A century of national forest inventories – informing past, present and future decisions

Breidenbach, Johannes; McRoberts, Ronald E.; Alberdi, Iciar; Antón-Fernández, Clara; Tomppo, Erkki

doi:10.1186/s40663-021-00315-x

Cited by 12 publications

(15 citation statements)

References 16 publications

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“…Site-specific information, such as information derived from forest inventories or soil mapping, are commonly gathered by national institutions, but not always openly accessible for every forestry stakeholder. National forest inventories were initiated in Europe already a century ago, providing detailed information on the forest condition and other related parameters for decision support, collected through sample plots, but also remote sensing approaches, continuously improving on the fine resolution of this data [101]. In addition, soil mapping on national level has a long tradition in Europe too, and initiatives are in place to merge national attempts to a digital and thematic soil map database-yet this is a long-term process, and the resulting spatial information will only be available at a coarse resolution [102].…”

Section: Discussionmentioning

confidence: 99%

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

et al. 2022

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Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

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Section: Discussionmentioning

confidence: 99%

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

et al. 2022

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“…Climatic conditions range between a mean annual temperature of −2 • C and about 6 • C, and a mean annual precipitation from about 400 mm up to 1300 mm. The permanent NFI plots used here cover the whole country from latitude 58.1 • N up to 71.1 • N and longitude 4.6 • E up to 31 • E (Breidenbach et al 2020). There are three main dominant species in Norway: Norway spruce (Picea abies (L.) Karst.…”

Section: Datamentioning

confidence: 99%

Building a high-resolution site index map using boosted regression trees: the Norwegian case

et al. 2023

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Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production ≥ 1 m3 ha-1 year-1) in a wall-to-wall high-resolution (16x16m) SI map for Norway. We investigate if remotely-sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine learning algorithm, to create high accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning (ALS) metrics as predictor variables. We use the SI observed at more than 10 000 national forest inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5 822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT have RMSE ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSE are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation.

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“…Taking into account the risks and rapidly changing situation in EU forests, forest disturbances and updated risk assessments will also be part of the yearly reports. (Breidenbach et al 2021). These programs have been progressively deployed and enforced throughout Europe since the early twentieth century, and up to very recently in Eastern Europe (Bontemps 2021; Supplemental Material), with a strategic view of documenting both the status and changes of forests and support forest and environmental policies (Tomppo et al 2010).…”

Section: Proposal 4 (P4)mentioning

confidence: 99%

“…The collection of forest ecosystem-, biodiversity-, and environment-oriented indicators by NFI surveys has also largely increased over time (Tomppo et al 2010, chapter 4;Breidenbach et al 2021), along with growing awareness for environmental issues (Forest Principles of the Rio conference, UN 1992). With the development of new, dedicated indicators, the use of NFI data for the quantification of forest biodiversity has experienced a strong development in recent years (e.g., Rondeux and Sanchez 2010, Storch et al 2018, Bertrand et al 2011.…”

Section: An Extended and Increasing Collection Of Forest Indicatorsmentioning

confidence: 99%

Offering the appetite for the monitoring of European forests a diversified diet

Bontemps

Bouriaud

Vega

et al. 2022

Annals of Forest Science

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Key message Forest monitoring in Europe is turning matter of renewed political concern, and a possible role for ICP Forests health monitoring has been suggested to meet this goal (Ann For Sci 78:94, 2021). Multipurpose national forest inventory (NFI) surveys yet offer a sampling effort by two orders of magnitude greater than ICP level 1, have accomplished substantial methodological and harmonization progresses in the recent years, and therefore form a decisive contributor to future European forest monitoring incentives. Possible paths for the future development of a pan-European, comprehensive and more accurate monitoring are designed that stress a crucial need to build on the assets of the existing forest monitoring programs and favor their cooperation, in order to limit the co-existence of distinct forest monitoring processes.

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A century of national forest inventories – informing past, present and future decisions

Cited by 12 publications

References 16 publications

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

Building a high-resolution site index map using boosted regression trees: the Norwegian case

Offering the appetite for the monitoring of European forests a diversified diet

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