Applications of Unpiloted Aerial Vehicles (UAVs) in Forest Hydrology

Part of the Ecological Studies book series (ECOLSTUD, volume 240)


Unpiloted aerial vehicles (UAVs) are transforming the field of ecohydrology as remote sensing platforms. UAV flight characteristics bring them closer to the land surface, further increasing the achievable spatial resolution of multi-/hyperspectral, thermal infrared (TIR) and light distancing and ranging (LiDAR) measurements and enabling measurement of the land surface at times when clouds obscure the view of satellite- and conventional aircraft-borne sensors. However, it is the low cost of UAVs and the lower operator risk associated with their use that truly define their role as more than a novelty. This chapter focuses on consumer-grade UAVs as platforms for remote sensing, because this category of UAV is much more accessible in terms of cost and regulation to use by the ecohydrologist than military- or aerospace-industry-derived UAVs. The chapter explores the impact of UAV technology on advancing the field of ecohydrology by first reviewing the state of development of consumer-grade UAVs and the remote sensors that can be integrated into these platforms and then discussing the role of UAVs in advancing methods to map forest stand composition, evapotranspiration and snowpack hydrology, respectively. The chapter concludes with a discussion of future needs and potential research directions.



This work is supported in part by the Natural Science and Engineering Research Council of Canada through the following research grants: RGPIN 2014-06114 (Hill), EGP 502265-16 (Hill), RGPIN-2018-06766 (Pypker) and EGP 505371-16 (Pypker). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the authors.


  1. Aeronautics Act: Regulations Amending the Canadian Aviation Regulations (Part VI) (2002) Canada Gazette Part II, 136(11). Retrieved January 16, 2019, from the Canada Gazette website:
  2. Alonzo MH, Andersen E, Morton DC, Cook BD (2018) Quantifying boreal forest structure and composition using UAV structure from motion. Forests 9:119. CrossRefGoogle Scholar
  3. Ambrosia VG, Wenger SS, Sullivan DV, Buechel SW, Dunagan SE, Brass JA et al (2003) Demonstrating UAV-acquired real-time thermal data over fires. Photogramm Eng Remote Sens 69:391–402. CrossRefGoogle Scholar
  4. Ambrosia VG, Wegener SS, Zajkowski T, Sullivan DV, Buechel S, Enomoto F et al (2011) The Ikhana UAS western states fire imaging missions: from concept to reality (2006–2011). Geocarto Int 26:85–101. CrossRefGoogle Scholar
  5. Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11:138–146. CrossRefGoogle Scholar
  6. Anderson MC, Norman JM, Kustas WP, Houborg R, Starks PJ, Agam N (2008) A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens Environ 112:4227–4241. CrossRefGoogle Scholar
  7. Arora VK, Montenegro A (2011) Small temperature benefits provided by realistic afforestation efforts. Nat Geosci 1:514. CrossRefGoogle Scholar
  8. Avanzi F, Bianchi A, Cina A, De Michele C, Maschio P, Pagliari D et al (2018) Centimetric accuracy in snow depth using unmanned aerial system photogrammetry and a multistation. Remote Sens 10:765. CrossRefGoogle Scholar
  9. Babcock CAO, Finley H-E, Andersen R, Pattison R, Cook BD, Morton M et al (2018) Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations. Remote Sens Environ 212:212–230. CrossRefGoogle Scholar
  10. Baldocchi DD (1997) Flux footprints within and over forest canopies. Boundary-Layer Meteorol 85:273–292. CrossRefGoogle Scholar
  11. Baldocchi DD, Hicks BB, Meyers TP (1988) Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology 69:1331–1340. CrossRefGoogle Scholar
  12. Baron J, Hill DJ, Elmigili H (2018) Combining image processing and machine learning to identify invasive plants in high resolution images. Int J Remote Sens 39:5099–5118. CrossRefGoogle Scholar
  13. Becker RH, Zmijewski KA, Crail T (2013) Seeing the forest for the invasives: mapping buckthorn in the oak openings. Biol Invasions 15:315–326. CrossRefGoogle Scholar
  14. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65:2–16. CrossRefGoogle Scholar
  15. Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E et al (2014) Geographic object-based image analysis – towards a new paradigm. ISPRS J Photogramm 87:180–191. CrossRefGoogle Scholar
  16. Boudreau J, Nelson RF, Margolis HA, Beaudoin A, Guindon L, Kimes DS (2008) Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sens Environ 112:3876–3890. CrossRefGoogle Scholar
