Geospatial embeddings offer a promising shortcut. By compressing large volumes of raw spatial data into compact numerical representations, geospatial embeddings can simplify analysis workflows and improve efficiency at scale. But this is a young field, and there are trade-offs to choosing embeddings over traditional spatial datasets.

Public embedding datasets are now available from Google DeepMind (AlphaEarth Foundations), Clay, and the University of Cambridge (TESSERA), among others. At Cecil, we’re exploring how these datasets can support the teams we work with. This newsletter shares what we’ve learned so far.

Fingerprints for a place

A geospatial embedding is a set of numbers that summarises the characteristics of a location, as observed by multiple input datasets over a given period. Each embedding is a point in multi-dimensional space, where the closer two points are, the more similar the locations they represent. In this way, an embedding acts as a fingerprint of a place.

Imagine sitting back-to-back with a partner. In front of you is a landscape painting. Your task is to get your partner to recreate it, but you can only pass them a single post-it note. To do this, you must compress the essential structure of the painting into a few words: mountain ridge, river bend, seasonal snow line, and so on. That post-it note is like an embedding. It preserves the information most important for distinguishing this landscape from others, while discarding redundant details.

Geospatial embeddings are generated by geospatial foundation models, which use self-supervised learning (i.e. unsupervised by a human) to compress many spatial datasets into a vector of fewer dimensions. Input datasets typically include multispectral imagery, radar backscatter, elevation models, and climate variables.

Different models use different architectures, training strategies, and input datasets, resulting in embedding datasets with a variety of properties and dimensions. These design choices also affect which workflows each model is suited to. For instance, AlphaEarth Foundations and TESSERA produce one embedding per pixel, making them suited for pixel-level classification and fine-grained mapping. Most other models embed image patches, making them better suited to scene classification and monitoring large areas.

The table below summarises some key distinctions between models available today.

Unlike a single satellite sensor band, which corresponds to a fixed property like near-infrared reflectance, no single embedding dimension has a fixed meaning. The representation is distributed, so the “meaning” only exists in the relationships between all dimensions together. You cannot use a single dimension to identify a forest. Instead, you must use the full vector and apply analytical techniques to derive this answer.

Where embeddings add practical value

Geospatial embeddings address some common challenges facing data teams today (see our newsletters on spatial data foundations and spatial indexes):

Similarity search. Consider a known reference location, like a mangrove stand or deforestation frontier. By comparing its embedding with those of other locations, it is possible to identify sites with similar properties in embedding space.

Clustering. Without any labelled data, embedding vectors can be clustered to produce first-pass maps of landscape types. Clusters typically correspond with meaningful land cover categories, making this valuable for stratified survey design or initial site baselining.

Change detection. Comparing embeddings of the same location over time can reveal change, for instance by calculating similarity between two years to create a change score for each pixel. This is an effective triage tool for identifying where to focus further analysis.

Label efficiency. This is perhaps the most significant advantage for nature data teams, given that ground-truth data is expensive to collect. For instance, AlphaEarth Foundations reduced the number of samples needed to classify 87 crop categories from thousands per class to approximately 150 per class. The Element84 team also found that a simple logistic regression trained on AlphaEarth Foundations’ embeddings could detect bracken (an aggressive fern that degrades the UK uplands) from a small number of training samples. Embeddings significantly lower the barrier to entry for organisations that can’t justify tens of thousands of dollars on field data collection.

Trade-offs with geospatial embeddings

Embeddings are not a like-for-like replacement for existing spatial datasets or methods. Similar to our recommendations for dataset evaluation, it is important to be clear-eyed about the trade-offs:

Interpretability. When a statistical model trained on embeddings labels a pixel as “degraded peatland,” you cannot trace that decision back to specific source data. Traditional spectral indices, like the Normalized Digital Vegetation Index (NDVI), have a direct biophysical interpretation, whereas embeddings do not. This is a constraint for applications that require explainability, such as regulatory reporting or auditing.

Temporal resolution. Most public embedding datasets are delivered at annual temporal resolution. This works for tracking slow changes like urban expansion, but seasonal changes like winter to summer crop rotations are compressed away. Unlike traditional annual composites, there is no way to inspect which seasonal signals the model retained. This also affects what “similar” means in embedding space. Models encoding a full year may place two sites close together because they share seasonal dynamics, not because they look alike on any one date. For higher frequency use cases like deforestation, flood, or fire alerts, raw imagery or purpose-built datasets are still required.

Geographic bias. All foundation models carry the biases of their input data. For instance, Prithvi v1.0 was pre-trained exclusively on US data, while SatCLIP uses only 100,000 scenes globally. It is essential to validate model performance in your region of interest before relying on analysis results.

Platform dependency. AlphaEarth Foundations embeddings are available through Google Earth Engine, Google Cloud Storage, and Source Cooperative (thanks to Taylor Geospatial Engine and Radiant Earth, with Jeff Albrecht leading the migration plus the CNG community). Alternatives like Clay, TESSERA, and Prithvi are earlier in development and offer more flexibility. Cost is also a factor. The full AlphaEarth Foundations dataset is hundreds of terabytes, and migrating it out of Google Earth Engine cost tens of thousands of dollars in egress fees alone.

Looking ahead

Geospatial embeddings offer advantages where ground-truth data is scarce, preprocessing is complex, and the problem is not well understood… in other words, in many conservation and environmental monitoring applications.

However, embeddings trade interpretability, temporal granularity, and geographic coverage for convenience and efficiency. And while geospatial embedding models often report error reduction against specific benchmarks, carefully curated datasets and robust analysis workflows can still outperform embeddings while remaining biophysically interpretable. Embeddings are a new tool for compressing complexity, but they are not a silver bullet that eliminates it.

At Cecil, we’re monitoring these developments closely as we consider how embeddings fit into our product. If you’ve been experimenting with embedding models, we’d love to hear what you’ve found. Share your experiences in our Slack community, or explore Cecil’s documentation to discover related datasets available today.

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Special thanks to Isaac Corley and the Cloud-Native Geospatial Forum for their work on embeddings product interoperability, Element84 for their examples of AlphaEarth Foundations embeddings in practice, and Simon Ilyushchenko at Google Earth Engine for his friendly review.

Recent updates at Cecil:

• Selected by Isometric as one of their Earth Observation data partners

• New datasets:

  • IBAT STAR Threat Abatement

  • PlanetSapling Global Major Mines

  • Sylvera Biomass Atlas

  • WRI Tropical Tree Cover 10 m and 70 m

  • WCS Forest Landscape Integrity Index

  • USGS Annual National Landcover Database

  • USDA Cropland Data Layer 10 m and 30 m

Hello Cecil community, I’m Chris, Lead Scientist of the Forest Ecosystems team at Planet. It’s nice to meet you!

So I read this great book as a grad student, Writing Science by Joshua Schimel. I loved it because it reframes how scientists can construct and define their identities. Early career scientists are asked to do so many things these days: developing hypotheses, teaching, keeping up with new research, raising funding, writing code, mentoring, collecting data, performing statistical analyses, reviewing manuscripts, and writing papers. For example.

Schimel’s book challenged scientists to think of writing as more than something you do: writing is central to your identity. As a scientist, you are a writer. All the aforementioned tasks — the synthesis of existing knowledge with new data and analysis — most effectively advance a field when the results are clearly communicated. The strategies for communication can change based on the audience, from your peers in a reviewed scientific publication, to the prospective funders of a new proposal, or to a government agency that might implement new findings as policy recommendations. I really connected with this perspective. I love to think of writing as more than something I do; it is a part of who I am.

That’s because, like everyone else with a job, I have many things to do. My job is to lead an exceptionally talented team that builds satellite-based forest monitoring systems. I’ve had to develop many new skills as I grew into this role. My background in plant ecophysiology did not prepare me to learn about API design patterns, event-driven workflows, or really any form of software architecture. With some experience — it’s been about 10 years since I wrote my first line of code — I now find software architecture and software ecology fascinating.

Reaching further from my roots, I recently found myself reading a book about the evolution and marketing of technology products, Crossing the Chasm by Geoffrey Moore. This book seems to be de rigueur for anybody in corporate technology management. I didn’t expect to like it as much as I did. I thought it would be rather dry, with lots of acronyms and fluff. I was wrong! It’s full of interesting and thoughtful lessons on the growth, competition, and success of technology systems.

Moore articulated a useful distinction between continuous innovations and discontinuous innovations. A continuous innovation simply improves on what came before. You could upgrade from an HDTV to a 4K TV and keep using your peripherals and streaming services, for example. A discontinuous innovation, on the other hand, requires behavioral or technological changes to use the new system. Upgrading to a 3D TV would be discontinuous because you need special viewing systems and specialized content providers. And you may need to rearrange your living room. Discontinuous innovations must provide a compelling value proposition for users to adopt because they merit changing habits. And many folks are skeptical of change.

I bring this up because it relates to the last Cecil Newsletter by Tom Walker that discussed the challenges of evaluating nature datasets. Walker articulated a series of fundamental and persistent challenges to quantify and communicate the accuracy and uncertainty of nature datasets, particularly global ones. As developers of such data products, our team at Planet has had to address each of these challenges when building our Forest Carbon data products.

We recently published a manuscript detailing how we approach dataset inspection, intercomparison, and benchmarking. It describes the concepts behind our methods, it details the known and expected limitations of the product, and articulates a vision for the role that rigorous uncertainty quantification will play in enabling the use of satellite-derived carbon estimates in different policy contexts. I won’t recap it here, since the 15,000 words, 29 figures and 61 total pages of the manuscript was the most concise expression we could muster. I hope you find the opportunity to read it, particularly the Discussion section.

Instead of discussing the manuscript, I want to examine an implicit assumption we often make in benchmarking and evaluation. Here’s a trimmed-down summary from Tom’s post on the topic:

“The gold standard of dataset evaluation is to benchmark accuracy against an accepted source of truth… For nature data, this creates a challenge: the source of truth is usually field data that contains its own uncertainties. Field surveys [...] converting tree basal area measurements into biomass estimates carries inherent uncertainty. Even so, high quality field data remains the best available reference point for evaluating dataset performance.”

Well put. But let’s consider this point closely.

By implicitly framing satellite biomass datasets as a continuous innovation over field data — as a product that can only be understood in terms of its fidelity to field data — we adopt the perspective that satellite data is primarily an advance upon methods developed and refined in the field. What are the implications here? Champions of remote sensing might extrapolate from this premise to promise that, with further innovation, satellite data might one day surpass the quality of field measurements and make field data collection obsolete.

If you’ve spent any time in the field, you likely regard such a perspective with a great deal of skepticism. I too am a skeptic of this promise. That’s because it’s based on a false premise.

Field and satellite observations are fundamentally distinct modalities. They are different methods of estimation: they do not measure the same things, nor do they measure on the same spatial or temporal scales. Field and satellite measurements respectively represent bottom-up and top-down approaches to estimation, both literally and epistemologically. There are some things you can easily measure in the field but struggle to estimate from space, like understory stem counts or species turnover following a disturbance. Likewise, estimating annual deforestation rates across the tropics is much easier from space than it is from the field.

I believe we all know that these things are different, and that each presents a clear value proposition. Where we seem to encounter tension is in efforts to reconcile the bottom-up and top-down understandings afforded by these modalities.

One source of tension is the one-way nature of the relationship. Field data are typically required to calibrate remote sensing measurements. Yet I can’t think of an example where an ecological field measurement was calibrated by remote sensing observations (the closest might be the use of satellite-derived maps to inform sampling strategies). Some seem to interpret this relationship as parasitic; I interpret it as largely commensal; more on this later.

This one-way dynamic is somewhat inevitable. Satellites measure in units of energy (like at-sensor radiance in W/m2/sr/nm, or backscatter power in dB), not in ecological units (like abundance in species counts, or basal area in m2/ha). We must convert measurements from satellites to make sense of what they might mean in the field. This is hard.

I often describe my job as akin to a translator: converting data collected from space into calibrated estimates of forest structure, function, and change. This can only be credibly done based on a deep knowledge of how vegetation interacts with energy in the form of absorbed and reflected electromagnetic radiation. It’s a translation that needs to be performed at scale, running signal processing algorithms on trillions of pixels worth of data every year to represent global forest change.

Translators often describe their job as an imperfect art, where the unique contours of specific turns of phrase in their original language are often rendered dull or indistinct upon translation. So it goes when joining field and satellite data. Lost is the precision of the original measurement, as are the stories of the flooded camp sites, the lost permits, the things found in shoes, the quiet murmurs of nature transcribed into data. But what we gain from this translation is a new way to see our changing planet and quantify the consequences of change.

Plants interact with diurnal variations in electromagnetic radiation in profound and complex ways. By measuring these patterns continuously over time and space, satellite data offers a discontinuous innovation over what is measured in the field.

We should not think of remote sensing as a replacement to field data, but as a complement.

