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

Abstract

BackgroundRemote 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.MethodsThis 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.ResultsPrediction 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.ConclusionsDespite 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.

Highlights

Remote sensing-based mapping of forest Ecosystem Service (ES) indicators has become increasingly popular
Increased attention should especially be Vauhkonen Forest Ecosystems (2018) 5:24 focused on quantifying and communicating the resulting uncertainties to the decision makers in order to make informed decisions. Accounting for these aspects, the present study examines the robustness of forest land-use prioritization based on maps of the provisioning potential of forest ESs (Vauhkonen and Ruotsalainen 2017a), i.e., the fitness of forest patches to provide goods and services typical to the ESs occurring in the studied area, re-considering the methodological and data workflow proposed in the earlier study
The present study examines whether changes to each of the three steps listed above could improve pixel-wise analyses of the provisioning potential of forest ESs: 1) What data to use for the expert models of the provisioning potential: A consolidated approach to obtain grid-based, wall-to-wall predictions for the tessellated landscapes would be to use forest resource maps based on generalizing field sample plot measurements to larger areas using coarse to medium resolution Remote Sensing (RS) images and other numeric map data (Tomppo et al 2008a, 2008b, 2014)

Summary

Introduction

Remote sensing-based mapping of forest Ecosystem Service (ES) indicators has become increasingly popular. Vauhkonen Forest Ecosystems (2018) 5:24 focused on quantifying and communicating the resulting uncertainties to the decision makers in order to make informed decisions (see Eigenbrod et al 2010; Schulp et al 2014; Foody 2015) Accounting for these aspects, the present study examines the robustness of forest land-use prioritization based on maps of the provisioning potential of forest ESs (Vauhkonen and Ruotsalainen 2017a), i.e., the fitness of forest patches to provide goods and services typical to the ESs occurring in the studied area, re-considering the methodological and data workflow proposed in the earlier study. Such data have been used to map attributes related to habitat (Melin et al 2013, 2016; Vauhkonen and Imponen 2016), structural (Valbuena et al 2016b; Vauhkonen and Imponen 2016) and aesthetic (Vauhkonen and Ruotsalainen 2017b) properties of the forest

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