title: Development of an innovative and efficient urban vegetation monitoring system for sustainable urban ecology

authors: Alighaleh, Pejman

subjects: Urban Vegetation

abstract: In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing
a more efficient, accurate, and less labor-intensive approach using deep learning technology.
Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species.
The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balance of precision (0.95), recall (0.77), and accuracy (0.86), while YOLOv8m with Transfer Learning excelled in recall (0.93), proving useful for applications requiring high true positive identification. For specific tree detection, the YOLOv8m_Bigger_size model led with high precision (0.95), recall (0.98), and the top mAP50-95 score (0.79) in validation. These results underscore the models' applicability in diverse urban scenarios and reinforce the feasibility of this approach for large-scale urban vegetation monitoring. The research findings indicate that deep learning technology, particularly the YOLOv5m and YOLOv8m models, significantly enhances the efficiency and accuracy of urban vegetation monitoring. The innovative use of car-mounted cameras for data collection marks
a significant advancement over traditional methods, enabling the capture of large-scale urban vegetation data with minimal intrusion and high efficiency.
In summary, this study demonstrates that applying advanced computer vision systems can markedly improve urban vegetation monitoring. The integration of YOLOv5 and YOLOv8 models in New Zealand's urban landscapes offers a novel approach to address the challenges of urbanization. The models are adaptable for use in other New Zealand cities with access to similar urban vegetation datasheets, crucial for evaluating model accuracy and enhancing precision in future models. This thesis advances our understanding of urban vegetation monitoring using computer vision and highlights the potential of deep learning models in enhancing environmental conservation efforts. The insights and methodologies presented provide valuable tools for urban environmental management and suggest new directions for future research and applications in this critical area.

text: Development of an innovative and efficient urban vegetation monitoring system for sustainable urban ecology In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing
a more efficient, accurate, and less labor-intensive approach using deep learning technology.
Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species.
The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balance of precision (0.95), recall (0.77), and accuracy (0.86), while YOLOv8m with Transfer Learning excelled in recall (0.93), proving useful for applications requiring high true positive identification. For specific tree detection, the YOLOv8m_Bigger_size model led with high precision (0.95), recall (0.98), and the top mAP50-95 score (0.79) in validation. These results underscore the models' applicability in diverse urban scenarios and reinforce the feasibility of this approach for large-scale urban vegetation monitoring. The research findings indicate that deep learning technology, particularly the YOLOv5m and YOLOv8m models, significantly enhances the efficiency and accuracy of urban vegetation monitoring. The innovative use of car-mounted cameras for data collection marks
a significant advancement over traditional methods, enabling the capture of large-scale urban vegetation data with minimal intrusion and high efficiency.
In summary, this study demonstrates that applying advanced computer vision systems can markedly improve urban vegetation monitoring. The integration of YOLOv5 and YOLOv8 models in New Zealand's urban landscapes offers a novel approach to address the challenges of urbanization. The models are adaptable for use in other New Zealand cities with access to similar urban vegetation datasheets, crucial for evaluating model accuracy and enhancing precision in future models. This thesis advances our understanding of urban vegetation monitoring using computer vision and highlights the potential of deep learning models in enhancing environmental conservation efforts. The insights and methodologies presented provide valuable tools for urban environmental management and suggest new directions for future research and applications in this critical area.

year: 2024

title: Students’ Voice on Mobile Technology and Web 2.0 Tools for Learning

authors: Vesey, Patricia Edna

subjects: students' voice

abstract: Literature tells us, and from my own observations as an experienced teacher, when students have a mobile device in hand they show signs of being motivated, and a phenomenon takes place where students display a natural curiosity and engagement with the device through various interactions. The purpose of this study was to validate students’ voice by allowing them to express their views on the use of mobile technology and Web 2.0, whether they found them motivating and engaging, and how they perceived them as being useful for learning. Students’ perceptions of current and emerging technologies and their usefulness can differ to teachers’ perceptions, therefore research involving conversations with students are needed to better understand how they perceive and relate to these technologies. This study has had such conversations in order to uncover students’ perceptions. Gathering students’ views gives us a first-hand view on what affects them. It identifies factors that perpetuate or diminish their learning, that educators may not have considered before. Knowing and understanding how it is for students, helps to improve learning conditions for them.

While there is an initial phase of excitement and curiosity that students have when using mobile technology and Web 2.0, this quickly passes and there are learning conditions that need to be met in order for motivation and engagement to be sustained. To sustain their motivation, students need to have self-determination. For students to be self-determining, their needs for autonomy, competence, and relatedness must be met. This thesis has determined, through the voice of students, that their need for autonomy can be met through the affordances of mobile technology and Web 2.0, but the teacher’s role is vital for meeting their needs for competence and relatedness.

