Article Open Access

Enhancing the Monitoring Protocols of Intermittent Flow Rivers with UAV-Based Optical Methods to Estimate the River Flow and Evaluate Their Environmental Status

Drones and Autonomous Vehicles. 2024, 1(1), 10006; https://doi.org/10.35534/dav.2023.10006
Department of Marine Sciences, University of the Aegean, 81100 Mytilene, Greece
*
Authors to whom correspondence should be addressed.

Received: 18 Sep 2023    Accepted: 23 Nov 2023    Published: 27 Nov 2023   

Abstract

Temporary streams are a key component of the hydrological cycle in arid and semi-arid regions, but their flow is highly variable and difficult to measure. In this paper, we present a novel approach that could be used to assess the flow of temporary streams this allowing to characterize their environmental status. Specifically, we apply the Image Velocimetry (IV) method to estimate surface velocity in temporary streams using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors (IV-UAV method). The IV-UAV method enables the easy, safe and quick estimation of the velocity on the water’s surface. This method was applied in different temporary streams in Lesvos Island, Greece. The results obtained indicate that the IV-UAV can be implemented at low discharges, temporary streams and small streams. Specifically, the water depth ranged from 0.02 m to 0.28 m, while the channel width ranged from 0.6 m to 4.0 m. The estimated surface velocity ranged from 0.0 to 5.5 m/s; thus, the maximum water discharge was 0.60 m3/s for the largest monitored stream of the island. However, there were many occasions that measurements were unable due to various reasons such as dense vegetation or archaeological sites. Despite of this, the proposed methodology could be incorporated in optical protocols which are used to assess the environmental status of temporary streams of Mediterranean conditions. Finally, this would become a valuable tool for understanding the dynamics of these ecosystems and monitoring changes over time.

References

1.
Pumo D, Lo Conti F, Viola F, Noto LV. An automatic tool for reconstructing monthly time-series of hydro-climatic variables at ungauged basins.  Environ. Model. Softw. 2017, 95, 381–400. [Google Scholar]
2.
Stein R, Dittmers K, Fahl K, Kraus M, Matthiessen J, Niessen F, Fütterer DK. Arctic (palaeo) river discharge and environmental change: evidence from the Holocene Kara Sea sedimentary record.  Quat. Sci. Rev. 2004, 23, 1485–1511. [Google Scholar]
3.
Auble GT, Friedman JM, Scott ML. Relating riparian vegetation to present and future streamflows.  Ecol. Appl. 1994, 4, 544–554. [Google Scholar]
4.
Zaimes GN, Schultz RC, Isenhart TM. Riparian land uses and precipitation influences on stream bank erosion in Central IOWA.  J. Am. Water Resour. Assoc. 2006, 42, 83–97. [Google Scholar]
5.
Al-Mamari MM, Kantoush SA, Kobayashi S, Sumi T, Saber M. Real-time measurement of flash-flood in a Wadi Area by LSPIV and STIV.  Hydrology 2019, 6, 27. [Google Scholar]
6.
Stamataki MD, Tzoraki O, Sauqeut E. Time-lapse graphical representation methods for mapping of Intermittent Rivers and Ephemeral Streams (IRES).  Eur. J. Geogr. 2021, 12, 51–67. [Google Scholar]
7.
Martin J, Kurc SA, Zaimes G, Crimmins M, Hutmacher A, Green D. Elevated air temperatures in riparian ecosystems along ephemeral streams: The role of housing density.  J. Arid. Environ. 2012, 84, 9–18. [Google Scholar]
8.
Datry T, Singer G, Sauquet E, Capdevilla DJ, Von Schiller D, Subbington R, et al. Science and management of intermittent rivers and ephemeral streams (SMIRES).  Res. Ideas Outcomes 2017, 3, 1–23. [Google Scholar]
9.
Nabih S, Tzoraki O, Zanis P, Tsikerdekis T, Akritidis D, Kontogeorgos I, et al. Alteration of the Ecohydrological Status of the Intermittent Flow Rivers and Ephemeral Streams due to the Climate Change Impact (Case Study: Tsiknias River).  Hydrology 2021, 8, 43. [Google Scholar]
10.
Tzoraki O, Amaxidis Y, Skoulikidis NT, Nikolaidis NP. In-Stream Geochemical Processes of Temporary Rivers—Krathis River Case Study. In Proceedings of the Restoration and Protection of the Environment VII Conference, Mykonos, Greece, 28 June–1 July 2004.
11.
Tramblay Y, Rutkowska A, Sauquet E, Sefton C, Laaha G, Osuch M, et al. Trends in flow intermittence for European rivers.  Hydrol. Sci. J. 2021, 66, 37–49. [Google Scholar]
12.
