133 citations as recorded by crossref.

1. Airborne LiDAR-derived elevation data in terrain trafficability mapping M. Niemi et al. https://doi.org/10.1080/02827581.2017.1296181
2. A topographic moisture index explains understory vegetation response to retention harvesting L. Echiverri & S. Ellen Macdonald https://doi.org/10.1016/j.foreco.2020.118358
3. Spatio-temporal controls of dissolved organic carbon stream water concentrations A. Musolff et al. https://doi.org/10.1016/j.jhydrol.2018.09.011
4. A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management T. Service et al. https://doi.org/10.1016/j.jenvman.2024.122110
5. Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content K. Liao et al. https://doi.org/10.1016/j.jhydrol.2017.01.043
6. Groundwater flow paths drive longitudinal patterns of stream dissolved organic carbon (DOC) concentrations in boreal landscapes A. Lupon et al. https://doi.org/10.5194/hess-27-613-2023
7. Mapping Earth Hummocks in Daisetsuzan National Park in Japan Using UAV-SfM Framework Y. Meng et al. https://doi.org/10.3390/rs16193610
8. How much data is enough? Sensor choice, scale, and point-density effects on terrain metrics in steep mountain forests G. Grube et al. https://doi.org/10.1080/02827581.2026.2715106
9. Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta A. Onojeghuo et al. https://doi.org/10.1080/15481603.2021.1952541
10. Whether the Rock Fragment Content Should Be Considered When Investigating Nitrogen Cycle in Stony Soils? K. Liao et al. https://doi.org/10.1029/2018JG004780
11. Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology A. Salmivaara et al. https://doi.org/10.1093/forestry/cpaa010
12. An approach to select optimum inputs for hydrological modeling to improve simulation accuracy in data-scarce regions J. Zhou et al. https://doi.org/10.1016/j.ejrh.2023.101447
13. Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest S. Paul et al. https://doi.org/10.5194/bg-23-683-2026
14. A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan N. Shahzad et al. https://doi.org/10.3390/app12052280
15. Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps M. Schönauer et al. https://doi.org/10.1016/j.jag.2021.102614
16. Prediction of soil drainage classes in Denmark by means of decision tree classification A. Møller et al. https://doi.org/10.1016/j.geoderma.2017.10.015
17. Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model G. O'Neil et al. https://doi.org/10.1029/2019WR024784
18. Assessing Land Degradation Through Remote Sensing and Geospatial Techniques for Sustainable Development Under the Mediterranean Conditions E. Ali et al. https://doi.org/10.3390/su17136087
19. Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices J. Larson et al. https://doi.org/10.5194/hess-26-4837-2022
20. Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment E. Silveira et al. https://doi.org/10.1016/j.jag.2019.02.004
21. A Citizen-Contributed GIS Approach for Evaluating the Impacts of Land Use on Hurricane-Harvey-Induced Flooding in Houston Area D. Yang et al. https://doi.org/10.3390/land8020025
22. Soils of Cuttings and Secondary Forests A. Dymov https://doi.org/10.1134/S1064229323700205
23. Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning J. Pohjankukka et al. https://doi.org/10.1016/j.geoderma.2025.117216
24. Identifying and assessing the potential hydrological function of past artificial forest drainage E. Hasselquist et al. https://doi.org/10.1007/s13280-017-0984-9
25. Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals M. Schönauer et al. https://doi.org/10.1016/j.jag.2022.102730
26. The role of biogeochemical hotspots, landscape heterogeneity, and hydrological connectivity for minimizing forestry effects on water quality H. Laudon et al. https://doi.org/10.1007/s13280-015-0751-8
27. Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification G. O'Neil et al. https://doi.org/10.1016/j.jhydrol.2018.02.009
28. GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed V. Rana & T. Suryanarayana https://doi.org/10.1080/19475683.2020.1733083
29. High-resolution harvester data for estimating rolling resistance and forest trafficability A. Salmivaara et al. https://doi.org/10.1007/s10342-024-01717-6
30. Assessing the Potential of EMIT Hyperspectral Data Combined with DEM-Derived Terrain Variables for Predicting Soil As, Cu and Zn Concentrations in a Mountainous Region of Southwest China G. Qie et al. https://doi.org/10.3390/rs18132211
31. Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover H. Haugen et al. https://doi.org/10.3390/rs14205207
32. Effect of bound water on the quality of dried Lentinus edodes during storage W. Yang et al. https://doi.org/10.1002/jsfa.10210
33. Landslides on dry badlands: UAV images to identify the drivers controlling their unexpected occurrence on vegetated hillslopes E. Rodriguez-Caballero et al. https://doi.org/10.1016/j.jaridenv.2020.104434
