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Analysis of Landsat for monitoring vegetables in New York mucklandsThis pilot study assessed the feasibility of relying on Landsat multispectral scanner data for inventorying vegetables grown in mucklands,in variably shaped, variably sized fields. Classification of muckland vegetables using a Euclidean distance classifier and a parallelepiped classifier was performed with reasonable accuracy (generally over 60 percent) based on only one date of Landsat data. Prior canonical and principal component analyses did not improve the classification accuracy but did reduce the dimensionality of the data.
Document ID
19840050558
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Zhu, M. H.
(Cornell Univ. Ithaca, NY, United States)
Yan, S. Y.
(Cornell Univ. Ithaca, NY, United States)
Philipson, W. R.
(Cornell Univ. Ithaca, NY, United States)
Yen, C. C.
(Cornell Univ. Ithaca, NY, United States)
Philpot, W. D.
(Cornell University Ithaca, NY, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1983
Subject Category
Earth Resources And Remote Sensing
Accession Number
84A33345
Funding Number(s)
CONTRACT_GRANT: NGL-33-010-171
Distribution Limits
Public
Copyright
Other

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