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Monitoring Sahelian floodplains using Fourier analysis of MODIS time-series data and artificial neural networks

Toon Westra UGent and Robert De Wulf UGent (2007) INTERNATIONAL JOURNAL OF REMOTE SENSING. 28(7-8). p.1595-1610
abstract
Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time-series data was applied to monitor the flooding extent of the Waza-Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MODIS, Fourier transform, wetlands, remote sensing, PLAINS, AVHRR NDVI DATA, VEGETATION
journal title
INTERNATIONAL JOURNAL OF REMOTE SENSING
Int. J. Remote Sens.
volume
28
issue
7-8
pages
1595 - 1610
Web of Science type
Article
Web of Science id
000246208000011
JCR category
IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
JCR impact factor
0.987 (2007)
JCR rank
5/11 (2007)
JCR quartile
2 (2007)
ISSN
0143-1161
DOI
10.1080/01431160600887698
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
749864
handle
http://hdl.handle.net/1854/LU-749864
date created
2009-09-18 09:42:48
date last changed
2009-09-24 12:21:02
@article{749864,
  abstract     = {Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time-series data was applied to monitor the flooding extent of the Waza-Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.},
  author       = {Westra, Toon and De Wulf, Robert},
  issn         = {0143-1161},
  journal      = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
  keyword      = {MODIS,Fourier transform,wetlands,remote sensing,PLAINS,AVHRR NDVI DATA,VEGETATION},
  language     = {eng},
  number       = {7-8},
  pages        = {1595--1610},
  title        = {Monitoring Sahelian floodplains using Fourier analysis of MODIS time-series data and artificial neural networks},
  url          = {http://dx.doi.org/10.1080/01431160600887698},
  volume       = {28},
  year         = {2007},
}

Chicago
Westra, Toon, and Robert De Wulf. 2007. “Monitoring Sahelian Floodplains Using Fourier Analysis of MODIS Time-series Data and Artificial Neural Networks.” International Journal of Remote Sensing 28 (7-8): 1595–1610.
APA
Westra, T., & De Wulf, R. (2007). Monitoring Sahelian floodplains using Fourier analysis of MODIS time-series data and artificial neural networks. INTERNATIONAL JOURNAL OF REMOTE SENSING, 28(7-8), 1595–1610.
Vancouver
1.
Westra T, De Wulf R. Monitoring Sahelian floodplains using Fourier analysis of MODIS time-series data and artificial neural networks. INTERNATIONAL JOURNAL OF REMOTE SENSING. 2007;28(7-8):1595–610.
MLA
Westra, Toon, and Robert De Wulf. “Monitoring Sahelian Floodplains Using Fourier Analysis of MODIS Time-series Data and Artificial Neural Networks.” INTERNATIONAL JOURNAL OF REMOTE SENSING 28.7-8 (2007): 1595–1610. Print.