The key advantages of satellite data are the scales of measurement: the frequency, resolution, and volume of observations. Yet evaluating data quality with a sparse sample of tens to hundreds of field plots will almost always provide noisy and underdetermined results. The scale mismatches are simply too great. So how should we do things differently?

Acknowledging satellite data as a discontinuous innovation would mean developing different usage patterns for space-based and field-based estimates. I think such patterns can be quantitatively informed by the uncertainties that emerge when linking these data together.

When it comes to carbon, it will probably always be the case that satellite-based carbon estimations report higher uncertainty at fine scales than field measurements. There is plenty of uncertainty in field measurements to begin with, which are further compounded when modeling these data with proxies from remote sensing (see Réjou-Méchain et al. for more). But as we wrote in our Forest Carbon Diligence paper, the fact that satellite carbon estimates have higher uncertainty at fine scales does not mean high uncertainty persists across all scales. Spatial aggregation plays a key mechanistic role here.

“Aggregating pixels over larger areas — field plots, carbon projects, protected areas, or states, for example — systematically decreases uncertainty when estimating the standard error of the mean, or of the sum of total carbon stocks. What’s most critical is that uncertainty measurements are well-calibrated; that multiple sources of uncertainty are propagated to the prediction intervals; that the spatial distribution of errors is quantified; and that any comparison between projects or between time periods robustly tests for differences based on this aggregated uncertainty.”

This is where I both converge with — and diverge from — Tom’s earlier quote: “field data remains the best available reference point for evaluating dataset performance.” Indeed, field data are unequivocally the standard for how we define and quantify carbon stocks. However, what I believe should change is how we evaluate performance. Since satellite-based precision will always be lower than field precision, we should not expect satellite data to perfectly align with plot data at the pixel level. Instead, the statistical power required to estimate carbon stocks — and to detect significant changes in stocks — will almost always require aggregation to coarser scales to sufficiently minimize uncertainty.

Some models for elegantly aligning satellite and field data already exist. Neha Hunka et al., for example, recently used thousands of National Forest Inventory (NFI) plots in Mexico to recalibrate satellite observations. This recalibrated model then provided out-of-sample, NFI-like estimates at unmeasured locations with empirically tested uncertainties.

Their approach to aligning field and satellite data addresses some key points. First, satellite estimates become consistent with state- or country-level protocols, such as the allometries used to convert field data to biomass. Second, while field data alone can estimate summaries over large areas with the right sampling design, fusing field and remote sensing data in model-based inference ensures that the resulting estimates are informed both by the sampled plots and by unsampled areas for which remote sensing data are available. This enables estimation for small areas — like Improved Forest Management projects — where the sample size might not be large enough to use field data alone (a strength of model-based inference noted by Ståhl et al.).

This approach could also kickstart a positive feedback cycle. As Hunka et al. point out, “...due to various financial and logistical constraints, only 10,959 of the 26,220 proposed NFI locations were sampled in the 3rd NFI cycle between 2015 and 2019, resulting in spatially-irregular, non-random gaps in the probability design across the domain.” Assuming similar constraints will apply in the future, which locations should be prioritized for the next cycle?

This is where the positive feedback begins. Since their approach provides spatially explicit uncertainties, this kind of information could prioritize sampling in locations that have relatively high expected information gain. More field data will yield better models, which benefits remote sensing, and better models can guide sampling to provide more informative data in future field collection efforts.

This example also highlights that satellite carbon mapping merits the collection of thousands of additional field plots. We need much more field data, not less! Field data are incredibly valuable, and provide the foundation of remote sensing science. And their value increases further when integrated with satellite data, providing the kind of detailed insights into global stocktakes that are not possible with either dataset independently. For me, the future is in fusion.

I would love to see the community advance to where field and satellite data coexist in a sort of adversarial mutualism. Remote sensing missions need thousands of field measurements for calibration and validation, and these measurements should be continuously funded and made accessible to support multiple missions (like BIOMASS, NISAR, GEDI, and private constellations). These plots should reveal where satellite observations get it wrong, spurring research into potential improvements. More field data should then be collected in high uncertainty areas to test these improvements and identify future frontiers for R&D. This process of continuous evaluation and data collection could create a virtuous cycle of benefits for both communities — and would represent another discontinuous innovation over the status quo.

Let’s work together to make this a reality. As a satellite data provider, we want to advance the science and advance the impact of the great work done in this domain through collaboration. It’s one of the many reasons we’re excited to be a part of the growing Cecil community. We can go farther if we go together.

h/t to Max Joseph for his inputs on model-assisted design and sample prioritization, and to the Cecil team for their feedback during the writing process.

Recent updates at Cecil:

• The IBAT Alliance’s core datasets: IUCN Red List of Threatened Species, World Database of Key Biodiversity Areas, and the World Database on Protected Areas

• The Natural History Museum’s 1 km and 10 km Biodiversity Intactness Index

• Impact Observatory’s 9-class land cover

• JRC global forest cover and global forest type

• University of Maryland (GLAD) global forest change

• Lobelia’s surface soil moisture 30 m

• Planet’s 20 m and 1,000 m soil water content

Join our Slack Community to join the conversation with over 430 nature data enthusiasts

Sign up for a Cecil account and get started here.

Two major collaborations announced

We just announced collaborations with the IBAT Alliance and the Natural History Museum (NHM) London. This means that Cecil now supports initial screening for nature risk across portfolios and supply chains.

These announcements cap off a huge few months of new datasets available on the platform:

• The IBAT Alliance’s core datasets: IUCN Red List of Threatened Species, World Database of Key Biodiversity Areas, and the World Database on Protected Areas

• The Natural History Museum’s 1 km and 10 km Biodiversity Intactness Index

• Impact Observatory’s 9-class land cover

• JRC global forest cover and global forest type

• University of Maryland (GLAD) global forest change

• Lobelia’s surface soil moisture 30 m

• Planet’s 20 m and 1,000 m soil water content

Get started with Cecil today, explore our documentation to learn more about these and other datasets available on the Cecil platform, or join our Slack community to connect with like-minded nature data enthusiasts.

New York Climate Week

We’re looking forward to this year’s New York Climate Week. Myself, Tom Walker, our Head of Science (tom@cecil.earth), and Connor Jordan, our Strategy & Partnerships Manager (connor@cecil.earth), are in NYC with me. Drop us a line to set up a meeting or say hi!

Here are the events we’re speaking at:

AI Explorers Summit 2025

September 21 9:00 am - 4:00 pm

Hosted by the Climate Collective, Conservation International, TechChange, and UNDP. Tom will give a short talk about Cecil’s role in helping the nature data sector reach scale.

NYCW Climate Tech Kickoff

September 22 6:30 pm - 9:30 pm

This event is co-hosted by one of our investors, Superorganism. I’ll be talking about our vision for the future.

GeoNYC Relaunch: Climate Week Edition

September 23 6:00 pm - 8:00 pm

This event brings together people passionate about the power of location data. I’ll be giving a lightning talk on understanding nature risk for global supply chains.

Data and Dumplings

September 24 8:30 am - 10:00 am

This is our event! Come by to meet fellow geospatial nature data nerds as we mingle, eat dumplings, and hear from Arbonics, GBIF, and the Natural History Museum (London).

Tech Workshop: Open Data in Applied Risk Analysis

September 24 1:00 pm - 4:30 pm

This will be a great event for the geospatial community. I’ll give a demo Cecil’s IBAT collaboration.

Earth Observation for Climate

September 25 3:30 pm - 7:30 pm

Hosted by TerraWatch Space, this event brings together a variety of folks to explore the role of satellite data in powering climate solutions. I’ll be speaking on a forest data panel.

You can also find us at:

• The Nature Hub (Sep 22-24)

• Deloitte’s Horizons Climate Week conference (Sep 22-25)

• CI’s and NTC’s Finding the Signal in the Noise (Sep 25)

• Superorganism’s Superbloom Rooftop Cocktails (Sep 25)

This expression from Alfred Korzybski – that a map is a model of reality, not reality itself – could not be more apt for nature data. Every dataset we create is a model of nature. But nature is a mosaic of interconnected ecosystems, each containing many thousands of interacting components that vary over time and space. This staggering complexity makes it impossible to model nature perfectly. Every measurement, no matter how sophisticated, is still an estimate.

As nature data users, we must operate in spite of this uncertainty. We must decide which datasets to apply to different contexts, the level of accuracy needed for a use case, and how much to work and pay for higher performance. These decisions matter, with poor dataset choices contributing to overclaimed carbon credits, delayed wildfire warnings, and misdirected conservation efforts.

We believe that this decision process – i.e., dataset evaluation – must be standard practice in nature data workflows, yet many teams lack the tools to do it. This newsletter introduces how we are thinking about dataset evaluation at Cecil. We first explore different sources of uncertainty and how they manifest in nature data. We then propose a three-pillar framework for evaluating nature datasets, which we’re operationalising on the Cecil platform.

Sources of uncertainty in nature data

Generating nature data is a multistep journey, from deciding what to measure, to designing protocols, to collecting and processing data. External factors and decisions along this journey create different sources of uncertainty:

• Conceptual mismatches – not measuring a variable directly (e.g., using spectral diversity as a proxy for species diversity).

• Instrument limitations – biases or limits in equipment (e.g., soil temperature probes only measuring to 10 cm depth where plant roots extend to 30 cm).

• Environmental conditions – local conditions affecting measurements (e.g., cloud cover partially obscuring satellite sensor readings).

• Methodology – design decisions and tradeoffs (e.g., optimising models for time series change detection at the expense of individual observation accuracy).

• Data processing – artefacts introduced by data wrangling (e.g., sharp lines appearing on rasters due to mosaicking of input data tiles).

• Human error – mistakes in measuring or recording data (e.g., species misidentification in field surveys).

These uncertainties primarily affect dataset performance in two ways:

• Accuracy – the proximity of a measurement to the true value (e.g., a rainfall sensor reads 80 mm when rainfall is actually 100 mm).

• Precision – the similarity/consistency of repeat measurements (e.g., repeat readings of 95 mm, 105 mm, 114 mm).

Data providers often express these uncertainties through confidence intervals, which represent the range within which the true value falls (e.g., 100 ± 20 mm).

Sources of uncertainty can occur individually or together, with multiple sources often having compounding effects on performance. For example, even modest errors in vegetation height and wood density measurements can propagate into larger errors when combined in a biomass calculation.

Uncertainty also varies at different scales of data aggregation. For instance, in remote sensing datasets pixel-level uncertainty is often high, but uncertainty decreases as pixel values (and their uncertainties) are aggregated to make estimates over larger areas.

A framework for nature data evaluation

It is impossible to solve for all uncertainty in nature data – and it’s not necessary to. Yet to make informed decisions, users need a systematic way to evaluate how uncertainty impacts dataset performance. Industries like pharmaceutical research and weather forecasting have made dataset evaluation standard practice. But the nature data sector has yet to do so. Our users regularly ask "which dataset performs best?" And without a shared framework and resources, there’s no obvious answer.

At Cecil, we're building tools to change this, which revolve around a three-pillar framework of dataset evaluation: (1) dataset inspection for initial screening, (2) cross-comparisons among multiple options, and (3) benchmarking accuracy against a source of truth.

Dataset inspection

Dataset inspection is the process of checking the details and behaviour of a single dataset to confirm it is suitable for a use case:

• Dataset applicability – checking documentation to confirm a dataset measures what you need it to (e.g., has spatial coverage for your area of interest) and the methodology is appropriate (e.g., has sensitivity to detect expected values or change). Reveals conceptual mismatches, instrument limitations, and methodology issues.

• Expected behaviour – exploring patterns in dataset values and confidence intervals that may signal a problem (e.g., step changes in values due to updates in sensor technology). Reveals effects of environmental conditions, data processing issues, and human error.

• Use case sensitivity – confirming that a dataset is able to detect a relevant event (e.g., deforestation in your region of interest). Reveals how all sources of uncertainty impact suitability.

At Cecil, we enable dataset inspection by providing standardised dataset documentation for all datasets, as well as making it straightforward to sign up, request data for an area of interest, and begin working with the data. See our documentation to get started.

Cross-comparisons

Cross-comparisons go beyond dataset inspection to examine how datasets perform relative to each other. Cross-comparisons help to reveal how different sources of uncertainty together affect dataset performance. For instance:

• Spatial and temporal comparisons – using pixel-level correlations, difference maps, and time series plots to identify where and when datasets agree or disagree.

• Multidimensional comparisons – applying approaches like multidimensional scaling to examine whether datasets with multiple variables and time points show similar patterns overall.

• Uncertainty analysis – exploring uncertainty values to identify where confidence intervals overlap (e.g., to flag where high uncertainty may be responsible for disagreement between datasets).

• Relative use case sensitivity – comparing the ability of different datasets to detect a relevant event (e.g., deforestation in your region of interest).