  17. Calderón R, Navas-Cortés JA, Lucena C, Zarco-Tejada PJ (2013) High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens Environ 139:231–245. CrossRefGoogle Scholar
  18. Cao C, Lee X, Muhlhausen J, Bonneau L, Xu J (2018a) Measuring landscape albedo using unmanned aerial vehicles. Remote Sens 10:1812. CrossRefGoogle Scholar
  19. Cao J, Leng W, Liu K, Liu L, He Z, Zhu Y (2018b) Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens 10:89. CrossRefGoogle Scholar
  20. Cardil A, Vepakomma U, Brotons L (2017) Assessing pine processionary moth defoliation using unmanned aerial systems. Forests 8:402. CrossRefGoogle Scholar
  21. Carrivick JL, Smith MW, Quincey DJ (2016) Structure from motion in the geosciences. Wiley, HobokenCrossRefGoogle Scholar
  22. Casbeer DW, Kingston DB, Beard RW, McLain TW (2006) Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int J Syst Sci 37:351–360. CrossRefGoogle Scholar
  23. Chan FCC, Arain MA, Khomik M, Brodeur JJ, Peichl M, Restrepo-Coupe N et al (2018) Carbon, water and energy exchange dynamics of a young pine plantation forest during the initial fourteen years of growth. For Ecol Manag 410:12–26. CrossRefGoogle Scholar
  24. Chávez JL, Neale CMU, Prueger JH, Kustas WP (2008) Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values. Irrig Sci 27:67–81. CrossRefGoogle Scholar
  25. Chen S, McDermid GJ, Castilla G (2017) Measuring vegetation height in linear disturbances in the boreal forest with UAV photogrammetry. Remote Sens 9:1257. CrossRefGoogle Scholar
  26. Chianucci F, Disperati L, Guzzi D (2015) Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. Int J App Earth Obs 47:60–68. CrossRefGoogle Scholar
  27. Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm 92:79–97. CrossRefGoogle Scholar
  28. Coops NC, Gillanders SN, Wulder MA (2010) Assessing changes in forest fragmentation following infestation using time series Landsat imagery. For Ecol Manag 259:2355–2365. CrossRefGoogle Scholar
  29. Costa JM, Grant OM, Chaves MM (2013) Thermography to explore plant–environment interactions. J Exp Bot 64:3937–3949. CrossRefGoogle Scholar
  30. Cristiano PM, Campanello PI, Bucci SJ, Rodriguez SA, Lezcano OA, Scholz FG et al (2015) Evapotranspiration of subtropical forests and tree plantations: a comparative analysis at different temporal and spatial scales. Agric For Meteorol 203:96–106. CrossRefGoogle Scholar
  31. Dandois JP, Ellis EC (2013) High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens Environ 136:259–276. CrossRefGoogle Scholar
  32. David LCG, Ballado AH (2016) Vegetation indices and textures in object-based detection from UAV imagery. In: 6th IEEE international conference on control system, computing and engineering (ICCSCE), 25–27 Nov. 2016, Batu Ferringhi, Malaysia.
  33. De Michele C, Avanzi F, Passoni D, Della Vedova G (2015) Microscale variability of snow depth using UAS technology. Cryosphere Discuss 9:1047–1075. CrossRefGoogle Scholar
  34. Deems JS, Painter TH, Finnegan DC (2013) Lidar measurement of snow depth: a review. J Glaciol 59:467–479. CrossRefGoogle Scholar
  35. Deguchi A, Hattori S, Park HT (2006) The influence of seasonal changes in canopy structure on interception loss: application of the revised Gash model. J Hydrol 318:80–102. CrossRefGoogle Scholar
  36. Demir N (2018) Using UAVs for detection of trees from digital surface models. J For Res 29:813–821. CrossRefGoogle Scholar
  37. Detert M, Weitbrecht V (2015) A low-cost airborne velocimetry system: proof of concept. J Hydraul Res 53:532–539. CrossRefGoogle Scholar
  38. Eisenbeiss H, Zhang L (2006) Comparison of DSMs generated from mini UAS imagery and terrestrial laser scanner in a cultural heritage application. Int Arch Photogramm 36:90–96Google Scholar
  39. Fernández-Guisuraga JM, Sanz-Ablanedo E, Suárez-Seoane CL (2018) Using unmanned aerial vehicles in postfire vegetation survey campaigns through large and heterogeneous areas: opportunities and challenges. Sensors 18:586. CrossRefGoogle Scholar
  40. Ferreira MP, Féret JB, Grau E, Gastellu-Etchegorry J-P, do Amaral CH, Shimabukuro YE et al (2018) Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy. Remote Sens Environ 211:276–291. CrossRefGoogle Scholar
  41. Finnigan JJ (2004) A re-evaluation of long-term flux measurement techniques part II: coordinate systems. Bound-Layer Meteorol 113(1).
