# Terrestrial laser scanning predicts forest surface fuel loads in 53 Central European plots, R² up to 0.72

- Published: 2026-11-11 15:34 UTC
- Section: Planet › Ecology & recycling
- Tags: terrestrial laser scanning, surface fuel load, forest fire behavior, fuel mapping, Pinus sylvestris, Quercus, random forest model, forest inventory metrics, Central European forests, litter and needles
- URL: https://realtech.media/news/rt-922da620910984b0a7b5bc74_terrestrial_laser_scanning/

**Forest structure measured by laser scanning predicted leaf, needle and total surface fuel loads in German and Czech forests, where fuel distribution is rarely known in detail.**

![Terrestrial laser scanning predicts forest surface fuel loads in 53 Central European plots, R² up to 0.72](https://realtech.media/media/2026/11/5e3b5dbf9d8fed09.jpg)
*Department of the Interior. National Park Service. Alaska Region. (12/2/1980 - 5/1995), Wikimedia Commons, Public domain*

Researchers assessed how forest structure relates to surface fuel loads across 53 plots in Germany and Czechia, according to a study in Fire Ecology. The plots lay along a gradient from stands dominated by Scots pine (Pinus sylvestris) to stands dominated by oak (Quercus spp.). Random forest models predicted the fuel loads with R² values up to 0.72 [1].

Surface fuels strongly influence forest fire behavior, yet their distribution and composition are often not known in detail. The authors hypothesized that fuel loads vary systematically with forest structure and composition, so that structural characteristics could be used to predict them [1].

## Laser scanning and destructive sampling

The team used terrestrial laser scanning (TLS) to derive detailed, observer-independent structural and compositional metrics for every plot. Plots in Brandenburg, Germany, were sampled in July and August 2023, and plots in Czechia in July 2024. Scanning used a RIEGL VZ-400i terrestrial laser scanner.

Surface fuel loads were measured by destructive sampling in replicated microplots. The material was dried and sorted by hand into classes: leaves, needles, cones, acorns, woody debris up to 2.5 cm in diameter, moss, grass and herbs, and the complete fuel load. The predictors fell into three groups: conventional forest inventory metrics, three-dimensional vegetation density metrics, and statistical descriptors of the laser point cloud [1].

## Which metrics predicted fuel load

Predictive performance was strongest for fuel classes made of smaller, evenly distributed particles: leaves, needles and the complete fuel load [1].

When all plots along the species gradient were modeled together, simple inventory metrics that distinguish coniferous from deciduous trees were the most important predictors. When pure-species plots were modeled separately, more complex descriptors of vegetation density and height became essential. Vegetation density in the canopy was an important predictor of surface fuel load there [1].

The authors conclude that relatively simple inventory-based metrics, many of which exist in forest databases, can predict key surface fuel components across the surveyed species gradient. In their assessment, this allows more accessible and scalable fuel mapping and could be integrated into fire behavior models. Models for single species and pure stands benefit from the additional information in 3D structural metrics [1].

## Sources

1. [Fire Ecology](https://doi.org/10.1186/s42408-026-00549-6)
2. [bioengineer.org](https://bioengineer.org/terrestrial-laser-scanning-predicts-forest-surface-fuels-across-temperate-tree-gradients)

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Source: RealTech, realtech.media — https://realtech.media/news/rt-922da620910984b0a7b5bc74_terrestrial_laser_scanning/