As the future of the world’s oil reserves becomes progressively more uncertain, it is becoming increasingly important that steps are taken to ensure that there are viable, attractive alternatives to travel by private motor vehicle. As with many of New Zealand’s major urban centres, Christchurch is still exceptionally reliant on private motor vehicles; although a significant proportion of the population indicate that they would like to cycle more, cycling is still an underutilised mode of transport. Following a series of fatal earthquakes that struck the city in 2010 and 2011, there has been the need to significantly redevelop much of the city’s horizontal infrastructure – subsequently providing the perfect platform for significant changes to be made to the road network. Many of the key planning frameworks governing the rebuild process have identified the need to improve Christchurch’s cycling facilities in order to boost cycling numbers and cyclist safety. The importance of considering future growth and travel patterns when planning for transport infrastructure has been highlighted extensively throughout literature. Accordingly, this study sought to identify areas where future cycle infrastructure development would be advantageous based on a number of population and employment projections, and likely future travel patterns throughout the city. Through the use of extensive GIS analysis, future population growth, employment and travel patterns for Christchurch city were examined in order to attain an understanding of where the current proposed major cycleways network could be improved, or extended. A range of data and network analysis were used to derive likely travel patterns throughout Christchurch in 2041. Trips were derived twice, once with a focus on simply finding the shortest route between each origin and destination, and then again with a focus on cyclist safety and areas where cyclists were unlikely to travel. It was found that although the proposed major cycleways network represents a significant step towards improving the cycling environment in Christchurch, there are areas of the city that will not be well serviced by the current proposed network in 2041. These include a number of key residential growth areas such as Halswell, Belfast and Prestons, along with a number of noteworthy key travel zones, particularly in areas close to the central city and key employment areas. Using network analysis, areas where improvements or extensions to the proposed network would be most beneficial were identified, and a number of potential extensions in a variety of areas throughout the city were added to the network of cycle ways. Although it has been found that filling small gaps in the network can have considerable positive outcomes, results from the prioritisation analysis suggested that initially in Christchurch demand is likely to be for more substantial extensions to the proposed major cycleways network.
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.
