Mapping Forest Structure: How Sylvera Provides Accurate Forest Biomass Estimates Across Landscapes

We can learn a lot by mapping forest structure. What is the average biomass of this species of tree in the Northeastern United States or Brazil? And, consequently, how much carbon can they store? Also, what can the data tell us about the environment, soil fertility, and our overall forest management tactics? Is there anything we can do to improve forest productivity in the region?

Mapping the structure of forests is an important pursuit, which is why many different entities, from individual corporations to the USDA Forest Service, engage in the task.

What is biomass?

Biomass can be defined as the standing dry mass of live or dead matter from woody plants, and it is usually expressed as a mass per unit area (e.g. Megagrams per hectare: Mg ha-1). It is an essential variable used to monitor the carbon released and sequestered by forest land ecosystems because approximately 50% of biomass is carbon. In this article we refer to aboveground biomass, as this is the only portion we can “see” using satellite remote sensing technology. 

Measuring forest biomass

Measuring biomass of trees and shrubs is not easy. In fact, the only way to directly measure biomass is through destructive sampling, where trees and shrubs are cut down and weighed.

This destructive approach is time-consuming, costly, and counterproductive, as we want to keep the carbon in vegetation rather than releasing it back into the atmosphere (which will likely happen to those cut down trees over time). However, this method is necessary to develop allometric models.

Allometries are used to estimate biomass from easy-to-measure parameters such as tree diameter and tree height. It is based on biological scaling theory and describes the dependencies of living organisms in terms of body mass, size and shape.

Allometric models are used by forest ecology experts and environmental scientists in traditional forest inventories to calculate tree biomass. Forest inventories are based on the establishment of field plots over the region of interest in order to estimate large-area forest statistics. However, many tropical countries rich in forests do not run forest inventory programs, or are just starting to implement them, due to the remoteness of the locations and the costs involved.

When measuring forest biomass using traditional forest inventory methods, we are exposed to several sources of error such as from the manual measurement of tree dimensions, the sampling strategies, and the allometric models. Allometric estimates of biomass are usually biased (Demol et al. 2022), with differences of 15% (Burt et al, 2021) or even up to 30% (Calders et al, 2015, Gonzalez de Tanago et al, 2018). Forest inventory plots are measured over long periods of time (e.g. 5 year cycles), using different methodologies, sampling designs, sizes/shapes, and operators, which can lead to large discrepancies when assessing different projects and regions. Additionally, we lack reference data from many areas of the world's forests due to inaccessibility and/or very high cost. This makes access to good-quality reference data one of the main challenges when monitoring biomass stocks.

Therefore we usually avoid the term ‘ground truth data' and prefer instead the term ‘reference data'. Reference data is crucial in remote sensing and data science, as for any given model you develop, the rule-of-thumb “garbage in equals garbage out” always applies.  This is why, at Sylvera, we put special care on our reference data.

Nuestro intento de construir el mejor conjunto de datos de referencia: lidar multiescala

For this purpose, we visit forests around the world, and laser scan them from the ground and air using our proprietary multi-scale lidar (MSL) methods. We collect 3D data (i.e. point clouds) on the ground using our terrestrial laser scanners (TLS). These scanners can record the structure of individual trees with millimeter-level accuracy, right down to individual twigs and leaves. We also collect similar data from our airborne laser scanners (ALS) mounted on unoccupied aerial vehicles (UAVs), which enables us to collect data over larger areas.

These novel datasets contain large amounts of information on forest structure and aboveground biomass. However, accessing this information is complex.

Pulling out data on individual trees enables us to carefully reconstruct and model tree-scale parameters such as aboveground biomass. We are able to measure the biomass of trees with a margin error potentially as low as 3% (Burt et al, 2021) when compared to destructive tree measurements (vs the up to 30% error previously mentioned when using allometries).

Using this MSL technology we are aiming to build the most accurate reference biomass dataset ever assembled. We are able to scan up to 50,000 ha of forest in one MSL field campaign. MSL reference data can be produced at different spatial resolutions which enables a better upscaling of the data using satellite imagery. Using MSL biomass data we are also able to create our own biomass calibration of spaceborne LiDAR footprints acquired by the Global Ecosystem Dynamics Investigation (GEDI) sensor, and enhance our reference dataset. (These datasets also help us monitor forest health over time and deliver trustworthy Ratings for all forestry-based carbon credits projects.)

¿Cómo podemos ampliar nuestras mediciones de biomasa a otros periodos de tiempo y a zonas más extensas?

Our MSL technology can measure biomass with incredible accuracy but the amount of area we can cover and the number of times we can measure is limited by the amount of time we can spend collecting data and the cost of this activity (ca. tens of thousands of hectares per field campaign).

Satellite remote sensing technology is crucial to monitor biomass stocks because it allows us to do it more frequently (e.g. annually), across longer periods of time (e.g. 2000 to present), and over larger spatial scales (e.g. regional/national jurisdictions) when compared to forest inventories.

Current carbon accounting standards rely on the use of satellite imagery to detect activities within carbon offsetting projects (e.g. deforestation, new planted forest), and combine these with averaged values of biomass or carbon emissions factors to determine the amount of carbon being stored by forests and the amount of carbon released by each activity. These average values are calculated at project level every 5-10 years and are based on manual field plot measurements.