A Catalogue of European Intermittent Rivers and Ephemeral Streams. Available online: https://hal.archives-ouvertes.fr/hal-02914572/file/Sauquet_et_al_2020Catalogue_SMIRES.pdf (accessed on 5 August 2022).
13.
Tzoraki O, Nikolaidis NP, Amaxidis Y, Skoulikidis NT. In-stream biogeochemical processes of a temporary river.  Environ. Sci. Technol. 2007, 41, 1225–1231. [Google Scholar]
14.
Tzoraki O, Nikolaidis NP. A generalized framework for modeling the hydrologic and biogeochemical response of a Mediterranean temporary river basin.  J. Hydrol. 2007, 346, 112–121. [Google Scholar]
15.
Garcia C, Amengual A, Homar V, Zamora A. Losing water in temporary streams on a Mediterranean island: Effects of climate and land-cover changes.  Glob. Planet Change 2017, 148, 139–152. [Google Scholar]
16.
Tzoraki O. Operating small hydropower plants in Greece under intermittent flow uncertainty: The case of Tsiknias River (Lesvos).  Challenges 2020, 11, 17. [Google Scholar]
17.
Myronidis D, Nikolaos T. Changes in climatic patterns and tourism and their concomitant effect on drinking water transfers into the region of South Aegean, Greece.  Stoch. Environ. Res. Risk Assess. 2021, 35, 1725–1739. [Google Scholar]
18.
Stathi E, Kastridis A, Myronidis D. Analysis of Hydrometeorological Trends and Drought Severity in Water-Demanding Mediterranean Islands under Climate Change Conditions.  Climate 2023, 11, 106. [Google Scholar]
19.
Costigan KH, Kennard MJ, Leigh C, Sauquet E, Datry T, Boulton AJ. Flow Regimes in Intermittent Rivers and Ephemeral Streams. In Intermittent Rivers and Ephemeral Streams; Academic Press: Cambridge, MA, USA, 2017.
20.
Gutiérrez‐Jurado KY, Partington D, Batelaan O, Cook P, Shanafield M. What triggers streamflow for intermittent rivers and ephemeral streams in low‐gradient catchments in Mediterranean climates.  Water Resour. Res. 2019, 55, 9926–9946. [Google Scholar]
21.
Boulton AJ, Rolls RJ, Jaeger KL, Datry T. Hydrological connectivity in intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams; Academic Press: Cambridge, MA, USA, 2017.
22.
Datry T, Boulton AJ, Bonada N, Fritz K, Leigh C, Sauquet E, et al. Flow intermittence and ecosystem services in rivers of the Anthropocene.  J. Appl. Ecol. 2018, 55, 353–364. [Google Scholar]
23.
Chiu MC, Leigh C, Mazor R, Cid N, Resh V. Anthropogenic threats to intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams; Academic Press: Cambridge, MA, USA, 2017.
24.
Aslam S, Tzoraki O, Krasakopoulou E. Anthropogenic litter in freshwater bodies and their estuaries: an empirical analysis in Lesvos, Greece. Environ. Sci. Pollut. Res. 2021, 29, 1–13. [Google Scholar]
25.
Brownson RC, Hoehner CM, Day K, Forsyth A, Sallis JF. Measuring the built environment for physical activity: State of the science.  Am. J. Prev. Med. 2009, 36, S99–S123. [Google Scholar]
26.
Sallis J, Bauman A, Pratt M. Environmental and policy interventions to promote physical activity.  Am. J. Prev. Med. 1998, 15, 379–397. [Google Scholar]
27.
De’ath G. Fabricius KE. Classification and regression trees: A powerful yet simple technique for ecological data analysis.  Ecology 2000, 81, 3178–3192. [Google Scholar]
28.
Moretti M, Dias AT, De Bello F, Altermatt F, Chown SL, Azcárate FM, et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits.  Funct. Ecol. 2017, 31, 558–567. [Google Scholar]
29.
Qureshi A, Badola R, Hussain SA. A review of protocols used for assessment of carbon stock in forested landscapes.  Environ. Sci. Policy 2012, 16, 81–89. [Google Scholar]
30.
Forsyth A, Schmitz KH, Oakes M, Zimmerman J, Koepp J. Standards for environmental measurement using GIS: Toward a protocol for protocols.  J. Phys. Act. Health 2006, 3, S241–S257. [Google Scholar]
31.
Tmušić G, Manfreda S, Aasen H, James MR, Gonçalves G, Ben-Dor E, et al. Current practices in UAS-based environmental monitoring.  Remote Sens. 2020, 12, 1001. [Google Scholar]
32.
