As a global phenomenon, many cities are undergoing urban renewal to accommodate rapid growth in urban population. However, urban renewal can struggle to balance social, economic, and environmental outcomes, whereby economic outcomes are often primarily considered by developers. This has important implications for urban forests, which have previously been shown to be negatively affected by development activities. Urban forests serve the purpose of providing ecosystem services and thus are beneficial to human wellbeing. Better understanding the effect of urban renewal on city trees may help improve urban forest outcomes via effective management and policy strategies, thereby maximising ecosystem service provision and human wellbeing. Though the relationship between certain aspects of development and urban forests has received consideration in previous literature, little research has focused on how the complete property redevelopment cycle affects urban forest dynamics over time. This research provides an opportunity to gain a comprehensive understanding of the effect of residential property redevelopment on urban forest dynamics, at a range of spatial scales, in Christchurch, New Zealand following a series of major earthquakes which occurred in 2010 – 2011. One consequence of the earthquakes is the redevelopment of thousands of properties over a relatively short time-frame. The research quantifies changes in canopy cover city-wide, as well as, tree removal, retention, and planting on individual residential properties. Moreover, the research identifies the underlying reasons for these dynamics, by exploring the roles of socio-economic and demographic factors, the spatial relationships between trees and other infrastructure, and finally, the attitudes of residential property owners. To quantify the effect of property redevelopment on canopy cover change in Christchurch, this research delineated tree canopy cover city-wide in 2011 and again in 2015. An object-based image analysis (OBIA) technique was applied to aerial imagery and LiDAR data acquired at both time steps, in order to estimate city-wide canopy cover for 2011 and 2015. Changes in tree canopy cover between 2011 and 2015 were then spatially quantified. Tree canopy cover change was also calculated for all meshblocks (a relatively fine-scale geographic boundary) in Christchurch. The results show a relatively small magnitude of tree canopy cover loss, city-wide, from 10.8% to 10.3% between 2011 and 2015, but a statistically significant change in mean tree canopy cover across all the meshblocks. Tree canopy cover losses were more likely to occur in meshblocks containing properties that underwent a complete redevelopment cycle, but the loss was insensitive to the density of redevelopment within meshblocks. To explore property-scale individual tree dynamics, a mixed-methods approach was used, combining questionnaire data and remote sensing analysis. A mail-based questionnaire was delivered to residential properties to collect resident and household data; 450 residential properties (321 redeveloped, 129 non- redeveloped) returned valid questionnaires and were identified as analysis subjects. Subsequently, 2,422 tree removals and 4,544 tree retentions were identified within the 450 properties; this was done by manually delineating individual tree crowns, based on aerial imagery and LiDAR data, and visually comparing the presence or absence of these trees between 2011 and 2015. The tree removal rate on redeveloped properties (44.0%) was over three times greater than on non-redeveloped properties (13.5%) and the average canopy cover loss on redeveloped properties (52.2%) was significantly greater than on non-redeveloped properties (18.8%). A classification tree (CT) analysis was used to model individual tree dynamics (i.e. tree removal, tree retention) and candidate explanatory variables (i.e. resident and household, economic, land cover, and spatial variables). The results indicate that the model including land cover, spatial, and economic variables had the best predicting ability for individual tree dynamics (accuracy = 73.4%). Relatively small trees were more likely to be removed, while trees with large crowns were more likely to be retained. Trees were most likely to be removed from redeveloped properties with capital values lower than NZ$1,060,000 if they were within 1.4 m of the boundary of a redeveloped building. Conversely, trees were most likely to be retained if they were on a property that was not redeveloped. The analysis suggested that the resident and household factors included as potential explanatory variables did not influence tree removal or retention. To conduct a further exploration of the relationship between resident attitudes and actions towards trees on redeveloped versus non-redeveloped properties, this research also asked the landowners from the 450 properties that returned mail questionnaires to indicate their attitudes towards tree management (i.e. tree removal, tree retention, and tree planting) on their properties. The results show that residents from redeveloped properties were more likely to remove and/or plant trees, while residents from non- redeveloped properties were more likely to retain existing trees. A principal component analysis (PCA) was used to explore resident attitudes towards tree management. The results of the PCA show that residents identified ecosystem disservices (e.g. leaf litter, root damage to infrastructure) as common reasons for tree removal; however, they also noted ecosystem services as important reasons for both tree planting and tree retention on their properties. Moreover, the reasons for tree removal and tree planting varied based on whether residents’ property had been redeveloped. Most tree removal occurred on redeveloped properties because trees were in conflict with redevelopment, but occurred on non- redeveloped properties because of perceived poor tree health. Residents from redeveloped properties were more likely to plant trees due to being aesthetically pleasing or to replace trees removed during redevelopment. Overall, this research adds to, and complements, the existing literature on the effects of residential property redevelopment on urban forest dynamics. The findings of this research provide empirical support for developing specific legislation or policies about urban forest management during residential property redevelopment. The results also imply that urban foresters should enhance public education on the ecosystem services provided by urban forests and thus minimise the potential for tree removal when undertaking property redevelopment.
