Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data
The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usuall...
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Finnish Society of Forest Science
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ftdoajarticles:oai:doaj.org/article:c5186019bc964945864c8a98510ea55c 2023-05-15T17:00:22+02:00 Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data Maltamo, Matti Peuhkurinen, Jussi Malinen, Jukka Vauhkonen, Jari Packalén, Petteri Tokola, Timo 2009-01-01T00:00:00Z https://doi.org/10.14214/sf.203 https://doaj.org/article/c5186019bc964945864c8a98510ea55c EN eng Finnish Society of Forest Science https://www.silvafennica.fi/article/203 https://doaj.org/toc/2242-4075 2242-4075 doi:10.14214/sf.203 https://doaj.org/article/c5186019bc964945864c8a98510ea55c Silva Fennica, Vol 43, Iss 3 (2009) Forestry SD1-669.5 article 2009 ftdoajarticles https://doi.org/10.14214/sf.203 2022-12-31T04:53:21Z The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch. Article in Journal/Newspaper karelia* Directory of Open Access Journals: DOAJ Articles Silva Fennica 43 3 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Forestry SD1-669.5 |
spellingShingle |
Forestry SD1-669.5 Maltamo, Matti Peuhkurinen, Jussi Malinen, Jukka Vauhkonen, Jari Packalén, Petteri Tokola, Timo Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
topic_facet |
Forestry SD1-669.5 |
description |
The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch. |
format |
Article in Journal/Newspaper |
author |
Maltamo, Matti Peuhkurinen, Jussi Malinen, Jukka Vauhkonen, Jari Packalén, Petteri Tokola, Timo |
author_facet |
Maltamo, Matti Peuhkurinen, Jussi Malinen, Jukka Vauhkonen, Jari Packalén, Petteri Tokola, Timo |
author_sort |
Maltamo, Matti |
title |
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
title_short |
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
title_full |
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
title_fullStr |
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
title_full_unstemmed |
Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data |
title_sort |
predicting tree attributes and quality characteristics of scots pine using airborne laser scanning data |
publisher |
Finnish Society of Forest Science |
publishDate |
2009 |
url |
https://doi.org/10.14214/sf.203 https://doaj.org/article/c5186019bc964945864c8a98510ea55c |
genre |
karelia* |
genre_facet |
karelia* |
op_source |
Silva Fennica, Vol 43, Iss 3 (2009) |
op_relation |
https://www.silvafennica.fi/article/203 https://doaj.org/toc/2242-4075 2242-4075 doi:10.14214/sf.203 https://doaj.org/article/c5186019bc964945864c8a98510ea55c |
op_doi |
https://doi.org/10.14214/sf.203 |
container_title |
Silva Fennica |
container_volume |
43 |
container_issue |
3 |
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1766053023973376000 |