34. Soil Moisture Variations From Boreal Forests to the Tundra J. Kemppinen et al. https://doi.org/10.1029/2022WR032719
35. Predicting bearing capacity and trafficability classes of forest roads using road properties and surrounding terrain information V. Karjalainen et al. https://doi.org/10.1080/14680629.2024.2434945
36. Groundwater inflows control patterns and sources of greenhouse gas emissions from streams A. Lupon et al. https://doi.org/10.1002/lno.11134
37. Dwarf Shrubs Impact Tundra Soils: Drier, Colder, and Less Organic Carbon J. Kemppinen et al. https://doi.org/10.1007/s10021-020-00589-2
38. Topographic indices and ERA5-Land data to describe soil moisture variability in a Central European beech forest M. Schönauer et al. https://doi.org/10.1016/j.ejrh.2025.102456
39. Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture Y. Xu et al. https://doi.org/10.1109/TGRS.2019.2944421
40. Spatial optimization of ground-based primary extraction routes using the BestWay decision support system P. Flisberg et al. https://doi.org/10.1139/cjfr-2020-0238
41. Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration L. Ikkala et al. https://doi.org/10.3390/rs14133169
42. Large-scale mapping of existing and lost wetlands: Earth Observation data and tools to support restoration in the Sebou and Medjerda river basins A. Guelmami https://doi.org/10.1007/s41207-023-00443-6
43. Delineating northern peatlands using Sentinel-1 time series and terrain indices from local and regional digital elevation models M. Karlson et al. https://doi.org/10.1016/j.rse.2019.111252
44. Comparing buffer zone alternatives in forest planning using a decision support system J. Lundström et al. https://doi.org/10.1080/02827581.2018.1441900
45. Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux E. Vainio et al. https://doi.org/10.5194/bg-18-2003-2021
46. Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data J. Kemppinen et al. https://doi.org/10.1002/esp.4301
47. A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines A. Hosseini et al. https://doi.org/10.1139/cjfr-2018-0258
48. An inverse shortest path approach to find forwarder productivity functions N. Søvde et al. https://doi.org/10.1016/j.compag.2018.05.023
49. The effects of an auxiliary axle on forwarder rut development – a Norwegian field study D. Fjeld & Ø. Østby-Berntsen https://doi.org/10.1080/14942119.2020.1765645
50. A tree-based intelligence ensemble approach for spatial prediction of potential groundwater M. Avand et al. https://doi.org/10.1080/17538947.2020.1718785
51. Are dissolved organic carbon concentrations in riparian groundwater linked to hydrological pathways in the boreal forest? S. Ploum et al. https://doi.org/10.5194/hess-24-1709-2020
52. Divergent drivers of temporal stability for gravel and sandy desert ecosystems around mobile deserts: Implications for ecosystem conservation and desertification management L. Wei et al. https://doi.org/10.1016/j.catena.2025.109088
53. Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms F. Kaya et al. https://doi.org/10.1016/j.geodrs.2022.e00584
54. A web application for hydrogeomorphic flood hazard mapping R. Tavares da Costa et al. https://doi.org/10.1016/j.envsoft.2019.04.010
55. Forest harvest contribution to Boreal freshwater methyl mercury load R. Kronberg et al. https://doi.org/10.1002/2015GB005316
56. Évaluation du risque d’inondation par intégration du SAGA Wetness Index (SAGAWI) et de l’Analyse Hiérarchique des Procédés (AHP) : cas du District Autonome d’Abidjan K. Traoré https://doi.org/10.4000/belgeo.60310
57. Delineating retention tree groups based on open spatial data and ecosystem service indicators M. Kesälä et al. https://doi.org/10.1016/j.foreco.2025.123041
58. Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes J. Vollering et al. https://doi.org/10.5194/soil-11-763-2025
59. Using a digital elevation model to place overland flow fields and uncleaned ditch sections for water protection in peatland forest management M. Niemi et al. https://doi.org/10.1016/j.ecoleng.2023.106945
60. Remote sensing and forest inventories in Nordic countries – roadmap for the future A. Kangas et al. https://doi.org/10.1080/02827581.2017.1416666
61. TWI computation: a comparison of different open source GISs P. Mattivi et al. https://doi.org/10.1186/s40965-019-0066-y
62. GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques I. Bashir et al. https://doi.org/10.21523/gcj5.20040201
63. Relating Bryophyte Assemblages to a Remotely Sensed Depth-to-Water Index in Boreal Forests S. Bartels et al. https://doi.org/10.3389/fpls.2018.00858
64. Topographic conditions of soil drainage of Vladimir Opolie P. Shilov https://doi.org/10.19047/0136-1694-2020-105-28-56
65. Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry S. Hoffmann et al. https://doi.org/10.1007/s40725-021-00153-8
