Description
Earth-orbiting satellites capture data about the planet’s surface using optical, radar, and spectral sensors. Three access tiers apply for community use:
| Tier | Data type | Access | Expertise | Cost |
|---|---|---|---|---|
| Public | Optical (Google Earth, Sentinel-2) | Free, browser-based | Minimal | Free |
| Technical | SAR (Sentinel-1), hyperspectral | Free download | Heavy — physics/data science | Free data, costly expertise |
| Premium | High-res optical, soil/biomass products | Vendor licensing | Very high | Significant payment |
Key sensor modalities:
- Optical: Visible-light; land cover, terrain, vegetation, landslides
- SAR (synthetic aperture radar): All-weather, cloud-penetrating; ground movement, flood extents, soil moisture, surface change
- Hyperspectral/multispectral: Vegetation health (NDVI), crop stress, water quality indicators
NZ-specific context
New Zealand owns zero satellites. Sentinel-1 (European Space Agency) images European locations approximately every 6 days; NZ may receive 0–1 usable passes per equivalent cycle. Coverage frequency depends on which constellation built the satellite and whose priorities it serves. No national Earth-observation platform exists; government space strategy documents mention sovereign EO capability as an aspiration, but no formal commitment has been made.
Community use cases
Site selection:
- Terrain stability: landslide history, slope analysis, earthquake ground deformation (SAR interferometry)
- Water resources: river course, flood-plain extent, rainfall capture potential
- Note: solar radiance and climate parameters are generally simpler to source from MetService/NIWA than to construct from satellite data
Operational monitoring:
- Agricultural performance: crop quality and size assessment, soil moisture, vegetation stress — well-demonstrated at commercial scale
- Biodiversity: forest cover change, invasive species extent, land-use transitions
- Environmental health: waterway discharge detection, pollution indicators
Disaster and resilience:
- Post-event terrain change (before/after earthquake, landslide, flood)
- Flood extent mapping via SAR (works through cloud cover when optical cannot)
- Ongoing landscape and river-course change tracking
Not suited for at community scale:
- Real-time early warning (revisit interval too slow — use IoT ground sensors instead)
- Long-range climate modelling (computationally heavy; use NIWA/MetService)
Access model for communities
Raw satellite data is not a practical DIY resource for non-technical communities. Three viable pathways:
- Partner organisation — research institution, iwi environmental body, or NGO provides processed outputs
- Productised tool — purpose-built analytics product presenting derived insights rather than raw data
- Embedded specialist — contracted expert downloads, processes, and interprets on behalf of the community
AI-assisted tools lower the code-writing barrier but not the domain knowledge requirement. Without understanding a dataset’s limitations, AI-generated satellite analysis produces confident but unreliable results.
Relationship to ground-based sensing
Satellite EO and IoT ground sensors (e.g., LoRa mesh) are complementary layers:
| Layer | Strength | Limitation |
|---|---|---|
| Satellite | Broad area, historical change, cloud-penetrating SAR | Infrequent NZ revisit, large files, expertise required |
| IoT/LoRa | Real-time, hyperlocal, community-owned, low-cost | Point measurements only, no area coverage |
Optimal community architecture uses both: satellite for site assessment and periodic environmental audit; IoT for daily operational monitoring and real-time alerts.
Relevance to Neobiome
Satellite EO is a D09 enabling layer that amplifies D02 (food production monitoring), D03 (water resource tracking), and D08 (biodiversity/nature-based systems). Access via the partner or productised-tool model is the realistic pathway for Neobiome-scale communities given NZ’s coverage constraints and the expertise barrier. Satellite EO is not currently a DIY community technology — Neobiome’s D09 architecture should plan for it as a periodic audit layer complementing the real-time, hyperlocal LoRa mesh network.
Open questions
[NZ sovereign satellite capability timeline; productised community-scale EO tools for NZ regions; cost structures for community satellite analytics; whether AI-assisted spatial ML platforms will make DIY analysis viable within 5 years; integration with LoRa/IoT sensing architectures.]
Connections
Referenced by
EDT domains (1): D09: Digital Intelligence & Connectivity