Multitemporal lidar data can effectively monitor forest and carbon dynamics, but requires field surveys or modeling efforts to enhance trustworthiness of lidar-based inferences.

Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux

LiDAR applications show potential for accurate forest biomass estimation at the individual tree scale, with improvements in spatial resolution and evaluation accuracy.

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

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.

Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers

Our method accurately estimates single-tree biomass non-destructively using terrestrial LiDAR scan data, improving efficiency in single-tree biomass estimation.

Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR

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.

Lidar biomass index: A novel solution for tree-level biomass estimation using 3D crown information

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).

Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

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.

Forest biomass estimation from airborne LiDAR data using machine learning approaches

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.

Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data

LiDAR can accurately estimate forest biomass, but its accuracy varies among different software programs and forest types.

Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements

Combining lidar and radar data shows potential for accurate forest biomass mapping, with results within 10% of a reference map derived from LVIS data.

Forest biomass mapping from lidar and radar synergies

Top-of-canopy height from canopy height models with fine to relatively coarse pixel resolutions (1 to 10 m) yields the most accurate biomass predictions at the 1-ha scale, with nRMSE decreasing with spatial scale.

Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states

Backpack LiDAR can nondestructively estimate individual tree aboveground biomass, providing a reliable basis for forest resource management and carbon stock estimation.

Estimation of Aboveground Biomass of Individual Trees by Backpack LiDAR Based on Parameter-Optimized Quantitative Structural Models (AdQSM)

The first training data-based algorithm for determining forest biomass using small footprint LiDAR data is a success, with potential for future refinement and testing.

Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data

Space-based forest inventories can efficiently estimate aboveground biomass and carbon stocks in northern forests, with non-commercial forests contributing 36% of the biomass.

Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec.

Airborne lidar can accurately estimate aboveground biomass and tree species richness in tropical Ghana forests, with MARS regression providing higher accuracy than multilinear regression.

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

LiDAR-assisted estimation of aboveground forest biomass has potential for improved forest management, but its uncertainty assessment needs improvement.

Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass

LiDAR data can accurately estimate crown biomass components in coniferous forests, particularly when using the green channel.

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

Reducing LiDAR point density can improve regional-scale forest assessment accuracy without compromising biomass estimates, and these estimates can be further improved using development density.

Effects of LiDAR point density and landscape context on estimates of urban forest biomass

Airborne lidar data can accurately estimate individual pine tree biomass and component biomass, with linear regression models explaining 93% of the variability in individual tree biomass and dbh.

Estimating biomass of individual pine trees using airborne lidar

The random forest model is the most accurate method for estimating Eucalyptus biomass using LiDAR data and aerial images, providing data reference for detailed forest biomass distribution in most provinces.

Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

These studies suggest that LiDAR is effective for estimating tree and forest biomass, with accuracy enhanced by various methods such as multitemporal data, scale-invariant models, machine learning algorithms, and combining with other data types.