Singhal G, Bansod B, Mathew L. Unmanned aerial vehicle classification, applications and challenges: A review. Preprints 2018. doi:10.20944/preprints201811.0601.v1.
33.
Seier G, Hödl C, Abermann J, Schöttl S, Maringer A, Hofstadler DN, et al. Unmanned aircraft systems for protected areas: Gadgetry or necessity?  J. Nat. Conserv. 2021, 64, 126078. [Google Scholar]
34.
Fallati L, Polidori A, Salvatore C, Saponari L, Savini A, Galli P. Anthropogenic Marine Debris assessment with Unmanned Aerial Vehicle imagery and deep learning: A case study along the beaches of the Republic of Maldives.  Sci. Total Environ. 2019, 693, 133581. [Google Scholar]
35.
Hughes M, Hornby DD, Bennion H, Kernan M, Hilton J, Phillips G, et al.  The development of a GIS-based inventory of standing waters in Great Britain together with a risk-based prioritisation protocol.  Water Air Soil Pollut. 2004, 4, 73–84. [Google Scholar]
36.
Doukari M, Batsaris M, Papakonstantinou A, Topouzelis K. A protocol for aerial survey in coastal areas using UAS.  Remote Sens. 2019, 11, 1913. [Google Scholar]
37.
Rivas Casado M, Ballesteros Gonzalez R, Kriechbaumer T, Veal A. Automated identification of river hydromorphological features using UAV high resolution aerial imagery.  Sensors 2015, 15, 27969–27989. [Google Scholar]
38.
Lane SN, Gentile A, Goldenschue L. Combining UAV-based SfM-MVS photogrammetry with conventional monitoring to set environmental flows: modifying dam flushing flows to improve alpine stream habitat.  Remote Sens. 2020, 12, 3868. [Google Scholar]
39.
Manfreda S, Dor EB. Remote sensing of the environment using unmanned aerial systems. In Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments; Elsevier: Amsterdam, The Netherlands, 2023.
40.
Gray PC, Windle AE, Dale J, Savelyev IB, Johnson ZI, Silsbe GM, et al. Robust ocean color from drones: Viewing geometry, sky reflection removal, uncertainty analysis, and a survey of the Gulf Stream front.  Limnol. Oceanogr. Methods 2022, 20, 656–673. [Google Scholar]
41.
Dimitriou E, Stavroulaki E. Assessment of riverine morphology and habitat regime using unmanned aerial vehicles in a Mediterranean environment.  Pure Appl. Geophys. 2018, 175, 3247–3261. [Google Scholar]
42.
De Keukelaere L, Moelans R, Knaeps E, Sterckx S, Reusen I, De Munck D, et al. Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation.  Remote Sens. 2023, 15, 1345. [Google Scholar]
43.
Islam MT, Yoshida K, Nishiyama S, Sakai K, Adachi S, Pan S. Promises and uncertainties in remotely sensed riverine hydro‐environmental attributes: Field testing of novel approaches to unmanned aerial vehicle‐borne lidar and imaging velocimetry.  River Res. Appl. 2022, 38, 1757–1774. [Google Scholar]
44.
Román A, Tovar-Sánchez A, Gauci A, Deidun A, Caballero I, Colica E, et al. Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters.  Remote Sens. 2022, 15, 237. [Google Scholar]
45.
Windle AE, Silsbe GM. Evaluation of unoccupied aircraft system (UAS) remote sensing reflectance retrievals for water quality monitoring in coastal waters.  Front. Environ. Sci. 2021, 9, 674247. [Google Scholar]
46.
Borg Galea A, Sadler JP, Hannah DM, Datry T, Dugdale SJ. Mediterranean intermittent rivers and ephemeral streams: Challenges in monitoring complexity.  Ecohydrology 2019, 12, e2149. [Google Scholar]
47.
Tauro F, Piscopia R, Grimaldi S. Streamflow observations from cameras: Large-scale particle image velocimetry or particle tracking velocimetry?  Water Resour. Res. 2017, 53, 10374–10394. [Google Scholar]
48.
Kim Y, Muste M, Hauet A, Krajewski WF, Kruger A, Bradley A. Stream discharge using mobile large‐scale particle image velocimetry: A proof of concept.  Water Resour. Res. 2008, 44, 1–6. [Google Scholar]
49.
Tauro F, Petroselli A, Grimaldi S. Optical sensing for stream flow observations: A review.  J. Agric. Eng. 2018, 49, 199–206. [Google Scholar]
50.
