This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73⁻1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Department of Physical Geography and Geoecology Faculty of Science Charles University Albertov 6 128 43 Prague Czech Republic

Zobrazit více v PubMed

Wang D., Hollaus M., Puttonen E., Pfeifer N. Automatic and self-adaptive stem reconstruction in landslide-affected forests. Remote Sens. 2016;8:974. doi: 10.3390/rs8120974. DOI

Dong C. Remote sensing, hydrological modeling and in situ observations in snow cover research: A review. J. Hydrol. 2018;561:573–583. doi: 10.1016/j.jhydrol.2018.04.027. DOI

Dong C., Menzel L. Snow process monitoring in montane forests with time-lapse photography. Hydrol. Process. 2017;31:2872–2886. doi: 10.1002/hyp.11229. DOI

Jenicek M., Pevna H., Matejka O. Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches. J. Hydrol. Hydromech. 2018;66 doi: 10.1515/johh-2017-0027. DOI

Pomeroy J.W., Gray D.M., Hedstrom N.R., Janowicz J.R. Physically based estimation of seasonal snow accumulation in the boreal forest; Proceedings of the 59th eastern snow conference; Stowe, VT, USA. 5–7 June 2002; pp. 93–108. DOI

Ellis C.R., Pomeroy J.W., Essery R.L.H., Link T.E. Effects of needleleaf forest cover on radiation and snowmelt dynamics in the Canadian Rocky Mountains. Can. J. For. Res. 2011;41:608–620. doi: 10.1139/X10-227. DOI

Melloh R.A., Hardy J.P., Davis R.E., Robinson P.B. Spectral albedo/reflectance of littered forest snow during the melt season. Hydrol. Process. 2001;15:3409–3422. doi: 10.1002/hyp.1043. DOI

Elder K., Dozier J., Michaelsen J. Snow accumulation and distribution in an Alpine Watershed. Water Resour. Res. 1991 doi: 10.1029/91WR00506. DOI

Strasser U., Warscher M., Liston G.E. Modeling Snow–Canopy Processes on an Idealized Mountain. J. Hydrometeorol. 2011;12:663–677. doi: 10.1175/2011JHM1344.1. DOI

Garvelmann J., Pohl S., Weiler M. From observation to the quantification of snow processes with a time-lapse camera network. Hydrol. Earth Syst. Sci. 2013;17:1415–1429. doi: 10.5194/hess-17-1415-2013. DOI

Moeser D., Roubinek J., Schleppi P., Morsdorf F., Jonas T. Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images. Agric. For. Meteorol. 2014;197:158–168. doi: 10.1016/j.agrformet.2014.06.008. DOI

Lundquist J.D., Dickerson-Lange S.E., Lutz J.A., Cristea N.C. Lower forest density enhances snow retention in regions with warmer winters: A global framework developed from plot-scale observations and modeling: Forests and Snow Retention. Water Resour. Res. 2013;49:6356–6370. doi: 10.1002/wrcr.20504. DOI

Revuelto J., López-Moreno J.-I., Azorin-Molina C., Alonso-González E., Sanmiguel-Vallelado A. Small-Scale Effect of Pine Stand Pruning on Snowpack Distribution in the Pyrenees Observed with a Terrestrial Laser Scanner. Forests. 2016;7:166. doi: 10.3390/f7080166. DOI

Stähli M., Gustafsson D. Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrol. Process. 2006;20:411–428. doi: 10.1002/hyp.6058. DOI

Stähli M., Jonas T., Gustafsson D. The role of snow interception in winter-time radiation processes of a coniferous sub-alpine forest. Hydrol. Process. 2009;23:2498–2512. doi: 10.1002/hyp.7180. DOI

Varhola A., Coops N.C., Weiler M., Moore R.D. Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results. J. Hydrol. 2010;392:219–233. doi: 10.1016/j.jhydrol.2010.08.009. DOI

Essery R., Morin S., Lejeune Y., B Ménard C. A comparison of 1701 snow models using observations from an alpine site. Adv. Water Resour. 2013;55:131–148. doi: 10.1016/j.advwatres.2012.07.013. DOI

Avanzi F., Bianchi A., Cina A., De Michele C., Maschio P., Pagliari D., Passoni D., Pinto L., Piras M., Rossi L. Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation. Remote Sens. 2018;10:765. doi: 10.3390/rs10050765. DOI

Jin S., Qian X., Kutoglu H. Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data. Remote Sens. 2016;8:63. doi: 10.3390/rs8010063. DOI

Larson K.M. GPS interferometric reflectometry: Applications to surface soil moisture, snow depth, and vegetation water content in the western United States: GPS interferometric reflectometry. Wiley Interdiscip. Rev. Water. 2016;3:775–787. doi: 10.1002/wat2.1167. DOI