• Ensemble approaches – treating multiple datasets as an ensemble (e.g., using the median as an estimate and spread between datasets as an indicator of uncertainty).

To do cross-comparisons, you must first align datasets. Cecil handles this by delivering all data into one database and allowing users to join datasets at the pixel level by transforming them to the same coordinate reference system and spatial resolution.

Try this Jupyter Notebook to get started with basic cross-comparisons of plant biomass datasets on Cecil.

Benchmarking

The gold standard of dataset evaluation is to benchmark accuracy against an accepted source of truth (e.g., validating weather models against meteorological station observations).

For nature data, this creates a challenge: the source of truth is usually field data that contains its own uncertainties. Field surveys to assess bird diversity can miss species that rarely sing, and converting tree basal area measurements into biomass estimates carries inherent uncertainty. Even so, high quality field data remains the best available reference point for evaluating dataset performance.

A typical benchmarking analysis evaluates how dataset values deviate from those of a field reference dataset while accounting for uncertainty in both datasets. The more comprehensive the field reference dataset the better – benchmarking should ideally consider all regions, ecosystems, and contexts (e.g., coastlines, steep slopes) covered by the test dataset. This poses another challenge: generating a comprehensive reference dataset of sufficient scale for global usage is an expensive and monumental task.

We are working with the community to resolve this, and expect to bring reference datasets onto the Cecil platform soon. Join our Slack community if you’d like to stay updated and join the conversation with over 250 nature data enthusiasts.

Towards standardised dataset evaluation

The nature data sector has yet to implement dataset evaluation as standard practice. Data providers inconsistently publish performance metrics, and licensing restrictions can hinder third-party assessments. At the same time, the challenges of benchmarking against imperfect field data make it easy to avoid dataset evaluation entirely.

Thankfully, this is changing. Organisations like Sylvera and Adventure Scientists are building rigorous field reference datasets. Companies like Equitable Earth (formerly ERS) have begun to publish their benchmarking reports. At Cecil, we are making dataset evaluation accessible by building tools to inspect, compare, and benchmark datasets in nature data workflows.

Dataset evaluation will ultimately reveal limitations and uncertainties, but this transparency will yield more reliable and actionable insights. Better decisions come from understanding what our data can and cannot tell us.

If every map is imperfect, we should strive to know where the imperfections lie.

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Recent updates at Cecil:

• Planet’s two soil moisture content datasets are now available on Cecil

• WRI’s SBTN Natural Lands dataset is now available on Cecil

• Join our Slack Community to join the conversation with over 250 nature data enthusiasts

Sign up for a Cecil account and get started here.

I want to acknowledge that an image previously included in this newsletter was missing information and context. As a result, it may have led to confusion or incorrect assumptions about the analysis in the image.

The image represented pairwise scatter plots of aboveground biomass (AGB; Mg/ha) values from Chloris Geospatial, Kanop, and Planet datasets (dotted line = 1:1). Points are all annual pixel values spanning 2013-2024 from one AOI (118 ha) in Kakadu National Park, Australia (N = 316536). Datasets were transformed to the same coordinate reference system (EPSG:4326), spatial resolution (0.00025º; ~ 30 m on the equator), and pixel grid.

To provide more context, I’ve included an aerial image of the AOI.

At Cecil, we’re fortunate to work with world-class data providers who are actively shaping the future of environmental markets. We’re grateful not just for our partnerships, but for their transparency and commitment to building a robust ecosystem.

Our partners are already deeply engaged in efforts aligned with the framework we outlined, including:

• Publishing detailed dataset pages on our platform, with usage notes, accuracy assessments, technical specs, and fact sheets

• Participating in initiatives like the Equitable Earth AGB benchmark: https://docs.ers.org/standard1.0/agb-benchmark.pdf

There’s still a long road ahead and collaboration will be key to getting it right. Thank you for all your comments and discussions from this newsletter.

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A number without context has little meaning at all. Consider a temperature value of 23.5 – is it today’s weather or the mean temperature of the past century? Measured by hand or satellite sensor? In celsius or fahrenheit? Without these details, it’s impossible to know what this number means and how to use it in a data analysis workflow.

These details are called metadata. Metadata is the information that provides context to numbers and datasets, telling users what they are, how they were generated, and how to interpret them. Metadata is critical to all domains that use data, including environmental and geospatial data science. And metadata is becoming even more essential as AI begins to leverage nature data at unprecedented scale and without human supervision.

At Cecil, we think about metadata every day and continue to explore the best ways to represent it in our platform. This newsletter shares some of the key things we’ve learnt along the way. We first examine different types of metadata, then introduce some of the most relevant metadata standards and frameworks for nature data. Finally, we touch on why metadata is becoming even more important as AI transforms how nature data is used.

Types of metadata

Nature data is multidimensional, typically consisting of a range of measurements (variables) taken at multiple locations over time (observations). Metadata can exist on all dimensions of this data.

Consider a simple case where a dataset is delivered in a table, with observations as rows and variables as columns. Metadata can apply to a single cell in that table, a single row or column, groups of rows or columns, or the entire table:

Datapoints

Datapoint level metadata describes properties of specific observations for specific variables (i.e. individual table cells). Examples include flagging outliers, sensor failures, or other problems isolated to individual datapoints. Cell-wise metadata is usually stored in a separate table with the same row-column structure as the main data, which is then indexed to filter datapoints as required.

Observations

Observation level metadata describes properties of observations that apply to all variables (i.e. row-wise). Examples include latitude/longitude locations, sampling dates, or quality flags that apply to groups of pixels from the same raster tile (e.g. cloud cover). Increasingly, row-wise metadata is stored as additional variables in the main data table (i.e. denormalised data), which maximises the efficiency of data operations.

Variables

Variable level metadata describes properties of variables that apply to all observations (i.e. column-wise). Examples include measurement units and limits (e.g. maximum 100%), or identifiers for variables from the same measurement platform (e.g. Sentinel-2 bands). Column-wise metadata is usually stored in a lookup table, where each row represents a variable in the main data table and columns contain metadata about each variable.

Variable level metadata can also allow for grouping of variables to simplify interpretation. One example is the IUCN’s Global Ecosystem Typology dataset, where the presence/absence of 109 ecosystem functional groups can be simplified to 25 biomes or 10 realms using a metadata lookup table:

Datasets

Dataset level metadata describes properties of the entire dataset (i.e. table-wise). Common examples include dataset name or version, as well as how missing data is encoded. Table-wise metadata can be stored in a variety of different ways, including dataset or file level tags, changelogs, or product documentation.

Common metadata standards

It is challenging enough to organise metadata for one dataset, let alone multiple datasets that draw on different measurement technologies and bridge geospatial and environmental science domains. In fact, in our experience no two nature datasets deliver metadata the same way.

Thankfully, recent efforts to align on metadata standards and frameworks have begun to enable greater data sharing, interoperability, and automation across the nature sector. While new resources are still emerging, here are a few of the common standards and frameworks that are used for nature data today:

• Geospatial data:

  • ISO 19115 – the global metadata standard for geospatial datasets

  • SpatioTemporal Asset Catalogs (STAC) – a metadata specification for indexing geospatial data

  • CF Conventions – a metadata standard for climate model data

• Biological data:

  • Darwin Core – standard identifiers, labels, and definitions for biodiversity data

  • Ecological Metadata Language (EML) – a standard vocabulary and syntax for ecological research data

  • MIxS – a metadata specification for genomic samples (e.g. eDNA)

• General principles:

  • FAIR principles – guidelines to ensure data is findable, accessible, interoperable, and reusable

  • CARE principles – a framework to ensure Indigenous data sovereignty

  • DataCite – a framework for citing and tracking research datasets and assets

Metadata and AI

Organising metadata effectively is already important today. However, as AI becomes more prevalent in nature data applications, metadata plays an increasingly critical role in ensuring that these systems work as intended – i.e. accurately and ethically in absence of human supervision. Some examples include:

• Training data – metadata is needed to constrain predictions based on what the model was trained on (e.g. satellite-based models need metadata about sensor calibration, cloud masking, etc.)

• Data quality – metadata is needed to improve predictions by flagging outliers and poor quality values (e.g. weighting gap-filled observations differently)

• Automated data discovery – metadata allows AI systems to automatically find and use relevant datasets (e.g. STAC allows AI to automate discovery of satellite data for specific regions / dates)

At Cecil, we build data infrastructure to make nature data accessible and interoperable. To this end, we have the privilege of supporting the whole sector in its efforts to build a standard and mature approach to metadata. We are excited to be working alongside our partners and peers on this, and look forward to the opportunity to share more soon.

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Recent updates at Cecil:

• The GLAD laboratory Global Forest Change dataset (from Hansen et al. 2013) is now available on Cecil

• The IUCN Global Ecosystem Typology dataset (from Keith et al. 2022) is now available on Cecil

• Join our Slack Community to learn from other teams tackling nature data

Get in touch if you’d like to learn more about the Cecil platform, or the work we’re doing to prepare the nature data industry for scale.

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“$30,000 for soil baseline data.”

We recently brought together a group of nature professionals, where a regenerative agriculture project developer mentioned they had sold soil sample data from a baselining assessment to two remote sensing companies for $30,000 each, covering the cost of the initial baselining work. This anecdote signals a greater trend: robust in situ measurements of nature are valuable, and this value is increasingly recognised by the nature sector.

A nature data economy – i.e. the trade of nature data among willing participants – is here. This brings both exciting opportunities for the nature tech and data sector and raises questions about the readiness of the industry to deliver on the rigour, scale, ethics, and governance required.

This newsletter is the first in a new series focusing on the opportunities and challenges of the emerging nature data economy. In this first edition, we cover the foundations of data exchange. We explore why a nature data economy is needed, introduce the roles that exist within it, and touch on considerations for responsible data sharing. We then provide examples of successful data sharing as a foundation for a responsible and effective nature data economy.

This newsletter expands on previous newsletters covering specific aspects of sharing nature data, including nature data licensing and the Nature Data Sharing Initiative.

Why is a nature data economy needed?

Representing the natural world through data requires combining a mosaic of digitised information collected using different technologies and delivered in a spatially explicit, temporally explicit, and accurate way. Although large amounts of environmental data exist today, most of it is collected to order, remains siloed in private repositories, and is in technical formats unfamiliar to even advanced data scientists. Teams frequently tell us they struggle to find, access, and use the data they need.

The nature data economy bridges this gap. By incentivising the exchange of data and providing the mechanisms to do so, the nature data economy facilitates the seamless sharing of nature data between organisations in a rigorous, equitable, and useful manner.

But the sharing of nature data is still in its infancy, and multiple technical, scientific, and ethical challenges prevent it from reaching scale. At Cecil, we believe an equitable data sharing culture is the foundation for enabling the exchange of data because it standardises the language, roles, responsibilities, and practices among participants.

Roles in the nature data economy

There are three basic roles involved in the exchange of nature data:

• Nature Practitioners: groups that collect nature data through their work managing and conserving natural environments (e.g. land stewards, Indigenous Peoples and Local Communities, not-for-profit organisations, researchers, and private sector entities).

• Data Providers: organisations that generate and transform field observations and other measurements into meaningful variables and datasets (e.g. see our documentation).

• End Users: groups that use nature data (e.g. forest managers, farmers, insurance providers, and companies preparing for like EUDR and TNFD reporting). This group also includes service providers that translate data products into insights for other End Users.

A single organisation can play all three roles: a nature conservation charity may collect in situ observations for their own use (Nature Practitioner), sell this data for use by others (Data Provider), and use additional data generated by third parties (End User).

Yet these personas typically lack the resources and incentives to fully explore their data sharing options. In situ observations collected to baseline a nature conservation project are used once then archived, while organisations building global datasets struggle to find sufficient in situ observations to train and validate models. With no common vocabulary or framework for sharing data, it is hard for different groups to align on interests, price, and mutual motivations. So data is simply not shared.

Responsible data sharing

Much like privacy concerns surrounding online personal data, the exchange of nature data raises ethical and governance considerations that the sector has yet to resolve. At the highest level, responsible nature data sharing must consider1:

• Sovereignty: the rights of individuals, groups, and organisations to exercise ownership and control over data related to them, their communities, and their lands.

• Permanence: the ability to control, govern, and provide access to data, knowledge, and resources in perpetuity.

• Provenance: an audit trail of the data supply chain going back to the origins of the data.

• Licensing: the terms for data sharing, ranging from Public to Open to bespoke options.

• Sensitive Data: data with greater risk of intended or unintended harm and misuse. Examples include: Personally Identifiable Information (e.g., addresses); bioculture knowledge (e.g., traditional ecological knowledge); species location data (e.g., the location of endangered black rhinos); and commercial and competitive data.

Similar to the challenge above, the lack of a common understanding hinders this process.

