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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21257
Title: Moist deciduous forest identification using MODIS temporal indices data
Authors: Upadhyay P.
Ghosh, Sanjay Kumar
Kumar A.
Krishna Murthy Y.V.N.
Raju P.L.N.
Published in: International Journal of Remote Sensing
Abstract: The present research aims to extract moist deciduous forest (MDF) from Moderate Resolution Imaging Spectroradiometer (MODIS) temporal data by using the fuzzy c-means (FCM)-based noise clustering (NC) soft classification approach. Seven-date temporal MODIS data were used to identify MDF, and temporal Advanced Wide Field Sensor (AWiFS) data were used as reference data for testing. Different types of spectral indices were used to generate the temporal data set combinations for both MODIS and AWiFS. The NC resolution parameter delta was optimized to achieve the best output. It was found that for both AWiFS and MODIS data, optimum NC outputs were obtained when reached close to 105. For assessment of the accuracy, NC classified outputs were optimized using the entropy approach. The optimized data set of AWiFS was then used for assessing the accuracy of the optimized data set of MODIS using fuzzy error matrix (FERM), composite operators (MIN-MIN, MIN-PROD, and MIN-LEAST), and a sub-pixel confusion-uncertainty matrix (SCM). It was found that the temporal data set combination corresponding to 'Three' date yields the highest overall accuracy for all accuracy assessment techniques. In all cases, the 'Three' date combination corresponds to the three scenes pertaining to different phenological activity of the MDF. This 'Three' date combination, along with the soil-adjusted vegetation index (SAVI), yielded the highest overall accuracy values, namely 94.88% and 94.84% for MIN-LEAST and MIN-PROD, respectively. ¬© 2014 Taylor & Francis.
Citation: International Journal of Remote Sensing, 35(9): 3177-3196
URI: https://doi.org/10.1080/01431161.2014.903438
http://repository.iitr.ac.in/handle/123456789/21257
Issue Date: 2014
Publisher: Taylor and Francis Ltd.
Keywords: Forestry
Fuzzy inference
Radiometers
Satellite imagery
Uncertainty analysis
Accuracy assessment
Composite operators
Moderate resolution imaging spectroradiometer
Noise clustering
Overall accuracies
Resolution parameters
Soft classification
Spectral indices
Matrix algebra
accuracy assessment
deciduous forest
fuzzy mathematics
MODIS
remote sensing
Forestry
Image Analysis
Optimization
Radiometry
Satellites
ISSN: 1431161
Author Scopus IDs: 55660194200
55478984700
57214420708
15839910400
56448256400
Author Affiliations: Upadhyay, P., Civil Engineering Department, Indian Institute of Technology, Roorkee, India
Ghosh, S.K., Civil Engineering Department, Indian Institute of Technology, Roorkee, India
Kumar, A., Indian Institute of Remote Sensing, Dehradun, India
Krishna Murthy, Y.V.N., Indian Institute of Remote Sensing, Dehradun, India
Raju, P.L.N., Indian Institute of Remote Sensing, Dehradun, India
Corresponding Author: Upadhyay, P.; Civil Engineering Department, , Roorkee, India; email: priyadarshiu@gmail.com
Appears in Collections:Journal Publications [CE]

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