McCreight J.L., Small E.E., Larson K.M. Snow depth, density, and SWE estimates derived from GPS reflection data: Validation in the western US. Water Resour. Res. 2014;50:6892–6909. doi: 10.1002/2014WR015561. DOI

Deems J.S., Painter T.H., Finnegan D.C. Lidar measurement of snow depth: A review. J. Glaciol. 2013;59:467–479. doi: 10.3189/2013JoG12J154. DOI

Grünewald T., Lehning M. Are flat-field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale: Representativeness of flat field snow depth measurements. Hydrol. Process. 2015;29:1717–1728. doi: 10.1002/hyp.10295. DOI

Grünewald T., Schirmer M., Mott R., Lehning M. Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment. Cryosphere. 2010;4:215–225. doi: 10.5194/tc-4-215-2010. DOI

Jaakkola A., Hyyppa J., Puttonen E. Measurement of Snow Depth Using a Low-Cost Mobile Laser Scanner. IEEE Geosci. Remote Sens. Lett. 2014;11:587–591. doi: 10.1109/LGRS.2013.2271861. DOI

Grünewald T., Stötter J., Pomeroy J.W., Dadic R., Moreno Baños I., Marturià J., Spross M., Hopkinson C., Burlando P., Lehning M. Statistical modelling of the snow depth distribution in open alpine terrain. Hydrol. Earth Syst. Sci. 2013;17:3005–3021. doi: 10.5194/hess-17-3005-2013. DOI

Bühler Y., Marty M., Egli L., Veitinger J., Jonas T., Thee P., Ginzler C. Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere. 2015;9:229–243. doi: 10.5194/tc-9-229-2015. DOI

Nolan M., Larsen C., Sturm M. Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry. Cryosphere Discuss. 2015;9:333–381. doi: 10.5194/tcd-9-333-2015. DOI

Prokop A., Schirmer M., Rub M., Lehning M., Stocker M. A comparison of measurement methods: Terrestrial laser scanning, tachymetry and snow probing for the determination of the spatial snow-depth distribution on slopes. Ann. Glaciol. 2008;49:210–216. doi: 10.3189/172756408787814726. DOI

Machguth H., Eisen O., Paul F., Hoelzle M. Strong spatial variability of snow accumulation observed with helicopter-borne GPR on two adjacent Alpine glaciers. Geophys. Res. Lett. 2006;33 doi: 10.1029/2006GL026576. DOI

Farinotti D., Magnusson J., Huss M., Bauder A. Snow accumulation distribution inferred from time-lapse photography and simple modelling. Hydrol. Process. 2010;24:2045–2201. doi: 10.1002/hyp.7629. DOI

Parajka J., Haas P., Kirnbauer R., Jansa J., Blöschl G. Potential of time-lapse photography of snow for hydrological purposes at the small catchment scale: Potential of time-lapse photography of snow for hydrological purposes. Hydrol. Process. 2012;26:3327–3337. doi: 10.1002/hyp.8389. DOI

Dietz A.J., Kuenzer C., Gessner U., Dech S. Remote sensing of snow—A review of available methods. Int. J. Remote Sens. 2012;33:4094–4134. doi: 10.1080/01431161.2011.640964. DOI

Cimoli E., Marcer M. Digital Elevation Model Reconstruction of a Glaciarized Basin Using Land-Based Structure from Motion. [(accessed on 28 February 2019)]; Available online: https://pdfs.semanticscholar.org/8757/eb995c9538664abb477287b473b4fac82a2c.pdf?_ga=2.114940608.653756116.1551329016-1294446320.1529472521.

Elder K., Rosenthal W., Davis R.E. Estimating the spatial distribution of snow water equivalence in a montane watershed. Hydrol. Process. 1998;12:1793–1808. doi: 10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP695>3.0.CO;2-K. DOI

Vander Jagt B.J., Durand M.T., Margulis S.A., Kim E.J., Molotch N.P. On the characterization of vegetation transmissivity using LAI for application in passive microwave remote sensing of snowpack. Remote Sens. Environ. 2015;156:310–321. doi: 10.1016/j.rse.2014.09.001. DOI

De Michele C., Avanzi F., Passoni D., Barzaghi R., Pinto L., Dosso P., Ghezzi A., Gianatti R., Della Vedova G. Microscale variability of snow depth using U.A.S. technology. Cryosphere. 2015;9:1047–1075. doi: 10.5194/tcd-9-1047-2015. DOI

Bühler Y., Adams M.S., Bösch R., Stoffel A. Mapping snow depth in alpine terrain with unmanned aerial systems (UAS): Potential and limitations. Cryosphere Discuss. 2016:1–36. doi: 10.5194/tc-2015-220. DOI

