1. Introduction
Accurate river discharge data play a critical role in hydrological processes and they are necessary for a variety of practical hydrological applications, such as hydrological modeling for water resources assessment and management, reservoir operation and flood management, design of hydraulic infrastructures, environmental changes and riparian areas adaptation, strategic planning and policies for civil protection and water management activities [
1,
2,
3,
4]. Such data are indispensable in arid or semi-arid regions, especially in islands, where flash flood warning systems are still limited and not effective due to the limitation of discharge monitoring and observational networks [
5].
Intermittent and ephemeral streams (IRES) are dominant in arid or semi-arid regions [
6]. The flow in ephemeral streams is episodic while intermittent streams are characterized by flow cessation, usually followed by a complete drying of the channels [
7,
8]. The temporary streams constitute more than 50% of the global hydrologic network while this percentage is growing due to the impacts of the climatic crisis which alters the status of the majority of rivers from perennial to intermittent [
9]. Temporary streams are prevalent in many countries across Europe but also in Mediterranean countries outside Europe; reaching a 26% coverage for the south Mediterranean terrestrial area [
10,
11]. IRES are also found in the headwaters of many drainage basins in wetter climates [
12]. The area of the watersheds of temporary rivers that drain into the sea is estimated to be around 43% of the total area of Greece excluding the mountainous tributaries (steep slopes) on the karstic geology [
13,
14]. Lack of temporary stream monitoring is particularly acute on islands [
15]. Furthermore, the water bodies of the Greek islands reveal high variability of the streamflow and water supply through the year [
16].
The flow regimes (e.g., frequency, magnitude, duration, and timing of flow events) of IRES are very vulnerable [
17]. Climate is the main force affecting flow regime of IRES through precipitation, temperature and wind [
18]. In addition, slope, geology, and geomorphology are factors that influence the physical and biological attributes of IRES [
19]. Furthermore, humans alter the flow regimes of IRES by altering runoff patterns in catchments through various land uses and by direct impacts on instream flows from dams and extraction of surface and ground water [
20]. Anthropogenic threats to IRES can be broadly classified into hydrological, physical, chemical, and biological alterations on the flow regime [
21]. Anthropogenic litter, especially macro and micro plastic pollution, is also a serious threat of IRES ecological and environmental status of IRES [
22]; thus monitoring, evaluation and hierarchy of their environmental status is a “must”.
The environmental status requires data collection, measurement of variables based on the datasets, and analysis of the produced variables [
23]. The physical environment is complex and has so many different variables that is difficult to be assessed without a protocol with guidelines to be followed [
24,
25]. Such protocols for environmental measurements exist focused on different aspects e.g., terrestrial invertebrate functional traits [
26], carbon stock in forests [
27], for GIS applications [
28], for drone missions and pre-flight conditions [
29,
30]. There are also protocols focused on specific areas; e.g., for monitoring protecting areas by drone technology [
31], for marine debris assessment [
32], for lakes and other standing waters [
33] for coastal areas which incorporate the morphology of the study area, the environmental conditions, and flight parameters [
34,
35], for dams constructed on stream network and how affect environmental flows [
36], among others.
Recent developments on photogrammetry and image-based velocimetry monitoring techniques (IVMT) offer a great potential for IRES monitoring [
37,
38]. These techniques offer massive, easy, safe and low-cost measurements in real time, at any flow conditions and at high spatial resolution [
39]. Such techniques have rapidly evolved because of the widely availability of a new generation of optical sensors, digital cameras, unmanned aerial systems (UAS), software and methodologies. There is a significant development of optical-based monitoring methods during the last years [
40,
41]. IVMT use a sequence of images that can be captured by various ways such as digital permanent gauge-cameras installed close to the river, mobile-devices with operators standing on the banks and on bridges, or cameras attached to unmanned aerial vehicles (UAVs) [
42,
43,
44,
45,
46,
47]. The surface velocity field in IVMT is indirectly obtained by measuring the velocity of floating tracer particles, naturally present (e.g., foam, light debris, vegetation, wood, surface waves) or artificially introduced to the flow [
48]. The water surface is recorded by cameras, through a sequence of consecutive frames (extracted from a video), reconstructing the local flow velocity starting from the identification of the tracer particles displacements between pairs of subsequent frames. Among the various IVMT applications [
49,
50,
51], there are three different approaches that have gained wide consensus for natural rivers monitoring: a) the large-scale particle image velocimetry (LSPIV) b) the particle tracking velocimetry (LSPTV) and c) the Space-Time Image Velocimetry (STIV) technique [
52]. These techniques were originally developed for laboratory scale experiments under controlled conditions e.g., as particle image velocimetry (PIV) and particle tracking velocimetry (PTV) techniques [
53]. LSPIV is characterized by the Eulerian point of view, while LSPTV uses a Lagrangian point of view [
54]. The two techniques have several common characteristics, while the main difference is that LSPIV estimates the velocity at image sub-regions, while LSPTV reconstructs the trajectory of individual particles transiting in the field of view [
55]. Furthermore, STIV identifies the brightness variation in a searching line set parallel to the main flow direction compared to the continuous images [
56]. This method is more suitable, when the stream flow is not complex, while it can provide a high calculation speed and computation efficiency [
57]. All above methods need floating tracers in order to be implemented [
58]. In the field applications, tracer particles must be ecologically inert as in most situations it is not possible to be recovered and reused (e.g., tree leaves) [
59]. In addition, they need to be large enough to be visible and captured by the UAV’s camera. Generally, the necessary steps of IVMT implementation require the following [
60]: a) seeding of tracers with adequate geometry and density; b) recording of images with an adequate temporal resolution; c) processing (i.e., elaboration of recorded images including pre-processing procedures such as images stabilization, orthorectification, image enhancement if needed, and estimation of the tracer displacements between pairs of consecutive images; and d) evaluation of velocity data with post-processing.
