title: Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

authors: Pham, Thi Hong Lien

subjects: object-based classification

abstract: Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. 

OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). 

The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR.
 
The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy.

text: Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. 

OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). 

The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR.
 
The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy.

year: 2018

title: On the precipitability of binary Ti alloys bearing 4-period d-metal eutectoid stabilisers

authors: Bolzoni, Leandro

subjects: Titanium alloys

abstract: There is no specific, simple approach for predicting whether the addition of an eutectoid beta stabiliser to Ti leads to an ‘active’ eutectoid transformation upon primary processing (e.g., sintering). Here, we demonstrate that, among theoretical/empirical models, phase diagram features, and electronic structure parameters, the hypoeutectoid area is the best predictor, followed by the molybdenum equivalent parameter (MoE), of the precipitability for 4-period d-metals. As the area increases, which corresponds to the addition of progressively stronger β-eutectoid stabilisers, the less active the eutectoid phase transformation, changing from pearlitic to bainitic (i.e., need for an ageing heat treatment). This occurs if the MoE weighted coefficient is, respectively, lower than or higher than 1. This is because molybdenum is taken as reference, and the higher the coefficient, the larger the drop of the β transus temperature (i.e., higher stabilisation). Valid for 4-period d-metals, it remains to be proven for 5-period and 6-period d-metals.

text: On the precipitability of binary Ti alloys bearing 4-period d-metal eutectoid stabilisers There is no specific, simple approach for predicting whether the addition of an eutectoid beta stabiliser to Ti leads to an ‘active’ eutectoid transformation upon primary processing (e.g., sintering). Here, we demonstrate that, among theoretical/empirical models, phase diagram features, and electronic structure parameters, the hypoeutectoid area is the best predictor, followed by the molybdenum equivalent parameter (MoE), of the precipitability for 4-period d-metals. As the area increases, which corresponds to the addition of progressively stronger β-eutectoid stabilisers, the less active the eutectoid phase transformation, changing from pearlitic to bainitic (i.e., need for an ageing heat treatment). This occurs if the MoE weighted coefficient is, respectively, lower than or higher than 1. This is because molybdenum is taken as reference, and the higher the coefficient, the larger the drop of the β transus temperature (i.e., higher stabilisation). Valid for 4-period d-metals, it remains to be proven for 5-period and 6-period d-metals.

year: 2026-06

title: Ati Ue: Te Aitanga a Tuoro

authors: James, Steven (Tipene) Robert

subjects: Ngati Whakaue

abstract: This thesis is based on the manuscripts of my tupuna Tuoro Akapita Pango that are held in a treasured whānau box. The manuscripts contain history and kōrero of Te Arawa written by multiple generations of the whānau Pango. Within this thesis I explore the value of inter-generational knowledge through considering what lies within these whānau manuscripts and taking the opportunity to discover more history about Ngāti Whakaue through a whānau lens. Researching these manuscripts enables reconnection of the past and the present; the reconnection between forgotten knowledge and whānau. 
Specifically, this thesis proposes a Ngāti Whakaue approach to engaging historical materials and research. It centres the voices of the people, such as the many family members involved within the process, as well as the box of writings and the many gems that hide within. Also, it asks what the manuscripts mean for the current descendants involved in the research. 
The research has been a means of reconnecting with the past to give relevance and context to the present. The objective is demonstrating the value of iwi-specific research methods so that whānau feel encouraged to embark on their own journey.

text: Ati Ue: Te Aitanga a Tuoro This thesis is based on the manuscripts of my tupuna Tuoro Akapita Pango that are held in a treasured whānau box. The manuscripts contain history and kōrero of Te Arawa written by multiple generations of the whānau Pango. Within this thesis I explore the value of inter-generational knowledge through considering what lies within these whānau manuscripts and taking the opportunity to discover more history about Ngāti Whakaue through a whānau lens. Researching these manuscripts enables reconnection of the past and the present; the reconnection between forgotten knowledge and whānau. 
Specifically, this thesis proposes a Ngāti Whakaue approach to engaging historical materials and research. It centres the voices of the people, such as the many family members involved within the process, as well as the box of writings and the many gems that hide within. Also, it asks what the manuscripts mean for the current descendants involved in the research. 
The research has been a means of reconnecting with the past to give relevance and context to the present. The objective is demonstrating the value of iwi-specific research methods so that whānau feel encouraged to embark on their own journey.

year: 2021