Manfreda S, McCabe MF, Miller PE, Lucas R, Pajuelo Madrigal V, Mallinis G, et al. On the use of unmanned aerial systems for environmental monitoring.  Remote Sens. 2018, 10, 641. [Google Scholar]
51.
Koutalakis P, Tzoraki O, Zaimes GN. Software utilized for image-based velocimetry methods focused on water resources.  Desalin. Water Treat. 2021, 218, 1–17. [Google Scholar]
52.
Muste M, Fujita I, Hauet A. Large‐scale particle image velocimetry for measurements in riverine environments.  Water Resour. Res. 2008, 44, 1–14. [Google Scholar]
53.
Bechle AJ, Wu CH, Liu WC, Kimura N. Development and application of an automated river-estuary discharge imaging system.  J. Hydraul. Eng. 2012, 138, 327–339. [Google Scholar]
54.
Dramais G, Le Coz J, Camenen B, Hauet A. Advantages of a mobile LSPIV method for measuring flood discharges and improving stage–discharge curves.  J. Hydro-Environ. Res. 2011, 5, 301–312. [Google Scholar]
55.
Rozos E, Dimitriadis P, Mazi K, Lykoudis S, Koussis A. On the Uncertainty of the Image Velocimetry Method Parameters.  Hydrology 2020, 7, 65. [Google Scholar]
56.
Gleason CJ, Durand MT. Remote sensing of river discharge: A review and a framing for the discipline.  Remote Sens. 2020, 12, 1107. [Google Scholar]
57.
Zhu X, Lipeme Kouyi G. An analysis of LSPIV‐based surface velocity measurement techniques for stormwater detention basin management.  Water Resour. Res. 2019, 55, 888–903. [Google Scholar]
58.
Fujita I, Muste M, Kruger A. Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications.  J. Hydraul. Res. 1998, 36, 397–414. [Google Scholar]
59.
Tauro F, Petroselli A, Arcangeletti E. Assessment of drone‐based surface flow observations.  Hydrol. Process. 2016, 30, 1114–1130. [Google Scholar]
60.
Perks MT, Fortunato Dal Sasso S, Hauet A, Jamieson E, Le Coz J, Pearce S, et al. Towards harmonisation of image velocimetry techniques for river surface velocity observations.  Earth Syst. Sci. Data 2020, 12, 1545–1559. [Google Scholar]
61.
Han X, Chen K, Zhong Q, Chen Q, Wang F, Li D. Two-Dimensional Space-Time Image Velocimetry for Surface Flow Field of Mountain Rivers Based on UAV Video.  Front. Earth Sci. 2021, 9, 686636. [Google Scholar]
62.
Raffel M, Willert CE, Scarano F, Kähler C, Wereley S, Kompenhans J. Particle Image Velocimetry: A Practical Guide; Springer International Publishing: Cham, Switzerland, 2018.
63.
Patalano A, Garcia CM, Brevis W, Bleninger T, Guillen N, Moreno L, et al. Recent advances in Eulerian and Lagragian large-scale particle image velocimetry. In Proceedings of the 36th IAHR World Congress, The Hauge, Netherlands, 28 June–3 July 2015.
64.
Gollin D, Brevis W, Bowman ET, Shepley P. Performance of PIV and PTV for granular flow measurements.  Granul. Matter. 2017, 19, 1–18. [Google Scholar]
65.
Fujita I, Watanabe H, Tsubaki R. Development of a non-intrusive and efficient flow monitoring technique: The space-time image velocimetry (STIV).  Int. J. River Basin Manag. 2007, 5, 105–114. [Google Scholar]
66.
Yu K, Kim S, Kim D. Correlation analysis of spatio-temporal images for estimating two-dimensional flow velocity field in a rotating flow condition.  J. Hydrol. 2015, 529, 1810–1822. [Google Scholar]
67.
Cameron SM. PIV algorithms for open-channel turbulence research: Accuracy, resolution and limitations.  J. Hydro-Environ. Res. 2011, 5, 247–262. [Google Scholar]
68.
Tauro F. Particle tracers and image analysis for surface flow observations.  Wiley Interdiscip. Rev. Water 2016, 3, 25–39. [Google Scholar]
69.
Muste M, Xiong Z, Bradley A, Kruger A. Large-Scale Particle Image Velocimetry–a reliable tool for physical modeling. In Proceedings of the ASCE 2000 Joint Conference on Water Resources Engineering and Water Resources Planning & Management, Minneapolis, MN, USA, 30 July–2 August 2000. 
70.
Koutalakis P, Zaimes GN. River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar. Hydrology 2022, 9, 148. [Google Scholar]
71.
