UC Research Repository

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The UC Research Repository collects, stores and makes available original research from postgraduate students, researchers and academics based at the University of Canterbury.

 

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Assessing paropsine damage on Eucalyptus trees with remote sensing
(Forest Growers Research, 2022) Mann L; morgenroth, justin; Xu, Cong; Pawson S
EXECUTIVE SUMMARY New Zealand forests comprise 10.1 million hectares of forests. Due to the sheer scale of managing these forests, remote sensing is increasingly used as a source of information for decision-making. Information on tree growth, mortality, and health related to climate or pest activity can be monitored and quickly mapped. Currently, no remote sensing methods exist to quantify foliar browse by paropsine beetles on Eucalyptus. Currently, defoliation assessments are performed through visual methods by ground-based observers. Such methods, like the Crown Damage Index (CDI), are time consuming, particularly at larger spatial scales, and potentially suffer from observer bias. Paropsine damage does not induce a colour change in foliage as would occur with a leaf-sucking insect. Instead, paropsines reduce canopy density by eating parts of leaves, thus altering their shape and area. Hence, LiDAR could be a suitable tool for paropsine defoliation assessment. This study aimed to evaluate the potential for LiDAR as a quantitative assessment of paropsine defoliation of Eucalyptus crowns as a replacement for the CDI. Three LiDAR scanners (VUX-240, VUX-1LR and L1) were used to collect data from a Eucalyptus trial in the Canterbury region (43°11'47.2"S 172°39'06.1"E) in September 2021 and March 2022. To measure the defoliation prediction accuracy of LiDAR we simultaneously collected CDI data for 55 tree crowns at the same date as the LiDAR data. A total of 57 LiDAR metrics were extracted for each of the 55 tree crowns. The best metrics model to predict CDI was statistically analysed with a Partial Least Squares Regression (PLSR). Results: The results demonstrated 18 LiDAR metrics of interest and showed that LiDAR scanners could predict CDI with ±19.1-23.6 % error from the actual CDI observed in the field, with VUX-240 having the smallest error prediction (Root Mean Square Error (RMSE)=9.5 CDI units in September 2021), followed by the L1 scanner (RMSE=10.5 CDI units in March 2022), and VUX-1LR having the highest error prediction (RMSE=11.8 CDI units in September 2021 and RMSE=11.6 CDI units in March 2022). Key conclusions are: • All three scanners had comparable predictive abilities, meaning that all could possibly be used for paropsine defoliation assessment. • The actual error prediction shows promise as a healthy tree could be distinguished from a heavily defoliated tree. • More testing needs to be undertaken to increase the LiDAR defoliation prediction accuracy. These tests should occur in sites with a broader CDI range (e.g., the Marlborough region). • Future work needs to move away from the CDI and use a quantitative method of assessing crown defoliation that can be compared with the remotely sensed LiDAR data. This is important as the CDI is semi-quantitative and potentially subject to observer bias. • More testing needs to be undertaken to determine whether LiDAR can differentiate between paropsine beetle defoliation and trees where abiotic stresses have led to small leaves and/or sparse crowns
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The Perspectives of Māori and Pasifika Mate Kirikōpū (Endometriosis) Patients in Aotearoa New Zealand
(MDPI AG, online-publication-date) Ellis K; Tewhaiti-Smith J; Munro, Deborah; Wood, Rachael
Experiences with endometriosis have been understudied in indigenous and people of colour populations. This study aimed to investigate the experiences of Māori and Pasifika endometriosis patients in Aotearoa New Zealand. Twenty-seven Māori endometriosis participants from 21 iwi (tribes), and 10 Pasifika participants from 8 different island nations participated in online, asynchronous, anonymous text-based discussions about their endometriosis journeys. Their explanations were analysed qualitatively with an inductive thematic approach. The average delay from symptom onset to a confirmed or suspected endometriosis diagnosis was 11.6 ± 7.8 years in the Māori cohort and 12.4 ± 6.2 years in the Pasifika cohort. There were high levels of dissatisfaction with the availability of treatment, with 66.7% of Māori participants and 60.0% of Pasifika participants feeling that endometriosis treatment was not readily available to them. Poor experiences with the medical profession might dissuade Māori and Pasifika patients from seeking care, exacerbating a culture of distrust and perpetuating healthcare inequities. This could potentially be improved by increasing the capacity to take time for relationship building within general practice or through the incorporation of cultural advisors to support relationship establishment that emphasises holistic consideration of patient well-being and culturally safe care.
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Listening to the Voices of Rangatahi: Sexual Health Promotion in the Digital Age.
(2023) CLELLAND, Tracy ; O'Reagan , Abagail; Gilson , Fabian; Clark , Adrian
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Reinvestigating social vulnerability from the perspective of Critical Disaster Studies (CDS): directions, opportunities and challenges in Aotearoa disaster research
(Informa UK Limited, online-publication-date) Uekusa, Shinya; Wynyard , Matthew; Matthewman , Steve
This article argues that resilience has been overemphasised in popular and scholarly discourse, while social vulnerability has been comparatively overlooked. We therefore need to shift the focus from resilience and adaptation towards vulnerability and the various structures that engender and maintain systemic inequality and disadvantage. This necessitates a shift from strict hazard management and resilience building to considerations of social justice. People should not have to be resilient to ongoing marginalisation and stigmatisation, and, in focusing on individual resilience, systemic disadvantage is obscured. Disaster scholars here must also reckon with the structural violence of colonisation. Aotearoa New Zealand has a unique hazard profile, and it has unique social infrastructures that can help deal with them. The best disaster mitigation and recovery programmes are inclusive and equity driven. Greater attention to Indigenous Knowledge – Mātauranga Māori – and Indigenous institutions, such as marae and the myriad relationships and connections that such institutions support, might potentially play a crucial role in future disaster mitigation and response.