Use of Remote Sensing for Verra-aligned Project Design Documents (PDDs) & Monitoring Reports: Looking at VM0047

In 2022, Verra’s Digital Measurement, Reporting, and Verification (DMRV) Working Group was formed to explore the integration of DMRV technologies within the Voluntary Carbon Standard (VCS). Among these technologies, remote sensing stands out for its potential to streamline and enhance environmental monitoring. This content piece delves into how Verra currently embraces remote sensing in its Afforestation, Reforestation, and Revegetation (ARR) methodology, VM0047, and how you can benefit from remote sensing beyond Verra.

How and Where to Use Remote Sensing in VM0047

The VM0047 methodology under Verra’s Afforestation, Reforestation, and Revegetation (ARR) guidelines highlights three applications of remote sensing: Assessment of historical land cover, monitoring of burned areas, and calculation and monitoring of the stocking index.

1. Assessment of Historical Land Cover (Applicability Conditions, p. 9)

Projects that use the census-based approach must demonstrate that the activity area was non-forested for the past decade either by third-party attestation or pre-project photos as evidence. Satellite imagery facilitates this by providing historical data on the land cover and land use, which Verra accepts as evidence upon validation by a Verified Carbon Standard (VCS) Validation and Verification Body (VVB). Utilising datasets such as Dynamic World, WorldCover, or indices such as the Normalized Difference Vegetation Index (NDVI) based on Landsat and Sentinel imagery allows for assessing land cover history, all available on Maya. However, Verra does not specify a minimum resolution for this imagery, as acceptance depends on the quality of data and the presence of obstructions like cloud cover. As we will explain later, it can be assumed that a minimum resolution of 30m is expected, while not always sufficient.

‍2. Monitoring of Burned Areas (Monitoring, pp. 42–43)

For periodic monitoring, remote sensing can assess burned areas, minimising the need for direct field measurements. However, publicly available datasets for this use case either struggle with spatial resolution (like MODIS) or temporal resolution (like GABAM), so purchasing commercial data for your area of interest might be necessary.

The MODIS dataset from NASA and USGS, despite its 500m resolution, serves as a useful preliminary tool for identifying past forest fires. This can inform your early-stage project screening and your fire mitigation strategy. However, for the monitoring report, an area burned calculation based on a 500m resolution dataset is likely to be insufficient, and finer resolution satellite imagery should be used. The ​​Global Annual Burned Area Map (GABAM), on the other hand, comes at a 30m resolution but is only available until 2021 and thus not suited for current monitoring periods.

As with the pre-project photos for evidence of the project site’s land cover, Verra does not provide any guidance on the required minimum resolution for calculating the area burned.

3. Calculation of the Stocking Index to Assess the Performance Benchmark and Dynamic Baseline Scenario (Appendix 1: Performance Method, pp. 54–55)

The strongest remote sensing application in VM0047 involves calculating the Stocking Index (SI), an “unspecified remote sensing metric that has demonstrated correlation with terrestrial aboveground carbon stocks (e.g., normalized difference fraction index from Landsat imagery, or average canopy height derived from LiDAR)” (p. 7). Ideally, the Stocking Index would measure forest carbon directly, but this is difficult with remote sensing. Therefore, the Stocking Index uses a related metric as a proxy to estimate biomass, making it easier to calculate the dynamic baseline.

Remote sensing technologies like LiDAR, particularly GEDI data, are utilized for detailed 3D mapping of forest canopies, aiding in the estimation of average canopy height. Alternatively, the NDFI based on Landsat imagery provides insights into forest canopy damage and recovery. Although Verra considers a 30m resolution a minimum for these analyses, finer resolutions, such as the 10m offered by Sentinel-2 imagery, might yield more reliable correlations between the SI and carbon stocks. 

However, correlating between optical indices such as NDFI based on Landsat or Sentinel-2 and actual carbon stock becomes challenging once the canopy cover reaches a density where most pixels are already covered by a canopy. Further changes in tree size and carbon stock may not be correctly portrayed in the SI in that case. Synthetic Aperture Radar (SAR) imagery from ALOS PALSAR or the BIOMASS instrument, scheduled for launch by the European Space Agency in 2024, could improve correlation accuracy. As with all remotely sensed evidence, it is upon the VVB to accept the suitability of the SI metric.

Please note that remote sensing is used to estimate relative stock changes between control and project plots but that the carbon stock calculations within the project area still depend on direct field measurements.

Implications for Your Carbon Project

1. Validation and Verification: While remote sensing provides extensive area coverage and data collection capabilities, the validation of this data is subject to audits by VVBs. The resolution and quality of data, relevance to specific geographic and ecological contexts, and the presence of obstacles like clouds or canopy cover can influence the acceptance of remotely sensed data. Thus, there is a lack of guidance when it comes to minimum resolution or accepted datasets, as it currently remains a case by case decision.
2. Complementary, Not Replacement: Remote sensing is valuable for preliminary assessments and monitoring changes over time. However, it cannot entirely replace the need for on-ground verification, especially for detailed and accurate measurements of variables like carbon stocks and biomass. Field measurements are still crucial for providing ground truth data that complements and validates the remotely sensed data.
3. Technological Advancements and Adaptation: As remote sensing technology advances, the potential for its use in environmental monitoring and carbon crediting also expands. However, project developers must stay updated with the latest technologies and adapt their monitoring strategies accordingly to ensure compliance with evolving standards and methodologies.
4. Broader Use Cases: Beyond compliance with Verra’s standards, remote sensing can serve broader environmental and conservation goals. It enables project developers to conduct risk assessments, plan resource allocation, and implement conservation strategies effectively. In the end, it’s not just about Verra's verification. There are many more use cases for geospatial insights for assessing and monitoring Nature-based Solutions.

Conclusion

While Verra provides a framework for utilising remote sensing in project validation and monitoring, it’s essential to recognise the broader potential of these technologies beyond mere compliance. It’s not just about adhering to Verra’s standard but also about leveraging geospatial insights for a more comprehensive understanding of Nature-based Solutions.

Project developers and investors can proactively utilise remote sensing for early-stage site and project screening and to inform their mitigation strategies against environmental risks such as fires, droughts, and floods. This proactive approach allows for better planning and implementation of mitigation strategies, ensuring that projects are not only compliant with Verra's requirements but also robust against environmental uncertainties.