Lidar in tree biomass
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LiDAR in Tree Biomass Estimation
Introduction to LiDAR and Tree Biomass
Light Detection and Ranging (LiDAR) technology has revolutionized the way we estimate forest biomass, providing a non-destructive, accurate, and efficient method for measuring tree and forest attributes. This article synthesizes recent research on the application of LiDAR in tree biomass estimation, highlighting key methodologies, challenges, and advancements.
Multitemporal LiDAR for Forest and Carbon Monitoring
Multitemporal LiDAR surveys have proven effective in monitoring forest growth, biomass dynamics, and carbon flux over time. A study conducted in Scotland utilized airborne LiDAR surveys over a decade to map tree growth and biomass changes. The research demonstrated that LiDAR-detected tree heights correlated well with field measurements, although biases due to pulse density variations were noted. Correcting these biases allowed for accurate detection of sub-annual tree growth and carbon sequestration rates, affirming the utility of repeat LiDAR data for resource monitoring and carbon management1.
Individual Tree Biomass Estimation Techniques
Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS)
LiDAR applications at the individual tree scale have shown significant promise. Techniques such as Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) have been used to estimate tree volume and aboveground biomass (AGB). These methods leverage high-resolution 3D structural data to provide accurate biomass estimates. However, challenges remain in terms of spatial resolution and evaluation accuracy2.
Non-Destructive Methods
Non-destructive methods using TLS have been developed to estimate single-tree biomass efficiently. For instance, a study on lodgepole pine trees used voxelization and the Outer Hull Model (OHM) to estimate branch, foliage, and trunk volumes. This approach yielded nearly unbiased biomass estimates, demonstrating strong agreement with destructive sampling data4.
Scale-Invariant Biomass Estimation
Researchers have developed scale-invariant models for forest biomass estimation using LiDAR-derived canopy height distributions (CHD) and canopy height quantile functions (CHQ). These models have shown consistent predictive performance across various scales, making them useful for forest inventory tasks where analysis units vary in size and shape3.
LiDAR Biomass Index (LBI)
A novel parameter called the LiDAR Biomass Index (LBI) has been proposed to estimate tree-level AGB using 3D crown information. This index has been tested on both coniferous and broadleaf species, showing high accuracy in explaining variations in field data, particularly for coniferous species5.
Machine Learning Approaches
Machine learning techniques have been increasingly applied to LiDAR data for biomass estimation. Methods such as random forest (RF), support vector regression (SVR), and Cubist have been compared, with SVR showing the highest accuracy at the plot level. These approaches highlight the potential of combining LiDAR data with advanced computational methods to improve biomass estimation accuracy7.
Multispectral LiDAR and Biomass Estimation
Multispectral LiDAR data have been used to estimate stem biomass in uneven-aged forests. Studies have shown that algorithms like RF can provide accurate predictions for both total and barkless stem biomass, demonstrating the utility of multispectral LiDAR in complex forest structures8.
Synergies Between LiDAR and Radar
Combining LiDAR with radar data has been explored to enhance biomass mapping. This synergy leverages the strengths of both technologies, with LiDAR providing accurate height measurements and radar sensing canopy volume. Preliminary results indicate that this combined approach can predict biomass with reasonable accuracy, suggesting potential for future satellite missions10.
Conclusion
LiDAR technology has significantly advanced the field of forest biomass estimation, offering precise, non-destructive methods for measuring tree and forest attributes. While challenges such as spatial resolution and bias correction remain, ongoing research and the integration of machine learning and multispectral data continue to enhance the accuracy and applicability of LiDAR in forest biomass monitoring.
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Most relevant research papers on this topic
Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux
Multitemporal lidar data can effectively monitor forest and carbon dynamics, but requires field surveys or modeling efforts to enhance trustworthiness of lidar-based inferences.
Highly CitedLiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives
LiDAR applications show potential for accurate forest biomass estimation at the individual tree scale, with improvements in spatial resolution and evaluation accuracy.
Literature ReviewHighly CitedLidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers
Scale-invariant models using lidar data effectively estimate forest biomass at various scales, making them useful for forest inventory tasks and estimating other forest characteristics.
Highly CitedNon-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR
Our method accurately estimates single-tree biomass non-destructively using terrestrial LiDAR scan data, improving efficiency in single-tree biomass estimation.
Highly CitedLidar biomass index: A novel solution for tree-level biomass estimation using 3D crown information
The Lidar Biomass Index (LBI) effectively estimates tree-level aboveground biomass using 3D crown information, improving our understanding of forest carbon sequestration and climate change interactions.
Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR
Airborne scanning LiDAR can accurately map forest biomass and stem volume, with visually corrected individual tree detection (ITDvisual) providing cost-efficient training data for area-based approach (ABA).
Highly CitedForest biomass estimation from airborne LiDAR data using machine learning approaches
Support vector regression (SVR) is the most accurate machine learning approach for estimating forest biomass from LiDAR data, with manual delineation of tree crowns not always producing superior models.
Highly CitedEstimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data
Multispectral LiDAR data can accurately estimate individual tree stem biomass in uneven-aged structured forests, with the Random Forest algorithm providing the best predictive performance.
Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements
LiDAR can accurately estimate forest biomass, but its accuracy varies among different software programs and forest types.
Highly CitedForest biomass mapping from lidar and radar synergies
Combining lidar and radar data shows potential for accurate forest biomass mapping, with results within 10% of a reference map derived from LVIS data.
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