The findings from this study strongly argue that the teacher has a vital role to play in ensuring students’ learning experiences supported by technologies are effective, and assumptions around students’ knowledge, skills and understanding of using technology in formal learning situations needs to be checked. Teachers need to robustly scaffold the use of technology in learning situations until students have built up competence. Teachers need to keep relating to and conversing with students, and not assume students do not need support when using technologies characterised by autonomy and independence. It also found that the learning conditions must support students’ needs for self-determination if students are to stay motivated and engaged – the use of technology alone is insufficient. It also highlighted equity and access to technology learning opportunities for all students, students’ informal learning, and influences on students’ perceptions as to whether they considered technologies were useful for learning.

text: Students’ Voice on Mobile Technology and Web 2.0 Tools for Learning Literature tells us, and from my own observations as an experienced teacher, when students have a mobile device in hand they show signs of being motivated, and a phenomenon takes place where students display a natural curiosity and engagement with the device through various interactions. The purpose of this study was to validate students’ voice by allowing them to express their views on the use of mobile technology and Web 2.0, whether they found them motivating and engaging, and how they perceived them as being useful for learning. Students’ perceptions of current and emerging technologies and their usefulness can differ to teachers’ perceptions, therefore research involving conversations with students are needed to better understand how they perceive and relate to these technologies. This study has had such conversations in order to uncover students’ perceptions. Gathering students’ views gives us a first-hand view on what affects them. It identifies factors that perpetuate or diminish their learning, that educators may not have considered before. Knowing and understanding how it is for students, helps to improve learning conditions for them.

While there is an initial phase of excitement and curiosity that students have when using mobile technology and Web 2.0, this quickly passes and there are learning conditions that need to be met in order for motivation and engagement to be sustained. To sustain their motivation, students need to have self-determination. For students to be self-determining, their needs for autonomy, competence, and relatedness must be met. This thesis has determined, through the voice of students, that their need for autonomy can be met through the affordances of mobile technology and Web 2.0, but the teacher’s role is vital for meeting their needs for competence and relatedness.

The findings from this study strongly argue that the teacher has a vital role to play in ensuring students’ learning experiences supported by technologies are effective, and assumptions around students’ knowledge, skills and understanding of using technology in formal learning situations needs to be checked. Teachers need to robustly scaffold the use of technology in learning situations until students have built up competence. Teachers need to keep relating to and conversing with students, and not assume students do not need support when using technologies characterised by autonomy and independence. It also found that the learning conditions must support students’ needs for self-determination if students are to stay motivated and engaged – the use of technology alone is insufficient. It also highlighted equity and access to technology learning opportunities for all students, students’ informal learning, and influences on students’ perceptions as to whether they considered technologies were useful for learning.

year: 2013

title: Operational forest restoration plan for Kukutaaruhe Gully: Part 2

authors: Elliot Noe, Elizabeth

abstract: Kukutaaruhe Gully is a minor branch of Hamilton’s extensive gully system, and runs from the corner of Bankwood and Clarkin Road, behind Fairfield College, connecting with Donny Park in the north and out into the Waikato River. The Kukutaaruhe Education Trust was established to support the implementation of the Fairfield Project, which is a partnership between Ngati Wairere, local community and Fairfield College. The project’s goals include the establishment of an ecological and environmental education centre on the approximately 12 ha of land behind Fairfield College and restoration of the adjacent Kukutaaruhe Gully. In 2019, the University of Waikato’s Environmental Research Institute was contracted by the Kukutaaruhe Education Trust to create an Operational Forest Restoration plan for Kukutaaruhe Gully. This is one in a three-part series created together under one contract:
• Part 1 of 3 is an Overview of the Kukutaaruhe Gully Restoration Initiative
• Part 2 of 3 is this document, the Operational Forest Restoration plan for Kukutaaruhe Gully
• Part 3 of 3 is an Ecological Restoration Plan Template.

text: Operational forest restoration plan for Kukutaaruhe Gully: Part 2 Kukutaaruhe Gully is a minor branch of Hamilton’s extensive gully system, and runs from the corner of Bankwood and Clarkin Road, behind Fairfield College, connecting with Donny Park in the north and out into the Waikato River. The Kukutaaruhe Education Trust was established to support the implementation of the Fairfield Project, which is a partnership between Ngati Wairere, local community and Fairfield College. The project’s goals include the establishment of an ecological and environmental education centre on the approximately 12 ha of land behind Fairfield College and restoration of the adjacent Kukutaaruhe Gully. In 2019, the University of Waikato’s Environmental Research Institute was contracted by the Kukutaaruhe Education Trust to create an Operational Forest Restoration plan for Kukutaaruhe Gully. This is one in a three-part series created together under one contract:
• Part 1 of 3 is an Overview of the Kukutaaruhe Gully Restoration Initiative
• Part 2 of 3 is this document, the Operational Forest Restoration plan for Kukutaaruhe Gully
• Part 3 of 3 is an Ecological Restoration Plan Template.

year: 2020-10