66. Using Random Forest Classification and Nationally Available Geospatial Data to Screen for Wetlands over Large Geographic Regions B. Felton et al. https://doi.org/10.3390/w11061158
67. Soils of Post-Pyrogenic Forests A. Dymov https://doi.org/10.1134/S1064229323700217
68. Cost of riparian buffer zones: A comparison of hydrologically adapted site‐specific riparian buffers with traditional fixed widths T. Tiwari et al. https://doi.org/10.1002/2015WR018014
69. A decision support model for individual tree stump harvesting options based on criteria for economic return and environmental protection B. Olsson et al. https://doi.org/10.1080/02827581.2016.1236983
70. Analyzing and Projecting Soil Moisture and Cone Penetrability Variations in Forest Soils M. Jones & P. Arp https://doi.org/10.4236/ojf.2019.92005
71. Determining diagnostic indicators for fine-scale short vegetation aboveground biomass inversion using a HVRU-based analysis approach J. Wang et al. https://doi.org/10.1016/j.ecolind.2019.106033
72. Effects of stump harvest and site preparation on N2O and CH4 emissions from boreal forest soils after clear-cutting M. Strömgren et al. https://doi.org/10.1016/j.foreco.2016.03.019
73. GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study C. Akumu et al. https://doi.org/10.1016/j.geoderma.2016.07.028
74. Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices L. Rimondini et al. https://doi.org/10.5194/essd-15-3473-2023
75. Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest W. Nijland et al. https://doi.org/10.1016/j.foreco.2015.08.027
76. GIS-based planning of buffer zones for protection of boreal streams and their riparian forests H. Mykrä et al. https://doi.org/10.1016/j.foreco.2022.120639
77. Understanding the hydrological valley landscape: A multi-scenario adaptive framework for delineating valley floors W. Sun et al. https://doi.org/10.1016/j.catena.2025.109111
78. Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications? A. Salmivaara et al. https://doi.org/10.1080/14942119.2018.1419677
79. Scale‐dependent effects of environmental factors on soil organic carbon, soil nutrients and stoichiometry under two contrasting land‐use types Q. Zhu et al. https://doi.org/10.1111/sum.12695
80. Delineating wetland areas from the cut-and-fill method using a Digital Elevation Model (DEM) Z. Lidzhegu et al. https://doi.org/10.1080/03736245.2019.1638825
81. Depth‐to‐water mediates bryophyte response to harvesting in boreal forests S. Bartels et al. https://doi.org/10.1111/1365-2664.13359
82. Utilization of Image, LiDAR and Gamma-Ray Information to Improve Environmental Sustainability of Cut-to-Length Wood Harvesting Operations in Peatlands: A Management Systems Perspective T. Palander & K. Kärhä https://doi.org/10.3390/ijgi10050273
83. Correlation between morfometry features and soil taxonomy indexes on the example of chernozems on the left bank of Don river (Voronegh region) L. Mirny et al. https://doi.org/10.55959/MSU0137-0944-17-2025-80-1-122-132
84. Drone-borne ground-penetrating radar reveals spatiotemporal moisture dynamics in peatland root zones M. Henrion et al. https://doi.org/10.1016/j.srs.2025.100311
85. Growth and Yield Characterization of Soybean (Glycine max L.) and Adzuki Bean (Vigna angularis L.) Cultivated from Paddy Fields with Different Topographic Features H. Chun et al. https://doi.org/10.7745/KJSSF.2018.51.4.536
86. Modelling forest road trafficability with satellite-based soil moisture variables D. Fjeld et al. https://doi.org/10.1080/14942119.2023.2276628
87. Explorative analysis of depth-to-water index in identifying rewettable agricultural peat soils H. Kekkonen et al. https://doi.org/10.1016/j.jenvman.2025.125443
88. Landscape-scale factors driving plant species composition in mountain streamside and spring riparian forests R. Pielech et al. https://doi.org/10.1016/j.foreco.2015.03.038
89. A comprehensive strategy for modeling watershed restoration priority areas under epistemic uncertainty: A case study in the Atlantic Forest, Brazil I. Possantti et al. https://doi.org/10.1016/j.jhydrol.2022.129003
90. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape A. Ågren et al. https://doi.org/10.1016/j.geoderma.2021.115280
91. Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data E. Silveira et al. https://doi.org/10.1080/10106049.2019.1594394
92. Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations S. Mohtashami et al. https://doi.org/10.1016/j.jag.2022.102728
93. Applying continuous-cover forestry on drained boreal peatlands; water regulation, biodiversity, climate benefits and remaining uncertainties H. Laudon & E. Maher Hasselquist https://doi.org/10.1016/j.tfp.2022.100363
94. Terrain wetness indices derived from LiDAR to inform soil moisture and corrosion potential for underground infrastructure D. Bretreger et al. https://doi.org/10.1016/j.scitotenv.2020.144138
