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Cambridge Centre for Carbon Credits (4C)


Methods of gathering data on nature ecosystems

Conventional methods for measuring the carbon stored in natural systems centre on traditional field-inventory techniques, but this is difficult to scale up globally. Modern technology provides alternative solutions that have some clear advantages. All the carbon stored in ecosystems – whether alive or dead, below- or above-ground – is ultimately derived from photosynthesis.

Current practice is to measure the density and size of carbon pools using techniques originally designed to measure timber volumes. While field-based measurements are the current basis of carbon measurements, new techniques allow reliable translation to landscape-level carbon densities in ways which are better than relying on ground-measurements alone:

- Macroscopic monitoring from space has produced time-series observations with no analogue in terms of spatio-temporal coverage.
- Airborne monitoring using LiDAR scans from planes or drones, provides an excellent source of data for measuring the vegetation structure within forests, which is vital for estimating current and potential carbon density.  However, this approach is expensive to deploy and is seldom used to track changes over time.
- Organismal scale collection using traditional field techniques in combination with technologies that increase precision and trustworthiness, including smartphones, LiDAR and deployable soil and climate sensors.

We are initially using space-borne technologies to track the above-ground carbon density (ACD) of forests.  Our primary source of data is GEDI (NASA's LIDAR sampler), which is operational onboard the International Space Station, taking billions of measurements of forest structure from which ACD can be mapped.

We fuse GEDI estimates of ACD with data from remote sensing systems that map where forests are being cleared, degraded and recovering, to keep track of forest carbon over time at a global scale. We are also developing new systems that will track ACD directly by fusing GEDI data with long-term radar time-series products.

Once we have this data, the next step is to use it to classify projects for their carbon sequestration values.