Characterizing fire-related spatial patterns in fire-prone ecosystems using optical and microwave remote sensing

Persistent Link:
http://hdl.handle.net/10150/280006
Title:
Characterizing fire-related spatial patterns in fire-prone ecosystems using optical and microwave remote sensing
Author:
Henry, Mary Catherine
Issue Date:
2002
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:
The use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Geography.; Physical Geography.; Agriculture, Forestry and Wildlife.; Remote Sensing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Geography and Regional Development
Degree Grantor:
University of Arizona
Advisor:
Yool, Stephen R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleCharacterizing fire-related spatial patterns in fire-prone ecosystems using optical and microwave remote sensingen_US
dc.creatorHenry, Mary Catherineen_US
dc.contributor.authorHenry, Mary Catherineen_US
dc.date.issued2002en_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.abstractThe use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectGeography.en_US
dc.subjectPhysical Geography.en_US
dc.subjectAgriculture, Forestry and Wildlife.en_US
dc.subjectRemote Sensing.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineGeography and Regional Developmenten_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorYool, Stephen R.en_US
dc.identifier.proquest3053870en_US
dc.identifier.bibrecord.b4281165xen_US
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