Towards in-field live fuel moisture content estimation using multi-wavelength terrestrial laser scanning

Abstract

The impact of climate change on vegetation dynamics and wildfire risk has been a subject of considerable research interest. Live fuel moisture content (LFMC) is a critical factor in assessing fire risk and influencing fire ignition and behaviour. Satellite remote sensing techniques provide information on LMFC dynamics, but spatial and temporal resolution hinder understanding in structurally complex forests with interconnected tree and shrub layers. Multi-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC. However, studies of LFMC in shrubs are scarce despite their key role in fire spread. In this study, we investigated the capacity of a dual-wavelength SALCA (Salford Advanced Laser Canopy Analyser) TLS (1063 and 1545 nm) and the single-wavelength Trimble X6 (1500 nm) to estimate LFMC in six Mediterranean forest plots (135 individual plants, 18 species). Analysis at different separate plots and individual-species levels identified key factors affecting LFMC prediction using TLS. At plot level, linking spectral indices and LFMC is challenging due to species diversity in crown structures, ages, sizes and leaf types. Our results suggest that detector heating by solar radiation could alter the sensor calibration and reduce model accuracy. Nevertheless, in some areas the multiple linear regression models achieved an R adj 2 up to 0.82 and an RMSE of 7.66%. At the species level, models showed stronger relationships with LFMC ( R adj 2 ranging from 0.43 to 0.88) and a relatively low RMSE (RMSE from 1.92 to 3.97%). Overall, univariate relationships between LFMC and individual-wavelength reflectance were not consistent across species or most plots. Considering these results, combining the capacity of dual TLS devices to estimate LFMC with the structural information that they provide, open a potential to improve field work sampling for wildfire risk assessment. Extending the research to cover a wider range of tree, shrub and herbaceous species in the future will advance our understanding of LFMC dynamics and contribute to more accurate fire behaviour modelling.

Plant phenotyping relevance

TLSセンサーを用いて個体・種・プロットレベルの生植物含水率（LFMC）を推定し、モデル精度やセンサー校正の影響を評価しているため、植物状態の取得・推定方法が中心です。

Code and data availability

The paper's TLS intensity and LFMC field dataset is deposited on Zenodo (10.5281/zenodo.16739493), but the data availability statement specifies a 12-month embargo from publication, so the asset is not yet publicly actionable and authors/repository access must be awaited. No author analysis code with a public URL is描述d

No evidence-backed public reproduction asset is currently recorded.