  42. Flerchinger GN, Saxton KE (1989) Simultaneous heat and water model of a freezing snow-residue-soil system. I. Theory and development. Trans ASAE 32:565–571. CrossRefGoogle Scholar
  43. Ganthaler A, Losso A, Mayer S (2018) Using image analysis for quantitative assessment of needle bladder rust disease of Norway spruce. Plant Pathol 67:1122–1130. CrossRefGoogle Scholar
  44. Getzin S, Wiegand K, Schӧning I (2012) Assessing biodiversity in forests using very high resolution images and unmanned aerial vehicles. Methods Ecol Evol 3:397–404. CrossRefGoogle Scholar
  45. Goldmean Sachs (2016) Drones reporting for work. Accessed online June 27, 2018:
  46. González-Jorge H, Martínez-Sánchez J, Bueno M, Arias P (2017) Unmanned aerial systems for civil applications: a review. Drones 1:2. CrossRefGoogle Scholar
  47. Goulden ML, Field CB (1994) Methods for monitoring the gas-exchange of individual tree canopies – ventilated-chamber, sap-flow and Penman-Monteith measurements on evergreen oaks. Funct Ecol 8:125–135. CrossRefGoogle Scholar
  48. Granier A (1985) Une nouvelle méthode pour la mesure du flux de sève brute dans le tronc des arbres. Annals For Sci 42:193–200. CrossRefGoogle Scholar
  49. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A et al (2013) High resolution global maps of 21st-century forest cover change. Science 342:850–853. CrossRefGoogle Scholar
  50. Harrison D, Rivard B, Sànchez-Azofeifa A (2018) Classification of tree species based on long wave hyperspectral data from leaves, a case study for a tropical dry forest. Int J Appl Earth Obs Geoinf 66:93–105. CrossRefGoogle Scholar
  51. Hawrylo P, Wężyk P (2018) Predicting growing stock volume of Scots Pine stands using Sentinel-2 satellite imagery and airborne image-derived point clouds. Forests 9:274. CrossRefGoogle Scholar
  52. Hedrick A, Marshall H-P, Winstral A (2015) Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements. Cryosphere 9:13–23. CrossRefGoogle Scholar
  53. Hill DJ, Tarasoff C, Whitworth GE, Baron J, Bradshaw JL, Church J (2017) Utility of unmanned aerial vehicles for mapping invasive plant species: a case study on yellow flag iris (Iris pseudacorus L.). Int J Remote Sens 38:2083–2105. CrossRefGoogle Scholar
  54. Iizuka K, Yonehara T, Itoh M, Kosugi Y (2018) Estimating tree height and diameter at breast height (DBH) from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese Cypress (Chamaecyparis obtusa) forest. Remote Sens 10:13. CrossRefGoogle Scholar
  55. Ishida T, Kurihara J, Vivray FA, Namuco SB, Paringit EC, Perez Y et al (2018) A novel approach for vegetation classification using UAV-based hyperspectral imaging. Comput Electron Agric 144:80–85. CrossRefGoogle Scholar
  56. Jaakkola A, Hyyppä J, Kukko A, Yu X, Kaartinen H, Lehtomäki M et al (2010) A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J Photogramm 65:514–522. CrossRefGoogle Scholar
  57. Jarvis PG, McNaughton KG (1986) Stomatal control of transpiration: scaling up from leaf to region. Adv Ecol Res 15:1–49. CrossRefGoogle Scholar
  58. Jayathunga S, Owari T, Tsuyuki S (2018) Evaluating the performance of photogrammetric products using fixed-wing UAV imagery over mixed conifer-broadleaf forest: comparison with airborne laser scanning. Remote Sens 10:187. CrossRefGoogle Scholar
  59. Jetten VG (1996) Interception of tropical rainforest: performance of a canopy water balance model. Hydrol Process 10:671–685.<671::AID-HYP310>3.0.CO;2-A CrossRefGoogle Scholar
  60. Keim RF, Skaugset AE, Weiler M (2005) Temporal persistence of spatial patterns in throughfall. J Hydrol 314:263–274. CrossRefGoogle Scholar
  61. Klosterman S, Richardson A (2017) Observing spring and fall phenology in a deciduous forest with aerial drone imagery. Sensors 17:2852. CrossRefGoogle Scholar