Data sharing initiatives in practice

Despite the challenges ahead, initiatives are now emerging to align language and standards across the ecosystem, showcase what successful nature data sharing looks like, and call the sector to action in the pursuit of a mature nature data economy. Some examples are:

The CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) support Indigenous Peoples’ self-determination, right to resiliency development, and the right to control access to, use of, and benefit from research data.

The Enhanced Rock Weathering (ERW) Data Quarry is one of the first systems of its kind to proactively share commercial data for the benefit of scientific learning in the Carbon Dioxide Removal (CDR) space.

The FAIR data principles (Findable, Accessible, Interoperable, and Reusable) provide a framework for making research data more discoverable and usable by ensuring data can be easily located, accessed, and reused across different systems and contexts.

Finally, Cecil, alongside a group of 16 cross-sector organisations, recently launched the Nature Data Sharing Initiative (NDSI). The NDSI provides guidance notes, a data sharing worksheet, and a template agreement to enable effective and equitable sharing of Nature Practitioners' field data.

The rise of the nature data economy

At Cecil, we believe that a responsible, equitable, and effective nature data economy is here – and is essential for the planet. The sharing of existing siloed data would help fill critical gaps, like tracking the one in six IUCN-listed species that lack sufficient data. New pathways to finance data generation by Indigenous Peoples and Local Communities would create best-in-class data and well-paying jobs that require their intimate knowledge of the land. Innovations in science and tech would bloom alongside Nature Practitioners, being fed by both the new data and the economy.

Perhaps most importantly, the rise of a mature nature data economy could be just in time to give us the depth of information we need to act – to work alongside nature effectively to mitigate the worst effects of the global polycrisis.

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This newsletter is created by Cecil. We have some exciting announcements coming soon about our data infrastructure platform, stay tuned!

Here are our recent updates:

• We announced our destination partnership with Felt

• We announced data partnerships with Impact Observatory and Space Intelligence

• Two Joint Research Council (JRC) datasets are now available on Cecil: Global Forest Cover 2020 (v2) and Global Forest Type 2020 (v0).

Previous newsletters have explored the foundations of nature data – from spatial data standards and libraries, to nature data sharing. Our last newsletter explored nature data licensing as a key resource in our Nature Data Sharing Initiative (NDSI).

Understanding LULC

The Earth’s surface is a mosaic of natural and human features. Land use and land cover are two overlapping concepts that systematically classify the land surface into a set of named classes: what physically exists (land cover) and how humans are using it (land use).

Land cover: The physical reality

Land cover describes what’s physically present on the land surface. Common classes include:

Land use: Human activity

Land use describes how humans use the land surface. While classifications vary between data sources, common classes include:

How land cover and land use interact

Through the use of land cover and land use together, LULC acts as a standard language for describing what exists at any location at a given point in time.

Land cover is a spatially explicit concept, meaning that a single location cannot be classified as more than one land cover class (e.g. water and forest). Land use also tends to be spatially explicit, although it is possible for there to be some overlap between land use classes (e.g. recreational and protected areas).

In combination, land use and land cover are not spatially explicit, in that it is common for a single location to have both a land use and land cover class. A real-world example of this is a forest national park, which is forest (land cover) and a protected area (land use).

Read our LULC concept note to learn more about LULC as a scientific concept.

LULC use cases

LULC data serves multiple purposes in nature data workflows:

Operational value

LULC is of direct value to operational workflows. Examples include enabling natural hazards monitoring (e.g. landslips), guiding management planning (e.g. new planting), tracking change over time (e.g. forest growth), and informing urban and regional planning.

Regulatory compliance

LULC data supports compliance with multiple regulatory frameworks. For instance, land cover change from forest to another LULC class is necessary to identify historic deforestation for the EU’s Deforestation Regulation (EUDR). LULC is also used by companies evaluating their dependencies and impacts on nature through the Taskforce for Nature-Related Financial Disclosures (TNFD) LEAP approach.

Supporting other scientific concepts

LULC provides useful context to other scientific concepts. For instance, when combined with plant biomass data, LULC data can help explain why biomass varies over space and time, support the selection of appropriate data sources (e.g. products optimised for moist tropical forest), and guide stratified sampling strategies for field measurements.

LULC also unlocks new use cases that require LULC plus plant biomass data, such as identifying candidate sites for forest regeneration.

LULC datasets

Quantifying LULC

LULC is usually represented as a grid of pixels, where each pixel is assigned a single class, probabilities for multiple classes, or even a fractional cover for one or more classes. These classes can be used directly or aggregated to calculate the proportion of each class in a given area, such as a forestry asset or national park.

Most LULC datasets are generated using machine learning models trained on multispectral satellite data. While land cover classifications can be determined from satellite data alone, additional data sources, like socioeconomic information, government data, and road networks, are usually necessary for determining land use classifications.

Dataset performance varies by region and class type. Global products like Dynamic World enable standardised analysis across regions by using consistent methods and classes worldwide. In contrast, national datasets, like the USGS National Land Cover Database, optimise their methods and classifications for local contexts, offering deeper insight within specific regions but reduced comparability between them.

Common LULC datasets

A slew of LULC datasets have become available in recent years, differing in methodology, coverage and resolution, and the classes they use. Some examples include:

Example: Dynamic World

To illustrate how LULC datasets inform nature analysis, let's explore Google and WRI’s Dynamic World dataset – which is available on the Cecil platform. The dataset delivers global land cover data at 10 m spatial resolution with 2-5 day temporal frequency from 2015 to present, using Sentinel-2 satellite data to classify nine land cover types.

One strength of the Dynamic World dataset is its probabilistic approach. As well as assigning a land cover classification for a pixel, it provides probability scores for each land cover class. This flexibility enables teams to use consensus classifications, set user-defined accuracy thresholds, or create custom classifications by combining probabilities with other datasets.

Looking ahead

Land Use and Land Cover is the second scientific concept we are introducing, alongside Plant Biomass. In future newsletters, we'll explore how different datasets compare, best practices for working with LULC data, and analysis approaches combining LULC with data from other concepts.

Visit our documentation to learn more about our scientific approach, read concept notes on the scientific concepts we support, and explore the datasets available on Cecil today.

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Over the past 18 months, we've worked with nature practitioners, legal teams, data providers, and data users to understand considerations for the appropriate use of nature data. This newsletter shares our learnings and a resource to help anyone in the ecosystem better share or use nature data.

This is the second edition in our multi-part series on data sharing. Our previous newsletter introduced the concept of nature data sharing, explored its challenges, and explained its importance. We also launched the Nature Data Sharing Initiative (NDSI) - a living collection of resources to promote responsible and impactful data sharing among teams working on nature.

Who’s involved in nature data licensing today?

Nature data licensing has become crucial for organisations worldwide - from nature-tech companies building models to support with site-level analysis for corporations making decisions on nature risks. Many of these organisations increasingly have questions about data availability and thoughtful access that respects legal, ethical, and cultural considerations.

Similarly, nature practitioners such as Indigenous Peoples, land stewards, community action groups, nonprofits, and private organisations are looking to share more of their data while also ensuring it is appropriately protected and/or that they are appropriately compensated.

What are the different licensing approaches?

In general, there are three main licensing approaches for publishing data with different benefits and considerations:

• Public dedication: data is dedicated to the global public domain with no restrictions;

• Open licence: highly permissive licence that allows unrestricted reuse but may include conditions such as a requirement to attribute the data holder; and

• Bespoke licence terms: named party-to-party relationship negotiated with flexible permitted uses and/ or restrictions, including commercial terms if relevant.

Case study: Licensing forest data

In our last newsletter, we introduced a forest data sharing use case involving land stewards and data providers. Today we also introduce the potential use of the data provider's model in a corporate reporting, and outline the licensing considerations for all parties.

Field measurements for model training

Land stewards gather baseline field data before launching a forest carbon project. This data typically includes biomass and tree metrics, geospatial coordinates, text records, georeferenced images, and other relevant metadata.

This field data serves multiple purposes: it provides evidence for forest carbon projects and can support data providers in training and validating their models. For example, field measurements serve as inputs and benchmarks for models that estimate various forest metrics — canopy cover, canopy height, forest cover, and living aboveground biomass. Examples of these provider datasets can be found in Cecil's documentation.

When collecting and sharing field measurements, it is important to carefully consider the legal and ethical dimensions involved. Data sharing often requires formal agreements or licenses between data owners and users. These agreements help address key issues such as copyright, privacy, sovereignty, and ownership. By navigating these considerations thoughtfully, both parties can ensure compliance and build trust in the data-sharing process.

This is particularly crucial in situations with multiple stakeholders and sensitive information, such as protecting Indigenous Peoples’ traditional ecological knowledge through Biocultural Intellectual Property Rights (BCIPR) or obfuscating location data of endangered species. Standardised frameworks like Creative Commons licenses for open data sharing, the sample template in NDSI for open or commercial data sharing, and frameworks guiding best practices like GBIF’s on sensitive species occurrence data can be utilised to appropriately facilitate this exchange.

Corporate reporting

Once a data provider develops a model for forest variables, it can support a wide variety use cases in carbon markets and corporate reporting of supply chain emissions and removals by:

• Improving project assessment and due diligence

• Ensuring effective dynamic baselining and removals monitoring

• Enhancing supply chain carbon footprinting by going beyond deforestation monitoring and working with spatially explicit carbon dynamics

• Ensuring GHG Protocol aligned forest carbon removals monitoring

When corporate and commercial users, such as data analysts and sustainability managers, utilise nature data, additional considerations and safeguards are required. These users need clear terms that define how the data can be processed and analysed. It is also important to establish guidelines for how any derived insights can be shared.

End-user licensing agreements play a key role in addressing these requirements. These agreements should cover commercial usage rights, data attribution requirements, and potential revenue-sharing mechanisms. By implementing these safeguards, organisations can ensure responsible and transparent use of nature data.

In this example for the forest carbon analysis, the agreement should also outline how the traditional ecological knowledge and local community data that trained the model will be protected from misuse. For these relationships to thrive, the parties must strike a balance between enabling valuable commercial applications while protecting the rights and interests of the original data providers and knowledge holders.

The Nature Data Sharing Initiative

If you want to keep learning about nature data sharing, please see the Nature Data Sharing Initiative (NDSI). NDSI is a collection of resources designed to promote data literacy and foster a culture of responsible, impactful data sharing among teams working on nature.

​​NDSI includes the following key resources:

• Overview: An introduction, overview, and guidance notes around nature data and Nature Practitioners’ field data;

• Guide: A guide on practical steps for sharing data;

• Template: A data sharing worksheet and sample data sharing agreement template.

View NDSI

If you’d like to contribute your expertise to this initiative, please reach out to us at alex@cecil.earth.

In future newsletters, we'll continue our focus on data sharing by exploring topics such as the significance of indigenous rights and field data, and offer other practical solutions for improving access to nature data.

Notices

• Cecil + Google are partnering to provide Google Earth Engine (GEE) datasets on Cecil's nature data platform. Learn more.

• Chloris Geospatial share how their datasets empower organisations in carbon markets with trustworthy forest carbon data. Watch here

• Kanop release groundbreaking research on the innovative InSAR2InSAR model—a leap forward for accurate environmental monitoring. Learn more.

• Planet’s next generation, high-resolution satellite and 36 SuperDoves has arrived at Vandenberg Space Force Base ahead of next month’s launch. Learn more.

Thank yous

A special thank you to everyone who supported us this month with feedback, introductions, and advice.

Adam Weiner, Coby Strell, David Marvin, Danielle Rappaport, Elaine Mitchell, Florian Reber, Gregg Treinish, Helen Crowley, Kevin Webb, Mac Bryla, Matthias Mohr, Nate Trappe, Noah Golmant, Peter Levine, Romain Fau, Syakira Syafiqah, Sylvain Vaquer, Tom Quigley, and Wade Cooper.

Keep reading

• Making nature data accessible

• Nature data documentation: 3 key challenges

• Exploring spatial indexes

• Spatial data foundations for nature analysis

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

This newsletter is curated by Cecil, a team on a mission to make nature data accessible. Their platform helps data teams access analysis-ready commercial and public nature datasets, eliminating the need for cleaning, harmonising, and pre-processing tasks. Whilst currently focused on aboveground biomass, they will soon launch land cover and land use datasets to support market-leading nature-tech applications, consultants, and nature restoration professionals.

Enjoy the newsletter? Please forward to a friend.

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Most nature data is siloed in repositories and used by a very narrow audience today. Yet, if properly activated, this data could greatly improve our ability to understand and value natural ecosystems. At Cecil, we believe that an equitable data sharing culture can celebrate and appropriately compensate land stewards, improve cross-sector collaboration, and lead to new insights and knowledge — ultimately raising the bar on integrity in nature markets.

Today we're excited to start a new multi-part series focused on data sharing. We'll begin by introducing the concept of nature data sharing, the challenges faced and explain its importance. We’ll then share details on the launch of our Nature Data Sharing Initiative - a collection of resources to promote responsible, impactful data sharing among teams working on nature.