Harder P., Schirmer M., Pomeroy J., Helgason W. Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle. Cryosphere. 2016;10:2559–2571. doi: 10.5194/tc-10-2559-2016. DOI

Bühler Y., Adams M.S., Stoffel A., Boesch R. Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery. Int. J. Remote Sens. 2017;38:3135–3158. doi: 10.1080/01431161.2016.1275060. DOI

Cimoli E., Marcer M., Vandecrux B., Bøggild C.E., Williams G., Simonsen S.B. Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic. Remote Sens. 2017;9:1144. doi: 10.3390/rs9111144. DOI

Metcalfe R.A., Buttle J.M. A statistical model of spatially distributed snowmelt rates in a boreal forest basin. Hydrol. Process. 1998;12:1701–1722. doi: 10.1002/(SICI)1099-1085(199808/09)12:10/11<1701::AID-HYP690>3.0.CO;2-D. DOI

Hedstrom N.R., Pomeroy J.W. Measurements and modelling of snow interception in the boreal forest. Hydrol. Process. 1998;12:1611–1625. doi: 10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4. DOI

Jonckheere I., Fleck S., Nackaerts K., Muys B., Coppin P., Weiss M., Baret F. Review of methods for in situ leaf area index determination. Agric. For. Meteorol. 2004;121:19–35. doi: 10.1016/j.agrformet.2003.08.027. DOI

Manninen T., Korhonen L., Voipio P., Lahtinen P., Stenberg P. Airborne estimation of boreal forest LAI in winter conditions: A test using summer and winter ground truth. IEEE Trans. Geosci. Remote Sens. 2012;50:68–74. doi: 10.1109/TGRS.2011.2173939. DOI

Roubínek J., Moeser D., Pavlásek J., Jonas T. Linking Snow Distribution and Forest Canopy Characteristics by Way of Hemi-Spherical Photography. [(accessed on 28 February 2019)]; Available online: http://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_O4-26.pdf.

Miřijovský J., Langhammer J. Multitemporal Monitoring of the Morphodynamics of a Mid-Mountain Stream Using UAS Photogrammetry. Remote Sens. 2015;7:8586–8609. doi: 10.3390/rs70708586. DOI

Agisoft PhotoScan User Manual. [(accessed on 14 March 2016)]; Available online: http://www.agisoft.com/pdf/photoscan-pro_1_2_en.pdf.

Verhoeven G. Taking computer vision aloft - archaeological three-dimensional reconstructions from aerial photographs with photoscan. Archaeol. Prospect. 2011;18:67–73. doi: 10.1002/arp.399. DOI

Koutsoudis A., Vidmar B., Ioannakis G., Arnaoutoglou F., Pavlidis G., Chamzas C. Multi-image 3D reconstruction data evaluation. J. Cult. Herit. 2014;15:73–79. doi: 10.1016/j.culher.2012.12.003. DOI

CloudCompare. [(accessed on 10 July 2018)]; Available online: http://www.cloudcompare.org/

Zhang W., Qi J., Wan P., Wang H., Xie D., Wang X., Yan G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens. 2016;8:501. doi: 10.3390/rs8060501. DOI

Jain A.K. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 2010;8:651–666. doi: 10.1016/j.patrec.2009.09.011. DOI

ESRI ArcGIS Desktop: Release 10.4 Redlands, CA: Environmental Systems Research Instiute. [(accessed on 18 January 2017)]; Available online: http://www.esri.com.

Jenicek M., Hotovy O., Matejka O. Snow accumulation and ablation in different canopy structures at a plot scale: Using degree-day approach and measured shortwave radiation. AUC Geogr. 2017;52:61–72. doi: 10.14712/23361980.2017.5. DOI

Jenicek M., Pevna H., Matejka O. Snow accumulation and ablation in three forested mountain catchments. Acta Hydrol. Slovaca. 2015;16:208–216.

Danner M., Locherer M., Hank T., Richter K. Measuring Leaf Area Index (LAI) with the LI-Cor LAI 2200C or LAI-2200 (+2200Clear Kit) [(accessed on 28 February 2019)]; Available online: http://gfzpublic.gfz-potsdam.de/pubman/item/escidoc:1381850/component/escidoc:1388296/EnMAP_FieldGuide_LAI_2015_009.pdf.

Morsdorf F., Kötz B., Meier E., Itten K.I., Allgöwer B. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens. Environ. 2006;104:50–61. doi: 10.1016/j.rse.2006.04.019. DOI

Persson S. Master’s Thesis. LUND UNIVERSITY; Lund, Sweden: 2014. Estimating Leaf Area Index from Satellite Data in Deciduous Forests of Southern Sweden.