  62. Klosterman S, Melaas E, Wang JA, Martinez A, Frederick S, O’Keefe J (2018) Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agric For Meteorol 248:397–407. CrossRefGoogle Scholar
  63. Köstner B, Granier A, Cermák J (1998) Sapflow measurements in forest stands: methods and uncertainties. Ann Sci For 55:13–27. CrossRefGoogle Scholar
  64. Kunert N, Aparecido LMT, Wolff S, Higuchi N, dos Santos J, de Araujo C et al (2017) A revised hydrological model for the Central Amazon: the importance of emergent canopy trees in the forest water budget. Agric For Meteorol 239:47–57. CrossRefGoogle Scholar
  65. Laliberte AS, Rango A (2017) Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GISci Remote Sens 48(1):4–23. CrossRefGoogle Scholar
  66. Leduc M-B, Knudby AJ (2018) Mapping wild leak though the forest canopy using a UAV. Remote Sens 10:70. CrossRefGoogle Scholar
  67. Lee X (2004) A model for scalar advection inside canopies and application to footprint investigation. Agric For Meteorol 127:131–141. CrossRefGoogle Scholar
  68. Lefsky MAA (2010) A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys Res Lett 37.
  69. Lefsky MA, Harding DJ, Keller M, Cohen WB, Carabajal CC, Del Bom E-SF et al (2005) Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32:L22S02. CrossRefGoogle Scholar
  70. Leitão JP, Moy de Vitry M, Scheidegger A (2016) Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas. Hydrol Earth Syst Sci 20:1637–1653. CrossRefGoogle Scholar
  71. Levia DF Jr, Frost EE (2003) A review and evaluation of stemflow literature in the hydrologic and biogeochemical cycles of forested and agricultural ecosystems. J Hydrol 274:1–29. CrossRefGoogle Scholar
  72. Levy CR, Burakowski E, Richardson AD (2018) Novel measurements of fine-scale albedo: using a commercial quadcopter to measure radiation fluxes. Remote Sens 10:1303. CrossRefGoogle Scholar
  73. Liang S (2000) Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sens Environ 76:213–238. CrossRefGoogle Scholar
  74. Link TE, Unsworth M, Marks D (2004) The dynamics of rainfall by a seasonal temperate rainforest. Agric For Met 124(3–4):171–191. CrossRefGoogle Scholar
  75. Lu D, Weng Q (2005) A survey of image classification method and techniques for improved classification performance. Int J Remote Sens 28:823–870. CrossRefGoogle Scholar
  76. Lucieers A, de Jong SM, Turner D (2013) Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAS photography. Prog Phys Geogr 38:97–116. CrossRefGoogle Scholar
  77. Maes WH, Steppe K (2012) Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. J Exp Bot 63:4671–4712. CrossRefGoogle Scholar
  78. Martin RE, Asner GP, Francis E, Ambrose A, Baxter DAJ et al (2018) Remote measurement of canopy water content in giant sequoias (Sequoiadendron giganteum) during drought. For Ecol Manag 419–420:279–290. CrossRefGoogle Scholar
  79. Matthews AJ, Jensen LJR (2013) Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens 5:2164–2183. CrossRefGoogle Scholar
  80. McPherson RA (2007) A review of vegetation-atmosphere interactions and their influences on mesoscale phenomena. Prog Phys Geogr 31:261–285. CrossRefGoogle Scholar
  81. Meijerink AMJ (2002) Satellite eco-hydrology – a review. Trop Ecol 43:91–106Google Scholar
  82. Meinzer FC, Goldstein G, Andrade JL (2001) Regulation of water flux through tropical forest canopy trees: do universal rules apply? Tree Physiol 21:19–26. CrossRefGoogle Scholar
  83. Merlin PW (2009) Ikhana: unmanned aircraft system western states fire missions. Monographs in aerospace history no. 44 (SP-2009-4544). National Aeronautics and Space Administration (NASA) History Office, Washington, DCGoogle Scholar
  84. Mlambo RI, Woodhouse H, Gerard F, Anderson K (2017) Structure from motion (SfM) photogrammetry with drone data: a low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests 8:68. CrossRefGoogle Scholar
  85. Monteith JL (1965) Evaporation and environment. Symo Soc Exp Biol 19:205–234Google Scholar