Nature data sharing: addressing the gap

Petabytes of nature data have already been generated, including remote sensing data and a large volume of field data from sampling sites distributed across the globe. Yet, one of the most common challenges we hear from teams working in this space is that they don’t have the data they need to make progress.

From companies building nature-tech applications, to corporates wanting to assess their nature-related risks, more questions are being asked about the availability of data and how it can be accessed thoughtfully - ensuring legal, ethical, and cultural considerations are met.

At the same time, various groups are generating nature data every day. They include land stewards, Indigenous Peoples, customary and traditional owners, community action groups, nonprofits, and private sector organisations. However, these groups often lack the resources and data capability to fully grasp their data sharing options. This results in missed opportunities, duplicated work, and siloed data.

For example:

• Land stewards gather baseline field data before launching a forest carbon project. This data could include biomass and tree metrics, geospatial coordinates, text records, georeferenced images, and other relevant metadata.

• Commercial data providers need field data for model training and validation, but find it challenging to source and collect new data.

As a result, without a shared vocabulary or standardised framework, it is challenging for different groups to align on interests, risks, and mutual motivations for data sharing.

Similar challenges once existed in venture capital, where early-stage startup founders lacked the resources and knowledge to negotiate early funding rounds. Tools like Y Combinator’s SAFE notes were created to offer balanced terms for both startups and investors.

The Importance of Nature Data Sharing

Representing our natural world through data requires a mosaic of digitised information that is collected in a spatially explicit, temporally explicit, accurate, and precise manner. As a result, it is hard to imagine a future that doesn’t include a framework for data sharing.

To achieve this, we believe coordinated action is essential. Here are a few examples of steps we can take together.

Fill data gaps and increase accessibility 

Field data is typically collected by individual organisations or community groups, siloed, and often remains fragmented and inaccessible. Making this data available can help fill critical gaps, such as for the one in six IUCN-listed species that lack sufficient data.

Create new revenue streams for data collection

Collecting field data is costly and time consuming. There is a need for new pathways to finance and compensate data generation by Indigenous Peoples and Local Communities. Implementing principles like CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) can promote responsible data management and ensure fair compensation for this specialised work.

Support innovation

Field data is essential for driving innovation in nature conservation, restoration, and stewardship. It serves as the gold standard for measuring natural systems, validating new technologies, and improving machine learning models that rely on ex-situ data (e.g satellites). Field data also supports scientific research, helping us better understand ecosystems, biodiversity, and the services they provide.

Announcing the Nature Data Sharing Initiative (NDSI)

We are excited to announce the Nature Data Sharing Initiative (NDSI). NDSI is a collection of resources designed to promote data literacy and foster a culture of responsible, impactful data sharing among teams working on nature.

​​NDSI includes the following key resources:

• Overview: An introduction, overview, and guidance notes around nature data and Nature Practitioners’ field data;

• Guide: A guide on practical steps for sharing data;

• Template: A data sharing worksheet and sample data sharing agreement template.

View NDSI

Cecil developed the NDSI in collaboration with numerous experts. A special thank you to: David Rountree and Emily Cravigan from Allens; Anthony Lloyd and Kendrick Deng from Ashurst; Alex Logan, Connor Jordan, and Tom Walker from Cecil; Florian Reber from Chloris Geospatial; Climateworks; Megan Pillsbury from Dunya Analytics; Gabi Ceregra from Food Agility; Sarah Schmude and Bert Glover from Impact Ag; Kate Wing from Intertidal Agency; Victor Alloy and Romain Fau from Kanop; Paul Young from Kita; Ňkwi Flores; Karl Burkart from One Earth; Alexis Moyer and Ed Mitchard from Space Intelligence; Samichhya Khadka; Camille Goldstone-Henry from Xylo Systems; and Chris Tolles from Yard Stick.

If you’d like to contribute your expertise to this initiative, please reach out to us at alex@cecil.earth.

In future newsletters, we'll continue our focus on data sharing by exploring topics such as the significance of indigenous rights and field data, and offer other practical solutions for improving access to nature data.

Keep reading

• Making nature data accessible

• Nature data documentation: 3 key challenges

• Exploring spatial indexes

• Spatial data foundations for nature analysis

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

This newsletter is curated by Cecil, a team on a mission to make nature data accessible. Their platform helps data teams access analysis-ready commercial and public nature datasets, eliminating the need for cleaning, harmonising, and pre-processing tasks. Whilst currently focused on aboveground biomass, they will soon launch land cover and land use datasets to support market-leading nature-tech applications, consultants, and nature restoration professionals.

Enjoy the newsletter? Please forward to a friend.

Subscribe now

We envision a world where nature data improves the way we understand and value nature. This future drives Cecil’s mission to make nature data accessible.

Over the past six months, we've explored the technical, scientific, and commercial challenges of accessing nature data. This edition summarises these accessibility barriers and explains Cecil's approach to addressing them.

We’re also excited to give our subscribers the first look at Cecil documentation. Get in touch if you want to join our Early Access Program (EAP).

Why is nature data hard to access?

Finding & selecting datasets

With thousands of nature datasets available, it is difficult to choose a dataset that meets the right criteria to support an analysis workflow. A lack of transparent dataset documentation also makes it hard to identify important details of methodologies, like locations of ground truth data, post-processing to correct for sensor biases, or handling of NoData values.

End users are left unable to confidently evaluate trade-offs when deciding between nature datasets. It’s common to spend weeks trawling through websites, documents, and academic papers, as well as engaging directly with providers – all to get the information needed to start an analysis workflow.

Integrating and preparing datasets 

Building integrations to work with one nature dataset is expensive and time-consuming – and that cost grows exponentially with each additional dataset. Each integration requires managing thousands of files, unpacking and processing the data, and incorporating the outputs into internal data infrastructure.

The lack of standardisation across datasets, such as measurement units, spatial resolution, CRS, or bounding box, makes it challenging to build a scalable solution with different data providers. Months can be wasted cleaning, pre-processing, and harmonising data before any data is ready for analysis.

Navigating commercial agreements and usage rights

Data providers employ a variety of business models and apply their own usage rights to data, making it complex to understand the range of applications each dataset can be used for, and what the total data usage costs will be.

In addition, commercial data providers often have a minimum spend requirement for data acquisition. This can create prohibitive costs for teams seeking small amounts of data for their AOIs (e.g. less than 100,000 ha).

These barriers collectively hinder teams looking to use nature data. As a result, vast segments of the market lack the means to properly understand, account for, or value nature.

Introducing Cecil

Cecil’s data platform makes nature data consistent, accessible, and ready for analysis. This includes the entire process of data acquisition from selected providers, data preparation using a preferred CRS and spatial resolution, and secure data access to connect nature data directly to analysis workflows. With Cecil, teams can easily monitor and track changes to nature at scale.

Curated datasets

Cecil identifies the core scientific concepts that represent nature, determines the most effective variables and technologies for measuring them, and curates multiple datasets to baseline and track change in these variables at the site level. We currently support a range of plant biomass datasets. Land use and land cover, as well as deforestation, are next on our roadmap.

Search the curated list of datasets by spatial and temporal attributes and assess datasets with Cecil’s documentation.

Analysis ready

Access nature data in a consistent format across all data providers. Cecil’s API allows users to acquire nature datasets from multiple providers and make all datasets joinable by configuring them to their preferred CRS and spatial resolution.

Cecil manages and processes files from chosen providers, delivering data into a database optimised for time-series and spatial analysis at scale. The platform allows users to securely connect programming languages and tools, such as Python, R, GIS, and BI tools, to deliver nature data directly into analysis workflows.

One stop for access

Build using Cecil with one contract, one integration, and no minimum usage requirements. Cecil partners with leading data providers to streamline contracting, compliance, integrations, and technical support.

Start accessing nature data today. Read the documentation to learn more about our API, the datasets we currently support, and the science behind our approach.

Read the documentation

Notices

• London's nature data community are gathering to geek out over dumplings and drinks this Wednesday 18th September. RSVP here.

• Cecil + The Landbanking Group are hosting a breakfast during New York Climate Week on Thursday 26th September. RSVP here.

• Cecil + Earthmover are bringing New York’s nature data community together to geek out over dumplings and drinks on Thursday 26th September. RSVP here.

• Chloris Geospatial is hosting a breakfast during New York Climate Week to share how new VCM standards, project developers and buyers of carbon credits are using new monitoring technologies. RSVP here.

• Kanop is running a pickleball, bagels and networking event at New York Climate Week. RSVP here.

Thank yous

A special thank you to everyone who supported us this month with feedback, introductions, and advice.

Adam Weiner, Alex Burns, Coby Strell, Danielle Rappaport, Elaine Mitchell, Gregg Treinish, Helen Crowley, Mac Bryla, Matthias Mohr, Nate Trappe, Noah Golmant, Peter Levine, Roberto Miethe, Romain Fau, Syakira Syafiqah, Sylvain Vaquer, and Wade Cooper.

Keep reading

• Nature data documentation: 3 key challenges

• Exploring spatial indexes

• Spatial data foundations for nature analysis

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

Sponsorship

Do you want to share monthly updates with our engaged audience of 1700+ nature data users and investors? Please contact rory@cecil.earth to find out more about our newsletter sponsorship packages.

This newsletter is curated by Cecil, a team on a mission to make nature data accessible. Their platform helps data teams access analysis-ready commercial and public nature datasets, eliminating the need for cleaning, harmonising, and pre-processing tasks. Whilst currently focused on aboveground biomass, they will soon launch land cover and land use datasets to support market-leading nature-tech applications, consultants, and nature restoration professionals.

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In our last newsletter, we explored challenges caused by the increased scale and complexity of nature datasets, and discussed spatial indexes. In this newsletter, we address one of the most common difficulties faced by individuals working with nature data: finding, accessing, and understanding documentation.

3 challenges faced when using nature data documentation

Nature data documentation provides essential information about datasets, enabling users to understand and effectively utilise the data. It explains what a dataset quantifies, defines each variable, guides data access, and supports analysis design. It highlights methodological or technical limitations, and indicates situations, or locations where the data may be more or less reliable.

Documentation is crucial at all stages of a nature data workflow because:

• Nature data is generated from a combination of technical inputs, including existing datasets, new measurements, and statistical models.

• Each input carries its own biases or limitations, and data providers vary in how they handle these elements.

• Data providers deliver data in a range of formats and parameters, often through custom user interfaces or data integrations.

Our research, which involved over 30 professionals working with nature data, revealed three common documentation challenges:

1. Incomplete or outdated: Important details are omitted, such as the regions covered by training data or basic criteria like spatial resolution. Documentation frequently lags behind live products and lacks version control for new features and changes to existing ones.

2. Fragmented: Users often need to switch between several documents simultaneously. Documentation can also be hidden in the bowels of websites or locked behind paywalls or email sign-ups.

3. Inconsistent or ambiguous: Users often need to grapple with technical language, dense prose, or inconsistent formatting. Naming conventions can be ambiguous, either because one term is used to represent multiple things or because names differ across API references, scientific documentation, and company websites.

These challenges arise because nature data products bridge different disciplines across environmental science, spatial data analysis, and software engineering. This complexity creates unique expectations for documentation.

As a result, individuals spend weeks trawling through websites, documents, and academic papers, as well as engaging directly with providers – all to get the information they need to start an analysis workflow.

Principles for nature data documentation

At Cecil, we've encountered these same challenges firsthand. We believe two key principles can help overcome them and create effective nature data documentation: completeness, and accessibility

Completeness

The cornerstone of good documentation is completeness. Users need to understand how a dataset was created, its limitations, and how to access it. Without this information, datasets are prone to misuse - such as applying them to regions where their accuracy is low.

Best in class examples of complete documentation include scientific papers, where every scientific paper includes a methods section that provides enough detail about data collection, processing, and validation to repeat the study, and software documentation, where information is up to date, consistently named, and well-organised with detailed examples throughout.

We've outlined the minimum content that we expect in nature data documentation:

• Data collection: the technologies and methods used to create input data

  • If using existing data, include source, version, and date accessed

  • If collecting new data, include devices (e.g. manufacturer, model), methods (e.g. ISO standard), and sampling details (e.g. location, plot sizes, dates)

• Data processing: the steps taken to generate output data from input data

  • How the input data is pre-treated

  • The calculations or statistical models applied, and in what tools (e.g. linear regression)

• Dataset validation: the benchmarking done to evaluate dataset accuracy

  • Include source, locations, and details of ground truth data, steps taken, and graphs and statistical test outputs showing performance

  • State dataset recommendations and limitations (e.g. Impact Observatory's Biodiversity Intactness Index is unsuitable for point-based data extraction)

• Dataset structure: how the data is organised

  • Include format, variable types, and metadata

  • Apply version control to datasets and documentation, including version numbers and change logs

  • For metadata, adhere to domain standards (e.g. the FGDC Geospatial Metadata Standards)

  • Provide key nature data criteria, which we introduced in Newsletter 2: selecting nature data sources

• Dataset access: how users access the dataset

  • Best practice is a step-by-step guide, including code snippets (e.g. Stripe)

• Usage rights: the protections that apply to the data

  • State the standardised (e.g. CC-BY-4.0) or bespoke licence set out in a commercial agreement

  • State additional terms of use (e.g. WWF Living Planet Index Data Use Policy)

Accessibility

Teams using nature data come from diverse backgrounds, including academic research, data system design, and financial services. Given this variety of expertise, it's crucial to balance completeness with accessibility. We believe this hinges on two key factors: the clarity of the documentation and sufficient context for interpretation.

