TREES 3.0 is live. Here are three opportunities that Chloris data unlocks for jurisdictions
By Florian Reber, SVP Partnerships & Business Development, Chloris Geospatial, and Jeremy Freund, Chief Climate Officer, Wildlife Works
In late June, ART released Version 3.0 of the REDD+ Environmental Excellence Standard, TREES. The biggest changes are about access. It opens a transition pathway for jurisdictions in the World Bank's Forest Carbon Partnership Facility (FCPF) and Initiative for Sustainable Forest Landscapes (ISFL) to register under TREES. And it gives subnational programs more time before they move to national accounting.
TREES 3.0 allows biomass maps to be used to derive emission factors, as long as the product is documented, calibrated and validated against national data, and its uncertainty is reported.
Chloris fits this precisely. The same documented, calibrated and validated record can serve as the primary carbon input a jurisdiction registers with. Applying a biomass threshold aligned to the national forest definition resolves each pixel’s stock and change into deforestation, degradation and reforestation, so the product yields both the activity data and the emission and removal factors, in the activity-data-times-emission-factor form TREES requires.
Chloris's direct biomass estimation methodology rests on more than two decades of peer-reviewed, IPCC-recognized and UNFCCC-compliant science developed by Chloris' Co-Founder, Co-CEO and Chief Science Officer, Dr. Alessandro Baccini and published in the world's leading scientific journals (1,2). It means that Chloris’ approach has been extensively stress-tested and vetted by the remote-sensing and carbon-cycle science communities. In the more recent past, Chloris time series data have been extensively validated against fully independent, highest-quality airborne-lidar and in-situ field measurements from sites worldwide.
States such as Goiás in Brazil already make use of Chloris data for its ART TREES registration. This post is about how Chloris data helps jurisdictions build robust baselines and crediting programs. By combining national ground data with Chloris data, a jurisdiction can unlock three opportunities: capture emissions from degradation, lower the reported uncertainty, and include removals from restoration.
(1) Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change2, 182–185 (2012).
(2) Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science358, 230–234 (2017).
1. Emissions from degradation
Legacy jurisdictional baseline approaches often use an activity data (AD) x emission factor (EF) approach. You map where forest was lost, then multiply by an average carbon density. That works for deforestation. It struggles with degradation. The reason sits in both terms of the calculation. Activity data is an area measurement of categorical change between land cover classes: forest area that becomes non-forest between two dates is counted as deforestation, but within-class biomass loss is not registered, so degradation area never gets included. The emission factor is an average emission value per broad land cover class, associated with the biomass that would be lost if that forest was destroyed, and this is normally calculated from ground plots. Chloris estimates biomass and biomass change directly at every pixel, which addresses both. For activity data, it brings degradation in by observing within-class biomass loss that does not cross the forest threshold, previously omitted by most legacy methods, alongside the loss that does. On emission factors, the Chloris biomass stock product provides a jurisdiction-wide pixel-level measurement of biomass, which is more comprehensive than a ground-plot inventory approach and often reaches a tighter uncertainty than field campaigns practically can, since plot data are time-consuming and costly to collect. And degradation often drives the biggest share of forest emissions, which you can see in the Chloris biomass change data. A baseline that misses it is incomplete. It means that legitimate carbon credits are being left on the table and conservation impact achieved by local communities misses an opportunity to be rewarded.
To illustrate what this means, we built TREES “Crediting Levels” with the Chloris carbon stock and change dataset for eight prominent REDD+ jurisdictions and compared them against historical emissions observed for the national Forest Reference Emission Levels (FRELs) governments have submitted to the UNFCCC. Jeremy Freund, Chief Climate Officer at Wildlife Works, led the work.
In Colombia, comparing the two baselines over the five-year reference period that would comprise a TREES Crediting Level (2018 to 2022), the methods agree on deforestation, within about 18 percent (105 versus 89 million tonnes CO₂e per year), but degradation splits by a factor of six: 285 million tonnes against 45 million (Figure 1). The same holds over the FREL’s full ten-year reference period (2013 to 2022), where historical deforestation agrees to within about five percent while degradation diverges nearly fivefold. Because the two methods agree on deforestation over two identical reference periods but not on degradation, we can conclude the gap is not a disagreement about how much forest area was lost, but rather the within-class ability to detect and quantify degradation that activity-data methods do not accurately register, and it is almost the entire difference between the two totals.