  86. Monteith JL, Unsworth MH (2007) Principles of environmental physics. Elsevier, New York, 418pGoogle Scholar
  87. Moore KE, Fitzjarrald DR, Sakai RK, Goulden ML, Munger JW, Wofsy SC (1996) Seasonal variation in radiative and turbulent exchange at a deciduous forest in Central Massachusetts. J Appl Meterol 40:1297–1309.<0122:SVIRAT>2.0.CO;2 CrossRefGoogle Scholar
  88. Muzylo A, Llorens P, Valente F, Keizer JJ, Domingo F, Gash JHC (2009) A review of rainfall interception modelling. J Hydrol 370:1–4. CrossRefGoogle Scholar
  89. Näsi R, Honkavaara E, Lyytikäinen-Saarenmaa P, Blomqvist M, Litkey P, Hakala T et al (2015) Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens 7:15467–15493. CrossRefGoogle Scholar
  90. Nelson R, Ranson KJ, Sun G, Kimes DS, Kharuk V, Montesano P (2009) Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sens Environ 113:691–701. CrossRefGoogle Scholar
  91. Nevalainen O, Honkavaara E, Tuominen S, Viljanen N, Hakala T, Yu X et al (2017) Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens 7:9185. CrossRefGoogle Scholar
  92. Newcome L (2000) Commercial UAV operations in civil airspace. In: Proceedings of SPIE 4127 airborne reconnaissance XXIV.
  93. Newcome LR (2004) Unmanned aviation: a brief history of unmanned aerial vehicles. American Institute of Aeronautics and Astronautics, Reston, VaCrossRefGoogle Scholar
  94. Ortiz S, Breidenbach J, Kändler G (2013) Early detection of bark beetle green attack using TerraSAR-X and RapidEye data. Remote Sens 5:1912–1931. CrossRefGoogle Scholar
  95. Page GFM, Liénard JF, Pruett MJ, Moffett KB (2018) Spatiotemporal dynamics of leaf transpiration quantified with time-series thermal imaging. Agric For Met 256–257(15):304–314. CrossRefGoogle Scholar
  96. Perks MT, Russell AJ, Large ARG (2016) Advances in flash flood monitoring using unmanned aerial vehicles (UAVs). Hydrol Earth Syst Sci 20:4005–4015. CrossRefGoogle Scholar
  97. Persson HJ, Perko R (2016) Assessment of boreal forest height from WorldView-2 satellite stereo images. Remote Sens Lett 7:1150–1159. CrossRefGoogle Scholar
  98. Puliti S, Ørka HO, Gobakken T, Næsset E (2015) Inventory of small forest areas using an unmanned aerial system. Remote Sens 7:9632–9654. CrossRefGoogle Scholar
  99. Pypker TG, Bond BJ, Link TE, Marks D, Unsworth MH (2005) The importance of canopy structure in controlling the interception loss: examples from a young and old-growth Douglas-fir forests. Agric For Meteorol 130:113–129. CrossRefGoogle Scholar
  100. Pypker TG, Levia DF Jr, Staelens J, Van Stan IIJT (2011) Chapter 18: canopy structure in relation to hydrological and biogeochemical fluxes. In: Levia DF Jr, Carlyle-Moses DE, Tanaka T (eds) Forest hydrology and biogeochemistry: synthesis of past research and future directions, Ecological studies series, vol 216. Springer, Berlin. CrossRefGoogle Scholar
  101. Rango A, Laliberte AS, Herrick JE, Havstad KM (2009) Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. J Remote Sens 3:033542. CrossRefGoogle Scholar
  102. Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417–1428. CrossRefGoogle Scholar
  103. Ruzgiené B, Berteška T, Gečyte S, Jakubauskiené E, Aksamitauskas VČ (2015) The surface modelling based on UAV photogrammetry and qualitative estimation. Measurement 73:619–627. CrossRefGoogle Scholar
  104. Saarinen N, Vastaranta M, Näsi R, Rosnell T, Hakala T, Honkavaara E et al (2018) Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens 10:338. CrossRefGoogle Scholar
  105. Sankey TT, McVay J, Swetnam TL, McClaran MP, Heilman P, Nichols M (2017) UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sen Ecol Conserv 4:20–33. CrossRefGoogle Scholar
  106. Sauer TJ, Horton R (2005) Soil heat flux. In: Hatfield JL, Baker JM (eds) Micrometerology in agricultural systems, ASA monograph 47. American Society of Agronomy, Madison, WI, pp 131–154. CrossRefGoogle Scholar
