How do we assure cost-effective digital sensors and measuring devices have high integrity as decision-grade data providers for scaling nature based solutions?
The Problem: Nature tech promises to make the case for deeper investments in nature-based solutions easier to justify, by providing better clarity about their impact over time and by helping make them even more cost-effective. However, inexpensive IoT sensors and digital measuring devices can lack credibility as comparable, trustable data sources when there are no consistent or standardised processes for authenticating, calibrating and validating them, or for guaranteeing their cybersecurity. This is compounded when each device manufacturer has differing methods and bespoke measurement units.
In 2021, IoT attacks rose 50% in just six months, due to configuration flaws (configuration, calibration and validation issues) and cryptographic key flaws (authentication and security issues). The lack of standardised processes for set-up, deployment and management currently weakens sensors’ and digital measurement devices’ viability as creators of decision-grade data. For example, this can cause investors in biodiversity net gain projects to demand more expensive or time-intensive methods to demonstrate biodiversity improvement, which in turn can make projects financially non-viable for farmers and land managers.
The Solution: Capital moves to nature at the speed of trust. These issues can be addressed by using standardised processes for configuring, calibrating, validating, authenticating and securing sensors. Implementing international open standardised processes improves the quality and trustability of the data collected from digital devices/instruments. This resource outlines how devices can be set up and deployed to accurately collect data according to scientifically agreed processes, as defined by existing and emerging high integrity carbon and biodiversity codes. These processes also provide evidence that devices have been properly calibrated, and are complying with strong authentication, data validation and verification, and governance protocols for cybersecurity and data governance.
These pages provide a knowledge base to guide you through applying the standardised processes for assuring the security, validation and management of data from digital devices and sensor instruments used on the ground, out in the field, manufactured by any provider.
The WWF and World Bank as well as UNEP have called for decision-grade data about nature, but what does that mean? It has to reference back to the decision in question that is being made, but there are likely to be common characteristics across all types of decision. The Taskforce for Nature-related Disclosures‘ Proposed Technical Scope (published 04.06.21) recommends data quality would be able to show:
Resolution (spatial and non-spatial) and scalability: Fit for use at the right scale for the decision. A balance may be struck between the resolution and the complexity of processing the data. For example, sourcing may not be better informed by <1km resolution geospatial data, where sub-regional data may be easier and more straightforward to process.
Geographic coverage: Globally consistent and comprehensive. Data should be collected in a fashion and using metrics that permit aggregation and dis-aggregation to allow for attribution across portfolios, corporate footprints etc.
Authoritativeness including traceability: Recognised as accurate and authoritive. This could be achieved through a peer-review process, whether published in the scientific literature or reviewed by peers, or a mandated process (e.g. CBD). In order to assess authoritativeness, the data must also be traceable. Data is traceable if the original data source is clear as well as the “data trail” which lays out how the data has been translated by different users to arrive in its final format.
These standardised processes will better enable investment in the nature-based solutions that will assist in the journey to nature positive net-zero. Reliable decision-grade data – gathered using calibrated and triangulated digital devices means nature tech can be an accountable, transparent and trustable tool.
Relevance: Formal recognition as appropriate to the decision context – for example, as part of the monitoring frameworks of multilateral environmental agreements or national policies – including the mechanism of recognition and the role of third parties in formal recognition of specific datasets.
Accessibility: Easily accessible online in different formats (e.g. direct downloads, web services, APIs) and multiple languages. Also requires considerations of costs where not open and freely accessible.
Comparability: Presented in comparable and interoperable formats and using consistent methods that enable integration of financial, socio-economic and ecological data across sectors to inform outcomes. Data should be comparable across and within industries.
Temporality: Time series data that can support trend analyses or real-time decision-making, and reflects not only the appropriate time scales for the indicator of interest (including forward-looking analysis where relevant), but also the feasibility of collecting data using current or emerging technologies and collection techniques (e.g. species abundance).
Frequency of update: Regularly updated or updated at appropriate timescales for the subject matter.
Thematic coverage: Address specific components of nature (encompassing species, ecosystems and contributions to people), pressures on nature (point and non-point source, direct and indirect) and responses (private, government, societal).
Example
We will get to a point where there is a grading of nature tech methodologies: e.g.
| Rating | Data item standards | Digital device standards |
| A grade | Collected from calibrated and authenticated IoT sensors, drones or satellite data, ag-tech robots or farm management software. Items collected are required by evidence based calculators, based on the the latest scientifically verified & IPCC validated Tier 2 or 3 methods. Provenance triangulated via date and time and GPS markers. Collection metrics configured to agreed vocabulary and scientific methods. High levels of coverage, representation, completeness and precision. Recorded and indexed on a verifiable governed /immutable registry which has equitable accessibility and environmental sustainability. Metadata descriptions include tags to agreed vocabulary. Data follows FAIR principles. | Strongly authenticated as valid source via security certificate or cryptographic public keys tested by API or data oracles. Provenance triangulated via date and time and GPS markers. Collection metrics configured to agreed vocabulary and scientific methods. Recorded and indexed on a verifiable governed / immutable registry which has equitable accessibility and environmental sustainability. Metadata descriptions include tags to agreed vocabulary. |
| D grade | Manual data input into unverified calculator. Low levels of coverage, representation, completeness and precision. Recorded and indexed locally. Metadata uses terms outwith vocabulary. Not FAIR principles compliant. | Unauthenticated devices. Not triangulated. Recorded and indexed locally. Metadata uses terms outwith vocabulary. Not FAIR principles compliant. |
The standardised processes will cover data gathering, managing and analysis and governance journeys, and could eventually cover quality of sampling, and aggregation and infrastructure tools and APIs used in dashboards.
Further Reading
Look to the following examples of specific device types for more detail of real world devices and what information to collect about the hardware.