Despite the technical complexity involved in creating such data, the documentation can be simplified by using clear and concise language and avoiding unnecessary jargon. Long paragraphs should be minimised and, where appropriate, text can be replaced with images or diagrams. Key details about the dataset, such as usage notes or spatial resolution, should be prominently displayed rather than embedded within dense blocks of text, ensuring that users can easily locate essential information and make decisions effectively.

It's not always possible to provide all necessary context in main documentation. Technical users may need more detail, such as equations for converting raw LiDAR waveforms into height values. Non-technical users may find such detail challenging, and instead benefit from additional learning materials to provide further background context – like our Plant Biomass concept note.

We suggest a set of simple best practices to make documentation more accessible:

• Limit jargon to essential words only, with acronyms spelt out on first use

• Use short paragraphs or bullets, replace blocks of text with tables or figures

• Highlight or pull-out essential details, like key criteria, variable descriptions, and usage notes

• Provide step-by-step guides, code snippets, and examples

• Follow a standard and conventional template (see Completeness, above)

• Provide URLs to deeper source documentation for technical users

• Provide learning resources for less technical users (e.g. scientific concept notes)

• Ensure the documentation is easily findable throughout the website and visible on the dataset page

• Avoid any stage-gates impeding access to documentation (e.g. requiring email sign-ups)

Notices

• Cecil is bringing London's nature data community together to geek out over dumplings and drinks on Wednesday 18th September. RSVP here.

• Cecil + The Landbanking Group are hosting a breakfast during New York Climate Week on Thursday 26th September. RSVP here.

• Cecil + Earthmover are bringing New York’s nature data community together to geek out over dumplings and drinks on Thursday 26th September. RSVP here.

• Chloris Geospatial is hosting a breakfast during New York Climate Week to share how new VCM standards, project developers and buyers of carbon credits are using new monitoring technologies. RSVP here.

• Kanop is running a pickleball, bagels and networking event at New York Climate Week. RSVP here.

Thank yous

A special thank you to everyone who supported us this month with feedback, introductions, and advice.

Adam Weiner, Alex Burns, Coby Strell, Danielle Rappaport, Elaine Mitchell, Gregg Treinish, Helen Crowley, Mac Bryla, Matthias Mohr, Nate Trappe, Noah Golmant, Peter Levine, Roberto Miethe, Romain Fau, Syakira Syafiqah, Sylvain Vaquer, and Wade Cooper.

Keep reading

• Exploring spatial indexes

• Spatial data foundations for nature analysis

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

Sponsorship

Do you want to share monthly updates with our engaged audience of 1700+ nature data users and investors? Please contact rory@cecil.earth to find out more about our newsletter sponsorship packages.

This newsletter is curated by Cecil, a team on a mission to make nature data accessible. Their platform helps data teams access analysis-ready commercial and public nature datasets, eliminating the need for cleaning, harmonising, and pre-processing tasks. Whilst currently focused on aboveground biomass, they will soon launch land cover and land use datasets to support market-leading nature-tech applications, consultants, and nature restoration professionals.

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Last month, we introduced the foundations of spatial data - a starting point for anyone with a nature data workflow. We discussed spatial data standards, libraries and formats commonly adopted across the nature data community.

This newsletter continues our series on nature data analysis workflows by focusing on spatial indexes. We explore common challenges caused by the increased scale and complexity of nature datasets and introduce spatial indexes as a possible solution.

Datasets are getting larger and more complex

Advanced measurement technologies like remote sensing satellites have greatly increased the amount of spatial data generated about nature. In 2023, over 100 TB of satellite imagery was collected daily, and an additional 20,000 satellites are predicted to be deployed by 2030. This growth is paired with the emergence of new data collection technologies such as hyperspectral sensors, camera traps, and bioacoustic devices.

Such large volumes of data are creating processing and analysis challenges for teams with raster-based nature data workflows:

Computation

Large datasets often exceed the capacity of file-based tools. In our research with over 30 subject matter experts, we found that storage or processing limitations slowed down raster-based analysis tasks - especially when working with many variables at high spatial or temporal resolutions.

"When conducting nature analysis at a continental scale, I'm often forced to downscale due to computational limits. These limitations affect other analysis decisions, such as the resolution I can work with."

Joining data

There are a range of parameters to consider when joining raster data from different sources, including the coordinate reference system (CRS), bounding box and spatial resolution. There is no industry standard for setting these parameters, so data teams must manually transform rasters from different sources in order to combine them in an analysis.

CRS selection

The right choice of CRS for a raster is important to accurately represent areas (e.g. farm boundaries), distances (e.g. bird migrations), or to compare sites across large areas (e.g. N. America). For instance, the global CRS EPSG 4326 is not an equal-area grid: on the equator, one degree of longitude is the same as one degree of latitude, but one degree of longitude becomes a smaller absolute distance as you move away from the equator. This means that sites in high latitude regions are represented less accurately by EPSG 4326 than those in the tropics.

If you’re looking for an introduction to coordinate systems, have a look at our newsletter on spatial data foundations.

After hearing from many experts and data teams about these emerging challenges, we decided to explore spatial indexes as a solution.

What are spatial indexes?

Spatial indexes, also referred to as Discrete Global Grid Systems (DGGS), are grid systems that organise spatial data into a discrete grid of cells with a unique identifier (index) that locates their position on Earth. Think of cells as pixels with a pre-defined size, shape and location that are shared between all datasets that use them. Cells must be a shape that can tesselate on a grid, such as a square, hexagon or triangle, so that no space is missed on the Earth’s surface. Grids are also usually hierarchical, in that they have multiple resolutions where higher resolution grids (with more cells) are nested within the cells of lower resolution grids. Since all cells have a unique ID, data teams can use spatial indexes to convert any raster grid onto a universal index, which can be used to easily combine data from many different data sources.

Common spatial indexes

Different spatial indexes have their own unique characteristics, usage scenarios and benefits. Below we explore two common spatial indexes – H3 and S2.

H3

H3 is a spatial index developed by Uber for the purpose of visualising and exploring spatial data patterns, such as distances and route finding.

H3 was designed to use hexagons because they are the closest tessellating shape to a circle. This makes them effective at representing distances between neighboring cells. A grid of hexagons also captures curved lines better than a grid of squares.

H3 is a hierarchical system with 16 nested resolutions. Indexing spatial data at a given resolution returns a list of one or more cell IDs that cover the whole area represented. For reference, a H3 cell at resolution 15 is approximately 0.9 m² (see here for more details about H3 resolutions). Converting between H3 resolutions is straightforward, but is imperfect because a hexagon at one resolution cannot be entirely filled by hexagons at a higher resolution.

H3 suffers from low area distortion (at the highest resolution, cells vary from 0.54 m² to 1.08 m² globally). However, cell areas still vary a little because the H3 system maps the grid onto an icosahedron rather than a sphere.

S2

S2 is a spatial index developed by Google to work with spherical projections. Unlike traditional geographic information systems, which represent data as flat two-dimensional projections (similar to an atlas), S2 represents all data on a three-dimensional sphere (like the globe).

S2 is made up of approximate squares, with the lowest resolution being the six faces of a cube projected onto the Earth’s spherical surface. Higher resolutions are then created by subdividing lower resolution cells into four.

As a grid of squares, S2 is not as good at preserving neighborhood relationships and curved lines as the hexagons of H3. However, S2 cells are perfectly nested between resolutions, meaning a cell at one resolution is entirely captured by four cells at the next resolution. S2 cells also suffer from less area distortion at the poles due to their spherical projection, although H3 cells are more uniform in size than S2 cells when considering the entire globe.

Using spatial indexes in nature data workflows

Spatial indexes have become increasingly popular in nature data workflows because of their ability to represent large volumes of data as a table of standardised cell IDs. However, these efficiency gains can also compromise accuracy for certain use cases. A thriving community of builders is working on fully understanding different trade-offs and building tools that overcome these challenges. As it stands, there are some considerations for using spatial indexes in nature data workflows:

Computation

Transforming raster or vector data into a spatial index can optimise it for certain data operations. However, the computational advantages of spatial indexes depend on the type of index. For example, H3 improves the efficiency of area and distance calculations. On the other hand, many existing spatial analysis libraries are designed to run on rasters and would either be slower or not work at all with a spatial index like H3.

Joining data

Spatial data is generated about a specific region with a specific CRS, bounding box, pixel grid and resolution, making it impossible to combine datasets without pre-processing. Transforming multiple rasters and vectors into spatial indexes standardises them into the same structure with identical cell references, making them immediately consistent and joinable.

Resolution

Spatial indexes usually have a resolution cap. This means they might not be suitable for analyses requiring extremely high precision (e.g., < 1 m²). H3 also has imperfect child containment between resolutions. As you increase H3 resolution, the cells at that resolution do not nest perfectly within those of the lower resolution.

Representing complex geometries

Spatial indexes might not accurately depict the exact spatial coverage of complex polygons and lines. For instance, the hexagons of H3 are better at representing curved lines than the squares of S2. However, S2 is better at representing straight lines running in cardinal directions (i.e., N, E, S, or W).

Data loss

Converting from a raster to a spatial index involves some data loss. For instance, if you convert from a raster to a spatial index and then back to a raster, you won't get the same raster you started with. This effect isn't unique to spatial indexes. There will also be data loss whenever you convert spatial data from one CRS or resolution to another.

Notices

• Cecil are bringing London's nature data community together to geek out over dumplings and cocktails on Thursday 25th July. RSVP here.

• Chloris Geospatial share thoughts on the role of tech innovation in scaling up private financing of natural climate solutions.

• Kanop is looking for a Chief Science & Policy Officer who could join the team as a Co-founder. Please email romain.fau@kanop.io for more details.

• Renoster launch REDD: Neutral’s high-integrity, transparent market for REDD+ projects to address long-standing challenges in the REDD+ credit market.

  

Thank yous

A special thank you to everyone who supported us this month with feedback, introductions, and advice:

Ali Swanson, Alex Burns, Ben Harries, Claire Wightman, David LeBauer, Fernando Mateos González, Mary Jane Wilder, Natasha Batista, Nitin Magima, Matthias Asemota, Maryn van der Laarse, Miguel Fernandez, Roberto Miethe, Steven Cramer, and Wade Cooper.

Keep reading

• Spatial data foundations for nature analysis

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

Sponsorship

Do you want to share monthly updates with our engaged audience of 1500+ nature data users and investors? Please contact rory@cecil.earth to find out more about our newsletter sponsorship packages.

This newsletter is curated by Cecil, a team on a mission to make nature data accessible. Their platform helps data teams access analysis-ready commercial and public nature datasets, eliminating the need for cleaning, harmonising, and pre-processing tasks. Whilst currently focused on aboveground biomass, they will soon launch land cover and land use datasets to support market-leading nature-tech applications, consultants, and nature restoration professionals.

Enjoy the newsletter? Please forward to a friend.

Subscribe now

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We envision a world where large-scale data analysis improves the way we understand and value nature. This future drives Cecil’s mission to make nature data accessible.

Spatial data sit at the core of all nature analysis. An up-to-date understanding of spatial data is essential for any team working with nature data.

This newsletter continues our series on nature data workflows by focusing on foundational spatial data concepts, standards, and libraries. Starting here prepares us to move into deeper topics, such as spatial data indices and creating an Area of Interest (AOI) for analysis workflows.

Coordinate systems

Spatial data include numeric information that allows you to position the data somewhere on Earth. These numbers are part of a coordinate system that provides a frame of reference to locate features, join data, and tie analysis to a specific location.

In nature data analysis, you'll encounter both Geographic Coordinate Systems and Projected Coordinate Systems. Below, we've provided a brief description of their use, their interaction, and considerations for their application.

Geographic Coordinate System

A Geographic Coordinate System uses a three-dimensional spherical or ellipsoid model of Earth’s surface - technically referred to as a ‘datum’ - to define locations on Earth. A point is referenced by its longitude and latitude values, which are angles measured from the Earth’s center to a point on the Earth’s surface. These angles are often measured in degrees.