  107. Scherrer D, Körner C (2010) Infra-red thermometry of alpine landscapes challenges climatic warming projections. Glob Chang Biol 16:2602–2613. CrossRefGoogle Scholar
  108. Scherrer D, Körner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38:406–416. CrossRefGoogle Scholar
  109. Seier G, Stangl J, Schöttl S (2017) UAV and TLS for monitoring a creek in an alpine environment, Styria, Austria. Int J Remote Sens 38(8–10):2903–2920. CrossRefGoogle Scholar
  110. Shaw M, Sandhoo K, Turner T (2000) Modernization of the global positioning system. GPS World 11:36–40Google Scholar
  111. Shrestha R, Wynne RH (2012) Estimating biophysical parameters of individual trees in an urban environment using small footprint discrete-return imaging lidar. Remote Sens 4:484–508. CrossRefGoogle Scholar
  112. Stafford N (2007) Spy in the sky. Nature 445:808–809. CrossRefGoogle Scholar
  113. Thumser P, Haas C, Tuhtan JA, Fuentes-Pérez JF, Toming G (2017) RAPTOR-UAV: real-time particle tracking in rivers using unmanned aerial vehicle. Earth Surf Process Landf 42:2439–2446. CrossRefGoogle Scholar
  114. Tuanmu M-N, Viña A, Bearer S, Xu W, Ouyang Z, Zhang H, Jianguo L (2010) Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sens Environ 114:1833–1844. CrossRefGoogle Scholar
  115. Van Stan II JT, Pypker TG (2015) A review and evaluation of forest canopy epiphyte roles in the partitioning and chemical alteration of precipitation. Sci Total Environ 236:813–824. CrossRefGoogle Scholar
  116. Vivoni ER, Rango A, Anderson CA, Pierini NA, Schreiner-McGraw AP, Saripalli S et al (2014) Ecohydrology with unmanned aerial vehicles. Ecosphere 5:130. CrossRefGoogle Scholar
  117. Wallace L, Lucier A, Watson C, Turner D (2012) Development of a UAV-LiDAR system with application to forest inventory. Remote Sens 4:1519–1543. CrossRefGoogle Scholar
  118. Wang R, Gamon JA, Schweiger AK, Cavender-Bares J, Townsend PA, Zygielbaum AI et al (2018) Influence of species richness, evenness, and composition on optical diversity: a simulation study. Remote Sens Environ 211:218–228. CrossRefGoogle Scholar
  119. Weathers KC, Cadenasso ML, Pickett STA (2001) Forest edges as nutrient and pollutant concentrators: potential synergisms between fragmentation, forest canopies, and the atmosphere. Conserv Biol 15:1506–1514. CrossRefGoogle Scholar
  120. Webster C, Westoby M, Rutter N, Jonas T (2018) Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sens Environ 209:835–8476. CrossRefGoogle Scholar
  121. Wing MG, Burnett J, Sessions J, Brungardt J, Cordell V, Dobler D et al (2013) Eyes in the sky: remote sensing technology development using small unmanned aircraft systems. J For 111:341–347. CrossRefGoogle Scholar
  122. Wright C, Kagawa-Viviani A, Gerlein-Safdi C, Mosquera GM, Poca M, Tseng H et al (2018) Advancing ecohydrology in the changing tropics perspectives from early career scientists. Ecohydrology 11:e1918. CrossRefGoogle Scholar
  123. Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006) Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manag 221:27–41. CrossRefGoogle Scholar
  124. Zakaria S, Hahadi MR, Abdullah AF et al (2018) Aerial platform reliability for flood monitoring under various weather conditions: a review. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-3/W4:591–602. CrossRefGoogle Scholar
  125. Zhou JM, Pavek J, Shelton SC, Holden ZJ, Sankaran S (2016) Aerial multispectral imaging for crop hail damage assessment in potato. Comput Electron Agric 127:406–412. CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Geography and Environmental StudiesThompson Rivers UniversityKamloopsCanada
  2. 2.Department of Natural Resource ScienceThompson Rivers UniversityKamloopsCanada

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