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Spatiotemporal Analysis for NDVI Time Series Using Local Binary Pattern and Daubechies Wavelet Transform

Bachir Kaddar(1*), Hadria Fizazi(2)

(1) Université d'Oran des Sciences et de la Technologie Mohamed Boudiaf, Faculty of Mathematics and Computer Sciences, Algeria
(2) Université d'Oran des Sciences et de la Technologie Mohamed Boudiaf, Faculty of Mathematics and Computer Sciences, Algeria
(*) Corresponding author


DOI: https://doi.org/10.15866/irease.v10i2.11873

Abstract


NDVI time series has shown to be very efficient for vegetation change dynamic analysis over a long period. However, noise and illumination variations present significant challenges to perform an accurate change detection. This paper aims at capturing global vegetation change dynamics within 16-days MODIS-NDVI time series by considering the inter-annual variations. To determine the appropriate scale that characterizes the long term variation, an efficient way relying on wavelet transform is used. First, the Daubechies 4 wavelet transform is employed to perform a multi-scale decomposition to extract the inter-annual variations and remove noise. Second, critical point theory is used to identify a set of points indicating potential vegetation change within time series, which, allows a time series reduction. Then, for each critical point, LBP code is computed to characterize the corresponding local patterns, which provides the ability to deal with illumination variations. Based on the extracted features, a change map is produced by computing similarity between neighboring time series, assessing dynamic vegetation change over the period of study. Experiment results using NDVI time series show clearly the potential of the proposed approach to detect change.
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Keywords


Change Detection; Multi-Scale Analysis; Wavelet Transform; Similarity Measure; NDVI Time Series

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References


Mas, J. F. (1999). Monitoring land-cover changes: a comparison of change detection techniques. International journal of remote sensing, 20(1), 139-152.
http://dx.doi.org/10.1080/014311699213659

Nielsen, A. A., Conradsen, K., Simpson, J. J. (1998). Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies. Remote Sensing of Environment, 64(1), 1-19.
http://dx.doi.org/10.1016/s0034-4257(97)00162-4

Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., Goossens, R. (2014). Burned area detection and burn severity assessment of a heath land fire in Belgium using airborne imaging spectroscopy (APEX). Remote Sensing, 6(3), 1803- 1826.
http://dx.doi.org/10.3390/rs6031803

DOI: 10.3390/rs6031803 • License: CC BY 4.0
http://dx.doi.org/10.1002/cber.19660991046

Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeill, S. J., Glassey, P. J. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. IEEE Geoscience and remote sensing letters, 33(2), 183-207.
http://dx.doi.org/10.1177/0309133309339563

Lu, P., Stumpf, A., Kerle, N., Casagli, N. (2011). Object-oriented change detection for landslide rapid mapping. Progress in Physical Geography, 33(2), 183-207.
http://dx.doi.org/10.1109/lgrs.2010.2101045

Van Westen, C. J., Castellanos, E., Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. International journal of remote sensing, 102(3), 112-131.
http://dx.doi.org/10.1016/j.enggeo.2008.03.010

Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing, 10(6), 989- 1003.
http://dx.doi.org/10.1080/01431168908903939

Jianya, G., Haigang, S., Guorui, M., Qiming, Z. (2008). A review of multi-temporal remote sensing data change detection algorithms. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), 757-762.
http://dx.doi.org/10.1002/cjg2.20231

Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., Worthy, L. D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote sensing of environment, 105(2), 142-154.
http://dx.doi.org/10.1016/j.rse.2006.06.018

Bruzzone, L., Prieto, D. F. (2002). An adaptive semi-parametric and context-based approach to unsupervised change detection in multi-temporal remote-sensing images. Image Processing, IEEE Transactions, 11(4), 452-466.
http://dx.doi.org/10.1109/tip.2002.999678

Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., ... Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote sensing of environment, 84(3), 471- 475.
http://dx.doi.org/10.1016/s0034-4257(02)00135-9

Lobell, D. B., Asner, G. P. (2004). Cropland distributions from temporal unmixing of MODIS data. Remote sensing of environment, 93(3), 412-422.
http://dx.doi.org/10.1016/j.rse.2004.08.002

Ozdogan, M. (2010). The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis. Remote sensing of environment, 114(6), 1190-1204.
http://dx.doi.org/10.1016/j.rse.2010.01.006