95. The Use of Multiscale Topographic Position Index for the Identification of Upstream Depressional Wetlands A. Broeckx et al. https://doi.org/10.1007/s13157-025-02022-7
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99. Application of the Conservation Planning Tool Zonation to Inform Retention Planning in the Boreal Forest of Western Canada F. Robinne et al. https://doi.org/10.3389/fevo.2020.584291
100. Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping J. Hird et al. https://doi.org/10.3390/rs9121315
101. Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia G. Blanchard et al. https://doi.org/10.1111/jvs.12781
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103. Occupancy and habitat associations of European hares and rabbits in mesic forests of the Northern Tablelands, New South Wales, Australia R. Stone et al. https://doi.org/10.1071/WR25093
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105. Depth-to-water maps as predictors of rut severity in fully mechanized harvesting operations J. Heppelmann et al. https://doi.org/10.1080/14942119.2022.2044724
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107. Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape W. Lidberg et al. https://doi.org/10.1007/s13280-019-01196-9
108. Moisture Stress Indicators in Giant Sequoia Groves in the Southern Sierra Nevada of California, USA R. Ray https://doi.org/10.2136/vzj2016.03.0018
109. Exploring the reliability of CAN-bus data in assessing forwarder rolling resistance under real working conditions F. Guerra et al. https://doi.org/10.3832/ifor4687-017
110. Towards an Improved Conceptualization of Riparian Zones in Boreal Forest Headwaters J. Ledesma et al. https://doi.org/10.1007/s10021-017-0149-5
111. Multiple stressors in small streams in the forestry context of Fennoscandia: The effects in time and space L. Kuglerová et al. https://doi.org/10.1016/j.scitotenv.2020.143521
112. Identifying Suitable Locations for Water Harvesting Structures in Dryland Watersheds to Mitigate Flooding and Erosion Using High-Resolution Topographic Data and Multi-Criteria Analysis K. Neupane et al. https://doi.org/10.3390/su18115495
113. Age-independent site index mapping with repeated single-tree airborne laser scanning S. Solberg et al. https://doi.org/10.1080/02827581.2019.1616814
114. An operational UAV-based approach for stand-level assessment of soil disturbance after forest harvesting B. Talbot et al. https://doi.org/10.1080/02827581.2017.1418421
115. AI-Driven Integration of Sentinel-1 SAR for High-Resolution Soil Water Content Estimation to Enhance Precision Irrigation in Smallholder Maize Systems, Vhembe District G. Nxumalo et al. https://doi.org/10.3390/w18040499
116. Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery I. Mienna et al. https://doi.org/10.1002/rse2.260
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118. Management perspectives onAqua incognita: Connectivity and cumulative effects of small natural and artificial streams in boreal forests L. Kuglerová et al. https://doi.org/10.1002/hyp.11281
119. Scale-dependent groundwater contributions influence patterns of winter baseflow stream chemistry in boreal catchments A. Peralta-Tapia et al. https://doi.org/10.1002/2014JG002878
120. Predicting forwarder rut formation on fine-grained mineral soils M. Sirén et al. https://doi.org/10.1080/02827581.2018.1562567
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123. Deep learning Using Physically-Informed Input Data for Wetland Identification G. O'Neil et al. https://doi.org/10.1016/j.envsoft.2020.104665
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126. Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation M. Schönauer et al. https://doi.org/10.3390/f12020113
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128. Carbon isotope discrimination by Picea glauca and Populus tremuloides is related to the topographic depth to water index and rainfall G. Oltean et al. https://doi.org/10.1139/cjfr-2015-0491
129. Depth-to-Water Maps to Identify Soil Areas That Are Potentially Sensitive to Logging Disturbance: Initial Evaluations in the Mediterranean Forest Context F. Latterini et al. https://doi.org/10.3390/land11050709
130. Controls on spatial and temporal variability of soil moisture across a heterogeneous boreal forest landscape F. Zignol et al. https://doi.org/10.5194/hess-29-5493-2025
131. High-Resolution GeoAI framework for automated rutting depth prediction on logging trails using TransRAUnet and LiDAR O. Abdi & J. Uusitalo https://doi.org/10.1016/j.compag.2026.111547
132. Multi‐Source Mapping of Peatland Types Using Sentinel‐1, Sentinel‐2, and Terrain Derivatives—A Comparison Between Five High‐Latitude Landscapes M. Karlson & D. Bastviken https://doi.org/10.1029/2022JG007195
133. Mapping Wet Areas and Drainage Networks of Data-Scarce Catchments Using Topographic Attributes H. Chaves et al. https://doi.org/10.3390/w17152298