Mitigating the climate crisis requires improved monitoring of the world’s forests
We are at a critical juncture, with little time remaining to limit temperature rise to 1.5 °C. Accurate, reliable, and up-to-date data are essential in tracking forest emissions and holding actors accountable for climate change impacts.
Shortcomings hinder current
Conventional methodologies result in either underreporting or overestimation of GHG emissions.
Limited data on the forest carbon cycle - geographically, and across ecosystems.
Data gaps, method variations, and reporting issues erode monitoring efforts.
Sensor technologies unlock monitoring efforts
We’ve pioneered the world’s most accurate measurements of above-ground biomass for forests, demonstrating that we’re significantly underestimating carbon stocks, and emissions from forest loss and degradation may be greater than what was previously thought.
Sylvera already uses cutting-edge laser scanning technology to collect this data on the ground. We’re now expanding our capabilities to collect calibration data for further carbon pools (below-ground biomass, soil, and others), and to validate the resulting models with direct measurements of carbon in the atmosphere.
We're seeking funding opportunities to make our proprietary data sets accessible and ensure this information is actionable for the widest possible audience.
Working with the Mozambique Government
In 2022, we partnered with the Government of Mozambique through the Monitoring, Reporting and Verification Unit of the National Fund for Sustainable Development to produce a highly-accurate 50,000 hectare map of the above-ground carbon stocks in Mozambique, using multi-scale lidar and machine learning. The data collection completed was equivalent to scanning seventy thousand football pitches, making this the most laser scanned forest in the world.
Our proprietary measurement system is underpinned by methods that are up to 13 times more accurate than conventional methodologies. Using these methods, we found classical approaches significantly underestimated the carbon stock of the sampled area. This was greatest for large trees (those above 40 cm in diameter), which only represent 10% of all trees in the study area by number, yet make up 50% of the total carbon stock.
These findings will be used to calibrate a state-of-the-art monitoring, reporting, and verification (MRV) system over the subtropical forests of Zambezia province, and demonstrate that these forests are more valuable as carbon stores than previously thought. These results are currently being prepared for publication (Demol et al., in prep).
Mozambique: Step-by-step process
Terrestrial laser scans were collected from six 1 hectare plots, providing high-density point clouds describing tree structure down to individual leaves.
Individual trees were segmented from these point clouds, and their woody structure modelled using cylinders, enabling whole-tree volume estimation.
These volumes were combined with species information from conventional inventory measurements, to generate highly-accurate 10m resolution above-ground maps.
To upscale our measurements, we collected 2,000 hectares of slow-flying and 50,000 hectares of fast-flying airborne lidar data, at different heights above the canopy.
We extracted various independent metrics from our airborne data, including canopy height maps, terrain, and forest structure…
…and used machine learning and our 1-hectare above-ground biomass maps as training data to upscale our estimates to six 300 hectare areas, and again to span an incredible 50,000 hectares of miombo woodland.
Finally, we used proprietary machine learning methods, applied to satellite data, to unlock full coverage over the entire province of Zambezia.
Map of previous and planned research sites
Explore the map to see where we’ve been, and where we’re planning on sampling next.
We have built a team of experts, including a dedicated field science unit who collect calibration data on month-long expeditions in key forested areas around the world. This capability allows our in-house data scientists to build, integrating data from satellites and other sources, to accurately predict forest carbon stocks.
Dr Andrew Burt
Research Lead (MSL)
Andrew heads Sylvera’s multi-scale lidar team. A remote sensing scientist and tropical forest ecologist, over the past decade he has helped to pioneer lidar-based methods for measuring forest structure and biomass.
Senior Research Software Engineer (MSL)
Gabija is responsible for processing our lidar data. She has a background in computer science and brings a wealth of knowledge from her experience in developing time-sensitive and safety-critical algorithms to process lidar point clouds for autonomous vehicle applications.
Dr Robin Upham
Senior Research Software Engineer (MSL)
Robin is responsible for processing our lidar data, focusing on quantitative modelling and uncertainty quantification. With a PhD in Astrophysics, Robin brings experience from developing data analysis methods for cosmological satellite missions for ESA.
