Science stretch

Integrating terrestrial measurements with novel remote-sensing technology

We will have a breakthrough for hydrology research internationally

Key to the success of Forest Flows is developing a wireless datalogger network that can collect sensor data from multiple locations in a catchment and transmit it back to the office. However, planted forests are a very difficult environment to operate in as electronics are damaged by the damp environment, locations are remote, and signal is reduced by the terrain and vegetation. Existing expensive datalogger technology does not work well in forests.

Scion collaborated with the New Zealand technology Research and Design company INFACT Ltd, who successfully developed a fit-for-purpose, low-cost dataloggers able to meet the above challenges. The FlowLab dataloggers were built by New Zealand company Quick Circuit Ltd, and can operate five different types of sensors. This is a great example of NZ Inc. that successfully developed a world-leading technology.

Wireless mesh network

The wireless datalogger network will be installed at each primary and secondary site to collect real time and concurrent measurements of precipitation, throughfall, transpiration, diurnal tree growth, canopy evaporation, soil moisture and water potential, stream flow, groundwater, and stream water nitrate.

Measuring soil moisture from the air

The jewel of the Forest Flows crown is SlimSAR L- and P-Band synthetic aperture radar (SAR). This new radar technology from the USA can measure soil moisture from a plane to a depth of 1.5m. If successful, SlimSAR will provide a faster way to map soil properties and soil moisture across forested catchments and provide new insights on the mysteries of water retention and release in planted forests. Once we have a licence to operate, we will start collecting data this year.

Digital Twin - Pulse of the forest

The bold goal of this programme is to create a new biophysical forest hydrology model combining cutting edge remote sensing techniques with terrestrial based measurements, integrating data and enabling scale, from tree, to stand, to forest to catchment, as a digital model applicable to planted forests all over New Zealand. 

These extrapolations will be made possible through detailed analysis and simulation capability, and coupled with powerful tools including visualisation and enabling access to insights, achieving this Forest Digital Twin will be a major breakthrough for hydrology research internationally. 

The Forest Digital Twin as a capability

Connect and collect

Remote-sensing measurements, including L- and P-Band radar, hyperspectral imaging and LiDAR will be linked to terrestrial measurements at six monitoring sites in forested catchments across New Zealand. 

NIWA has deployed sensors to measure the climate, groundwater, streamwater, and other measurements with their Lora sensor network. Together data collected by Scion and NIWA sensors will provide a real time understanding of water use, water retention and water release in catchments. Forest Flows has in total 19 different types of sensors and collectors at each primary site.

In addition, periodic terrestrial measurements will provide catchment spatial data including tree species biomass productivity, tree species leaf area index, soil physical properties mapping, bedrock and ground water mapping, precipitation soil residence time, soil and groundwater nitrate fluxes, and seasonal water sources for trees.


To manage the 300,000+ daily observations, a big data pipeline was developed from scratch. A New Zealand first, Scion has developed the Forest Flows Big Data Kafka Pipeline that can seamlessly stream clean, summarise, and store big data arriving in real-time from the forest. The cloud storage of terrestrial data will make it easier for national and international collaborators to access and use the data, as well as facilitate collaboration. 

Kafka will enable the fusion of intensive terrestrial site monitoring with remote sensing.

InfluxDB provides an easy interface with the Kafka database to query and extract large datasets from multiple sensors and sites in seconds.

Analyze and simulate

Success will require innovative analysis and processing methods to link the fine-scale terrestrial measurements to the remote sensing data to create robust relationships for extrapolation, enabling the characterization of planted-forest hydrology across different tree species, catchment positions, soil/geology, climates and seasons.

The bio-physical model will predict forest hydrological fluxes across a range of New Zealand planted forests by upscaling linked temporal and spatial data. 

Insights and visualisation

The output will enable fast, cost-effective collection of quality data from any area – including remote forests – that can then be quickly translated into accurate and defensible predictions of hydrology fluxes. 

Providing the required information to optimise water use and water quality in planted forests. 

Machine learning and AI

Forest Flows has developed a key collaboration with the MBIE Time-Evolving Data Science/Artificial Intelligence for Advanced Open Environmental Science (TAIAO) data science programme led by the University of Waikato’s Artificial Intelligence Institute. This collaboration provides valuable synergies to both programmes. 

Forest Flows will provide real time environmental data so that the Artificial Intelligence Institute can develop its TAIAO platform machine learning (ML)/artificial intelligence prototype. In conjunction with this, Forest Flows will develop a prototype cloud-based database frame that can house real time environmental big data for Forest Flows and the TAIAO platform. 

The TAIAO programme will develop ML tools to analyse Forest Flows real time big data that would be challenging to analyse with traditional statistical methods.

The latest on technology from our blog

Forest Flows has started its second aerial campaign collecting remote sensing data!

This spring campaign, led by The University of Auckland's Dr Delwyn Moller, provides key soil moisture and forest canopy data from various sites across NZ. Remote sensing data are collected whenRead more

Wireless soil moisture sensor deployment at Riverhead Forest

Last week, Research Leader Dean Meason worked with The University of Auckland colleagues installing Massachusetts Institute of Technology / USC SoilSCAPE wireless soil moisture sensorsRead more

Developing the novel integrated forest hydrology catchment model bespoke

Scion hosted Dr. Don White, Director and lead researcher of Whitegum Forest and Natural Resource Management for 6 weeks. He worked with inFact Limited and NIWA to develop and improved the novelRead more

Successful deployment of the TriOS Optical Sensors NICO nitrate sensor

The Forest Flows Ministry of Business, Innovation and Employment Endeavour programme has successfully deployed TriOS Optical Sensors NICO nitrate sensor to continuously measure stream nitrate in NZRead more

Forest Flows has now 4 of the 5 primary research sites up and running!

Thanks to the tireless work of Scion and NIWA field teams, Forest Flows has now 4 of the 5 primary research sites up and running! It is providing real time, big data data on water use, waterRead more

2nd primary research site sensor network is now live

Scion's FlowLab IOT wireless sensor network for the 2nd primary research site is now live! It is providing real time data for the Forest Flows Ministry of Business, Innovation andRead more

"Generation One" Tree Ecophysiology Sensor Network

In this video a Scion Field Technical Officer is working at "Generation One" Tree Ecophysiology Sensor Network. This network of 270 electronic dendrometers, 28 sap flow flux sensors, and 15 soilRead more

A new collaboration

This article talks about Scion's Forest Flows Programme collaboration with The University of Waikato's TAIAO Programme, led by Albert Bifet, to use cutting edge machine learning and artificialRead more

Our key remote sensing technology has arrived in the country

The key remote sensing technology for the Ministry of Business, Innovation and Employment Forest Flows programme, SimSAR L- and P-Band Synthetic Aperture Radar, has arrived in NZ. Once theRead more

FlowLab datalogger rollout suspended

NZ's COVID-19 Lockdown has suspended Scion's FlowLab datalogger rollout for the Forest Flows Ministry of Business, Innovation and Employment Endeavour programme. However, we were able to get aRead more

FlowLab IOT wireless sensor network fully operational at one site

The Forest Flows Ministry of Business, Innovation and Employment Endeavour programme has reached a milestone with the Scion's FlowLab IOT wireless sensor network fully operational at one site. TheRead more

Prototype wireless network set up

The Forest Flows programme set up a prototype IOT wireless network to test the functionality of the novel FlowLab dataloggers designed by inFact Limited with the electronic dendrometer and METERRead more

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