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Neural networks modelling of stream nitrogen using remote sensing information: model development and application Open Access

Descriptions

Other title
Subject/Keyword
Neural networks
Nitrogen
Remote sensing
Modelling
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Xiangfei
Supervisor and department
Gan, Thian (Civil and Environmental Engineering)
Smith, Daniel W. (Civil and Environmental Engineering)
Examining committee member and department
Fedorak, Phillip M. (Biological Sciences)
Buchanan, Ian D. (Civil and Environmental Engineering)
Gamal El-Din, Mohamed (Civil and Environmental Engineering)
Gan, Thian (Civil and Environmental Engineering)
Hettiaratchi, Patrick (Civil Engineering, University of Calgary)
Smith, Daniel W. (Civil and Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization

Date accepted
2009-09-04T16:26:08Z
Graduation date
2009-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
In remotely located forest watersheds, monitoring nitrogen (N) in streams often is not feasible because of the high costs and site inaccessibility. Therefore, modelling tools that can predict N in unmonitored watersheds are urgently needed to support management decisions for these watersheds. Recently, remote sensing (RS) has become a cost-efficient way to evaluate watershed characteristics and obtain model input variables. This study was to develop an artificial neural network (ANN) modelling tool relying solely on public domain climate data and satellite data without ground-based measurements. ANN was successfully applied to simulate N compositions in streams at studied watersheds by using easily accessible input variables, relevant time-lagged inputs and inputs reflecting seasonal cycles. This study was the first effort to take the consideration of vegetation dynamics into N modelling by using RS-derived enhanced vegetation index (EVI) that was capable of describing the differences of vegetation canopy and vegetation dynamics among watersheds. As a further study to demonstrate the applicability of the ANN models to unmonitored watersheds, the calibrated ANN models were used to predict N in other different watersheds (unmonitored watersheds in this perspective) without further calibration. A watershed similarity index was found to show high correlation with the transferability of the models and can potentially guide transferring the trained models into similar unmonitored watersheds. Finally, a framework to incorporate water quantity/quality modelling into forestry management was proposed to demonstrate the application of the developed models to support decision making. The major components of the framework include watershed delineation and classification, database and model development, and scenario-based analysis. The results of scenario analysis can be used to translate vegetation cut into values of EVI that can be fed to the models to predict changes in water quality (e.g. N) in response to harvesting scenarios. The results from this research demonstrated the applicability of ANNs for stream N modelling using easily accessible data, the effectiveness of RS-derived EVI in N model construction, and the transferability of the ANN models. The presented models have high potential to be used to predict N in streams in the real-world and serve forestry management.
Language
English
DOI
doi:10.7939/R3VC9T
Rights
License granted by Xiangfei Li (xiangfei@ualberta.ca) on 2009-09-02T22:34:50Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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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