Huang WC, Young CC, Liu WC. Application of an automated discharge imaging system and LSPIV during typhoon events in Taiwan.  Water 2018, 10, 280. [Google Scholar]
72.
Herzog A, Stahl K, Blauhut V, Weiler M. Measuring zero water level in stream reaches: A comparison of an image‐based versus a conventional method.  Hydrol. Process. 2022, 36, e14658. [Google Scholar]
73.
Guillén NF, Patalano A, García CM, Bertoni JC. Use of LSPIV in assessing urban flash flood vulnerability.  Nat. Hazards 2017, 87, 383–394. [Google Scholar]
74.
Perks MT, Russell AJ, Large AR. Advances in flash flood monitoring using unmanned aerial vehicles (UAVs).  Hydrol. Earth Syst. Sci. Discuss. 2016, 20, 4005–4015. [Google Scholar]
75.
Tauro F, Porfiri M, Grimaldi S. Surface flow measurements from drones.  J. Hydrol. 2016, 540, 240–245. [Google Scholar]
76.
Detert M, Weitbrecht V.  A low-cost airborne velocimetry system: Proof of concept.  J. Hydraul. Res. 2015, 53, 532–539. [Google Scholar]
77.
Lewis QW, Rhoads BL. LSPIV measurements of two‐dimensional flow structure in streams using small unmanned aerial systems: 1. Accuracy assessment based on comparison with stationary camera platforms and in‐stream velocity measurements.  Water Resour. Res. 2018, 54, 8000–8018. [Google Scholar]
78.
Unsworth CA. Particle Image Velocimetry. In Geomorphological Techniques; British Society for Geomorphology: London, UK, 1900.
79.
Cristo C. Particle Imaging Velocimetry and its applications in hydraulics: A state-of-the-art review. In Experimental Methods in Hydraulic Research. Geoplanet: Earth and Planetary Sciences; Springer: Berlin/Heidelberg, Germany, 2011.
80.
Koutalakis P, Tzoraki O, Zaimes GN. UAVs To Enhance Watershed Management. Examples From North Greece. In Proceedings of the 7th International Conference on Civil Protection & New Technologies “SAFE GREECE 2020”, Athens, Greece, 14–16 October 2020.
81.
Liu WC, Lu CH, Huang WC. Large-scale particle image velocimetry to measure streamflow from videos recorded from unmanned aerial vehicle and fixed imaging system.  Remote Sens. 2021, 13, 2661. [Google Scholar]
82.
Ran QH, Li W, Liao Q, Tang HL, Wang MY. Application of an automated LSPIV system in a mountainous stream for continuous flood flow measurements.  Hydrol. Process. 2016, 30, 3014–3029. [Google Scholar]
83.
Fovet O, Belemtougri A, Boithias L, Braud I, Charlier JB, Cottet M, et al. Intermittent rivers and ephemeral streams: Perspectives for critical zone science and research on socio‐ecosystems.  Wiley Interdiscip. Rev. Water 2021, 8, e1523. [Google Scholar]
84.
Le Boursicaud R, Pénard L, Hauet A, Thollet F, Le Coz J. Gauging extreme floods on YouTube: Application of LSPIV to home movies for the post‐event determination of stream discharges.  Hydrol. Process. 2016, 30, 90–105. [Google Scholar]
85.
Theule JI, Crema S, Marchi L, Cavalli M, Comiti F. Exploiting LSPIV to assess debris-flow velocities in the field.  Nat. Hazards Earth Syst. Sci. 2018, 18, 1–13. [Google Scholar]
86.
Thumser P, Haas C, Tuhtan JA, Fuentes‐Pérez JF, Toming G. RAPTOR‐UAV: Real‐time particle tracking in rivers using an unmanned aerial vehicle.  Earth Surf. Process. Landf. 2017, 42, 2439–2446. [Google Scholar]
87.
Lewis QW, Lindroth EM, Rhoads BL. Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging. J. Hydrol. 2018, 560, 230–246. [Google Scholar]
88.
Mizerakis V, Strachinis I. New record of Tarentola mauritanica (squamata: Phyllodactylidae) from Lesvos Island, Greece.  Herpetol. Notes 2017, 10, 157–159. [Google Scholar]
89.
Hellenic Statistical Authority—2011 Population-Housing Census. Available online: https://www.statistics.gr/en/2011-census-pop-hous (accessed on 20 November 2021).
90.
Tsartas P, Kyriakaki A, Stavrinoudis T, Despotaki G, Doumi M, Sarantakou E, et al. Refugees and tourism: A case study from the islands of Chios and Lesvos, Greece.  Curr. Issues Tour. 2020, 23, 1311–1327. [Google Scholar]
91.
