title: Feminist geographies in Aotearoa New Zealand: cultural, social and political moments

authors: Adams-Hutcheson, Gail

subjects: Social Sciences

abstract: Aotearoa New Zealand is a nation of promise, potential and enigma: it was the first country in the world where women gained the vote in 1893 and now boasts the youngest woman world leader in 2017. It is also a postcolonial nation where structural racism, homophobia, and sexism persist, yet it has also given legal personhood to a river. Our Country Report foregrounds Aotearoa New Zealand feminist geographic scholarship that responds to, reflects, and sometimes resists such contrasts and contradictions at the national scale. We employ the lens of the 2017 national election to critically engage with current gendered and indigenous politics in the country. Analyzing these politics through three ‘feminist moments,’ our paper highlights the breadth and scope of current Aotearoa New Zealand feminist geographic scholarship and directions.

text: Feminist geographies in Aotearoa New Zealand: cultural, social and political moments Aotearoa New Zealand is a nation of promise, potential and enigma: it was the first country in the world where women gained the vote in 1893 and now boasts the youngest woman world leader in 2017. It is also a postcolonial nation where structural racism, homophobia, and sexism persist, yet it has also given legal personhood to a river. Our Country Report foregrounds Aotearoa New Zealand feminist geographic scholarship that responds to, reflects, and sometimes resists such contrasts and contradictions at the national scale. We employ the lens of the 2017 national election to critically engage with current gendered and indigenous politics in the country. Analyzing these politics through three ‘feminist moments,’ our paper highlights the breadth and scope of current Aotearoa New Zealand feminist geographic scholarship and directions.

year: 2019

title: Investigating the Social Supports of Successful Māori Undergraduate Appellants at the University of Waikato

authors: Diamond, Danielle Jade

subjects: Social Supports

abstract: Educational disparities between Māori and non-Māori tertiary students are evident in regards to participation, retention, and completion rates at secondary and tertiary levels. Instead of focusing on negative statistics, it was of interest to investigate Māori students who had failed sufficient papers to initially be denied re-entry to university study, yet continued to attain their degrees, as a result of a successful appeal. Māori undergraduate students, who had successfully appealed from 2003-2012, were recruited for my research. The availability and utilisation of social supports were investigated, along with the decision to make an appeal, the role of cultural expectations, and barriers experienced by participants. A mixed method approach was utilised in this research through use of an adapted overarching Kaupapa Māori framework, statistical analysis, and 13 interviews. My research found that social supports were beneficial for participants throughout their appeal, as they provided: emotional, instrumental, appraisal, and informational support. Although the social supports utilised by participants were satisfactory, barriers to accessing formal supports were also found, which included: perceived stigma having failed their papers, pride, as well as being whakamā. The appeal process was a beneficial intervention from the University of Waikato, which provided participants another opportunity to overcome the factors that contributed to their initial failure. Post appeal, participants implemented strategies to address and reduce their factors in order to become academically engaged. Participants were deemed to be resilient from their continued pursuit of degree attainment, implementation of their strategies, and their use of perceived social supports.

text: Investigating the Social Supports of Successful Māori Undergraduate Appellants at the University of Waikato Educational disparities between Māori and non-Māori tertiary students are evident in regards to participation, retention, and completion rates at secondary and tertiary levels. Instead of focusing on negative statistics, it was of interest to investigate Māori students who had failed sufficient papers to initially be denied re-entry to university study, yet continued to attain their degrees, as a result of a successful appeal. Māori undergraduate students, who had successfully appealed from 2003-2012, were recruited for my research. The availability and utilisation of social supports were investigated, along with the decision to make an appeal, the role of cultural expectations, and barriers experienced by participants. A mixed method approach was utilised in this research through use of an adapted overarching Kaupapa Māori framework, statistical analysis, and 13 interviews. My research found that social supports were beneficial for participants throughout their appeal, as they provided: emotional, instrumental, appraisal, and informational support. Although the social supports utilised by participants were satisfactory, barriers to accessing formal supports were also found, which included: perceived stigma having failed their papers, pride, as well as being whakamā. The appeal process was a beneficial intervention from the University of Waikato, which provided participants another opportunity to overcome the factors that contributed to their initial failure. Post appeal, participants implemented strategies to address and reduce their factors in order to become academically engaged. Participants were deemed to be resilient from their continued pursuit of degree attainment, implementation of their strategies, and their use of perceived social supports.

year: 2013

title: Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

authors: Martin, Ross David

subjects: Seagrass

abstract: Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline.  Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. 
Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning.
This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand.
Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood.
For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations.  High density seagrass was distinct from sediment, but classification error arose for sparse seagrass.
Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale.
The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution.
The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application.

text: Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline.  Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. 
Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning.
This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand.
Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood.
For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations.  High density seagrass was distinct from sediment, but classification error arose for sparse seagrass.
Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale.
The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution.
The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application.

year: 2020