title: Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand.

authors: Pathayala Ralalage, Sumedha Kusum Amarasena

subjects: Wetlands

abstract: Wetlands are among the world's most valuable ecosystems. They provide numerous ecological and socio-economic benefits. However, wetlands continue to disappear due to the increasing demand for wetland resources. In New Zealand, more than 90% of the original extent of wetlands has been lost since the mid-eighteenth century. Therefore, legislation has been identified for the protection of wetlands as a matter of national importance.

Geographic Information System (GIS) and Remote Sensing (RS) techniques have proven helpful for mapping and monitoring wetland resources. This study aims to understand how RStechniques can classify wetlands in the Lake Whangape catchment, Waikato. The parameters that can be extracted from available data and their effectiveness in the classification process are also studied. 

Four types of input data are collectively employed in the study. The data types are optical RS data, Synthetic Aperture Radar (SAR) data, a Digital Elevation Model (DEM), and wetland polygons provided by the Waikato Regional Council (WRC). All the steps including, accessing satellite scenes and data processing were performed within Google Earth Engine (GEE) computing platform using JavaScript language. The classification process for this study includes feature extraction, feature selection, model training, classification, and validation. Finally, the accuracy of the classification results is checked visually and statistically. 

The classification was carried out in two stages. In Stage one, open water, wetland, and non-wetland areas are classified (simple classification). The combined wetlands class is separated into marsh and swamp in the second stage (detailed classification). Based on the results, the Topographic Position Index (TPI) is the most influential parameter in identifying wetlands, while the Modified Normalized Water Index (MNDWI) successfully identifies open water. The overall accuracy reached 91% at the simple classification stage. However, the detailed classification results received comparatively low classification accuracies (the overall accuracy is 76%).

text: Remote sensing of wetlands in the Lake Whangape catchment, Waikato, New Zealand. Wetlands are among the world's most valuable ecosystems. They provide numerous ecological and socio-economic benefits. However, wetlands continue to disappear due to the increasing demand for wetland resources. In New Zealand, more than 90% of the original extent of wetlands has been lost since the mid-eighteenth century. Therefore, legislation has been identified for the protection of wetlands as a matter of national importance.

Geographic Information System (GIS) and Remote Sensing (RS) techniques have proven helpful for mapping and monitoring wetland resources. This study aims to understand how RStechniques can classify wetlands in the Lake Whangape catchment, Waikato. The parameters that can be extracted from available data and their effectiveness in the classification process are also studied. 

Four types of input data are collectively employed in the study. The data types are optical RS data, Synthetic Aperture Radar (SAR) data, a Digital Elevation Model (DEM), and wetland polygons provided by the Waikato Regional Council (WRC). All the steps including, accessing satellite scenes and data processing were performed within Google Earth Engine (GEE) computing platform using JavaScript language. The classification process for this study includes feature extraction, feature selection, model training, classification, and validation. Finally, the accuracy of the classification results is checked visually and statistically. 

The classification was carried out in two stages. In Stage one, open water, wetland, and non-wetland areas are classified (simple classification). The combined wetlands class is separated into marsh and swamp in the second stage (detailed classification). Based on the results, the Topographic Position Index (TPI) is the most influential parameter in identifying wetlands, while the Modified Normalized Water Index (MNDWI) successfully identifies open water. The overall accuracy reached 91% at the simple classification stage. However, the detailed classification results received comparatively low classification accuracies (the overall accuracy is 76%).

year: 2021

title: K₁-congruences for three-dimensional Lie groups

authors: Delbourgo, Daniel

subjects: mathematics

abstract: We completely describe   K₁ (Zₚ [[G∞]])  and its localisations by using an infinite family of p-adic congruences, where   G∞  is any solvable p-adic Lie group of dimension 3. This builds on earlier work of Kato when   dim(G∞)=2 , and of the first named author and Lloyd Peters when   G∞≅Z×p⋉Z𝒹ₚ  with a scalar action of  Z×ₚ . The method exploits the classification of 3-dimensional p-adic Lie groups due to González-Sánchez and Klopsch, as well as the fundamental ideas of Kakde, Burns, etc. in non-commutative Iwasawa theory.

text: K₁-congruences for three-dimensional Lie groups We completely describe   K₁ (Zₚ [[G∞]])  and its localisations by using an infinite family of p-adic congruences, where   G∞  is any solvable p-adic Lie group of dimension 3. This builds on earlier work of Kato when   dim(G∞)=2 , and of the first named author and Lloyd Peters when   G∞≅Z×p⋉Z𝒹ₚ  with a scalar action of  Z×ₚ . The method exploits the classification of 3-dimensional p-adic Lie groups due to González-Sánchez and Klopsch, as well as the fundamental ideas of Kakde, Burns, etc. in non-commutative Iwasawa theory.

year: 2019

title: The experience of homemaking in unaffordable and precarious housing

authors: Barrett, Patrick

abstract: Housing is the platform on which people build their lives. The experience of homemaking in unaffordable and precarious housing (Barrett, P., Cram, F. & James, B, 2024) is a new monograph from the Affordable Housing for Generations programme. Across 110 pages, 10 housing researchers share experiences from different groups of people in Aotearoa, burdened by the insecurity of unaffordable housing. The findings show how institutional, social and market dynamics impacting on life circumstances can cause people to be in precarious housing situations. Yet people continue to love homemaking and being contributors to their communities. Reinforcing the pivotal nature of housing in supporting all peoples’ health and wellbeing, this research demonstrates how much more can be achieved through improving access to stable, affordable housing and by understanding what is at the heart of a home.

text: The experience of homemaking in unaffordable and precarious housing Housing is the platform on which people build their lives. The experience of homemaking in unaffordable and precarious housing (Barrett, P., Cram, F. & James, B, 2024) is a new monograph from the Affordable Housing for Generations programme. Across 110 pages, 10 housing researchers share experiences from different groups of people in Aotearoa, burdened by the insecurity of unaffordable housing. The findings show how institutional, social and market dynamics impacting on life circumstances can cause people to be in precarious housing situations. Yet people continue to love homemaking and being contributors to their communities. Reinforcing the pivotal nature of housing in supporting all peoples’ health and wellbeing, this research demonstrates how much more can be achieved through improving access to stable, affordable housing and by understanding what is at the heart of a home.

year: 2024