There are many different datums, and therefore, many different Geographic Coordinate Systems. The World Geodetic System 1984 (WGS84) is a universal Geographic Coordinate System that is suitable for mapping global data. Another example is the Australian Geodetic Datum 1984, which is tailored to fit the Earth’s surface around Australia, providing high precision for this continent but lower accuracy elsewhere.

Projected Coordinate System

A Projected Coordinate System uses a two-dimensional Cartesian coordinate system to represent locations on Earth as a flat surface, such as a map or a computer screen.

In addition to defining a Geographic Coordinate System that models the Earth's surface, a Projected Coordinate System applies a map projection. This mathematical transformation adjusts geographic coordinates to account for Earth's curvature and projects them onto a flat surface. Numerous map projections exist, each minimising distortion over a specific area or for a particular purpose. For example, some map projections preserve accurate areas across all regions, while others maintain precise angles or distances.

Considerations for your nature data analysis workflow

Selecting or transforming a coordinate system

When selecting or transforming a coordinate system, it is important to consider the units and spatial extent of the data, geographic region, and total area you aim to analyse.

When you receive a dataset, it will likely come with an existing coordinate system. Therefore, it's important to understand how to transform it.

As an example of how this relates to a data analysis workflow, Snowflake refers to coordinate systems as a Spatial Reference System. The Geography data type in Snowflake uses WGS84 as a standard Geographic Coordinate System. By contrast, the Geometry data type enables users to specify a Projected Coordinate System. Users can specify the Spatial Reference System of data by assigning a spatial reference identifier, allowing for the most suitable coordinate system to be chosen for a specific region and use case.

Vector and raster data

The vast majority of nature data analysis will utilise a combination of both vector and raster data types. Sometimes, these data types are used together in a single workflow, while other times, separate workflows are developed for each.

Vector

Vector data are lightweight data comprising individual points stored as coordinate pairs that indicate physical locations in the world. Vector data can take various forms: points, lines, or polygons.

In nature data analysis, vectors are typically used to represent features, such as site boundaries (polygons), sampling transect paths (lines), or measurement samples (points). Vector data representations are essential for defining an area of interest for specific data analysis workflows, such as a farm's property boundaries.

Raster

Raster data consists of a grid of pixels, with each pixel holding a specific value.

Raster data are usually used to represent continuous values, such as canopy cover, temperature, or rainfall. Categorical data, such as land cover class coverage, is often represented in raster format. Raster data is crucial in analysing nature data, as most remote sensing data is collected, stored, and delivered in this format.

Considerations for your nature data analysis workflow

Formats and storage

Vector and raster data are stored in many different formats that have been developed over several decades. Most formats were originally developed for specific software programs and optimised for data storage capacity. Recently, several initiatives have begun to support these data types on widely adopted cloud platforms.

Selection criteria

In a previous newsletter, we discussed how data format and storage should be considered when selecting nature datasets to use in your analysis workflow. We also created a criteria factsheet to support your next data search.

Formats, standards, and libraries

Below is a compiled list of common formats, standards, and libraries that you may encounter in your workflows with vector and raster data.

Vector

Shapefiles

A shapefile is an Esri vector file format for storing the location, shape, and certain attributes of geographic features.

KML

Keyhole Markup Language (KML) is an XML-based vector file format originally developed for Google Earth, but has since been widely adopted in the GIS ecosystem.

GeoJSON

GeoJSON is an open standard vector file format based on JSON (JavaScript Object Notation). GeoJSONs are used in spatial data analysis to share areas of interest, such as property boundaries, in a machine readable format.

GeoPackage

The GeoPackage encoding standard describes a set of conventions for storing vector data within a SQLite database.

GeoParquet

GeoParquet is a column-oriented data format. GeoParquet is an Open Geospatial Consortium (OGC) standard that adds interoperable geospatial types (Point, Line, Polygon) to Parquet.

Raster

NetCDF

NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that supports the creation, access, and sharing of array-oriented scientific data.

GeoTIFF and Cloud Optimised GeoTIFF

GeoTIFF and Cloud Optimised GeoTIFF (COG) are a widely adopted metadata standard that enables georeferencing information to be embedded within a raster file.

Zarr

Zarr is a community project aimed at developing specifications and software for storage of large N-dimensional typed arrays.

BIL, BIP, and BSQ

Developed by Esri, Band interleaved by Line (BIL), Band interleaved by Pixel (BIP), and Band Sequential (BSQ) are three common methods of organizing image data for multiband images.

Standards and libraries

Global Field ID

After ingesting and validating field boundaries, Global FieldID assigns a unique alphanumeric code to each field enabling field identification of agricultural land plots, globally.

fiboa

Fiboa (Field Boundaries for Agriculture) is a new collaborative project focused on improving the interoperability of farm field boundary data by representing them in GeoJSON & GeoParquet in a standardised way.

Xarray

Xarray provides data models for working with labeled arrays and datasets.

STAC

The STAC specification is a common language to describe geospatial information so that it can more easily be worked with, indexed, and discovered.

GeoPandas

GeoPandas is an open source project to make working with geospatial data in Python easier.

We'd love to hear from anyone working with these different spatial data standards and formats to analyse nature data – drop us a line at hello@cecil.earth.

This is the second newsletter in a series focusing on nature data workflows. In the next issue, we'll delve into creating Areas of Interest (AOIs) for detailed asset-level analysis, discuss spatial indexes like H3, and examine how these advancements contribute to positive action for nature.

Early User Testing Program

Support Cecil’s mission to make nature data accessible by joining our Early User Testing Program.

We are seeking individuals with diverse data skills (such as Python, SQL, and GIS) to test a range of nature data analysis workflows on our platform.

Join Early User Testing Program

Notices

• BloomLabs explore whether biodiversity credits are assets or commodities.

• Chloris Geospatial welcomes the Cisco Foundation and NextSTEP as new investors.

• Nature-Tech Memos publish a weekly newsletter on the latest and greatest in Nature Tech and Nature Finance.

• Planet record Forest Carbon Planetary Variable webinar to discuss their accurate estimates of measurements of the aboveground carbon stored in forests.

• Space Intelligence release a series of webinars on ways project developers and investors are using land cover data to maximise nature impact.

• The World Economic Forum explore how direct biomass estimation can improve forest carbon accounting.

Thank you

A special thank you to everyone who supported us this month with feedback, introductions, and advice:

Andrew Farnsworth, Chris Holmes, Claudia Röösli, Claire Wightman, Daniel Osgood, David Marvin, Edmund Pragnell, Franziska Schrodt, Felix Morsdorf, Gabi Ceregra, Isabelle Helfenstein, Jochem Braakhekke, Karl Burkart, Luc Sierro, Marius Vögtli, Merry Schuman, Niki Lewis, Nkwi Flores, Ryan Abernathey, and Steve Toben.

Keep reading

• To access nature data, you first need to find it

• Selecting nature data sources

• A scientific approach to nature data

This newsletter is created by Cecil. From curating data providers, to preparing data for analysis, Cecil’s nature data infrastructure helps teams access accurate and precise nature data. They are trusted by companies such as Foresight Sustainable Forestry, Kilter Rural, and Impact Ag Partners to manage data on 3,000+ natural assets and 3+ million hectares.

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We believe in a world where nature data is transparent, standardised, and easy to use.

As we build data infrastructure to make this future a reality, we are publishing what we learn along the way. So far, we have covered:

1. Scientific concepts - why we draw on scientific methods, tools, and theories to identify the core scientific concepts for interpreting nature; starting with plant biomass.

2. Domain specific criteria - how we organised a set of reproducible criteria into a structured methodology for selecting plant biomass data sources.

In this newsletter, we explore the first challenge faced by all nature data teams: locating nature data. The majority of the world's nature data is spread across hundreds of repositories, making it time-consuming to access relevant datasets for analysis.

In future newsletters, we'll delve into other nature data analysis workflow challenges - including integrating with data providers, data processing, data delivery, and more.

A practical example of locating nature data

Imagine you need plant biomass data to assess a forestry asset in Honduras. Your analysis requires data on plant aboveground biomass from the past five years, with at least annual temporal resolution.

A typical process to find datasets might look as follows:

Google Search

Recognising that a search based on your specific criteria might not yield useful results, you instead enter a more generic term, plant biomass data, into Google. The search returns:

• 16 links to academic papers (8) and datasets (8) with spatial coverage outside of your region of interest (e.g. the Arctic) or experimental manipulations/context (e.g. soil nitrogen addition).

• 2 links to teaching resources on what plant biomass is.

• 2 links to data repositories.

• 0 links to plant biomass data providers.

Google Earth Engine

Next, you try searching Google Earth Engine’s data catalog:

• Plant aboveground biomass, past five years, at least annual temporal resolution returns 0 results.

• Plant biomass returns 0 results.

• Biomass returns 10 datasets, but you need to dig into each one to verify it meets more specific criteria:

  • 3 datasets are not plant biomass data.

  • 3 datasets are raw GEDI instrument data.

  • The remaining 4 datasets are for single years, 1 of which has no spatial coverage in Central America.

Planetary Computer

Searching Microsoft’s Planetary Computer data catalog doesn’t provide the results you are looking for either:

• Plant aboveground biomass, past five years, at least annual temporal resolution returns 0 results.

• Plant biomass returns 0 results.

• Biomass returns 4 datasets, but there is no functionality to filter by specific criteria or compare among them:

  • 1 is raw Sentinel-3 satellite data.

  • The others are for a single year, 1 of which has no spatial coverage in Central America.

  • 1 is from a commercial data provider with multiple year coverage, but it is a low spatial resolution product (roughly 4.6 km pixel size).

After searching 3 major sources of nature data, you have yet to find data that’s suitable for your analysis. Finding what you need may require digging further into academic research, government departments, or looking into commercial products.

Why is finding and accessing nature data so difficult?

Finding nature data to apply to analysis workflows can take months due to the following roadblocks:

• Fragmented sources: Petabytes of nature data are scattered across hundreds of different repositories, archives, and organisations. Finding what you need takes time.

• Missing selection criteria: Many open data repositories were designed for use cases beyond nature and are broad in scope (e.g. Google Earth Engine and Microsoft Planetary Computer). With no way to filter by relevant criteria, shortlisting potential datasets becomes a manual task.

• Limited access: Acquiring datasets requires commercial agreements and technical integrations. This restricts data accessibility to teams with particular technical expertise, especially when dealing with multiple partners.

With no easy way to find, shortlist, and access nature data in one place, teams are forced to use workarounds. Spreadsheets listing known datasets alongside their criteria and limitations have become the industry standard.

We'd love to hear from others who are struggling to find and access nature data – drop us a line at hello@cecil.earth.

This newsletter is the first in a series that explores challenges in nature data workflows. We’ll soon discuss other issues that arise when integrating with data providers, processing data, delivering data, and beyond.

Coming soon: Datasets

After speaking with 50+ experts, data providers, and nature data community members about how they would like to find and access nature data, we decided to build Datasets.

Datasets is a public repository for easily accessing nature datasets.

This is the first component of Cecil's data infrastructure and is designed as the starting point for your nature data analysis workflow.

Join the Datasets waitlist

Here’s more on how we’re getting started:

• Scientific concepts: Our initial focus for Datasets is the scientific concept of plant biomass. Over time, Datasets will expand to cover other scientific concepts.

• Purpose-built criteria: Unlike broad scope data repositories, we are focused on improving how dataset information is organised using the criteria outlined in our last newsletter.

• Transparent documentation: We have initially partnered with Chloris Geospatial, Kanop, Planet Labs, and terraPulse to list their datasets. We are working closely with these data providers to ensure Datasets includes transparent and standardised documentation.

• Community contributions: Datasets will continue to be developed with the valuable feedback from our community. Please suggest any datasets you would like to see included here.

We’re currently testing Datasets with a small group of experts and will announce when it is live. Join the waitlist to be the first to access this public repository when we launch.

Join the Datasets waitlist

Notice Board

• Chloris Geospatial share thoughts on how algorithmic forest carbon data can add value to Voluntary Carbon Markets.

• Ecosia, the tree-planting search engine, has teamed up with Kanop to monitor its reforestation efforts from space.

• Mozaic Earth have signed a strategic partnership with rePLANET to bring a low-cost biodiversity baselining solution to market in the UK.

• Space4Good are offering a pilot program for our new deforestation alerts, prediction and reporting tools based on earth observation.

• Meta, World Resources Institute and Land & Carbon Lab release groundbreaking 1m resolution global map of tree canopy height.

• Planet release validation and intercomparison results for their Forest Carbon Diligence products.

• International Food Policy Research Institute and Land & Carbon Lab update their spatial production allocation model on global crop production.

Do you have a nature data announcement to share with this community? Please contact hello@cecil.earth and we’ll include it in the next issue.

