How Can We Address the Data Dilemma in Ecosystem Restoration?

Guest article by Mehdi Ajana, Head of Strategy, Nabat.ai

Every major restoration framework includes monitoring requirements. The UN Decade on Ecosystem Restoration, the Kunming-Montreal Global Biodiversity Framework, and national net-zero strategies treat monitoring as a component of responsible practice. And yet, the monitoring that most restoration programs deliver is structurally inadequate for the task it is meant to perform.

The standard model offers a baseline survey before intervention; periodic site visits at six or twelve-month intervals, and a final assessment at project close. Data is collected manually, aggregated into reports, and submitted to funders or regulatory bodies. The reports show survival rates, canopy cover estimates, and species counts. They look like evidence of progress.

But they rarely capture whether the ecosystem is recovering -whether the functional processes that make a healthy mangrove stand resilient, productive, and carbon-sequestering are re-establishing themselves, or whether what is being measured is a surface appearance of health masking a system still under stress.

The difference matters enormously, both ecologically and financially, yet it remains almost invisible to episodic monitoring conducted at the timescales most programs operate on.

The importance of timescales

Ecosystem recovery is not a linear process, and it does not unfold on the timescales of project funding cycles. A mangrove stand planted in one year may show strong early survival and healthy canopy development by year two. That same stand may be under severe salinity stress by year three following a shift in upstream freshwater flows -a shift that a quarterly site visit would detect months too late to address effectively.

This is not hypothetical. Along Sri Lanka’s coast, early plantings following the 2004 tsunami struggled for exactly this reason: teams lacked the soil and hydrological baselines needed to match species to site, and the monitoring infrastructure to detect emerging stress before it became mortality. A decade later, the same region shifted to data-led approaches, with continuous satellite imagery, sensory arrays, and transparent dashboards, and has since restored more than 500 hectares of mangroves, with a pathway to 10,000 hectares by 2030.

Continuous environmental intelligence

The case for continuous monitoring is sometimes framed as a data argument for more data points, better coverage, and richer datasets. The deeper argument is about decision quality that becomes possible when observation is continuous rather than episodic.
Consider what changes at each stage of the restoration cycle.

At the site selection stage, continuous Earth observation data, updated every few days across entire landscapes, allows teams to map soil salinity gradients, sediment dynamics, tidal inundation frequency, and historic canopy cover at a resolution that was unavailable a decade ago. Beyond better site selection, this approach eliminates a category of failure: the interventions that were always going to struggle because the baseline conditions were never right.

At the intervention stage, real-time sensor data from drones, autonomous surface vehicles, LiDAR, and SAR-enabled monitoring arrays means that stress signals, including rising turbidity, salinity spikes, and anomalies in surface reflectance associated with early canopy stress, reach field teams before they become visible to the human eye. The window between detectable signal and irreversible damage is where adaptive management can happen, and continuous intelligence keeps that window open longer.

For funders, policymakers, and market participants who need to trust restoration outcomes, the difference is most consequential at the verification stage. Carbon sequestration figures calculated from continuous, spatially comprehensive biomass proxies are defensible in ways that periodic transect samples cannot match. Biodiversity recovery tracked against consistent data standards over years produces metrics that support genuine accountability rather than the appearance of it.

The accountability gap

The credibility of restoration as an investable asset class depends on the credibility of its measurement infrastructure. Global funding for nature restoration currently stands at approximately $133 billion per year. To achieve international biodiversity and climate goals by 2030, the required annual investment is estimated at $536 billion. The assumption embedded in most discussions of this gap is that the problem is one of incentives, that with the right policy signals, carbon pricing, and financial instruments, capital will flow.

But capital will not flow sustainably into a sector where reported outcomes cannot be independently verified, or where the difference between a successful restoration project and failed one is invisible until years after the investment is made. It will not flow where the standard of evidence accepted for compliance purposes would not satisfy basic due diligence in any other asset class. Investors increasingly reference established standards -Isometric, Gold Standard, and the emerging ISO 14064 and SBTN frameworks -as the baseline for what defensible verification looks like. The accountability gap is, ultimately, a capital gap, and closing one requires closing the other.

Continuous environmental intelligence provides more than just better data. It is the infrastructure for a different relationship between restoration practitioners and the institutions whose confidence determines whether restoration can scale. Programs that embed monitoring from the outset, treat verification as an ongoing function rather than a terminal one, and report outcomes against consistent science-grounded standards are the ones that can offer the transparency serious capital requires.

At Nabat, we’re building the infrastructure needed for restoration at scale. The intelligence layer is Ether, our proprietary geospatial AI foundation model trained on multi-modal datasets across coastal, marine, and dryland ecosystems and validated by in-house ecologists. Ether powers each stage of the NabatOS pipeline (Map, Assess, Plan, Restore, Monitor, and Verify), combining AI-enabled habitat mapping with continuous field-validated data to guide where and how to intervene, and to provide genuinely defensible visibility into ecosystem recovery over time. In the UAE, Nabat’s work with the Environment Agency -Abu Dhabi covers more than 20,000 hectares under AI-led monitoring across a ten-year national program (2024–2033), with millimeter-resolution aerial surveys generating a continuous, independently auditable record of ecosystem change. Ambition without accountability infrastructure cannot compound.

Feedback is what turns a program into a learning system. Scaling restoration to meet climate and biodiversity goals requires the same things any critical infrastructure requires: a system capable of continuous observation, adaptation, and verification.