References
- Colliander A, Cosh MH, Misra S, Jackson TJ, Crow WT, Powers J, et al. Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP Validation Experiment 2016 (SMAPVEX16). Remote Sensing of Environment. 2019;227:137–50. https://doi.org/10.1016/j.rse.2019.04.004.
- Colliander A, Cosh MH, Kelly VR, Kraatz S, Bourgeau‐Chavez L, Siqueira P, et al. SMAP detects soil moisture under temperate forest canopies. Geophysical Research Letters. 2020;47:e2020GL089697. DOI: 10.1029/2020GL089697.
- Colliander A, Cosh MH, Kelly V, Kraatz S, Bourgeau-Chavez LL, Siqueira P, et al. SMAP Validation Experiment 2019–2022 (SMAPVEX19–22): Detection of Soil Moisture Under Temperate Forest Canopy. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021). Brussels, Belgium: IEEE; 2021. p. 6992–5. DOI: 10.1109/IGARSS47720.2021.9553613.
- Li Y, Zhao Z, Wei S, Sun D, Yang Q, Ding X. Prediction of regional forest soil nutrients based on Gaofen-1 remote sensing data. Forests. 2021;12(11):1430. DOI: 10.3390/f12111430.
- Segarra J, Buchaillot ML, Araus JL, Kefauver SC. Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy. 2020;10(5):641. DOI: 10.3390/agronomy10050641.
- Debaene G, Bartmiński P, Siłuch M. In Situ VIS-NIR spectroscopy for a basic and rapid soil investigation. Sensors. 2023;23(12):5495. https://doi.org/10.3390/s23125495.
- Roudier P, Hedley CB, Lobsey CR, Viscarra Rossel RA, Leroux C. Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon. Geoderma. 2017;296:98–107. DOI: 10.1016/j.geoderma.2017.02.014.
- Morellos A, Pantazi XE, Moshou D, Alexandridis TK, Whetton RL, Tziotzios G, et al. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering. 2016;152:104–16.
- Pietrzykowski M, Chodak M. Near infrared spectroscopy—A tool for chemical properties and organic matter assessment of afforested mine soils. Ecological Engineering. 2014;62:115–22. DOI: 10.1016/j.ecoleng.2013.10.025.
- Ludwig B, Vormstein S, Niebuhr J, Heinze S, Marschner B, Vohland M. Estimation accuracies of near infrared spectroscopy for general soil properties and enzyme activities for two forest sites along three transects. Geoderma. 2017;288:37–46. DOI: 10.1016/j.geoderma.2016.10.022.
- Thomas F, Petzold R, Becker C, Werban U. Application of low-cost MEMS spectrometers for forest topsoil properties prediction. Sensors. 2021;21(11):3927. DOI: 10.3390/s21113927.
- Thomas F, Petzold R, Landmark S, Mollenhauer H, Becker C, Werban U. Estimating forest soil properties for humus assessment—Is Vis-NIR the way to go? Remote Sensing. 2022;14:1368. DOI: 10.3390/rs14061368.
- Liu H, Wang Q, Peng J, Ji W, Li X, Viscarra Rossel RA. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations. Science China Earth Sciences. 2014;57(8):1701–10. DOI: 10.1007/s11430-013-4808-x.
- Gholizadeh A, Viscarra Rossel RA, Saberioon M, Boruvka L, Kratina J, Pavlu L. National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic. Geoderma. 2021;385:114832. DOI: 10.1016/j.geoderma.2020.114832.
- Murphy PNC, Ogilvie J, Meng F-R, White B, Bhatti JS, Arp PA. Modelling and mapping topographic variations in forest soils at high resolution: A case study. Ecological Modelling. 2011;222(14).
- Case BS, Meng F, Arp PA. Digital elevation modelling of soil type and drainage within small forested catchments. Canadian Journal of Soil Science. 2005;85(1):127– 37. DOI: 10.1093/forestry/cpaa010.
- Talbot B, Rahlf J, Astrup R. An operational UAV-based approach for stand-level assessment of soil disturbance after forest harvesting. Scandinavian Journal of Forest Research. 2018;33:387–96. DOI: 10.1080/02827581.2017.1418421.
- Nevalainen P, Salmivaara A, Ala-Ilomäki J, Launiainen S, Hiedanpää J, Finér L, et al. Estimating the rut depth by UAV photogrammetry. Remote Sensing. 2017;9:1279. DOI: 10.3390/rs9121279.
- Pierzchała M, Talbot B, Astrup R. Estimating soil displacement from timber extraction trails in steep terrain: Application of an unmanned aircraft for 3D modelling. Forests. 2014;5:1212–23. DOI: 10.3390/f5061212.
- James LA, Watson DG, Hansen WF. Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA. CATENA. 2007;71(1):132–44. DOI: 10.1016/j.catena.2006.10.010.
- Lunt IA, Hubbard SS, Rubin Y. Soil moisture content estimation using ground penetrating radar reflection data. Journal of Hydrology. 2005;307(1-4):254–69.
- Campbell DMH, White B, Arp PA. Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data. Journal of Soil and Water Conservation. 2013;68(6):460–73.
- Wehr A, Lohr U. Airborne laser scanning—an introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing. 1999;54(2-3):68–82.
- MacMillan RA, Martin TC, Earle TJ, McNabb DH. Automated analysis and classification of landforms using high-resolution digital elevation data: applications and issues. Canadian Journal of Remote Sensing. 2003;29(5):592–606.
- Murphy PNC, Ogilvie J, Arp P. Topographic modelling of soil moisture conditions: a comparison and verification of two models. European Journal of Soil Science. 2009;60(1):94–109.
- Lidberg W, Nilsson M, Lundmark T, Ågren AM. Evaluating preprocessing methods of digital elevation models for hydrological modelling. Hydrological Processes. 2017;31(26):4660–8.
- Lidberg W, Nilsson M, Ågren A. Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio. 2020;49(2):475–86.
- Mohtashami S, Eliasson L, Hansson L, Willén E, Thierfelder T, Nordfjell T. Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps for planning logging operations. International Journal of Applied Earth Observation and Geoinformation. 2022;108:102728. DOI: 10.1016/j.jag.2022.102728.
- Ring E, Ågren A, Bergkvist I, Finér L, Johansson F, Högbom L. A guide to using wet area maps in forestry. Skogforsk arbetsrapport. 2020;1051-2020.
- Mattila U, Tokola T. Terrain mobility estimation using TWI and airborne gamma-ray data. Journal of Environmental Management. 2019;232:531–6.
- White B, Ogilvie J, Campbell DM, Hiltz D, Gauthier B, Chisholm HKH, et al. Using the cartographic depth-to-water index to locate small streams and associated wet areas across landscapes. Canadian Water Resources Journal/Revue Canadienne des Ressources Hydriques. 2012;37(4):333–47.
- Labelle ER, Hansson L, Högbom L, Jourgholami M, Laschi A. Strategies to mitigate the effects of soil physical disturbances caused by forest machinery: a comprehensive review. Current Forestry Reports. 2022;8(1):20–37.
- Kuglerová L, Hasselquist EM, Richardson JS, Sponseller RA, Kreutzweiser DP, Laudon H. Management perspectives on Aqua incognita: Connectivity and cumulative effects of small natural and artificial streams in boreal forests. Hydrological Processes. 2017;31(23):4238–44.
- Mohtashami S, Eliasson L, Jansson G, Sonesson J. Influence of soil type, cartographic depth-to-water, road reinforcement and traffic intensity on rut formation in logging operations: a survey study in Sweden. Silva Fennica. 2017;51(5).