Verentzioti A, Stranjalis G, Kalamatianos T, Siatouni A, Sakas DE, Gatzonis S. Epidemiology of first epileptic seizures in the northern Aegean Island of Lesvos, Greece.  Clin. Pract. 2017, 7, 84–87. [Google Scholar]
92.
Zouros N, Mc Keever P. The European geoparks network.  Episodes 2004, 27, 165–171. [Google Scholar]
93.
Margaroni SG, Tzoraki O, Velegrakis A. Soil erosion risk of Lesvos Island. In Proceedings of the 11th Panhellenic Symposium on Oceanography and Fisheries, Mytilene, Lesvos, Greece, 13–17 May 2015.
94.
Papadopoulou A, Dikou A, Papapanagiotou V. A contribution to Cumulative Effects Assessment for regional sustainable development-the case of Panagiouda-Pamfilla bay, Lesvos Island, Greece.  Transit. Waters Bull. 2014, 8, 53–72. [Google Scholar]
95.
Karavitis CA, Kerkides P. Estimation of the water resources potential in the island system of the Aegean Archipelago, Greece.  Water Int. 2002, 27, 243–254. [Google Scholar]
96.
Simha P, Mutiara ZZ, Gaganis P. Vulnerability assessment of water resources and adaptive management approach for Lesvos Island, Greece.  Sustain. Water Resour. Manag. 2017, 3, 283–295. [Google Scholar]
97.
The DJI Mavic 2 Pro specifications. Available online: https://www.dji.com (accessed on 15 November 2021).
98.
Kantoush SA, Schleiss AJ. Large-scale PIV surface flow measurements in shallow basins with different geometries.  J. Vis. 2009, 12, 361–373. [Google Scholar]
99.
Fang W, Zheng L. Distortion correction modeling method for zoom lens cameras with bundle adjustment.  J. Opt. Soc. Korea 2016, 20, 140–149. [Google Scholar]
100.
Johnson BG. Recommendations for a system to photograph core segments and create stitched images of complete cores.  J. Paleolimnol. 2015, 53, 437–444. [Google Scholar]
101.
Bååth H, Gällerspång A, Hallsby G, Lundström A, Löfgren P, Nilsson M, et al. Remote sensing, field survey, and long-term forecasting: an efficient combination for local assessments of forest fuels.  Biomass Bioenergy 2002, 22, 145–157. [Google Scholar]
102.
Parker SCJ, Hickman DL, Smith MI. Real-time processing of dual band HD video for maintaining operational effectiveness in degraded visual environments. In Proceedings of the SPIE 9471, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions, Baltimore, MD, USA, 21 May 2015. 
103.
Thielicke W, Stamhuis E. PIVlab–towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB.  J. Open Res. Softw. 2014, 2, 1–10. [Google Scholar]
104.
Thielicke W. The flapping flight of birds: Analysis and application. PhD Thesis, University of Groningen, Groningen, The Netherlands, 31 October 2014.
105.
Kinzel PJ, Legleiter CJ. sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar.  Remote Sens. 2019, 11, 2317. [Google Scholar]
106.
Legleiter CJ, Kinzel PJ, Nelson JM. Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information.  J. Hydrol. 2017, 554, 490–506. [Google Scholar]
107.
Dal Sasso SF, Pizarro A, Manfreda S. Metrics for the quantification of seeding characteristics to enhance image velocimetry performance in rivers.  Remote Sens. 2020, 12, 1789. [Google Scholar]
108.
Hauet A, Creutin JD, Belleudy P. Sensitivity study of large-scale particle image velocimetry measurement of river discharge using numerical simulation.  J. Hydrol. 2008, 349, 178–190. [Google Scholar]
109.
Lewis QW, Rhoads BL. Resolving two‐dimensional flow structure in rivers using large‐scale particle image velocimetry: An example from a stream confluence.  Water Resour. Res. 2015, 51, 7977–7994. [Google Scholar]
110.
Pumo D, Noto LV, Viola F. Ecohydrological modelling of flow duration curve in Mediterranean river basins.  Adv. Water Resour. 2013, 52, 314–327. [Google Scholar]
111.
Gena AW, Voelker C, Settles GS. Qualitative and quantitative schlieren optical measurement of the human thermal plume.  Indoor Air 2020, 30, 757–766. [Google Scholar]
112.
Thielicke W, Sonntag R. Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab.  J. Open Res. Softw. 2021, 9, 1–14. [Google Scholar]
113.
