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

Hoffmann, Stephan and Schoenauer, Marian and Heppelmann, Joachim and Asikainen, Antti and Eberhard, Benno and Hasenauer, Hubert and Ivanovs, Janis and Jaeger, Dirk and Lazdins, Andis and Mohtashami, Sima and Moskalik, Tadeusz and Nordfjell, Tomas and Sterenczak, Krzysztof and Talbot, Bruce and Uusitalo, Jori and Astrup, Rasmus and Vuillermoz, Morgan and Cacot, Emmanuel (2022). Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry. Current Forestry Reports. 8 :1 , 55-71
[Research article]

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Abstract

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.

Authors/Creators:Hoffmann, Stephan and Schoenauer, Marian and Heppelmann, Joachim and Asikainen, Antti and Eberhard, Benno and Hasenauer, Hubert and Ivanovs, Janis and Jaeger, Dirk and Lazdins, Andis and Mohtashami, Sima and Moskalik, Tadeusz and Nordfjell, Tomas and Sterenczak, Krzysztof and Talbot, Bruce and Uusitalo, Jori and Astrup, Rasmus and Vuillermoz, Morgan and Cacot, Emmanuel
Title:Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry
Series Name/Journal:Current Forestry Reports
Year of publishing :2022
Volume:8
Number:1
Page range:55-71
Number of Pages:17
Publisher:SPRINGER INT PUBL AG
ISSN:2198-6436
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:Depth-to-water, Remote sensing, Digital terrain models, European forestry, Precision forestry, Trafficability prediction
URN:NBN:urn:nbn:se:slu:epsilon-p-115676
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-115676
Additional ID:
Type of IDID
DOI10.1007/s40725-021-00153-8
Web of Science (WoS)000740379200001
ID Code:27413
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:21 Mar 2022 10:25
Metadata Last Modified:21 Mar 2022 10:31

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