New Zealand is adversely affected by many natural hazards. New Zealand’s geologic environment means that it is susceptible to tectonic movement (e.g. earthquakes, tsunamis), volcanic eruptions, and mass wasting events (e.g. slips and landslides). In addition, weather events such as floods, droughts, and severe storms have caused extensive damage to New Zealand. Each year on average natural hazards cost New Zealand $1.6 billion (Insurance Council of New Zealand, 2014). Natural disasters cannot be prevented, but their impact can be mitigated through careful planning and a thorough response. Satellite remote sensing data has been heavily used to support natural hazard management such as:
- Mitigating the effect of natural hazards through risk modelling and hazard mapping (e.g. mapping flood-prone areas)
- Preparation and response to droughts through vegetation monitoring and crop water requirement mapping.
- Supporting disaster response and recovery through damage assessments in events such as droughts, earthquakes, fires, floods, mass wasting events, and volcanic eruptions.
Applications of satellite imagery for natural hazards management will not operate in a vacuum. For instance, recent work conducted by NASA and the United States Geological Survey (USGS) found that fire hazard probabilities are correlated with soil moisture as well as traditionally measured variables such as land cover (Skibba, 2015). CSST will therefore be able to produce valuable data for hazard management both as a primary goal and coincident with the creation of other data products.
Immediate goals for CSST would include:
- Investigate the use of CSST data products to support natural hazard management (e.g. maps of biomass and agricultural water stress to constrain and manage damage due to flood, drought, or fire; maps of soil moisture to inform flood and fire prediction models; etc.).
- The launch of New Zealand’s first satellite would provide dedicated, daily, high resolution imagery to support recovery efforts in the event of a natural disaster.
- Research the utility of new space-based remote sensing methods to support and enhance natural hazard management (e.g. the use of GNSS-R methods to identify flooding extent despite the presence of significant clouds).
- In the near-term the CSST team would use data fusion techniques to combine very high resolution optical imagery with airborne laser altimetry (LiDAR) to construct hydrologically conditioned digital elevation models (DEMs) of urban centres. These DEMs will then be used to implement multi-scale Level of Detail hydrological models using techniques developed at the National School of Surveying (Wright et al, 2014). These models in turn will be used to predict the response of drainage systems to flood events. Model output will be verified by comparing with the flooding extent observed in very high resolution imagery.