Kundela A. Leaf Area Index Estimation and Radiation Interception Measurements in Chinese Subtropical Forests: Assessment of Methods in Heterogeneous Topography. [(accessed on 28 February 2019)]; Available online: http://www.uwinst.uzh.ch/publications/Andreas_Kundela.pdf.

Manninen T., Korhonen L., Voipio P., Lahtinen P., Stenberg P. Leaf area index (LAI) estimation of boreal forest using wide optics airborne winter photos. Remote Sens. 2009;1:1380–1394. doi: 10.3390/rs1041380. DOI

Cutini A., Matteucci G., Mugnozza G.S. Estimation of leaf area index with the Li-Cor LAI 2000 in deciduous forests. For. Ecol. Manag. 1998;105:55–65. doi: 10.1016/S0378-1127(97)00269-7. DOI

Zhang Y., Chen J.M., Miller J.R. Determining digital hemispherical photograph exposure for leaf area index estimation. Agric. For. Meteorol. 2005;133:166–181. doi: 10.1016/j.agrformet.2005.09.009. DOI

Frazer G.W., Canham C.D., Lertzman K.P. Gap Light Analyzer (GLA), Version 2.0: Imaging Software to Extract Canopy Structure and Gap Light Transmission Indices from True-Colour Fisheye Photographs, Users Manual and Program Documentation. Volume 36 Simon Fraser University; Burnaby, BC, Canada: The Institute of Ecosystem Studies; Millbrook, NY, USA: 1999.

Frazer G., Trofymow J., Lertzman K. A Method for Estimating Canopy Openess, Effective Leaf Area Index and Hotosynthetically Active Photon Flux Density Using Hemispherical Photography and Computerized Image Analysis Techniques. Canadian Forest Service, Pacific Forestry Centre; Victoria, BC, Canada: 1997. p. 68. Technic Report; Information Report BC-X-373, Natural Resources Canada.

Lendzioch T., Langhammer J., Jenicek M. Tracking forest and open area effects on snow accumulation by unmanned aerial vehicle photogrammetry. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2016:917–923. doi: 10.5194/isprsarchives-XLI-B1-917-2016. DOI

Puntanen S. Linear Regression Analysis: Theory and Computing by Xin Yan, Xiao Gang Su. Int. Stat. Rev. 2010;78:144. doi: 10.1111/j.1751-5823.2010.00109_11.x. DOI

Pomeroy J., Fang X., Ellis C. Sensitivity of snowmelt hydrology in Marmot Creek, Alberta, to forest cover disturbance. Hydrol. Process. 2012;26:1891–1904. doi: 10.1002/hyp.9248. DOI

Vander Jagt B., Lucieer A., Wallace L., Turner D., Durand M. Snow depth retrieval with UAS using photogrammetrytechniques. Geosiences. 2015;5:264–285. doi: 10.3390/geosciences5030264. DOI

De Michele C., Avanzi F., Passoni D., Barzaghi R., Pinto L., Dosso P., Ghezzi A., Gianatti R., Vedova G.D. Using a fixed-wing UAS to map snow depth distribution: An evaluation at peak accumulation. Cryosphere. 2016;10:511–522. doi: 10.5194/tc-10-511-2016. DOI

Boesch R., Bühler Y., Marty M., Ginzler C. Comparison of digital surface models for snow depth mapping with UAV and aerial cameras. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2016;8 doi: 10.5194/isprsarchives-XLI-B8-453-2016. DOI

Matějková I., van Diggelen R., Prach K., Marrs R.H. An attempt to restore a central European species-rich mountain grassland through grazing. Appl. Veg. Sci. 2003;6:161–168. doi: 10.1111/j.1654-109X.2003.tb00576.x. DOI

Fernandes R., Prevost C., Canisius F., Leblanc S.G., Maloley M., Oakes S., Holman K., Knudby A. Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos. Cryosphere. 2018;12:3535–3550. doi: 10.5194/tc-12-3535-2018. DOI

Jost G., Dan Moore R., Smith R., Gluns D.R. Distributed temperature-index snowmelt modelling for forested catchments. J. Hydrol. 2012;420–421:87–101. doi: 10.1016/j.jhydrol.2011.11.045. DOI

Zheng G., Moskal L.M. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors. 2009;9:2719–2745. doi: 10.3390/s90402719. PubMed DOI PMC

Link T.E., Marks D. Point simulation of seasonal snow cover dynamics beneath boreal forest canopies. J. Geophys. Res. Atmos. 1999;104:27841–27857. doi: 10.1029/1998JD200121. DOI

Barry R., Prévost M., Stein J., Plamondon A.P. Application of a snow cover energy and mass balance model in a balsam fir forest. Water Resour. Res. 1990;26:1079–1092. doi: 10.1029/WR026i005p01079. DOI