Tauro F, Pagano C, Phamduy P, Grimaldi S, Porfiri M. Large-scale particle image velocimetry from an unmanned aerial vehicle.  IEEE ASME Trans. Mechatron. 2015, 20, 3269–3275. [Google Scholar]
114.
Saumier LP, Khouider B, Agueh M. Optimal transport for particle image velocimetry: real data and postprocessing algorithms.  SIAM J. Appl. Math. 2015, 75, 2495–2514. [Google Scholar]
115.
Sarno L, Carravetta A, Tai YC, Martino R, Papa MN, Kuo CY. Measuring the velocity fields of granular flows–Employment of a multi-pass two-dimensional particle image velocimetry (2D-PIV) approach.  Adv. Powder Technol. 2018, 29, 3107–3123. [Google Scholar]
116.
Pumo D, Alongi F, Ciraolo G, Noto LV. Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV.  Water 2021, 13, 247. [Google Scholar]
117.
Garcia D. Robust smoothing of gridded data in one and higher dimensions with missing values.  Comput. Stat. Data Anal. 2010, 54, 1167–1178. [Google Scholar]
118.
Nogueira J, Lecuona A, Rodriguez PA. Data validation, false vectors correction and derived magnitudes calculation on PIV data.  Meas. Sci. Technol. 1997, 8, 1493. [Google Scholar]
119.
Yao M, Nan J, Chen T. Effect of particle size distribution on turbidity under various water quality levels during flocculation processes.  Desalination 2014, 354, 116–124. [Google Scholar]
120.
Kaldellis JK, Kondili EM. The water shortage problem in the Aegean archipelago islands: Cost-effective desalination prospects.  Desalination 2007, 216, 123–138. [Google Scholar]
121.
Stathi E, Kastridis A, Myronidis D. Analysis of Hydrometeorological Characteristics and Water Demand in Semi-Arid Mediterranean Catchments under Water Deficit Conditions.  Climate 2023, 11, 137. [Google Scholar]
122.
Stream Assessment and Mitigation Protocols: A Review of Commonalities and Differences. Available online: https://www.epa.gov/sites/default/files/2015-07/documents/stream_protocols_2010.pdf (accessed on 6 June 2022).
123.
Gkiatas G, Kasapidis I, Koutalakis P, Iakovoglou V, Savvopoulou A, Germantzidis I, et al. Enhancing urban and sub-urban riparian areas through ecosystem services and ecotourism activities.  Water Supply 2021, 21, 2974–2988. [Google Scholar]
124.
Latsiou A, Kouvarda T, Stefanidis K, Papaioannou G, Gritzalis K, Dimitriou E. Pressures and status of the riparian vegetation in Greek rivers: Overview and preliminary assessment.  Hydrology 2021, 8, 55. [Google Scholar]
125.
Arnell NW, Gosling SN. The impacts of climate change on river flow regimes at the global scale.  J. Hydrol. 2013, 486, 351–364. [Google Scholar]
126.
Schneider C, Laizé CLR, Acreman MC, Flörke M. How will climate change modify river flow regimes in Europe?  Hydrol. Earth Syst. Sci. 2013, 17, 325–339. [Google Scholar]
127.
Fernández D, Barquin J, Raven P. A review of river habitat characterisation methods: indices vs. characterisation protocols.  Limnetica 2011, 30, 0217–0234. [Google Scholar]
128.
Belletti B, Rinaldi M, Buijse AD, Gurnell AM, Mosselman E. A review of assessment methods for river hydromorphology.  Environ. Earth Sci. 2015, 73, 2079–2100. [Google Scholar]
129.
MPCA Stream Habitat Assessment (MSHA) Protocol for Stream Monitoring Sites. Available online: https://www.pca.state.mn.us/sites/default/files/wq-bsm3-02.pdf (accessed on 6 June 2022).
130.
Woodget AS, Austrums R, Maddock IP, Habit E. Drones and digital photogrammetry: from classifications to continuums for monitoring river habitat and hydromorphology.  Wiley Interdiscip. Rev. Water 2017, 4, e1222. [Google Scholar]
131.
Monteiro JG, Jiménez JL, Gizzi F, Přikryl P, Lefcheck JS, Santos RS, et al. Novel approach to enhance coastal habitat and biotope mapping with drone aerial imagery analysis.  Sci. Rep. 2021, 11, 1–13. [Google Scholar]
132.
Rivas Casado M, Ballesteros Gonzalez R, Wright R, Bellamy P. Quantifying the effect of aerial imagery resolution in automated hydromorphological river characterisation.  Remote Sens. 2016, 8, 650. [Google Scholar]
133.
Hooper L, Hubbart JA. A rapid physical habitat assessment of wadeable streams for mixed-land-use watersheds.  Hydrology 2016, 3, 37. [Google Scholar]
134.
