Monitoring Locally Led Land Restoration at Scale
Restoring land breathes life back into ecosystems and improves the livelihoods of people near and far. Every $1 invested creates $7 to $30 in economic benefits.
But monitoring restoration has long been a major barrier to scale. Too expensive, complicated, and time-consuming, existing monitoring, reporting, and verification (MRV) systems that collect and analyze data do not match the needs of community organizations and local businesses. Local organizations suffer the burden of collecting, analyzing, and sharing data from the field.
At the same time, donors and private investors are clamoring for more reliable and independently verified data to evaluate projects. High-quality data can provide a “credit rating” for local organizations, signaling to funders which projects can hit targets in the near term and can deliver outcomes in the long run. But quality comes at a cost. Every dollar spent on collecting and analyzing data means one less dollar spent on preparing nurseries, planting saplings, and protecting trees.
Digital Monitoring, Reporting, and Verification
After four years of testing, WRI has made restoration monitoring faster, more accurate, and more cost-efficient — reducing expenses by up to 98% across a portfolio of projects. Revolutionary improvements in AI technologies and access to mobile phones have made this innovation possible.
Through TerraFund, a partnership that finances local restoration projects, WRI and its partners One Tree Planted and Realize Impact co-created a new approach with over 200 projects across 30 African countries – and is replicating it in Brazil and India.
A new guidebook, Land Restoration From Planting to Proof: A Guide to Monitoring, Reporting, and Verification, lays out exactly how local organizations, their funders, and their allies in government can use this system.
Monitoring Restoration in 4 Ingredients
MRV is a recipe with four ingredients, laying out exactly who collects, quality assures, and verifies what data at what time. Together, every piece of information collected, analyzed, and communicated tells the story of restoration, who it helps and why it matters.
First, local organizations set science-informed targets. Then, their staff gather data in the field. They count trees, collect the location of each site, discuss with community members, and tally their expenses. Finally, WRI and its partners analyze that data, visit each project to cross-check results, and then use cutting-edge satellite data to independently verify the outcomes of each project.
1) Set Indicators of Success
Restoration is certainly about growing trees and improving the health of ecosystems, but it also creates jobs for women and youth and generate revenues for local businesses. Through 9 indicators, WRI evaluates restoration’s environmental, social, economic, and financial benefits.
Before a project signs a contract, they set targets, marking how many trees they will grow, hectares they will restore, jobs they will create, and people they will benefit. As the project continues, WRI compares results against these initial targets to tell whether it is on or off track.
2) Collect Project Locations
Once the targets are set, projects collect precise geospatial boundaries surrounding each restoration site, called “polygons.” Location data creates a “virtual fence” around each restoration site, marking it for satellites and field crews.
Collecting polygons is especially challenging in Africa, India, and Latin America, where most projects work with hundreds of smallholder farmers – and where a single organization can restore more than a thousand sites. And spotty internet connectivity in rural areas makes most systems unusable in the field. The incomplete data that results makes independent verification impossible.
After trialing several systems, WRI partnered with Wells for Zoë, a community organization in Malawi, to develop a field data collection app, Flority, that makes collecting polygons as easy as taking a photo with a mobile phone.
WRI staff train local staff to collect the data, correct errors and add contextual information to each polygon, including the type of restoration — like agroforestry or reforestation — and the number of trees planted or regenerated.
By summing up the total area of all polygons collected from each project, WRI can determine the total hectares under restoration and compare that against the project’s goals. Polygons also bring a sense of place to the socio-economic indicators, helping projects and financiers understand where the benefits of a project are spreading across the landscape.
3) Gather Field Reports
Location data and satellite imagery can’t capture all of a project’s outcomes. To create a powerful picture of restoration’s impact on a community, WRI trains local organizations to submit high-quality progress reports.
These reports match quantitative data on the number of jobs created, volunteers engaged and people benefitted with narrative information that summarizes each project’s challenges and successes. Projects also report on the number of each tree species that they plant on each site, a critical check to ensure balance between biodiversity and economic goals.
Local organizations collect and share data through standard project, nursery, site, and financial reports every six months via TerraMatch, an online platform that integrates reporting and technical support into a single interface.
After review, WRI posts that data on a public dashboard for local organizations, financiers, and their allies to see. This brings an unprecedented level of transparency – and encourages local organizations to take their targets seriously and submit accurate data. In turn, this robust data enhances the investment case for restoration and catalyzes additional financing.
4) Verify Outcomes
Before it’s ready for publication, WRI and partners use three approaches to check if the submitted data reflects reality.
WRI verifies indicators where AI-powered satellite algorithms or systematic in-field data collection can independently assess whether a project’s claims are true. Where that is not possible, WRI validates indicators with structured field visits or spot checks to cross-check data quality. Where the costs of field validation outweigh the data’s utility, WRI quality assures indicators to correct minor errors, inconsistencies, or discrepancies through desk review or automated processes.
