Client: Christchurch City Council
Impervious surfaces are important in the context of stormwater planning with the higher the impervious surface area in a catchment leading to a greater quantity and intensity of runoff. The ratio of impervious area to pervious area within in a catchment is a good indicator of the level of effort urban planners need to manage water quality.
The impervious area of Christchurch City has been mapped in 2007 and 2012 (Pairman et. al., 2012), and 2020 (Christchurch City Council) using remote sensing classification methods applied to satellite imagery.
In this project Lynker Analytics used modern Machine Learning methods to generate a new detailed impervious area map of Christchurch City including the outlying urban areas covered by the extent of the 2023 Christchurch 0.075m aerial imagery survey.
Impervious area map, Christchurch City
The impervious area map was a derivative from the standard Lynker multi-class landcover map which includes eight categories including water, bare earth, grass, shrub/scrub, trees, paved, road, and building. Our approach used supervised Machine Learning (ML) models developed and trained for high resolution orthophotography. These models have also been used by Lynker in other New Zealand cities including Auckland, Hutt City, Whangarei, Tauranga, Kapiti Coast and others.
Our ML models can be transferred from area to area (or image set to image set) but to perform optimally need to be fine-tuned on the target imagery. The ML models generate a polygon feature class (prediction). Ancillary GIS data including kerblines, water bodies, building outlines and road centrelines were then incorporated within our post processing and data assembly process.
The impervious surface produced from this method includes all artificial structures—such as pavements, roads, sidewalks, driveways and parking lots, as well as industrial areas such as airports, ports and logistics and distribution centres, all of which use asphalt, concrete, brick, stone etc.
The model reported an overall weighted average F1 score of 0.95. The map generated using this method shows finer detail than the 2020 map which was generated using 10m resolution satellite imagery.
2023 impervious area map.
2020 impervious area map.
Validation data used to measure model performance were acquired by Lynker staff using aerial photo interpretation. Clear examples of each class were selected using the same imagery processed by the ML models.
Central urban area (red)
Using this approach we determined that the total percentage of composite impervious surface throughout the study area represents approximately 20% of the land area while the impervious area in the central urban area was 60%. Both figures represent growth in the city from the earlier assessments.
Examples from the model including canopy, composite impervious area and the full multi-class landcover are shown below.
Central urban area
Lyttleton Port
Composite impervious and urban forest canopy
This work extends previous modelling we have done using urban ortho-photography and these models will support the future development of a longitudinal data set of change in imperviousness across Christchurch City.
Deliverables
Multi-class land cover GIS data
Impervious surface GIS data
Technical report
References:
Pairman D, Bellis S, McNeill, S. Mapping of impervious surface cover for Christchurch City, 2012. CCC commissioned Landcare Report. LC1811.