Vegetation change and native forest restoration in urban environments: Man…

Research Papers, Lincoln University

Global biodiversity is threatened by human actions, including in urban areas. Urbanisation has removed and fragmented indigenous habitats. As one of the 25 biodiversity ’hot spots’, New Zealand is facing the problems of habitat loss and indigenous species extinction. In New Zealand cities, as a result of the land clearance and imported urban planning precepts, many urban areas have little or no original native forest remaining. Urbanisation has also been associated with the introduction of multitudes of species from around the world. Two large earthquakes shook Christchurch in 2010 and 2011 and caused a lot of damage. Parts of the city suffered from soil liquefaction after the earthquakes. In the most damaged parts of Christchurch, particularly in the east, whole neighbourhoods were abandoned and later demolished except for larger trees. Christchurch offers an excellent opportunity to study the biodiversity responses to an urban area with less intensive management, and to learn more about the conditions in urban environments that are most conducive to indigenous plant biodiversity. This study focuses on natural woody plant regeneration of forested sites in Christchurch city, many of which were also surveyed prior to the earthquakes. By repeating the pre-earthquake surveys, I am able to describe the natural regeneration occurring in Christchurch forested areas. By combining this with the regeneration that has occurred in the Residential Red Zone, successional trajectories can be described under a range of management scenarios. Using a comprehensive tree map of the Residential Red Zone, I was also able to document minimum dispersal distances of a range of indigenous trees in Christchurch. This is important for planning reserve connectivity. Moreover, I expand and improve on a previous analysis of the habitat connectivity of Christchurch (made before the earthquakes) to incorporate the Residential Red Zone, to assess the importance for habitat connectivity of restoring the indigenous forest in this area. In combination, these data sets are used to provide patch scenarios and some management options for biodiversity restoration in the Ōtākaro-Avon Red Zone post-earthquake.

Using machine learning techniques to predict seismic damage in Dunedin

Research papers, University of Canterbury Library

Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.