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Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning


Torabzadeh, Hossein; Leiterer, Reik; Hueni, Andreas; Schaepman, Michael E; Morsdorf, Felix (2019). Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning. Agricultural and Forest Meteorology, 279:107744.

Abstract

Knowledge of the spatial distribution of tree species is important for efficiently managing and monitoring forested ecosystems, especially in mixed forests of the temperate zone. In this study, we fused imaging spectroscopy (IS) data with leaf-on and off small-footprint airborne laser scanning (ALS) data, for tree species identification in a dense temperate forest in Switzerland. In addition to the spectral reflectance of the sunlit part of the tree crowns, structural features computed based on the height, intensity and point distribution of ALS data in both the vertical and horizontal dimensions are used as features. Features were extracted using a pixel-based (1 m × 1 m) and an individual tree crown approach. In addition, applying a floating forward feature selection approach revealed that the ALS-derived features provided relevant structural information for species identification, while IS-derived features added complementary biochemical information. Comparing the accuracies of three different combinations of ALS and IS data, shows the highest classification accuracy (kappa = 90.3%) was obtained by fusing a selected set of features at individual tree crowns (ITC), while the best kappa accuracies resulting from IS or ALS data alone were 74.7% and 75.1%, respectively. Inclusion of the ITC information improved the classification results for all datasets, however, this improvement is significantly higher for ALS derived datasets (+31%). Our results show that accurate ITC information drastically improves classification accuracy of tree species in dense forests and that multi-seasonal ALS structural attributes play a major part in species discrimination.

Abstract

Knowledge of the spatial distribution of tree species is important for efficiently managing and monitoring forested ecosystems, especially in mixed forests of the temperate zone. In this study, we fused imaging spectroscopy (IS) data with leaf-on and off small-footprint airborne laser scanning (ALS) data, for tree species identification in a dense temperate forest in Switzerland. In addition to the spectral reflectance of the sunlit part of the tree crowns, structural features computed based on the height, intensity and point distribution of ALS data in both the vertical and horizontal dimensions are used as features. Features were extracted using a pixel-based (1 m × 1 m) and an individual tree crown approach. In addition, applying a floating forward feature selection approach revealed that the ALS-derived features provided relevant structural information for species identification, while IS-derived features added complementary biochemical information. Comparing the accuracies of three different combinations of ALS and IS data, shows the highest classification accuracy (kappa = 90.3%) was obtained by fusing a selected set of features at individual tree crowns (ITC), while the best kappa accuracies resulting from IS or ALS data alone were 74.7% and 75.1%, respectively. Inclusion of the ITC information improved the classification results for all datasets, however, this improvement is significantly higher for ALS derived datasets (+31%). Our results show that accurate ITC information drastically improves classification accuracy of tree species in dense forests and that multi-seasonal ALS structural attributes play a major part in species discrimination.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
08 Research Priority Programs > Global Change and Biodiversity
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Forestry
Physical Sciences > Global and Planetary Change
Life Sciences > Agronomy and Crop Science
Physical Sciences > Atmospheric Science
Uncontrolled Keywords:Atmospheric Science, Agronomy and Crop Science, Global and Planetary Change, Forestry
Language:English
Date:1 December 2019
Deposited On:24 Feb 2022 14:04
Last Modified:27 Jun 2024 01:36
Publisher:Elsevier
ISSN:0168-1923
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.agrformet.2019.107744
Project Information:
  • : FunderEuropean Space Agency
  • : Grant ID
  • : Project Title
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