Unfortunately, the period between measurements is so long that a large amount of change due to forest disturbances (i.e. emissions) can be missed. More often than not, this type of work presents important sampling deficiencies (e.g. too few samples) due to cost, labor intensity and inaccessibility of some remote areas. Additionally, average values are an increasingly poor descriptor as variance increases (particularly re. aforementioned sampling deficiencies), and most forests we are interested in exhibit a lot of structural variances, which can impact the estimation of biomass stocks and carbon emissions. 

Forest inventory plots were never designed to be used in combination with pixels from satellite observations. Data manually collected on the ground can hugely differ from remote satellite measurements in terms of spatial resolution and coverage. So discrepancies are usually introduced when trying to generate wall-to-wall remote sensing-derived products.

At Sylvera, we train our models using our state-of-the-art MSL-based reference datasets and our in-house calibrated GEDI data with the best publicly available satellite imagery. This approach allows us to remove or minimize these discrepancies when training our models. 

We upscale our biomass estimations over large areas and time scales using long-wavelength synthetic aperture radar (SAR). This technology can “see” through clouds and has high sensitivity to biomass, and multispectral optical satellite imagery which, despite having less sensitivity to biomass, it has a longer temporal coverage and contains other useful information related to the chlorophyll content of vegetation. We use other information, such as digital terrain models and spatial texture analytics, too.

Los bosques son sistemas ecológicos muy diversos que muestran un comportamiento complejo en diferentes escalas temporales y espaciales. Por lo tanto, los algoritmos de aprendizaje automático no paramétricos, que hacen menos suposiciones sobre la forma y la distribución de los datos de referencia, suelen superar a los métodos paramétricos(Evans et al, 2009). Los modelos de aprendizaje automático pueden utilizarse para estimar la cantidad y la distribución espacial de la biomasa y su incertidumbre. Con estos métodos también podemos estimar otros parámetros estructurales del bosque, como la altura del dosel o la fracción de cubierta arbórea. 

At Sylvera, we use peer-reviewed state-of-the-art approaches for monitoring aboveground biomass (Rodriguez-Veiga et al, 2020, Meyer et al, 2019, Rodriguez-Veiga et al, 2019). We also perform statistically rigorous validations and uncertainty analysis, and follow best practices (Duncanson et al, 2021McRoberts et al, 2022). Our models are trained regionally to routinely and robustly estimate time series of forest aboveground biomass and carbon stocks from satellite data.

Nuestros mapas de series temporales de biomasa aérea se utilizan para controlar los cambios en las reservas de biomasa en las zonas de interés.

Nuestros métodos mejoran constantemente gracias a la adquisición continua de datos MSL para aumentar nuestra cobertura, a la preparación de las próximas misiones por satélite (por ejemplo, la misión NiSAR y Biomass) y a la incorporación de las últimas innovaciones procedentes de nuestra propia investigación y de la literatura científica. Nuestras metodologías son revisadas interna y externamente por destacados académicos en la materia. También hemos colaborado con equipos de investigación de la UCLA, la Universidad de Leicester y el University College de Londres.

Why do we need to monitor woody biomass at Sylvera?

At Sylvera we rate carbon projects belonging to carbon frameworks such as Reducing Emissions from Deforestation and forest Degradation. Two of the most important components of these projects are activity data and emission factors, which are then used to calculate emissions.

Activity data can be evaluated using land cover classification techniques on satellite imagery, while emission factors can be evaluated using our own biomass measurements. Alternatively, we can compare emissions reported by projects with our own estimates derived from biomass time series data.

These biomass time series products provide more insight into where and how much carbon is changing across project areas, and offer an opportunity to detect and evaluate carbon emissions derived from forest degradation (the second “D” in REDD+).

When a forest is degraded it still exists, but it has suffered a reduction in its capacity to produce ecosystem services such as carbon storage. This is of key importance because a large proportion of carbon emissions can originate from forest degradation, which in many cases is not reported, and at the same time, can be the stepping stone towards a deforestation process. 

El seguimiento de la biomasa por satélite nos permitirá mejorar nuestras calificaciones evaluando las emisiones derivadas de la deforestación y la degradación forestal notificadas por los proyectos, pero también las emisiones originadas por la degradación forestal en los proyectos que no la notificaron.

Acerca de los investigadores de Sylvera

The research scientists focusing on biomass at Sylvera are part of two teams: the MSL team, responsible for MSL data acquisition and processing, and the Machine Learning (ML) team, in charge of developing methods for upscaling biomass measurements to project and regional-level using ML technology.

The research team leadership includes:

El Dr. Miro Demol es un científico de MSL Lidar que investiga las aplicaciones del escaneado láser en la silvicultura, con especial interés en la estimación de la biomasa aérea y su incertidumbre.

El Dr. Andrew Burt es un científico especializado en teledetección y ecología forestal tropical del equipo MSL, que durante la última década ha contribuido a ser pionero en el uso del escaneado láser en los bosques.

El Dr. Pedro Rodríguez-Veiga es investigador principal de observación de la Tierra en el equipo de ML y cuenta con más de 12 años de experiencia en el campo de la silvicultura, la recuperación de biomasa aérea mediante teledetección y la vigilancia forestal.

Better forest management can help mitigate climate change

We can't help with urban wood waste. But if you want the most accurate forest biomass estimates available—whether it's because you're a forest landowner attempting to produce carbon credits or a company looking to buy them—you need access to Sylvera's in-depth research and datasets.

Our team of research scientists use a proprietary approach involving LiDAR technology to measure above ground biomass, which has led to greater accuracy than allometric models can provide.

Book a free demo of Sylvera to learn more about all our industry-leading platform has to offer.