A Hierarchy of Data Needs for Nature Restoration Projects

When it comes to data, most nature restoration projects do the basic stuff, while the harder-to-get data goes uncollected.

But if we want to run truly “high integrity” projects, we need to be able to show that we did what we said we’d do, and that we did it well. For that, we need to roll up our sleeves and dig deeper into data collection and monitoring.

I’ve developed a hierarchy of data needs for nature restoration projects. Just like Maslow’s hierarchy of needs, the most essential elements sit at the bottom and act as the foundation that unlocks the more powerful layers above. Reaching the top means you have the data and monitoring rigor to improve internal processes, appeal to funders, and maximize your project’s environmental impact.

1Quantified Statements

This is the starting point of data collection: how many trees were planted. But on its own it’s impossible to prove, which means donors and third parties can’t verify it. It won’t get you very far.

There are other problems with stopping here. This data by itself can’t tell us the likelihood of a positive impact. Monoculture planting has been a problem in the past, and destroying a marsh to plant saplings is missing the forest for the trees.

To understand and monitor our impact, we need more data.

2Geospatial Reference

If you want to know whether something will succeed in the long term, you have to know where you’re looking. After all, your funder can’t check a satellite image in five years if you don’t have coordinates.

The minimum here is dropping a pin on a map. Better yet, go to the field and capture a GPS point. But the gold standard is creating a polygon by walking the perimeter of the exact area where the intervention is taking place. You can go further by breaking the intervention into multiple polygons, separated by terrain, species planted, or other qualities (more on that in level 4).

Drop a pin

Good

Capture a GPS point

Better

Walk the polygon

Gold standard

Knowing the exact location of the intervention unlocks the ability to monitor it. And if you’re choosing tools for this step, I’ve written about field data capture apps before.

3Temporality

Now we have the what and the where. But without knowing when the intervention happened, we can’t make predictions, projections, or verifications. When will the trees mature? We don’t know. And if the intervention happened in stages, knowing when each stage occurred matters just as much.

Dates are useful beyond projections and verification; they also help us manage the intervention. If you plant a sapling in a grassy area, you want to make sure it isn’t swallowed up by that grass before it can mature. The planting date gives you a clue about when to revisit the area and keep the grass low. If you don’t know when you planted, you can’t know when to cut.

4Specificity

By layering in specificity, we get better at monitoring both our impact and our efficiency. Donors want high-impact interventions, and if we can work quicker, cheaper, or more effectively, we’re positioned to win their trust.

Here are some types of data that help:

• Which species were planted, and how many of each? In terms of carbon sequestration, not all trees are created equal. If you don’t know what you’ve planted, you don’t know what you’re offering donors.

• Did they come from a nursery, or were they transplanted? Transplanting is cheaper and faster, but it requires its own monitoring and may yield different results. Record it so you can account for any variability in growth down the line.

• Who participated? Knowing who was there and the roles they played helps you calculate the costs of past interventions and project costs for future ones.

5Data Across Time

If you’ve done the work to capture the four levels below, you’re now able to effectively monitor the area over time.

Why can’t we just skip the other levels and go straight to this one? Because without quantified statements, geospatial reference, temporality, and specificity, it’s difficult to show that our intervention improved upon what nature would have done anyway.

Satellite monitoring is good for this step. But we also want to know what’s happening on the ground across time, through drone footage across seasons or by visiting the area on foot. Has it been maintained? Were plots sampled to verify growth? Were there top-ups across seasons? Did trees die? If so, when, and how many?

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We all want to design nature restoration projects with high integrity. Shallow data means neither we nor the people funding our projects can clearly tell whether we’ve done a good job.

When we’ve got deep, well-organized data, demonstrating our impact is easy. We can show that we planted diverse native species, not a monoculture. That we reforested with the right spacing and distribution. That we planted the right species at the right elevation. That things are growing where they should be.

Where do you sit on the data needs hierarchy? Wherever you are, keep up the good work, and keep growing.