Rule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring.

Persistent Link:
http://hdl.handle.net/10150/187473
Title:
Rule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring.
Author:
Kliman, Douglas Hartley
Issue Date:
1996
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
A rule-based classification model was developed to derive land-cover information from a large set of hyper-temporal, multi-spectral satellite imagery encompassing the state of Arizona. The model uses Advanced Very High Resolution Radiometer (AVHRR) imagery and the 30-minute digital elevation model (DEM) from the EROS Data Center (EDC) Conterminous U.S. AVHRR Biweekly Composites. Sixty one images from 1990, 1991 and 1992 were analyzed using the Brown & Lowe (1973) Natural Vegetative Communities of Arizona map to identify temporal patterns of Normalized Difference Vegetation Index (NDVI) and thermal measurements for 13 land-cover classes. Fifteen characteristic layers were created to represent the spectral, thermal and temporal properties of the data set. These layers were inputs for the rule-based classification model. The model was run on three years of data, creating three single year land-cover maps. The modeling effort showed that NDVI, thermal and DEM characteristics are useful for discerning land-cover classes. The single year land-cover maps showed that the rule-based model could not detect land-cover change between years. The single year maps were combined to create a summary land-cover map. This map differs from the Brown and Lowe map in the shape, proportional size and spatial distribution of land-cover polygons. The rule-based model can discern more land-cover classes than spectral cluster classification. Ground observations and an aerial video was used to assess map accuracy. The same proportion of agreement was observed between the ground observations, the Brown and Lowe map, and the summary land-cover map. Agreement was higher between video and the summary map than between video and the Brown and Lowe map. With further refinements to the input data set, classification model rules and field accuracy assessment, higher levels of agreement can be expected. Overall results show that rule-based classification of hyper-temporal, multi-spectral satellite imagery is a desirable method for mapping global land-cover.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Geography and Regional Development; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Marsh, Stuart E.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleRule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring.en_US
dc.creatorKliman, Douglas Hartleyen_US
dc.contributor.authorKliman, Douglas Hartleyen_US
dc.date.issued1996en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractA rule-based classification model was developed to derive land-cover information from a large set of hyper-temporal, multi-spectral satellite imagery encompassing the state of Arizona. The model uses Advanced Very High Resolution Radiometer (AVHRR) imagery and the 30-minute digital elevation model (DEM) from the EROS Data Center (EDC) Conterminous U.S. AVHRR Biweekly Composites. Sixty one images from 1990, 1991 and 1992 were analyzed using the Brown & Lowe (1973) Natural Vegetative Communities of Arizona map to identify temporal patterns of Normalized Difference Vegetation Index (NDVI) and thermal measurements for 13 land-cover classes. Fifteen characteristic layers were created to represent the spectral, thermal and temporal properties of the data set. These layers were inputs for the rule-based classification model. The model was run on three years of data, creating three single year land-cover maps. The modeling effort showed that NDVI, thermal and DEM characteristics are useful for discerning land-cover classes. The single year land-cover maps showed that the rule-based model could not detect land-cover change between years. The single year maps were combined to create a summary land-cover map. This map differs from the Brown and Lowe map in the shape, proportional size and spatial distribution of land-cover polygons. The rule-based model can discern more land-cover classes than spectral cluster classification. Ground observations and an aerial video was used to assess map accuracy. The same proportion of agreement was observed between the ground observations, the Brown and Lowe map, and the summary land-cover map. Agreement was higher between video and the summary map than between video and the Brown and Lowe map. With further refinements to the input data set, classification model rules and field accuracy assessment, higher levels of agreement can be expected. Overall results show that rule-based classification of hyper-temporal, multi-spectral satellite imagery is a desirable method for mapping global land-cover.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGeography and Regional Developmenten_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairMarsh, Stuart E.en_US
dc.contributor.committeememberAltschul, D. Roberten_US
dc.contributor.committeememberReeves, Richard W.en_US
dc.contributor.committeememberHutchinson, Charles F.en_US
dc.identifier.proquest9626497en_US
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