother improved inter-class separability for corn–soybean class pair and significantly improved overall classification accuracy. When compared to the raw NDVI data as input, the overall classification accuracy from the Fourier transformation and Whittaker smoother improved performance by approximately 2–6% for some years. Conversely, the asymmetric Gaussian and double-logistic smoothing algorithms actually led to degradation of classification performance.

[1]  E. Jewett,et al.  Scientific assessment of freshwater harmful algal blooms , 2008 .

[2]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[3]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[4]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[5]  John F. Hermance,et al.  Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness , 2007 .

[6]  Yang Shao,et al.  Sub-Pixel Mapping of Tree Canopy, Impervious Surfaces, and Cropland in the Laurentian Great Lakes Basin Using MODIS Time-Series Data , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[8]  A. Mathur,et al.  Denoising and wavelet-based feature extraction of MODIS multi-temporal vegetation signatures , 2005, International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005..

[9]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[10]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[11]  Robert E. Wolfe,et al.  An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  J. Wickham,et al.  Thematic accuracy of the NLCD 2001 land cover for the conterminous United States , 2010 .

[13]  Bradley C. Reed,et al.  Trend Analysis of Time-Series Phenology of North America Derived from Satellite Data , 2006 .

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

[15]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[16]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

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

[18]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[19]  Nathan S. Bosch,et al.  Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions , 2013, Proceedings of the National Academy of Sciences.

[20]  H. Paerl,et al.  Blooms Like It Hot , 2008, Science.

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

[22]  D. H. Roberts,et al.  Time Series Analysis with Clean - Part One - Derivation of a Spectrum , 1987 .

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

[24]  P. Eilers.  A perfect smoother. , 2003, Analytical chemistry.

[25]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[26]  Yang Shao,et al.  Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

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

[29]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[30]  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.

[31]  Yang Shao,et al.  Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data , 2010 .

[32]  F. Veroustraete,et al.  Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China , 2006 .

[33]  Alexander J. Macpherson,et al.  Assessing Sediment Yield for Selected Watersheds in the Laurentian Great Lakes Basin Under Future Agricultural Scenarios , 2012, Environmental Management.

[34]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[35]  James Rowland,et al.  A weighted least-squares approach to temporal NDVI smoothing , 1999 .

[36]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[37]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .