Multi-Temporal Analysis of Remotely Sensed Information Using Wavelets

Alfredo Nicolás Campos, Carlos Marcelo Di Bella

Climate and Water Institute, Research Center of Natural Resources, National Institute of Agricultural Technology (CIRN-INTA), Hurlingham, Argentina.

DOI: 10.4236/jgis.2012.44044                

Abstract

Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occurrence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this article we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process without the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to improve an image classification.

Keywords

Land Use/Cover Change; Disturbs; Vegetation Dynamic; MODIS NDVI Series; Wavelet Transform

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys and E. Lambin, “Digital Change Detection Methods in Ecosystem Monitoring: A Review,” International Journal of Remote Sensing, Vol. 25, No. 9, 2004, pp. 1565-1596. doi:10.1080/0143116031000101675
[2]	R. Lunetta, J. Knight, J. Ediriwickrema, J. Lyon and L. D. Worthy, “Land-Cover Detection Using Multi-Temporal MODIS NDVI Data,” Remote Sensing of Environment, Vol. 105, No. 2, 2006, pp. 142-154. doi:10.1016/j.rse.2006.06.018
[3]	B. Martínez and M. A. Gilabert, “Vegetation Dynamics from NDVI Time Series Analysis Using the Wavelet Transform,” Remote Sensing of Environment, Vol. 113, No. 9, 2009, pp. 1823-1842. doi:10.1016/j.rse.2009.04.016
[4]	B. A. Bradley, R. W. Jacob, J. F. Hermance and J. F. Mustard, “A Curve Fitting Procedure to Derive Inter-Annual Phenologies from Time Series of Noisy Satellite NDVI Data,” Remote Sensing of Environment, Vol. 106, No. 2. 2007, pp. 137-145. doi:10.1016/j.rse.2006.08.002
[5]	P. J?nsson and L. Eklundh, “TIMESAT—A Program for Analyzing Time-Series of Satellite Sensor Data,” Computers & Geosciences, Vol. 30, No. 8, 2004, pp. 833-845. doi:10.1016/j.cageo.2004.05.006
[6]	R. Lasaponara, “On the Use of Principal Component Analysis (PCA) for Evaluating Interannual Vegetation Anomalies from SPOT/VEGETATION NDVI Temporal Series,” Ecological Modelling, Vol. 194, No. 4, 2006, pp. 429-434. doi:10.1016/j.ecolmodel.2005.10.035
[7]	J. Verbesselt, R. Hyndman, G. Newnham and D. Culvenor, “Detecting Trend and Seasonal Changes in Satellite Image Time Series,” Remote Sensing of Environment, Vol. 114, No. 1, 2010, pp. 106-115. doi:10.1016/j.rse.2009.08.014
[8]	D. P. Roy, J. S. Borak, S. Devadiga, R. E. Wolfe, M. Zheng and J. Descloitres, “The MODIS Land Product Quality Assessment Approach,” Remote Sensing of Environment, Vol. 83, No. 1-2, 2002, pp. 62-76. doi:10.1016/S0034-4257(02)00087-1
[9]	J. W. J. Rouse, R. H. Haas, J. A. Schell and D. W. Deering. “Monitoring Vegetation Systems in the Great Plains with ERTS,” 3rd Symposium, NASA SP-351, Washington DC, 1973, pp. 309-317.
[10]	C. J. Tucker and P. J. Sellers, “Satellite Remote Sensing of Primary Production,” International Journal of Remote Sensing, Vol. 7, No. 11, 1986, pp. 1395-1416. doi:10.1080/01431168608948944
[11]	Y. Hirosawa, S. E. Marsh and D. H. Kliman, “Application of Standardized Principal Component Analysis to Land-Cover Characterization Using Multitemporal AVHRR Data,” Remote Sensing of Environment, Vol. 58, No. 3, 1996, pp. 267-281. doi:10.1016/S0034-4257(96)00068-5
[12]	Q. Wang, J. Tenhunen, N. Q. Dinh, M. Reichstein, T. Vesala and P. Keronen, “Similarities in Ground- and Satellite-Based NDVI Time Series and Their Relationship to Physiological Activity of a Scots Pine Forest in Finland,” Remote Sensing of Environment, Vol. 93, No. 1-2, 2004, pp. 225-237. doi:10.1016/j.rse.2004.07.006
[13]	C. M. Di Bella, J. M. Paruelo, J. E. Becerra, C. Bacour and F. Baret, “Effect of Senescent Leaves on NDVI-Based Estimates of fAPAR: Experimental and Modelling Evidences,” International Journal of Remote Sensing, Vol. 25, No. 23, 2004, pp. 5415-5427. doi:10.1080/01431160412331269724
[14]	G. Posse, M. Oesterheld and C. M. Di Bella, “Landscape, Soil and Meteorological Influences on Canopy Dynamics of Northern Flooding Pampa Grasslands, Argentina,” Applied Vegetation Science, Vol. 8, No. 1, 2005, pp. 49-56. doi:10.1111/j.1654-109X.2005.tb00628.x
[15]	F. Baret, G. Guyot and D. J. Major, “Crop Biomass Evaluation Using Radiometric Measurements,” Photogrammetria, Vol. 43, No. 5, 1989, pp. 241-256. doi:10.1016/0031-8663(89)90001-X
