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.
Sewerage systems convey sewage, or wastewater, from residential or commercial buildings through complex reticulation networks to treatment plants. During seismic events both transient ground motion and permanent ground deformation can induce physical damage to sewerage system components, limiting or impeding the operability of the whole system. The malfunction of municipal sewerage systems can result in the pollution of nearby waterways through discharge of untreated sewage, pose a public health threat by preventing the use of appropriate sanitation facilities, and cause serious inconvenience for rescuers and residents. Christchurch, the second largest city in New Zealand, was seriously affected by the Canterbury Earthquake Sequence (CES) in 2010-2011. The CES imposed widespread damage to the Christchurch sewerage system (CSS), causing a significant loss of functionality and serviceability to the system. The Christchurch City Council (CCC) relied heavily on temporary sewerage services for several months following the CES. The temporary services were supported by use of chemical and portable toilets to supplement the damaged wastewater system. The rebuild delivery agency -Stronger Christchurch Infrastructure Rebuild Team (SCIRT) was created to be responsible for repair of 85 % of the damaged horizontal infrastructure (i.e., water, wastewater, stormwater systems, and roads) in Christchurch. Numerous initiatives to create platforms/tools aiming to, on the one hand, support the understanding, management and mitigation of seismic risk for infrastructure prior to disasters, and on the other hand, to support the decision-making for post-disaster reconstruction and recovery, have been promoted worldwide. Despite this, the CES in New Zealand highlighted that none of the existing platforms/tools are either accessible and/or readable or usable by emergency managers and decision makers for restoring the CSS. Furthermore, the majority of existing tools have a sole focus on the engineering perspective, while the holistic process of formulating recovery decisions is based on system-wide approach, where a variety of factors in addition to technical considerations are involved. Lastly, there is a paucity of studies focused on the tools and frameworks for supporting decision-making specifically on sewerage system restoration after earthquakes. This thesis develops a decision support framework for sewerage pipe and system restoration after earthquakes, building on the experience and learning of the organisations involved in recovering the CSS following the CES in 2010-2011. The proposed decision support framework includes three modules: 1) Physical Damage Module (PDM); 2) Functional Impact Module (FIM); 3) Pipeline Restoration Module (PRM). The PDM provides seismic fragility matrices and functions for sewer gravity and pressure pipelines for predicting earthquake-induced physical damage, categorised by pipe materials and liquefaction zones. The FIM demonstrates a set of performance indicators that are categorised in five domains: structural, hydraulic, environmental, social and economic domains. These performance indicators are used to assess loss of wastewater system service and the induced functional impacts in three different phases: emergency response, short-term recovery and long-term restoration. Based on the knowledge of the physical and functional status-quo of the sewerage systems post-earthquake captured through the PDM and FIM, the PRM estimates restoration time of sewer networks by use of restoration models developed using a Random Forest technique and graphically represented in terms of restoration curves. The development of a decision support framework for sewer recovery after earthquakes enables decision makers to assess physical damage, evaluate functional impacts relating to hydraulic, environmental, structural, economic and social contexts, and to predict restoration time of sewerage systems. Furthermore, the decision support framework can be potentially employed to underpin system maintenance and upgrade by guiding system rehabilitation and to monitor system behaviours during business-as-usual time. In conjunction with expert judgement and best practices, this framework can be moreover applied to assist asset managers in targeting the inclusion of system resilience as part of asset maintenance programmes.
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.
