Scholarlink

Background <p>Surface and ladder fuels play a significant role in controlling fire behavior, and their estimation is critical for fire modeling and management. Although airborne laser scanning (ALS) provides cost-effective, spatially explicit data on forest 3D structure, its utility for surface fuel estimation remains uncertain due to canopy occlusion and the presence of tree trunk points. We assessed the impact of tree trunk point filtering (TPF) on model performance for estimating surface fuel loads in strata within a vertical gradient of 0.0–2.0&#xa0;m, which includes litter, herbaceous, and shrub layers. We used high-density ALS data (~ 2500 points m<sup>−2</sup>) from boreo-nemoral mixed forests in southern Sweden. We compared the performance of 438 LiDAR (lidar) metrics in characterizing surface fuels using parametric (linear and non-linear) and nonparametric (random forest — RF) regressions.</p> Results <p>There was no significant impact of TPF when comparing lidar-derived metric distributions and model performance under filter types, although a minor improvement was observed in the 0.5–2.0-m stratum. The performance of surface fuel strata modeling was the&#xa0;highest for the litter layer depth (<i>R</i><sup>2</sup> = 0.39) and moderate for the herbaceous layer and branch biomass (<i>R</i><sup>2</sup> = 0.26–0.28). The linear regression model consistently outperformed the RF model and showed slightly better performance than the nonlinear regression. We obtained a negligible positive impact of TPF (<i>ΔR</i><sup>2</sup> = 0.02) on predicting the litter layer depth utilizing the parametric regression approaches. Intensity-based metrics calculated using a minimum 5-m buffer radius were instrumental in modeling fuel layers within the 0.0–0.5-m stratum.</p> Conclusions <p>Removing tree trunk points did not affect the representation of surface fuels in airborne lidar data. We suggest, however, that the correct classification of ground and no-ground points and detection of objects such as boulders and deadwood can have a major effect on the adequate prediction of surface fuels.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Tree trunks do not bias estimates of surface fuels by aerial lidar in southern Sweden

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月21日

查看全文
  • Roman M. Zadorozhniuk,
  • Maksym Matsala,
  • Olga T. Wepryk,
  • Igor Drobyshev

摘要

Background

Surface and ladder fuels play a significant role in controlling fire behavior, and their estimation is critical for fire modeling and management. Although airborne laser scanning (ALS) provides cost-effective, spatially explicit data on forest 3D structure, its utility for surface fuel estimation remains uncertain due to canopy occlusion and the presence of tree trunk points. We assessed the impact of tree trunk point filtering (TPF) on model performance for estimating surface fuel loads in strata within a vertical gradient of 0.0–2.0 m, which includes litter, herbaceous, and shrub layers. We used high-density ALS data (~ 2500 points m−2) from boreo-nemoral mixed forests in southern Sweden. We compared the performance of 438 LiDAR (lidar) metrics in characterizing surface fuels using parametric (linear and non-linear) and nonparametric (random forest — RF) regressions.

Results

There was no significant impact of TPF when comparing lidar-derived metric distributions and model performance under filter types, although a minor improvement was observed in the 0.5–2.0-m stratum. The performance of surface fuel strata modeling was the highest for the litter layer depth (R2 = 0.39) and moderate for the herbaceous layer and branch biomass (R2 = 0.26–0.28). The linear regression model consistently outperformed the RF model and showed slightly better performance than the nonlinear regression. We obtained a negligible positive impact of TPF (ΔR2 = 0.02) on predicting the litter layer depth utilizing the parametric regression approaches. Intensity-based metrics calculated using a minimum 5-m buffer radius were instrumental in modeling fuel layers within the 0.0–0.5-m stratum.

Conclusions

Removing tree trunk points did not affect the representation of surface fuels in airborne lidar data. We suggest, however, that the correct classification of ground and no-ground points and detection of objects such as boulders and deadwood can have a major effect on the adequate prediction of surface fuels.