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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation date "2007".
- aggregation format "application/pdf".
- aggregation hasFormat 749864.bibtex.
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- aggregation isPartOf urn:issn:0143-1161.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Agriculture and Food Sciences".
- aggregation title "Monitoring Sahelian floodplains using Fourier analysis of MODIS time-series data and artificial neural networks".
- aggregation 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.".
- aggregation authorList BK495410.
- aggregation endPage "1610".
- aggregation issue "7-8".
- aggregation startPage "1595".
- aggregation volume "28".
- aggregation aggregates 758086.
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- aggregation similarTo 01431160600887698.
- aggregation similarTo LU-749864.