Biological Heritage Challenge: Eco-Index Programme

Research team
Dr Kiri Joy Wallace (University of Waikato)
Dr John Reid (J D Reid LTD)
Ngāti Pikiao (Tainu and Ngai Tahu Research Centre, University of Canterbury).
Nathaniel Calhoun (Code Innovation)
Kevan Cote (Moose Engineering & Design)
Karen Denyer (Papawera Consulting Ltd)
Saif Khan (GRI)

New Zealand National Science Challenge

Project summary

The Eco-index programme is funded through the New Zealand’s National Science Challenge “New Zealand’s Biological Heritage – Ngā Koiora Tuku Iho”. Through this initiative, the Eco-index programme has set a long-term National Biodiversity Vision ‘protected, restored and connected by 2121’. The Protect-Tiaki entails maintaining current native biodiversity by abating threats. The Restore – Whakahou is based on a land cover target for native ecosystems in every catchment to be restored to a minimum of 15% of their original extent. The connect- tūhono means connecting native ecosystems from the mountain to the sea.

Figure 1. Geospatial analytical framework and dashboard for Eco-index programme

At GRI, we analyse spatial data to generate the information required to achieve these vision components. To protect, we need to know the current biodiversity status to plan for their maintenance. Along with the broader Eco-index team, we have developed an analytical framework using currently available land cover, potential native vegetation and wetlands (PNVW), and Eco-index catchments layer to calculate the shortfall to reach 15% of the original native ecosystem types per catchment, which can be visualised in a dashboard (Figure 1).

This data leads to calculating the required biodiversity investment. Work is currently underway to develop the biodiversity connectivity analysis.

Another ongoing effort under the Eco-index programme is developing ecosystem detectors based on remote sensing data (Figure 2). We use multi-spectral data from various satellite images (e.g., Sentinel, Planet) and develop machine learning algorithms to discriminate signatures for prioritised native ecosystem types such as Kahikatea, and freshwater wetlands. Alongside reflectance data, other bio-physical data such as soil moisture, elevation data also helps identifying ecosystems. With simultaneously ongoing projects such as Rongowai and access to LiDAR datasets, GRI is well placed for achieving such highly technical cutting-edge research in this area.

Figure 2. Workflow for ecosystem detector development
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