Dr Miro Demol
Lidar Scientist (MSL)
Miro has a PhD focused on reducing uncertainties in terrestrial lidar-derived estimates of above-ground biomass, and processes our lidar data at Sylvera. He has a background in bio-engineering and nature management, and tropical forestry.
Research Operations Manager (MSL)
Ashleigh is responsible for the fundraising and scaling of the MSL team, and enabling the safe and efficient operation of the field team. They have a background in biological sciences and experience in planning and conducting international field work across the globe.
Research Operations Associate (MSL)
Elise enables the logistical, budgetary, and fundraising operations of the MSL team. With a background in environmental management, she has substantial experience in stakeholder engagement and planning international field work in Mozambique, Belize, and Cameroon.
Dr Beisit Luz Puma Vilca
Senior Field Scientist (MSL)
Beisit leads the Sylvera field team. She has a PhD in Biology and over 10 years’ experience developing and applying forest mensuration protocols and collecting terrestrial and airborne lidar data in tropical forests in Peru, Brazil, Belize, and Mozambique.
Field Analyst (MSL)
With an MSc in Conservation Management of African Ecosystems, Annabel has extensive field experience across Africa. She previously worked for a biotech startup, where she focused on the uses of next-generation sequencing in ecology. Her role in the Sylvera field team involves collecting lidar data, which you can read about in her blog post.
Field Analyst (MSL)
Chloe has a background in Conservation and Animal Biology. At Sylvera she collects terrestrial and airborne lidar data from tropical forests, and brings a wealth of knowledge from her field research experience in Kenya, Madagascar, and Cameroon, as well as from her previous role as a bat conservationist in Malawi.
Advanced Projects R&D Lead (Soil)
Christie is responsible for the integration of new soil-based measurement techniques. With an MSc in Engineering, she brings experience from aerospace, robotics, and nanofabrication, as well as her previous roles at SpaceX, Marvel Fusion, and the FAO.
Dr Christopher Walter
Soil Carbon Lead (Soil)
Chris leads the analysis of soil carbon and nutrient cycling in grasslands, forests, and agricultural systems. With a PhD in Biology, Chris has led continental-scale experiments to measure and model the effects of global climate change on soil carbon.
Dr Hamidreza Omidvar
Head of Machine Learning (ML)
Hamid has a PhD from Princeton University in Environmental Engineering, and his research focuses on using machine learning and remote sensing in climate modelling, and climate-related problems such as land cover mapping and time series analysis.
Dr Pedro Rodríguez-Veiga
Senior Earth Observation Research Scientist (ML)
Pedro has a PhD in Physical Geography, his research focuses on analysing forest carbon dynamics using remote sensing technology. He has been a co-investigator on high-impact projects such as the ESA Biomass Climate Change Initiative (CCI+), the NERC-NCEO Carbon Cycle: Land, Atmosphere & Oceans Programme, and the UK Space Agency Forests 2020.
Dr Johannes Hansen
Machine Learning Research Lead (ML)
Johannes Hansen has a PhD in Computational Mathematics and Geosciences with experience in Earth Observation (using SAR data in particular), physical modelling, and high performance computing.
Machine Learning Engineer (ML)
Piotr is a recent MSc Data Science graduate with a Physics background. With an interest in Computer Vision and geospatial experience gained during his internship at Harwell Science Campus, he mainly contributes towards the research and development of in-house models.
Discover relevant publications featuring the contributions of our team to academic literature.
Estimating forest above‐ground biomass with terrestrial laser scanning: Current status and future directions
New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar
Altered plant carbon partitioning enhanced forest ecosystem carbon storage after 25 years of nitrogen additions
Stronger fertilization effects on aboveground versus belowground plant properties across nine U.S. grasslands
A new bioenergy model that simulates the impacts of plant-microbial interactions, soil carbon protection, and mechanistic tillage on soil carbon cycling
Get in touch
Sylvera offers the cutting-edge technology, expertise, and global project management experience to generate the data needed to unlock net zero. Join us as partners and donors in this mission.