Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Jari Vauhkonen

doi:10.1186/s40663-018-0143-1

Forest Ecosystems 2018, 5(3): 24 https://doi.org/10.1186/s40663-018-0143-1

Research |

Open Access | Issue | Published: 11 June 2018

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Show Author's Information Hide Author's Information Jari Vauhkonen(

)

Natural Resources Institute Finland (Luke), Bioeconomy and Environment Unit, P.O. Box 68, Yliopistokatu 6, FI-80101 Joensuu, Finland

Keywords:

Remote sensing, Light detection and ranging (LiDAR), Forestry decision making, Spatial prioritization

Cite this article:

Vauhkonen J. Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps. Forest Ecosystems, 2018, 5(3): 24. https://doi.org/10.1186/s40663-018-0143-1

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Abstract Full text About this article

Abstract

Background

Remote sensing-based mapping of forest Ecosystem Service (ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.

Methods

This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.

Results

Prediction models based on Airborne Laser Scanning (ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses (berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97% of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.

Conclusions

Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m², i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

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About this article

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Show Author's information Hide Author's Information Jari Vauhkonen(

)

Natural Resources Institute Finland (Luke), Bioeconomy and Environment Unit, P.O. Box 68, Yliopistokatu 6, FI-80101 Joensuu, Finland

Abstract

Background

Methods

Results

Conclusions

Keywords: Remote sensing, Light detection and ranging (LiDAR), Forestry decision making, Spatial prioritization

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Received: 14 March 2018

Accepted: 24 May 2018

Published: 11 June 2018

Issue date: September 2018

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Acknowledgements

The acquisition of the studied data was originally supported by the Research Funds of University of Helsinki.

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