Thank you

A special thank you to everyone who supported Cecil this month with feedback, introductions, and advice:

Florian Reber, Romain Fau, David Marvin, Joe Sexton, Saurabh Channan, Simas Gradeckas, Oliver Dauert, Lauren Brown, Merry Schumann, Duncan Walker, Patrick Vandesteen, Franzi Schrodt, Maryn van der Laarse, Sean Chua, Laura Garcia Velez, Jinha Jung, Tommy Leep, Tom Quigley, Jeremy Yap, Steve Toben, Peggy Brannigan, Gregg Treinish, Tia Miller, Will Turner, Catherine Workman, Paul Hawken, Lowell Powell, Dean, Karl Burkhart, Kate Wing, Peter Tittman, Ben Halpern, and Beckett Sterner.

This newsletter is curated by Cecil. From curating data providers, to preparing data for analysis, Cecil’s nature data infrastructure helps teams access accurate and precise nature data. They are trusted by companies such as Foresight Sustainable Forestry, Kilter Rural, and Impact Ag Partners to manage data on 3,000+ natural assets and 3+ million hectares.

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Selecting nature data is difficult.

Teams face obstacles when shortlisting and evaluating new data sources:

• No one dataset can serve all purposes - different sources vary across spatial and temporal factors.

• Nature data is specific to local context - the same source may yield different insight in different places.

We spoke to 30+ subject matter experts, nature practitioners, and financial institutions to learn how they are tackling these challenges today.

We heard stories about spreadsheet workarounds listing known datasets and painful searches for supporting documentation.

Dataset documentation is often locked away in the bowels of repository websites. You have to click 15 links to get to the appendix that you want. (Will Mulhern, Y Analytics)

In addition to identifying issues, we discovered that teams are using consistent and repeatable criteria to select plant biomass data sources for analysis.

In this newsletter, we summarise our findings to help you succeed in your next plant biomass data selection process.

View our plant biomass data source criteria here.

Using reproducible criteria to select plant biomass data sources

After identifying your required plant biomass variable and gathering potential data sources from open repositories (e.g., Google Earth Engine) or commercial data providers (e.g., Planet), the following criteria can be used to shortlist datasets for analysis:

1. Spatial

• Spatial coverage: The total geographic extent of the dataset. This determines whether the dataset covers the desired geographic area for analysis. Example: global.

• Spatial resolution: The linear distance covered by a single pixel. This determines if the data is at a sufficient spatial granularity for analysis. Example: 30 m.

2. Temporal

• Temporal coverage: The total time-period over which the data is available. This determines if the dataset covers the desired time-period for analysis. Example: 2013-2022.

• Temporal resolution: How often new data is released. This determines if the dataset is at a sufficient temporal granularity for analysis. Example: Every 5 days.

• Timeliness: The delay between data collection and the dataset being updated. This determines whether data can be accessed fast enough to make decisions about a use case. Example: Within one month.

3. Scientific

• Accuracy: The numerical proximity of the measurements to the true value. This determines how close to the truth a dataset is. Example: Within 10% of accepted reference standard, such as permanent field plots.

• Precision: The numerical similarity of multiple repeat measurements. This determines how repeatable the values in the dataset are. Example: Replicate measurements within 10% of each other.

• Uncertainty: The numeric confidence interval within which the true value sits. This determines the degree of confidence associated with a dataset. Example: 80 m ± 15 m (mean ± 95% confidence intervals).

4. Trust

• Transparency: The accessibility, completeness, and readability of the methods, metadata, validation process, and other information. Can you trust the data yourself? Example: Technical documentation.

• Informal endorsements: Peer, personal network, and professional community feedback on the dataset. Provides community validation of trustworthiness. Example: Trusted academic colleague uses regularly.

• Formal endorsements: Independent third party publications on the dataset. Provides formal institutional validation of trustworthiness. Example: Scientific papers.

5. Other factors

• Cost: The cost of accessing the data. This determines if the dataset is affordable. Example: $0.25 per ha per year.

• Completeness: The proportion of complete records in the dataset. This impacts the proportion of data that can inform the specific use case. Example: Random data corruption from sensor or transmission error.

• Local relevance: The location(s) of ground truth or other calibration data used to train or validate datasets. This indicates where data are likely to perform better or worse. Example: cloud forest, Ecuador.

• Restrictions: Are there limitations on the use or sharing of the data? This influences whether the dataset is usable for the specific use case. Example: Non-commercial use only.

• Data format: Structure and content of the data output. This determines compatibility with platforms, workflows, tools, and integrations. Example: GeoTIFF.

• Storage and compute: The load on storage and/or compute systems. This determines usability around additional costs, resources, and project timelines. Example: File size.

The criteria factsheet

We created a criteria factsheet to support your next plant biomass data search.

View criteria factsheet

Please send feedback to hello@cecil.earth so we can improve this resource for the nature data community.

In next month's newsletter, we'll share Cecil’s catalogue of quality plant biomass data sources.

Insights

1. No data is clean

“No data is clean, but most is useful.” Dean Abbott, Chief Data Scientist - SmarterHQ

2. Agreeing on nature data

“In order to build a greener economy we need to reconfigure the connections between Nature, Policy, and Capital. And the first step in the right direction is agreement on data.” Marcelo Bicalho Behar, Vice-President - Natura &Co

3. The demand for transparency

“Greater transparency of data product process and methodology should be addressed in tandem. Regardless of the type of data that is being used to assess nature-related risks, data use and methodological decisions should be fully transparent (including peer-review) to allow for a traceable and replicable assessment.” - TNFD Data Discussion Paper

Learn

• Nature data provider Kanop share their scientific approach to measuring ecosystem services.

• Peter Hurley outlines his approach to modelling ecosystem connectivity.

• ‘Forest 500: A Decade of Deforestation Data’ report uses 1.3 million data points to set out 10 lessons for accelerating action on tropical deforestation.

• Interested in biodiversity finance? The Bloomlabs newsletter by Simas Gradeckas is a great place to start.

Notice Board

• Cecil is seeking nature data enthusiasts to give feedback on their data infrastructure. More details here.

• terraPulse announce partnership with Island Conservation to monitor benefits of invasive mammal eradications on island ecosystems.

• Planet adds to its planetary variable product suite with automated field boundary detection.

• Cecil is seeking partners to develop resources that simplify the sharing of first party nature data. More details here.

• Broadbent et al. find that future climate change could diminish the capacity of alpine ecosystems to retain nutrients.

• The GBIF Secretariat is calling for proposals to analyse trends in use of GBIF-mediated data in peer-reviewed literature.

Thank you

Thanks for reading and for supporting Cecil. A special thank you to everyone who supported us this month with feedback, introductions, and advice:

Arthur Broadbent, Johnny Wilson, Romain Fau, Franzi Schrodt, Jamie Batho, Simas Gradeckas, Will Mulhern, Sean Chua, Matt Taylor and Elaine Mitchell.

Enjoy the newsletter? Please forward to a friend.

Do you have a nature data announcement to share with this community? Please contact hello@cecil.earth and we’ll include it in the next issue.

This newsletter is curated by Cecil. From curating data providers, to preparing data for analysis, Cecil’s nature data infrastructure helps data teams access accurate and precise nature data. They are trusted by teams such as Foresight Sustainable Forestry, Kilter Rural, and Impact Ag Partners to manage data on 3,000+ natural assets and 3+ million hectares.

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Nature is complex.

Ecosystems are dimensional systems. They consist of thousands of interacting abiotic and biotic components and processes.

Whilst these components are fairly consistent worldwide, localised differences exist. For example:

• Species and soil types differ among habitats, sites, biomes, and regions.

• Some components can be geographically constrained e.g., trees and ice.

• All components interact with each other on different spatial and temporal scales.

This complexity creates challenges for organisations seeking to assess, disclose, and manage nature-related risks. International goal-setting and reporting frameworks, such as SBTN and TNFD, offer a useful starting point. They spark a process of self-examination and strategic action.

However, these frameworks have been designed to meet global requirements. They are high-level guides, not 'how-to' manuals. To ensure corporate action on nature is effective, the private sector must make independent efforts to interpret natural ecosystems.

In this newsletter, we share Cecil’s science-based approach to understanding nature.

A scientific approach to understanding nature

Environmental science offers a strong conceptual basis for understanding nature.

Ecosystems follow general rules at large scales (e.g., latitude-based tree lines) and small scales (e.g., enzyme activities). Yet local context shapes how they behave at the site scale (e.g., plantation or farm).

At Cecil, we draw on these rules to translate environmental science into an operational system to understand nature at the site scale. We use the best available scientific methods, tools, and theories to identify scientific concepts (e.g., plant biomass, soil water content, biodiversity) that describe nature.

Against a method or technology-based approach, we see scientific concepts as a future-proof way to interpret nature:

• They can apply to new use cases, technologies, and frameworks that don’t yet exist.

• They ensure new science will fit modularly into what we can interpret now.

• They protect against methods or technologies being surpassed in the future.

Scientific concepts are guiding how we build data infrastructure for nature. We have started with plant biomass.

The foundational importance of plant biomass

Plant biomass refers to the total amount (volume or mass) of organic matter produced by plants. It is mostly made up of carbon, nitrogen, and phosphorus.

We have decided to focus on this scientific concept first for three reasons:

• Industries exposed to nature-related risks, such as forestry and agriculture, rely on plant biomass data to support business-level analysis. For example, foresters need accurate canopy cover measurements to track the health of their forests, whilst plant height is monitored by farmers all over the world.

• Plant biomass influences other scientific concepts of business-value. Examples include crop yield, ecosystem carbon stock, soil moisture, and fire/flood risk.

• We are on a mission to deliver precise and accurate nature data. We believe that in-depth understanding of scientific concepts, rather than superficial coverage, is required to achieve this ambitious goal.

Introducing: Cecil Epics

Scientific concepts can help anyone interpret nature. To further illustrate this belief, we’re publishing our first Epic. 

Cecil Epics share the details behind scientific concepts and how we create data infrastructure to support them.

View Plant Biomass Epic

Please help us improve our Plant Biomass Epic by sending feedback to hello@cecil.earth.

In newsletter #2, we'll explore criteria that can be used to select plant biomass datasets for analysis.

Insights

1. Why context matters

“Making mental connections is our most crucial learning tool. The essence of human intelligence; to forge links; to go beyond the given; to see patterns, relationships, context.” (Marilyn Ferguson)

2. There’s no shortage of nature data

“There is no shortage of nature data but rather a lack of understanding about how data can be used to derive information that is decision-relevant to end users.” (TNFD discussion paper)

3. The limitations of our quest for nature data

“We have to use proxies and related indicators to approximate this inherently abstract and complex (nature) system. We must not obsess about loading up with data for the sake of data. That is not to say we should discount complexity and rely on overly simplistic self-serving assumptions. But we must understand the limitations of this quest for more data.” (James d’Ath)

Learn

• TNFD paper summarising a landscape assessment of nature data and analytics availability.

• Cassidy Rankine explains why GIS and remote sensing professionals should explore satellite imagery time series analysis.

• Research paper on climate risks to soy-maize double-cropping due to Amazon deforestation.

• TerraWatch newsletter on the state of earth observation for climate.

Notice Board

• Cecil is seeking nature data enthusiasts to give feedback on their data infrastructure. Details here.

• terraPulse announce partnership with the U.S Environmental Protection Agency and the U.S Geological Survey to monitor American Black Duck habitats.

• Climate Collective and the World Bank launch the Green Accountability Technology Accelerator.

• Planet release datasets in Sentinel Hub for land surface temperature, soil water content, crop biomass, and forest carbon.

• Kanop’s aboveground biomass study is a significant milestone for ecosystem service quantification.

• Kita and Oxbow Partners discuss the data risks and opportunities of carbon credits.

• Carto release dates for their geospatial data conference in London.

• Space Intelligence chief scientist identifies flaws in West et Al paper.

• The Deep Earth System Data Lab Project is looking for researchers in the earth system sciences.

Thank you

Thanks for reading and for supporting Cecil. A special thank you to everyone who supported us this month with feedback, introductions, and advice:
Alicia Gibson, Andrew Farnsworth, Andrew Feierman, Andrew Ford, Alix Pouchol, Arthur Broadbent, Chad Burton, Claire Wightman, Colleen Spalding, Cori Grainger, David Heislers, Franziska Schrodt, Jeffrey Kuperman, Johnny Wilson, Mansi Agrawal, Maryn van der Laarse, Mateus Mendes, Matt Taylor, Miranda Joicey, Romain Fau, Sabine Nix, Sam Gregory, Sean Chua, Rosie French, and Will Mulhern.

Enjoy the newsletter? Please forward to a friend.

Do you have a nature data announcement to share with this community? Please contact hello@cecil.earth and we’ll include it in the next issue.

This newsletter is curated by Cecil. From curating data providers, to preparing data for analysis, Cecil’s nature data infrastructure helps data teams access accurate and precise nature data. They are trusted by teams such as Foresight Sustainable Forestry, Kilter Rural, and Impact Ag Partners to manage data on 3,000+ natural assets and 3+ million hectares.