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Permanent link (DOI): https://doi.org/10.7939/R36688R5D

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Predicting RF Path Loss in Forests Using Satellite Measurements of Vegetation Indices Open Access

Descriptions

Other title
Subject/Keyword
RF Propagation
Path Loss Exponent
Satellite Measurement
Vegetation Index
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Jiang, Sujuan
Supervisor and department
MacGregor, Mike H (Computing Science)
Examining committee member and department
Sanchez-Azofeifa, Arturo (Earth and Atmospheric Sciences)
Harms, Janelle (Computing Science)
MacGregor, Mike H (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-01-30T09:38:31Z
Graduation date
2015-06
Degree
Master of Science
Degree level
Master's
Abstract
In this thesis, we propose a novel method for predicting the value of the radio frequency (RF) path loss exponent (PLE) from satellite remote sensing observations. The value of the PLE is required when designing wireless sensor networks for environmental monitoring. By taking field path loss measurements in single cells and extracting values of vegetation indices (VIs) from satellite data, we successfully build correlation models between PLE and VIs of different dates. We also characterize the composite correlation of all data from all the filed measurements, which covers the whole in-leaf phrase in forests. The correlations are strong (R^2 > 0.77) and exhibit high statistical significance (p < 0.01). It enables us to characterize and predict the RF propagation environment in forested areas without the need for field measurements, given that satellite data are available any location on Earth. We also propose a method of predicting missing high-resolution 30m x 30m Landsat 8 data required by our method from lower-resolution 250m x 250m MODIS observations that are not as easily degraded. Finally, we use the composite correlation model to predict path loss across multiple cells. A weighted sum method is applied to calculate the overall PLE value for a path across multiple cells. We compare the predicted RSSI values against actual field data. The result shows that the predicted RSSI data are very close to the field data with error less than 5%.
Language
English
DOI
doi:10.7939/R36688R5D
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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File format: pdf (Portable Document Format)
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Last modified: 2015:10:22 07:20:31-06:00
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File title: Introduction
File title: Predicting RF Path Loss in Forests Using Satellite Measurements of Vegetation Indices
File author: Sujuan Jiang
Page count: 64
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