Lidar Remote Sensing for Forest Ecosystem Dynamics

Lidar (Light Detection and Ranging) remote sensing employs laser pulses to capture three-dimensional information on forest structure, from ground elevation to canopy top. Airborne systems deliver fine-scale digital terrain and surface models for local and regional studies, while spaceborne missions such as GEDI and ICESat-2 provide systematic, near-global sampling of vertical forest attributes. By measuring canopy height, vertical complexity and foliage density, lidar enables quantification of above-ground biomass, detection of deforestation or regrowth, and assessment of habitat quality. Integration with optical satellite imagery and advances in machine learning have refined height retrieval, extended coverage and enhanced uncertainty estimation. These developments underpin national carbon inventories, inform conservation planning, guide restoration efforts and improve climate-carbon-biodiversity modelling. As a result, lidar has become indispensable for monitoring forest dynamics across spatial and temporal scales, offering robust, three-dimensional insights into ecosystem health and services.

Research from Nature Portfolio

Recent studies have produced a globe-spanning canopy height map at 10 metre resolution by fusing spaceborne lidar waveforms with dense optical imagery via a probabilistic deep learning framework, quantifying uncertainty and revealing that only 5 percent of the land surface supports trees taller than 30 m, with merely one-third of these tall canopies under formal protection—critical information for carbon and biodiversity conservation. Continental-scale analysis of satellite lidar records across Europe has further shown that protected areas sustain forests on average 2 m taller and with greater vertical complexity than adjacent unprotected landscapes, highlighting the effectiveness of environmental policy in preserving three-dimensional forest structure at broad scales.

Research from all publishers

High-resolution mapping of trees outside designated forests using 3 m nanosatellite imagery has delivered detailed canopy cover, height and biomass estimates across Europe, demonstrating that urban and agricultural trees contribute substantially to national carbon stocks and should be incorporated into operational inventory schemes. At sub-national extents, self-supervised vision transformers trained on aerial lidar and high-resolution RGB imagery have generated metre-scale canopy height models for California and São Paulo, achieving mean errors below 3 m and enabling precise monitoring of deforestation, degradation and agroforestry practices. In East Africa, statistical matching of lidar-derived forest structural metrics between protected areas and comparable unprotected sites in Tanzania revealed that community-governed and multiply designated reserves often maintain higher biomass densities and structural integrity, underscoring the influence of governance on conservation outcomes.

Lidar Remote Sensing for Forest Ecosystem Dynamics publication trend

The graph below shows the total number of articles in lidar remote sensing for forest ecosystem dynamics across all publications each year (not limited to Nature Index journals).

Technical terms

LiDAR: A remote sensing technology emitting laser pulses to measure distances and derive three-dimensional structures.

Waveform LiDAR: Full-waveform recording of returned laser energy, allowing detailed vertical profiling of vegetation layers.

Canopy height: The vertical distance from ground level to the top of the vegetation canopy, indicative of forest maturity and biomass.

Above-ground biomass density: Mass of living vegetation per unit area above ground, critical for carbon stock estimation.

Probabilistic deep learning: A modelling approach that provides predictions with quantified uncertainty through ensemble or Bayesian methods.

References

1. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Science Advances (2023).
2. A high-resolution canopy height model of the Earth. Nature Ecology & Evolution (2023).
3. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment (2024).
4. Spaceborne LiDAR reveals the effectiveness of European Protected Areas in conserving forest height and vertical structure. Communications Earth & Environment (2023).