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Moist deciduous forest identification using temporal MODIS data - A comparative study using fuzzy based classifiers
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DOI：10.1016/j.ecoinf.2013.07.002.png)
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The two soft fuzzy based classifiers, Possibilistic c-Means (PCM) approach and Noise Clustering (NC) were compared for the Moist Deciduous Forest (MDF) identification from MODIS temporal data. Seven date temporal MODIS data were used to identify MDF and temporal Advanced Wide Field Sensor (AWiFS) data was used as reference data for testing. Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were used to generate the temporal spectral index datasets for both the MODIS and AWiFS. The parameter weighting exponent m for PCM and resolution parameter delta for NC were optimized. Results show that the optimized value of m for MDF is 2.1, while delta value is 3.6 x 10(4) for temporal MODIS data. For assessment of the accuracy AWiFS datasets were also optimized using entropy approach. The optimized dataset of AWiFS was then used for accuracy assessment of the soft classified outputs from MODIS using Fuzzy ERror Matrix (FERM). It was found from this study that, for PCM classifier the highest fuzzy overall accuracy of 97.44% was obtained using the SAVI for the temporal dataset 'Five' consisting to one scene of 'Full greenness', three scenes in 'Intermediate frequency stage of Onset of Greenness (OG) and End of Senescence (ES) activity' and the last image pertaining corresponds to the 'Maximum frequency stage of OG activity' as per phenology of MDF. Similarly, for NC classifier the highest fuzzy overall accuracy of 95.19% was obtained for the EVI2 with temporal dataset 'Five' consisting with two scene of 'Full greenness', two scenes in 'Intermediate frequency stage of OG and ES activity' and the last one corresponds to the 'Maximum frequency stage of OG activity as per phenology of MDF. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Moist deciduous forest
Temporal spectral indices
Possibilistic c-Means
Noise Clustering
MODIS
FERM
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