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Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees
Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airbo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310548/ https://www.ncbi.nlm.nih.gov/pubmed/28205107 http://dx.doi.org/10.1007/s10661-017-5816-7 |
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author | Price, B. Gomez, A. Mathys, L. Gardi, O. Schellenberger, A. Ginzler, C. Thürig, E. |
author_facet | Price, B. Gomez, A. Mathys, L. Gardi, O. Schellenberger, A. Ginzler, C. Thürig, E. |
author_sort | Price, B. |
collection | PubMed |
description | Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R (2) of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10661-017-5816-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5310548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-53105482017-02-28 Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees Price, B. Gomez, A. Mathys, L. Gardi, O. Schellenberger, A. Ginzler, C. Thürig, E. Environ Monit Assess Article Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R (2) of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10661-017-5816-7) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-02-15 2017 /pmc/articles/PMC5310548/ /pubmed/28205107 http://dx.doi.org/10.1007/s10661-017-5816-7 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Price, B. Gomez, A. Mathys, L. Gardi, O. Schellenberger, A. Ginzler, C. Thürig, E. Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title | Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title_full | Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title_fullStr | Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title_full_unstemmed | Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title_short | Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
title_sort | tree biomass in the swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310548/ https://www.ncbi.nlm.nih.gov/pubmed/28205107 http://dx.doi.org/10.1007/s10661-017-5816-7 |
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