Land cover change information in urban areas supports decision makers in dealing with public policy planning and resource management. Remote sensing has been demonstrated as an efficient and accurate way to monitor land cover change over large extents. The Canterbury Earthquake Sequence (CES) caused massive damage in Christchurch, New Zealand and resulted in significant land cover change over a short time period. This study combined two types of remote sensing data, aerial imagery (RGB) and LiDAR, as the basis for quantifying land cover change in Christchurch between 2011 – 2015, a period corresponding to the five years immediately following the 22 February 2011 earthquake, which was part of the CES. An object based image analysis (OBIA) approach was adopted to classify the aerial imagery and LiDAR data into seven land cover types (bare land, building, grass, shadow, tree and water). The OBIA approach consisted of two steps, image segmentation and object classification. For the first step, this study used multi-level segmentation to better segment objects. For the second step, the random forest (RF) classifier was used to assign a land cover type to each object defined by the segmentation. Overall classification accuracies for 2011 and 2015 were 94.0% and 94.32%, respectively. Based on the classification result, land cover changes between 2011 and 2015 were then analysed. Significant increases were found in road and tree cover, while the land cover types that decreased were bare land, grass, roof, water. To better understand the reasons for those changes, land cover transitions were calculated. Canopy growth, seasonal differences and forest plantation establishment were the main reasons for tree cover increase. Redevelopment after the earthquake was the main reason for road area growth. By comparing the spatial distribution of these transitions, this study also identified Halswell and Wigram as the fastest developing suburbs in Christchurch. These results provided quantitative information for the effects of CES, with respect to land cover change. They allow for a better understanding for the current land cover status of Christchurch. Among those land cover changes, the significant increase in tree cover aroused particularly interest as urban forests benefit citizens via ecosystem services, including health, social, economic, and environmental benefits. Therefore, this study firstly calculated the percentages of tree cover in Christchurch’s fifteen wards in order to provide a general idea of tree cover change in the city extent. Following this, an automatic individual tree detection and crown delineation (ITCD) was undertaken to determine the feasibility of automated tree counting. The accuracies of the proposed approach ranged between 56.47% and 92.11% in thirty different sample plots, with an overall accuracy of 75.60%. Such varied accuracies were later found to be caused by the fixed tree detection window size and misclassifications from the land cover classification that affected the boundary of the CHM. Due to the large variability in accuracy, tree counting was not undertaken city-wide for both time periods. However, directions for further study for ITCD in Christchurch could be exploring ITCD approaches with variable window size or optimizing the classification approach to focus more on producing highly accurate CHMs.
None
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.
The Mw 7.1 Darfield earthquake generated a ~30 km long surface rupture on the Greendale Fault and significant surface deformation related to related blind faults on a previously unrecognized fault system beneath the Canterbury Plains. This earthquake provided the opportunity for research into the patterns and mechanisms of co-seismic and post-seismic crustal deformation. In this thesis I use multiple across-fault EDM surveys, logic trees, surface investigations and deformation feature mapping, seismic reflection surveying, and survey mark (cadastral) re-occupation using GPS to quantify surface displacements at a variety of temporal and spatial scales. My field mapping investigations identified shaking and crustal displacement-induced surface deformation features south and southwest of Christchurch and in the vicinity of the projected surface traces of the Hororata Blind and Charing Cross Faults. The data are consistent with the high peak ground accelerations and broad surface warping due to underlying reverse faulting on the Hororata Blind Fault and Charing Cross Fault. I measured varying amounts of post-seismic displacement at four of five locations that crossed the Greendale Fault. None of the data showed evidence for localized dextral creep on the Greendale Fault surface trace, consistent with other studies showing only minimal regional post-seismic deformation. Instead, the post-seismic deformation field suggests an apparent westward translation of northern parts of the across-fault surveys relative to the southern parts of the surveys that I attribute to post-mainshock creep on blind thrusts and/or other unidentified structures. The seismic surveys identified a deformation zone in the gravels that we attribute to the Hororata Blind Fault but the Charing Cross fault was not able to be identified on the survey. Cadastral re-surveys indicate a deformation field consistent with previously published geodetic data. We use this deformation with regional strain rates to estimate earthquake recurrence intervals of ~7000 to > 14,000 yrs on the Hororata Blind and Charing Cross Faults.