Below we provide an example of how each approach is applied for three indicators.
Independent Verification
Trees grown
For the number trees grown—one of the key indicators of project success—cutting-edge satellite and artificial intelligence (AI) technology can verify the number of new trees within each polygon. With this approach, WRI counts trees at baseline (before the project starts), partway through the project to verify that planting happened, and again at the end of five years to assess the survival rate.
To do this, WRI partnered with Meta to develop DINOv3, an AI model that can analyze high-resolution satellite images in unprecedented detail. For most projects, where trees are planted in open ecosystems and where satellite imagery is available, the AI count is as accurate as traditional field measurements – at only 1% of the cost.
For the other 20% of projects, WRI employs local field crews to count the number of trees in sample plots and statistically determines the project’s total from there. As AI data analysis advances, fewer projects will have to budget for this expense, and more trees can start growing.
Validation
Jobs created
Restoration projects are just as important for people as they are for nature and the climate. Advances in AI will never replace the need for experienced field staff to advise and support projects as they mobilize communities. WRI visits each project three times over five years to validate the accuracy of polygons and the reported data.
Before and during these visits, for example, WRI validates the number of people each project employs by comparing reported job numbers with submitted employment records. Restoration projects create many part-time and full-time jobs, and WRI closely assesses the latter to understand which project designs lead to more permanent economic opportunities for local communities.
WRI cross-checks the gender and age of employees, too, to gauge whether projects are achieving their gender and social equity goals. This systematic approach also allows staff to catch errors, like sudden increases in the number of people employed outside of the planting season or repeated figures in reports.
Quality Assurance
People benefited
It’s important to understand how a project engages and benefits the wider community, not just the people it employs. But the number of “beneficiaries” is notoriously difficult to tally accurately. Every organization has its own way of estimating how many people its work touches, making it difficult to independently verify without expensive field surveys.
WRI works directly with each local organization to understand who they consider a direct beneficiary. Then, together, the partners set a meaningful target. When the reports come in, WRI staff assess the completeness of the data and whether it aligns with expectations. That provides WRI with enough assurance of the project’s wider benefits without adding a data collection burden to the local organization.
Results from the Field
Together, the four ingredients of WRI’s MRV system – indicators, location data, reports, and verification – allow anyone to assess whether a local land restoration project is achieving its goals.
But no matter how elegant it may sound, a system is only words on a page unless it is applied, tested, and improved. The three case studies below highlight how WRI has used this system to assess real TerraFund projects in Rwanda, Tanzania, and Uganda.
Finance for Restoration Outcomes
The system detailed here and in the accompanying guidebook provide a scalable system to monitor, report on, and verify local restoration projects, but the work is not yet done.
Restoration faces a finance gap of $278 billion – a chasm that limits the sector from delivering its promises for people, nature, and climate. To close that gap, private investors, public agencies, and philanthropies need to increase investment.
Yet, despite restoration’s clear benefits, uncertainty undermines donor and investor confidence. Trees die due to natural disasters, climate change, and social instability. That risk prevents many potential funders from providing the up-front financing needed to kickstart restoration.
WRI believes that outcome-based finance is the key to bringing new investors into the restoration economy. In these transactions, financiers pay for the verified social and environmental outcomes of a project rather than the upfront costs, limiting their risk.
First, philanthropies and other flexible financiers provide upfront funding and guide local organizations through the first critical years of project implementation, maximizing tree survival.
This is where MRV comes in. Strong data, provided through reliable systems, then signals which projects can and do make an impact. Using the data collection, analysis, and communication methods described in this guide and new datasets on biodiversity, water, and carbon, WRI or other independent institutions independently verifies projects’ results.
Once the results are verified, “outcome payers” reimburse the upfront financiers for the project’s costs and claim the impact that they generated, at no risk: No impact, no payment. Then, capital is recycled back into restoration, funding more projects.
To deepen this work, WRI is now exploring low-cost techniques that leverage the AI tree count data to measure complicated ecosystem services, such as water quality, biodiversity, and carbon storage.
After years of research and testing, the world is ready to embrace digital monitoring, reporting, and verification for restoration. It’s time to use that data to catalyze more investment and transform the planet’s vital landscapes, tree by tree.
The Bezos Earth Fund provided catalytic funding to develop the MRV innovations described in this playbook. WRI's work on this method is generously supported by The Audacious Project, Mastercard, Good Energies Foundation, Caterpillar Foundation, DOEN Foundation, Meta, AKO Foundation, Lyda Hill Philanthropies, Sequoia Climate Foundation, and Ikea Foundation. This work is inspired by the Tree Restoration Monitoring Framework created by Conservation International and WRI for the Priceless Planet Coalition.