Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps

[1]  P. Arp,et al.  Using the Cartographic Depth-to-Water Index to Locate Small Streams and Associated Wet Areas across Landscapes , 2012 .

[2]  J. Heikkonen,et al.  Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology , 2020, Forestry: An International Journal of Forest Research.

[3]  S. Zimmermann,et al.  Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland , 2021, Geoderma Regional.

[4]  Eric R. Labelle,et al.  Effects of Steel Flexible Tracks on Forwarder Peak Load Distribution: Results from a Prototype Load Test Platform , 2019 .

[5]  P. Arp,et al.  Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data , 2013, Journal of Soil and Water Conservation.

[6]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[7]  Kalle Einola,et al.  Fusion of open forest data and machine fieldbus data for performance analysis of forest machines , 2019, European Journal of Forest Research.

[8]  T. Tokola,et al.  Terrain mobility estimation using TWI and airborne gamma-ray data. , 2019, Journal of environmental management.

[9]  J. Seibert,et al.  Water storage in a till catchment. II: Implications of transmissivity feedback for flow paths and turnover times , 2011 .

[10]  Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation , 2021 .

[11]  S. Schrader,et al.  Evaluation of soil compaction effects on soil biota and soil biological processes in soils , 2010 .

[12]  Jae Ogilvie,et al.  Mapping wetlands: A comparison of two different approaches for New Brunswick, Canada , 2007, Wetlands.

[13]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[14]  P. Arp,et al.  Relating Cone Penetration and Rutting Resistance to Variations in Forest Soil Properties and Daily Moisture Fluctuations , 2017 .

[15]  Vijay K. Singh,et al.  Modelling of soil permeability using different data driven algorithms based on physical properties of soil , 2020 .

[16]  L. Eliasson,et al.  Influence of soil type, cartographic depth-to-water, road reinforcement and traffic intensity on rut formation in logging operations: a survey study in Sweden , 2017 .

[17]  M. A. Rab RECOVERY OF SOIL PHYSICAL PROPERTIES FROM COMPACTION AND SOIL PROFILE DISTURBANCE CAUSED BY LOGGING OF NATIVE FOREST IN VICTORIAN CENTRAL HIGHLANDS, AUSTRALIA , 2004 .

[18]  Francesco Neri,et al.  The impact of heavy traffic on forest soils: A review , 2015 .

[19]  Mikko T. Niemi,et al.  Airborne LiDAR-derived elevation data in terrain trafficability mapping , 2017 .

[20]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[21]  P. Arp,et al.  A modular terrain model for daily variations in machine-specific forest soil trafficability , 2009 .

[22]  D. Tarboton A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[23]  P. Comeau,et al.  Linking the Depth-to-Water Topographic Index to Soil Moisture on Boreal Forest Sites in Alberta , 2016 .

[24]  S. S. Paul,et al.  Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape , 2021 .

[25]  B. Matthews Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[26]  Rainer Horn,et al.  Impact of modern forest vehicles on soil physical properties , 2007 .

[27]  Eric R. Labelle,et al.  Soil displacement during ground-based mechanized forest operations using mixed-wood brush mats , 2018, Soil and Tillage Research.

[28]  J. Uusitalo,et al.  Predicting rut depth induced by an 8-wheeled forwarder in fine-grained boreal forest soils , 2020, Annals of Forest Science.

[29]  K. Katzensteiner,et al.  A European morpho-functional classification of humus forms , 2011 .

[30]  Michael A. Lefsky,et al.  Review of studies on tree species classification from remotely sensed data , 2016 .

[31]  T. L. Coleman,et al.  SPECTRAL DIFFERENTIATION OF SURFACE SOILS AND SOIL PROPERTIES: IS IT POSSIBLE FROM SPACE PLATFORMS? , 1993 .

[32]  K. Seki,et al.  SWRC fit - a nonlinear fitting program with a water retention curve for soils having unimodal and bimodal pore structure , 2007 .

[33]  L. Pari,et al.  Applications of GIS-Based Software to Improve the Sustainability of a Forwarding Operation in Central Italy , 2020, Sustainability.

[34]  D. H. McNabb,et al.  Soil Wetness and Traffic Level Effects on Bulk Density and Air‐Filled Porosity of Compacted Boreal Forest Soils , 2001 .

[35]  Z. Kurczyński THE SELECTION OF AERIAL LASER SCANNING PARAMETERS FOR COUNTRYWIDE DIGITAL ELEVATION MODEL CREATION , 2013 .

[36]  E. Ring,et al.  Mapping Temporal Dynamics in a Forest Stream Network—Implications for Riparian Forest Management , 2015 .

[37]  P. Arp,et al.  Evaluating digital terrain indices for soil wetness mapping – a Swedish case study , 2014 .

[38]  M. Saarilahti,et al.  Terrain Trafficability Prediction with GIS Analysis , 2009 .

[39]  P. Arp,et al.  Soil Trafficability Forecasting , 2019, Open Journal of Forestry.

[40]  S. Macdonald,et al.  Relating Bryophyte Assemblages to a Remotely Sensed Depth-to-Water Index in Boreal Forests , 2018, Front. Plant Sci..

[41]  H. Laudon,et al.  Evaluating topography‐based predictions of shallow lateral groundwater discharge zones for a boreal lake‐stream system , 2017 .

[42]  Luca Montanarella,et al.  A regional scale soil mapping approach using integrated AVHRR and DEM data , 2001 .

[43]  H. Laudon,et al.  Management perspectives on Aqua incognita: Connectivity and cumulative effects of small natural and artificial streams in boreal forests , 2017 .

[44]  M. Nilsson,et al.  Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape , 2019, Ambio.

[45]  Jae Ogilvie,et al.  Topographic modelling of soil moisture conditions: a comparison and verification of two models , 2009 .

[46]  P. Arp,et al.  Modelling and mapping topographic variations in forest soils at high resolution: A case study , 2011 .

[47]  R. Picchio,et al.  Earthworms as an Ecological Indicator of Soil Recovery after Mechanized Logging Operations in Mixed Beech Forests , 2020, Forests.

[48]  Yizong Cheng,et al.  Statistical characterization of remotely sensed soil moisture images , 1997 .

[49]  Jin Zhang,et al.  An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping , 2016 .

[50]  A. Alm,et al.  Harvesting impacts on quaking aspen regeneration in northern Minnesota , 1993 .