The estimation of the discharge is also possible by combining the bathymetry of a river cross section with the calculated surface velocity field [
61]. Image-based methods can be applied in both gauging and ungauged sites. At a gauging station, discharge measurement can be easily estimated by using a water depth meter while at an ungauged site, flow discharge can be obtained by more in-situ cross-channel measurements [
62,
63]. Furthermore, image-based methods can be applied to existing surveillance video images taken in cities to monitor surface flow-based urban flooding [
64]. There are various studies that combined a UAV with image-based methods (IV-UAV) to successfully estimate the surface velocity or discharge of a river based on low-altitude aerial imagery [
65,
66,
67,
68]. When utilizing IV-UAV, the images can be captured parallel to the water surface but in most cases the camera is at an oblique angle in regard to the measurement surface or with a wide-angle lens [
69]. This technique induces the error of perspective distortion which is the direct result of camera placement in relation to an object [
70]. For this reason, the captured images need to be ortho-rectified before image interrogation by using a few selected ground control points (GCPs), that can be either natural (trees, rocks, etc.) or man-made objects (buildings, pillars, targets, etc.) that are included in the images [
71]. Sometimes IV-UAV is the only available method in places where accessibility is limited, harsh or even dangerous [
72]. Another advantage of IV-UAV implementation is that if images are captured perpendicular to the stream, the image orthorectification is not required [
73].
The scope of this study was to implement IV-UAV technique at different locations of IRES in the Greek island of Lesvos. These techniques are frequently tested in rivers and streams that have perennial flow all the year and as a consequence there are limited studies focused on headwaters and temporary streams, based on our knowledge and a short review we have performed [
74,
75,
76,
77,
78,
79]. The datasets collected to monitor the water resources of Lesvos Island for both quality and quantity. These results will be further investigated and utilized in order to develop a UAV-based environmental protocol focused on IRES that will incorporate the water state, velocity and discharge along other parameters.
4. Discussion
The produced results show that the IV-UAV can be implemented at low discharges and temporary streams. Although, the measurements were performed during a period of low water level, this is the general conditions that we realize in the Greek Islands. There are many water shortages during Spring and Summer months and water supply relies on the groundwater resources or in occasion in desalination. The described UAV-based methodology is a proven and widely utilized tool that enables us to estimate fast, accurately and safe the surface velocity on streams/river but also the water discharge in order to further develop water resources management plans. Although successful, there were many occasions that measurements were unable due to various reasons from prohibited points (e.g., prohibited areas for flights such as airports, archaeological sites, military, etc.) to areas that internet signal was unavailable (only manual flights were possible) to morphologic issues. Next photos (
Figure 14) present places where although water conditions were good, it was not easy (or impossible) to calculate velocity due to other difficulties. Examples can be the high and dense vegetation that blocks the water visibility. In addition, water scarcity or standing water provides limited or even no flow to be recorded. The color of the water also plays an important role and it is difficult to utilize the method in poor water quality or when siltation alters the water visibility. Furthermore, in transitional waters (areas where freshwater meets the sea, such as estuaries, deltas, and lagoons) the frequent sea wind induces surface currents in the opposite direction of water movement. Finally, the reflectance from the sun or other shadows e.g., from tree canopy introduce errors in the vectors of moving particles.
The ecological health of streams and rivers is evaluated and tracked using a set of established processes and recommendations known as environmental protocols. These procedures are intended to guarantee quality and consistency in the gathering and analysis of environmental data as well as to serve as a benchmark for comparing outcomes across various regions and epochs [
110]. Moreover, with the rapidly growing technology of UAVs) existing protocols now include also remote sensing techniques [
111,
112]. As for future research directions, we want to include the described methodology in optical protocols used to classify the environmental quality of the low-order streams. Flow regimes can differ during seasonal changes, depending on weather conditions and the geographical longitude or latitude that the river body covers [
113,
114]. Larger or smaller rivers, intermittent or perennial streams, both are studied all over the globe, with most scientists creating methods or protocols in order to standardize the way that research should be conducted for optimizing results [
115]. These methods can be grouped into four categories as mentioned in [
116]:
physical habitat assessment;
riparian habitat assessment;
morphological assessment;
assessment of hydrological regime alteration.