Deconstruction, at the end of the useful life of a building, produces a considerable amount of materials which must be disposed of, or be recycled / reused. At present, in New Zealand, most timber construction and demolition (C&D) material, particularly treated timber, is simply waste and is placed in landfills. For both technical and economic reasons (and despite the increasing cost of landfills), this position is unlikely to change in the next 10 – 15 years unless legislation dictates otherwise. Careful deconstruction, as opposed to demolition, can provide some timber materials which can be immediately re-used (eg. doors and windows), or further processed into other components (eg. beams or walls) or recycled (‘cascaded’) into other timber or composite products (e.g. fibre-board). This reusing / recycling of materials is being driven slowly in NZ by legislation, the ‘greening’ of the construction industry and public pressure. However, the recovery of useful material can be expensive and uneconomic (as opposed to land-filling). In NZ, there are few facilities which are able to sort and separate timber materials from other waste, although the soon-to-be commissioned Burwood Resource Recovery Park in Christchurch will attempt to deal with significant quantities of demolition waste from the recent earthquakes. The success (or otherwise) of this operation should provide good information as to how future C&D waste will be managed in NZ. In NZ, there are only a few, small scale facilities which are able to burn waste wood for energy recovery (e.g. timber mills), and none are known to be able to handle large quantities of treated timber. Such facilities, with constantly improving technology, are being commissioned in Europe (often with Government subsidies) and this indicates that similar bio-energy (co)generation will be established in NZ in the future. However, at present, the NZ Government provides little assistance to the bio-energy industry and the emergence worldwide of shale-gas reserves is likely to push the economic viability of bio-energy further into the future. The behaviour of timber materials placed in landfills is complex and poorly understood. Degrading timber in landfills has the potential to generate methane, a potent greenhouse gas, which can escape to the atmosphere and cancel out the significant benefits of carbon sequestration during tree growth. Improving security of landfills and more effective and efficient collection and utilisation of methane from landfills in NZ will significantly reduce the potential for leakage of methane to the atmosphere, acting as an offset to the continuing use of underground fossil fuels. Life cycle assessment (LCA), an increasingly important methodology for quantifying the environmental impacts of building materials (particularly energy, and global warming potential (GWP)), will soon be incorporated into the NZ Green Building Council Greenstar rating tools. Such LCA studies must provide a level playing field for all building materials and consider the whole life cycle. Whilst the end-of-life treatment of timber by LCA may establish a present-day base scenario, any analysis must also present a realistic end-of-life scenario for the future deconstruction of any 6 new building, as any building built today will be deconstructed many years in the future, when very different technologies will be available to deal with construction waste. At present, LCA practitioners in NZ and Australia place much value on a single research document on the degradation of timber in landfills (Ximenes et al., 2008). This leads to an end-of-life base scenario for timber which many in the industry consider to be an overestimation of the potential negative effects of methane generation. In Europe, the base scenario for wood disposal is cascading timber products and then burning for energy recovery, which normally significantly reduces any negative effects of the end-of-life for timber. LCA studies in NZ should always provide a sensitivity analysis for the end-of-life of timber and strongly and confidently argue that alternative future scenarios are realistic disposal options for buildings deconstructed in the future. Data-sets for environmental impacts (such as GWP) of building materials in NZ are limited and based on few research studies. The compilation of comprehensive data-sets with country-specific information for all building materials is considered a priority, preferably accounting for end-of-life options. The NZ timber industry should continue to ‘champion’ the environmental credentials of timber, over and above those of the other major building materials (concrete and steel). End-of-life should not be considered the ‘Achilles heel’ of the timber story.
