Use of real-time remote sensing for engagement to improve water quality outcomes at an on-farm scale

Targeted Problem: There are possible major land use restrictions in many regions in New Zealand through regional plan changes to improve water quality. These changes will mean farmers will have to change farming practices. It is often hard for farmers to reconcile and quantify their effect on water quality.

Concept/solution to be analysed: Community awareness of water quality has increased exponentially in recent times. There are new water quality limits being set by government in the National Policy statements, which are enacted at a Regional level by regional councils. Many of the contaminants are not obvious or easy to see, and this means there is low trust and engagement in the community.

Methodological approach (Methods): A high level analysis done using GIS will classify land form and business type for the Upper Waipa. Waterways that meet the criteria from this analysis have been selected for a more intensive study. Once this is complete remote sensing sites will be set up that will stay in place and be able to be related to a particular farm(s), and continuously measure various parameters including but not limited to nitrogen, phosphate, temperature, turbidity, and flow 24/7 365 days a year. Intensive imagery collection will support this to help identify and communicate effects seen.

Results: The methodology in this project aims to make the invisible more visible with timely and repeated drone, video and time-lapse imagery, correlated geo-located remote sensing analysed and presented using GIS. The mix of place in space GIS analysis, individual farm imagery, and sensor networks placed in waterways that will only measure water quality parameters attributable to that particular farm will aim to make the invisible visible. Changes in land management will be able to be assessed in real time and actual measured amounts attributable to particular land practices. It is envisaged that this will engage the landowners in adapting their land management to improve water quality as it leaves their farm boundary and contribute to better water quality in the catchments they are in.

Chances for generalization of the Results: The imagery and GIS based data in tandem will greatly assist in communicating the messages of cause and effect and the value of adoption of mitigation in the wider lower Waikato and to further incentivise and demonstrate the value of farm planning to manage risk.