Forest Inventory Methods and Biodiversity Assessment

Forest inventories and biodiversity assessments have advanced from simple plot-based surveys towards integrated, multiscale approaches that combine field measurements, remote sensing and novel statistical models. Traditional methods such as fixed-area plots, cluster sampling and horizontal point sampling (Bitterlich) remain foundational for estimating tree density, basal area and above-ground biomass. Recent developments employ multistage and stratified sampling schemes guided by spatial autocorrelation analyses to reduce field effort while maintaining precision. Geostatistical techniques, including variogram modelling and sequential Gaussian simulation, enable optimised layout of sample points and more accurate maps of biomass and structural attributes. Concurrently, biodiversity assessments have grown beyond species lists to include metrics of richness, evenness and functional diversity, often derived from nested quadrats, plot-based structural indices and environmental DNA (eDNA) surveys. Advances in airborne and terrestrial LiDAR, unmanned aerial systems and high-resolution satellite imagery allow three-dimensional canopy structure to be characterised over extensive areas, bridging gaps between ground plots and landscape-scale biodiversity patterns. Such integrated workflows support carbon accounting, conservation planning and adaptive management under global change, offering cost-efficient, scalable solutions for monitoring forest health and resilience.

Research from Nature Portfolio

Recent studies have harnessed unmanned aerial vehicles equipped with multispectral and LiDAR sensors to derive high-resolution models of canopy height, gap dynamics and leaf-area distribution, significantly reducing uncertainty in above-ground biomass estimates. Other work has combined eDNA metabarcoding of soil and leaf-litter samples with traditional point plots to achieve comprehensive inventories of cryptic understorey taxa, revealing fine-scale patterns of alpha and beta diversity. A further contribution has refined sampling designs by coupling satellite-derived land-cover classifications with spatially balanced, stratified plot networks, demonstrating improved accuracy in estimating both forest carbon stocks and species richness across heterogeneous landscapes.

Research from all publishers

A multistage sampling framework applied to long-term forest inventory records from a mountainous region employed spatial autocorrelation analysis to group sampling units and adaptively select clusters, achieving similar biomass estimation accuracy with a sixfold reduction in sample size. Geostatistical sampling based on sequential Gaussian conditional simulation in subtropical forests showed that optimised spatial layouts require fewer field plots than traditional equidistant or stratified designs while maintaining target precision for biomass and structural attributes. Simulation studies of plot configuration in temperate woodlands demonstrated that square cluster plots of moderate ground area maximise accuracy and precision for species richness estimates, whereas elongated clusters better capture diversity metrics; these findings inform design choices for biodiversity monitoring programmes.

Forest Inventory Methods and Biodiversity Assessment publication trend

The graph below shows the total number of articles in forest inventory methods and biodiversity assessment across all publications each year (not limited to Nature Index journals).

Technical terms

Fixed-area plot: A circular or square plot of known area used to sample trees for diameter and density measurements.

Bitterlich sampling: A horizontal point sampling method where trees are selected by basal area factor at each sampling point.

Variogram: A function describing spatial variance as a function of distance, used in geostatistics to model spatial structure.

Sequential Gaussian conditional simulation: A geostatistical technique that generates multiple realisations of a spatially correlated variable for sampling design and uncertainty estimation.

Alpha and beta diversity: Metrics of species richness within a site (alpha) and turnover among sites (beta).

Environmental DNA (eDNA): Genetic material extracted from environmental samples, used to detect and monitor biodiversity without direct observation of organisms.

References

1. Multistage Sampling and Optimization for Forest Volume Inventory Based on Spatial Autocorrelation Analysis. Forests (2023).
2. Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation. Forests (2023).
3. Effects of Plot Design on Estimating Tree Species Richness and Species Diversity. Forests (2022).