Bhandari, S., Phinn, S., Gill, T. (2012). Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sensing, 4(6), 1856-1886.
http://dx.doi.org/10.3390/rs4061856

Simoncelli, E. P., Freeman, W. T., Adelson, E. H., Heeger, D. J. (1992). Shiftable multiscale transforms. Remote Sensing, 38(2), 587-607.
http://dx.doi.org/10.1109/18.119725

Palais, R. S. (1970). Critical point theory and the minimax principle. (pp. 185-212).
http://dx.doi.org/10.1090/pspum/015/0264712

Ojala, T., Pietikäinen, M., & Harwood, D. (1998). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
http://dx.doi.org/10.1016/0031-3203(95)00067-4

Kasetkasem, T., Arora, M. K., Varshney, P. K. (2005). Super-resolution land cover mapping using a Markov random field based approach. Remote Sensing of Environment, 96(3), 302-314.
http://dx.doi.org/10.1016/j.rse.2005.02.006

Bruzzone, L., Prieto, D. F. (2000). Automatic analysis of the difference image for unsupervised change detection. Geoscience and Remote Sensing, IEEE Transactions, 38(3), 1171-1182.
http://dx.doi.org/10.1109/36.843009

Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment, 114(1), 106-115.
http://dx.doi.org/10.1016/j.rse.2009.08.014

Small, C. (2012). Spatiotemporal dimensionality and Time-Space characterization of multi-temporal imagery. Remote sensing of Environment, 124, 793-809.
http://dx.doi.org/10.1016/j.rse.2012.05.031

Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H. (2005). A crop phenology detection method using time-series MODIS data. Remote sensing of Environment, 96(3), 366-374.
http://dx.doi.org/10.1016/j.rse.2005.03.008

Galford, G. L., Mustard, J. F., Melillo, J., Gendrin, A., Cerri, C. C., Cerri, C. E. (2008). Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote sensing of Environment, 112(2), 576- 587.
http://dx.doi.org/10.1016/j.rse.2007.05.017

Martnez, B., Gilabert, M. A. (2009). Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote sensing of Environment, 113(9), 1823-1842.
http://dx.doi.org/10.1016/j.rse.2009.04.016

Vicente-Guijalba, F., Martinez-Marin, T., & Lopez-Sanchez, J. M. (2015). Dynamical approach for real-time monitoring of agricultural crops. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3278-3293.
http://dx.doi.org/10.1109/tgrs.2014.2372897

Ault, T. R., Schwartz, M. D., Zurita-Milla, R., Weltzin, J. F., & Betancourt, J. L. (2015). Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices. Journal of Climate, 28(21), 8363-8378.
http://dx.doi.org/10.1175/jcli-d-14-00736.1

Lawley, E. F., Lewis, M. M., & Ostendorf, B. (2016). A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes. Ecological Indicators, 60, 1284-1297.
http://dx.doi.org/10.1016/j.ecolind.2015.01.042

Li, F., Song, G., Liujun, Z., Yanan, Z., & Di, L. (2017). Urban vegetation phenology analysis using high spatio-temporal NDVI time series. Urban Forestry & Urban Greening, 25, 43-57.
http://dx.doi.org/10.1016/j.ufug.2017.05.001

GLCF Global Land Cover Facility, http://glcf.umd.edu/data/ndvi/
http://dx.doi.org/10.1007/978-3-642-41714-6_71281

Daubechies, I. (1990). The wavelets transform time-frequency localization and signal analysis. Information Theory, IEEE Transactions, 36(5), 961-1005.
http://dx.doi.org/10.1109/18.57199

Sebe, N., Lew, M. S. (2003). Comparing salient point detectors. Pattern recognition letters,4(1), 89-96.
http://dx.doi.org/10.1016/s0167-8655(02)00192-7

Struzik, Z. R., & Siebes, A. (1999, September). The Haar wavelet transform in the time series similarity paradigm. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 12-22). Springer Berlin Heidelberg.
http://dx.doi.org/10.1007/978-3-540-48247-5_2

Bloomfield, P. (2004). Fourier analysis of time series: an introduction. John Wiley & Sons.
http://dx.doi.org/10.1002/0471722235


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