Figure 1: Colombia’s annual historical forest emissions under the latest jurisdictional FREL (NREF v2.0) and a Chloris-derived Crediting Level, each split into deforestation and degradation, over (a) the full 10-year NREF reference period (2013–2022) and (b) a 5-year TREES reference period (2018–2022). Deforestation estimates are similar for the 2 methods; degradation differs several-fold.
Colombia is not unusual. Across eight jurisdictions (Figure 2), official FRELs attribute an average of about 14% of their forest emissions to degradation, with the DRC and Cambodia omitting it from their FREL submissions, while TREES Crediting Levels calculated with the Chloris stock and change dataset attribute on average about 73%. To maximize the climate finance potential of J-REDD programs in countries with such significant degradation impact, capturing that impact is essential.
Figure 2: For each jurisdiction, the share of historical forest emissions from deforestation and degradation under the FREL and under a Chloris-derived TREES Crediting Level. Official FRELs attribute 0 - 34% of emissions to degradation – averaging about 14% (the DRC and Cambodia omit it), while Chloris-derived TREES Crediting Levels attribute 49 – 89% - averaging about 73%.
2. Lower uncertainty
Achieving robust, low uncertainty numbers is another key element of a successful J-REDD program. TREES 3.0 requires uncertainty to be quantified and propagated, and the deduction at issuance scales with it. Lower uncertainty means smaller deductions for countries, so a jurisdiction is credited for more of what it actually achieved rather than losing volume to the imprecision of its measurement system.
Chloris reports a standard error for every pixel, alongside the biomass estimate. Propagated to annual emissions, the Chloris-derived TREES Crediting Levels carry 95 percent confidence intervals averaging about ±2% across the eight jurisdictions, the one exception being Kenya at ±7.9%. The official FRELs, by contrast, report uncertainties averaging about ±14% (Figure 2). Again, that makes a big difference on the ability to mobilize climate finance.
3. Removals from restoration
Standard forest mapping is not designed to capture removals from restoration. Chloris is. The same Chloris record that tracks biomass losses pixel by pixel also tracks biomass gains and the related carbon removals. Where forests regrow, on restoration and reforestation sites, it measures the carbon those areas sequester, which feeds the removals side of a TREES Crediting Level. Removals run on the same calibrated dataset and carry the same uncertainty as the emissions side, so a TREES jurisdiction would credit restoration on the same basis as its reductions.
Figure 3: Cumulative Above-ground Biomass (AGB) change, Ethiopia (2000 to 2025), Chloris 30 m resolution. Cumulative AGB change shows the total net AGB change per pixel from 2000 to 2025, based on the annual pixel level change.
The point
A fair question about direct biomass estimation is whether it can be validated and whether it is ready for jurisdictional use. It can, and it is. For a jurisdiction, Chloris can be calibrated and validated against the country’s own forest-inventory ground plots and forest definition, or against independent datasets, either remotely sensed or collected in-situ using time-tested IPCC validation and error-quantification methods. These same accepted methods that jurisdictions already use for reference levels can be applied to the Chloris datasets and our preliminary assessment shows that the datasets are TREES 3.0 compliant.
Chloris works with a country’s MRV system, on its ground data and forest definition, and can serve as the primary measurement basis for the carbon accounting. When using Chloris for TREES Crediting Levels and MRV, Chloris data builds on national inventory and forest definition, and resolves into the AD x EF form TREES requires. The same dataset can also serve a country's UNFCCC FREL, so the national submission and the TREES Crediting Level could run on consistent input data. Combine the two and a jurisdiction gets a baseline that counts degradation, credits restoration and carries lower uncertainty. If you are building a TREES Crediting Level or an MRV system, this can be compared to your jurisdiction's FREL, allowing you to evaluate the value of aligning the two reference levels. Accuracy and completeness are our goals and by capturing degradation, lowering uncertainty and crediting restoration - all consequences of measuring well - we aim to support the market in rewarding the mitigation a country actually delivered.
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