Boitsidis AJ, Gurnell AM, Scott M, Petts GE, Armitage PD. A decision support system for identifying the habitat quality and rehabilitation potential of urban rivers.  Water Environ. J. 2006, 20, 130–140. [Google Scholar]
135.
Munné A, Prat N, Solà C, Bonada N, Rieradevall M. A simple field method for assessing the ecological quality of riparian habitat in rivers and streams: QBR index.  Aquat. Conserv. Mar. Freshw. Ecosyst. 2003, 13, 147–163. [Google Scholar]
136.
Richter BD, Baumgartner JV, Powell J, Braun DP. A method for assessing hydrologic alteration within ecosystems.  Conserv. Biol. 1996, 10, 1163–1174. [Google Scholar]
137.
Palau A, Alcázar J. The basic flow method for incorporating flow variability in environmental flows.  River Res. Appl. 2012, 28, 93–102. [Google Scholar]
138.
Clarke RT, Lorenz A, Sandin L, Schmidt-Kloiber A, Strackbein J, Kneebone NT, et al. Effects of sampling and sub-sampling variation using the STAR-AQEM sampling protocol on the precision of macroinvertebrate metrics. In The Ecological Status of European Rivers: Evaluation and Intercalibration of Assessment Methods; Springer: Dordrecht, The Netherlands, 2006.
139.
Healey M, Raine A, Parsons L, Cook N. River Condition Index in New South Wales: Method Development and Application; NSW Office of Water: Sydney, Australia, 2012
140.
Stamataki MD, Koutalakis P, Papadopoulos D, Tzoraki O. Protocols for the environmental monitoring of the coastal and transitional river ecosystems. In Proceedings of the 4th International Congress on Applied Ichthyology & Aquatic Environment—HydroMedit (Virtual), Mytilene, Greece, 4–6 November 2021.
141.
Rivas Casado M, González RB, Ortega JF, Leinster P, Wright R. Towards a transferable UAV-based framework for river hydromorphological characterization.  Sensors 2017, 17, 2210. [Google Scholar]
142.
Tabacchi E, Lambs L, Guilloy H, Planty‐Tabacchi AM, Muller E, Decamps H. Impacts of riparian vegetation on hydrological processes.  Hydrol. Process. 2000, 14, 2959–2976. [Google Scholar]
143.
Dosskey MG, Vidon P, Gurwick NP, Allan CJ, Duval TP, Lowrance R. The role of riparian vegetation in protecting and improving chemical water quality in streams.  J. Am. Water Resour. Assoc. 2010, 46, 261–277. [Google Scholar]
144.
Rajib A, Kim IL, Golden HE, Lane CR, Kumar SV, Yu Z, et al. Watershed modeling with remotely sensed big data: MODIS leaf area index improves hydrology and water quality predictions.  Remote Sens. 2020, 12, 2148. [Google Scholar]
145.
Zaimes GN, Gounaridis D, Fotakis D. Assessing riparian land-uses/vegetation cover along the Nestos river in Greece.  Fresenius Environ. Bull. 2011, 20, 3217–3225. [Google Scholar]
146.
Simmons T, Armstrong T, Hawkins CP. Using aquatic invertebrates to measure the health of stream ecosystems: New bioassessment tools for Alaska’s parklands.  Alaska Park Sci. 2021, 20, 96–103. [Google Scholar]
147.
Stefanidis K, Papastergiadou E. Linkages between macrophyte functional traits and water quality: insights from a study in freshwater lakes of Greece.  Water 2019, 11, 1047. [Google Scholar]
148.
Nihei Y, Kimizu A. A new monitoring system for river discharge with horizontal acoustic Doppler current profiler measurements and river flow simulation. Water Resour. Res. 2008, 44, doi:10.1029/2008WR006970.
149.
Reichl F, Hack J. Derivation of flow duration curves to estimate hydropower generation potential in data-scarce regions.  Water 2017, 9, 572. [Google Scholar]
150.
Blasch KW, Ferré TP, Christensen AH, Hoffmann JP. New field method to determine streamflow timing using electrical resistance sensors.  Vadose Zone J. 2002, 1, 289–299. [Google Scholar]
151.
What is the difference between land cover and land use? Available online: https://oceanservice.noaa.gov/facts/lclu.html (accessed on 20 January 2023).
152.
Poikane S, Herrero FS, Kelly MG, Borja A, Birk S, van de Bund W. European aquatic ecological assessment methods: A critical review of their sensitivity to key pressures.  Sci. Total Environ. 2020, 740, 140075. [Google Scholar]
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