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Articles by Johannes Uotila
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
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.
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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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