Noora Tienaho, Ninni Saarinen, Tuomas Yrttimaa, Ville Kankare, Mikko Vastaranta. (2024). Quantifying fire-induced changes in ground vegetation using bitemporal terrestrial laser scanning. Silva Fennica vol. 58 no. 3 article id 23061. https://doi.org/10.14214/sf.23061

Keywords: forest fires; biomass; boreal forest; LIDAR; controlled burning; surface differencing; surface fires

Highlights: Bitemporal terrestrial laser scanning provided a means for identifying surface areas exposed to fire by utilizing a surface differencing method developed in this study; The developed method allowed for the quantification of fire-induced volumetric changes in ground vegetation at high resolution, facilitating the assessment of the impact of surface fires on forest ecosystems.

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Forest fires pose a significant threat to forest carbon storage and sinks, yet they also play a crucial role in the natural dynamics of boreal forests. Accurate quantification of biomass changes resulting from forest fires is essential for damage assessment and controlled burning evaluation. This study utilized terrestrial laser scanning (TLS) to quantify changes in ground vegetation resulting from low-intensity surface fires. TLS data were collected before and after controlled burnings at eight one-hectare test sites in Scots pine (Pinus sylvestris L.) dominated boreal forests in Finland. A surface differencing-based method was developed to identify areas exposed to fire. Validation, based on visual interpretation of 1 × 1 m surface patches (n = 320), showed a recall, precision, and F1-score of 0.9 for the accuracy of identifying burned surfaces. The developed method allowed the assessment of the magnitude of fire-induced vegetation changes within the test sites. The proportions of burned 1 × 1 m areas within the test sites varied between 51–96%. Total volumetric change in ground vegetation was on average –1200 m³ ha^-1, with burning reducing the vegetation volume by 1700 m³ ha^-1 and vegetation growth increasing it by 500 m³ ha^-1. Substantial variations in the volumetric changes within and between the test sites were detected, highlighting the complex dynamics of surface fires, and emphasizing the importance of having observations from multiple sites. This study demonstrates that bitemporal TLS measurements provide a robust means for characterizing fire-induced changes, facilitating the assessment of the impact of surface fires on forest ecosystems.

Tienaho, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland https://orcid.org/0000-0002-6574-5797 E-mail: noora.tienaho@uef.fi
Saarinen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@uef.fi
Yrttimaa, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland https://orcid.org/0000-0003-2648-523X E-mail: tuomas.yrttimaa@uef.fi
Kankare, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland https://orcid.org/0000-0001-6038-1579 E-mail: ville.kankare@uef.fi
Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

Category : Research article

article id 22007, category Research article

Ilkka Korpela, Antti Polvivaara, Saija Papunen, Laura Jaakkola, Noora Tienaho, Johannes Uotila, Tuomas Puputti, Aleksi Flyktman. (2023). Airborne dual-wavelength waveform LiDAR improves species classification accuracy of boreal broadleaved and coniferous trees. Silva Fennica vol. 56 no. 4 article id 22007. https://doi.org/10.14214/sf.22007

Keywords: crown modeling; laser scanning; photogrammetry; individual tree detection; Scandinavia

Highlights: First study to assess dual-wavelength waveform data in tree species identification; New findings regarding waveform features of previously unstudied species; Waveform features correlated with tree size displaying wavelength- and species-specific differences linked to bark reflectance, height growth rate and foliage density; Effects by pulse length and beam divergence are highlighted.

Abstract | Full text in HTML | Full text in PDF | Author Info

Tree species identification constitutes a bottleneck in remote sensing applications. Waveform LiDAR has been shown to offer potential over discrete-return observations, and we assessed if the combination of two-wavelength waveform data can lead to further improvements. A total of 2532 trees representing seven living and dead conifer and deciduous species classes found in Hyytiälä forests in southern Finland were included in the experiments. LiDAR data was acquired by two single-wavelength sensors. The 1064-nm and 1550-nm data were radiometrically corrected to enable range-normalization using the radar equation. Pulses were traced through the canopy, and by applying 3D crown models, the return waveforms were assigned to individual trees. Crown models and a terrain model enabled a further split of the waveforms to strata representing the crown, understory and ground segments. Different geometric and radiometric waveform attributes were extracted per return pulse and aggregated to tree-level mean and standard deviation features. We analyzed the effect of tree size on the features, the correlation between features and the between-species differences of the waveform features. Feature importance for species classification was derived using F-test and the Random Forest algorithm. Classification tests showed significant improvement in overall accuracy (74→83% with 7 classes, 88→91% with 4 classes) when the 1064-nm and 1550-nm features were merged. Most features were not invariant to tree size, and the dependencies differed between species and LiDAR wavelength. The differences were likely driven by factors such as bark reflectance, height growth induced structural changes near the treetop as well as foliage density in old trees.

Korpela, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-1665-3984 E-mail: ilkka.korpela@helsinki.fi
Polvivaara, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: –
Papunen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0001-5383-4314 E-mail: saija.papunen@outlook.com
Jaakkola, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: laura.jaakkola@helsinki.fi
Tienaho, University of Eastern Finland, Faculty of Science and Forestry, P.O. Box 111, FI-80101 Joensuu, Finland 0000-0002-6574-5797 E-mail: noora.tienaho@uef.fi
Uotila, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: johannes.uotila@helsinki.fi
Puputti, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0003-1972-1636 E-mail: tuomas.puputti@helsinki.fi
Flyktman, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-5235-317X E-mail: aleksi.flyktman@helsinki.fi

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