[16]	J. M. Paruelo, M. Oesterheld, C. M. Di Bella, M. Arzadum, J. Lafontaine, M. Cahuepéand C. M. Rebella, “Estimation of Primary Production of Subhumid Rangelands from Remote Sensing Data,” Applied Vegtation Science, Vol. 3, No. 2, 2000, pp. 189-195. doi:10.2307/1478997
[17]	P. M. Cristiano, G. Posse, C. M. Di Bella and F. R. Jaimes, “Uncertainties in fPAR Estimation of Grass Canopies under Different Stress Situations and Differences in Architecture,” International Journal of Remote Sensing, Vol. 31, No. 15, 2010, pp. 4095-4109. doi:10.1080/01431160903229192
[18]	J. Wang, P. M. Rich and K. P. Price, “Temporal Responses of NDVI to Precipation and Temperature in the Central Great Plains, USA,” International Journal of Remote Sensing, Vol. 24, No. 11, 2003, pp. 2345-2364. doi:10.1080/01431160210154812
[19]	A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao and L. G. Ferreira, “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices,” Remote Sensing of the Environment, Vol. 83, No. 1-2, 2002, pp. 195-213. doi:10.1016/S0034-4257(02)00096-2
[20]	X. Zhang, M. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed and A. Huete, “Monitoring Vegetation Phenology Using MODIS,” Remote Sensing of Environment, Vol. 84, 3, No. 2003, pp. 471-475. doi:10.1016/S0034-4257(02)00135-9
[21]	S. Lhermitte, J. Verbesselt, I. Jonkheere, K. Nackaerts, J. van Aardt, W. W. Verstraeten and P. Coppin, “Hierarchical Image Segmentation Based on Similarity of NDVI Time Series,” Remote Sensing of Environment, Vol. 112, No. 2, 2008, pp. 506-521. doi:10.1016/j.rse.2007.05.018
[22]	T. Sakamoto, M. Yokosawa, H. Toritani, M. Shibayama, N. Ishitsuka and H. Ohno, “A Crop Phenology Detection Method Using Time-Series MODIS Data,” Remote Sensing of Environment, Vol. 96, No. 3-4, 2005, pp. 366-374. doi:10.1016/j.rse.2005.03.008
[23]	G. L. Galford, J. F. Mustard, J. Melillo, A. Gendrin, C. C. Cerri and C. E. P. Cerri, “Wavelet Analysis of MODIS Time Series to Detect Expansion and Intensification of Row-Crop Agriculture in Brazil,” Remote Sensing of Environment, Vol. 112, No. 2, 2008, pp. 576-587. doi:10.1016/j.rse.2007.05.017
[24]	J. F. Kirby, “Which Wavelet Best Reproduces the Fourier Power Spectrum?” Computer & Geosciences, Vol. 31, No. 7, 2005, pp. 846-864. doi:10.1016/j.cageo.2005.01.014
[25]	A. Gupta, S. D. Joshi and S. Prasad, “A New Method of Estimating Wavelet with Desired Features from a Given Signal,” Signal Processing, Vol. 85, No. 1, 2005, pp. 147-161. doi:10.1016/j.sigpro.2004.09.008
[26]	A. P. Bradley and W. J. Wilson, “On Wavelet Analysis of Auditory Evoked Potentials,” Clinical Neurophysiology, Vol. 115, No. 5, 2004, pp. 1114-1128. doi:10.1016/j.clinph.2003.11.016
[27]	M. O. Domingues, O. Mendes Jr. and A. Mendes da Costa, “On Wavelet Techniques in Atmospheric Sciences,” Advances in Space Research, Vol. 35, No. 5, 2005, pp. 831-842. doi:10.1016/j.asr.2005.02.097
[28]	T. Sakamoto, N. Van Nguyen, A. Kotera, H. Ohno, N. Ishitsuka and M. Yokozawa, “Detecting Temporal Changes in the Extent of Anual Flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS Times-Series Imagery,” Remote Sensing of Environment, Vol. 109, No. 3, pp. 295-313. doi:10.1016/j.rse.2007.01.011
[29]	S. Merino de Miguel, M. Huesca and F. González-Alonso, “Modis Reflectance and Active Fire Data for Burn Mapping and Assessment,” Ecological Modelling, Vol. 221, No. 1, 2010, pp. 67-74. doi:10.1016/j.ecolmodel.2009.09.015
[30]	M. L. Clark, T. M. Aide, H. R. Grau and G. Riner, “A Scalable Approach to Mapping Annual Land Cover at 250 m Using MODIS Time Series Data: A Case Study in the Dry Chaco Ecoregion of South America,” Remote Sensing of the Environment, Vol. 114, No. 11, 2010, pp. 2816-2832. doi:10.1016/j.rse.2010.07.001
[31]	H. He, J. Zhou, Y. Wu, W. Zhang and X. Xie, “Modelling the Response of Surface Water Quality to the Urbanization in Xi’an, China,” Journal of Environmental Management, Vol. 86, No. 4, 2008, pp. 731-749. doi:10.1016/j.jenvman.2006.12.043
[32]	I. Daubechies, “Ten Lectures on Wavelets,” CBMS-NSF Regional Conference Series in Applied Mathematics SIAM, No. 61, Society for Industrial and Applied Mathematics, Philadelphia, 1992.
[33]	MathWorks, “Wavelet Families: Additional Discussion,” MATLAB Help, 2010. http://www.mathworks.com/help/toolbox/wavelet/ug/f8-24282.html