[1]
|
Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
Hydrology and Earth System Sciences,
2024
DOI:10.5194/hess-28-2617-2024
|
|
|
[2]
|
High-resolution harvester data for estimating rolling resistance and forest trafficability
European Journal of Forest Research,
2024
DOI:10.1007/s10342-024-01717-6
|
|
|
[3]
|
Exploring the reliability of CAN-bus data in assessing forwarder rolling resistance under real working conditions
iForest - Biogeosciences and Forestry,
2024
DOI:10.3832/ifor4687-017
|
|
|
[4]
|
High-resolution harvester data for estimating rolling resistance and forest trafficability
European Journal of Forest Research,
2024
DOI:10.1007/s10342-024-01717-6
|
|
|
[5]
|
Forurenset grunn på avveier og nasjonalt datasett over dreneringslinjer
Kart og Plan,
2023
DOI:10.18261/kp.116.1-2.2
|
|
|
[6]
|
Impact on Soil Physical Properties Related to a High Mechanization Level in the Row Thinning of a Korean Pine Stand
Land,
2022
DOI:10.3390/land11030329
|
|
|
[7]
|
Depth-to-Water Maps to Identify Soil Areas That Are Potentially Sensitive to Logging Disturbance: Initial Evaluations in the Mediterranean Forest Context
Land,
2022
DOI:10.3390/land11050709
|
|
|
[8]
|
Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
International Journal of Applied Earth Observation and Geoinformation,
2022
DOI:10.1016/j.jag.2022.102730
|
|
|
[9]
|
Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps
International Journal of Applied Earth Observation and Geoinformation,
2021
DOI:10.1016/j.jag.2021.102614
|
|
|
[10]
|
Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation
Forests,
2021
DOI:10.3390/f12020113
|
|
|
[11]
|
Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation
Forests,
2021
DOI:10.3390/f12020113
|
|
|