The following
Table 4 presents eleven environmental protocols in which different methods of observation have been used. In several of these protocols either the entire length of the river or parts of it were studied. Each protocol suggests different variables to be studied (e.g., salinity/chemicals, morphology of channel, aquatic state, water velocity, slope, vegetation, tools, etc.) [
117,
118,
119,
120,
121,
122,
123,
124,
125,
126,
127]. Some of the variables are treated with the same monitoring tools, while others differ depending on the choice of the researcher. The aquatic state (existence of water, lakes and dry riverbed) as well as the velocity /discharge and water quality/appearance seem that have not been studied thoroughly yet (especially via UAV) [
128] although they are considered very important in hydrologic-ecologic studies. As a consequence, stream conditions and ecological flow monitoring are very important characteristics and the creation of a single UAV-based protocol for the environmental study of rivers is more necessary than ever [
129]. This study is the first step to define the aquatic stage and water velocity via IV-UAV methodology. These are initial results in order to further incorporate the methodology in the development of a pioneer environmental UAV-based protocol to access the environmental quality of streams. The above results demonstrate the potential of UAV-based optical methods for estimating flow in temporary streams and evaluating their environmental status. These methods can provide a valuable tool for understanding the dynamics of these ecosystems and monitoring changes over time.
The hydrological characteristics of
Table 4 appear in scientific articles as bibliographic references or as research methods or protocol suggestions [
117,
118,
119,
120,
121,
122,
123,
124,
125,
126,
127]. The majority of them refer to pieces of methodology performed in the field or with remote sensing methods, and they are mostly found in the study of intermittent, but also continuous flow rivers. Some of the characteristics identified in the chosen protocols are discussed further below. Slope, substrate, and bathymetry are crucial parameters in the study of rivers because they constitute the base of model creation as they are raw data for scientists to manage. They help a lot in the creation of mapping material. Also, they are needed as model input data in various models (InVest, SWAT, etc.) for water quality purposes. They play a significant role in shaping the physical, chemical, and biological conditions of river ecosystems. The slope of a river determines its flow velocity which the physical characteristics of the river such as its channel morphology, sediment transport, and water depth. Steeper slopes are associated with faster flow rates and shorter travel times between different parts of the river system. The substrate of a river refers to the material on the riverbed, which can include rocks, gravel, sand, and mud. The substrate is crucial for understanding the physical and chemical parameters of the river because it influences water quality, nutrient cycling, and aquatic life habitat availability. Bathymetry is the measuring of water depth in a river, which is vital for understanding physical river properties such as channel form, flow velocity, and sediment transport.
In environmental regulations, vegetation is an important indicator for classifying the environmental state of streams. Vegetation can reveal the stream's physical, chemical, and biological characteristics, as well as its ecological roles and services [
130,
131,
132]. Riparian vegetation is the vegetation that grows along the banks of a stream and plays an important part in the stream's ecological health [
133]. Riparian vegetation assessment protocols may identify; the species composition, estimating the width of the riparian zone, and assessing the vegetation cover. Aquatic invertebrates are key markers of stream health and can be used to evaluate the influence of human activities on stream ecosystems [
134]. Protocols for aquatic invertebrate evaluation may determine the number, diversity, and species composition of invertebrates in the stream. Macrophytes are freshwater plants that grow in stream channels and can be utilized to assess water quality and environmental health [
135]. Macrophyte evaluation techniques may involve measurements of macrophyte abundance, diversity, and species composition in the stream.
Flow type patterns in intermittent rivers can vary depending on the season and weather conditions. During the rainy season, some intermittent rivers may experience high flows, while others may experience only episodic flow. Most protocols detect the flow types by field research rather than remote sensing techniques. The following methods are suggested to monitor the flow patterns of intermittent rivers [
136,
137,
138]:
Flow measurement: Install a flow measurement system to determine the river's flow rate. This can be accomplished using a variety of tools, such as current meters, acoustic Doppler current profilers, and flow gauges;
Flow duration: Calculate the length of the river that is flowing at different times, as well as the duration of flow events and dry periods;
Flow timing: Keep track of the timing of flow events, such as the onset and cessation of flow;
Land use and land cover are two distinct but related concepts that describe how land is used and the physical characteristics of its surface. Land use refers to how humans use land, such as agricultural land, residential areas, industrial areas, or protected areas such as parks and reserves. It describes human activities on the land, as well as how it is managed and transformed for various purposes. In contrast, land cover refers to the physical characteristics of the land surface, such as vegetation, water bodies, soil type, and natural features such as mountains and valleys. It describes the land's natural and physical characteristics, as well as how they contribute to the environment. Land use and land cover are related but not the same thing. Natural processes and features determine land cover, whereas human activities and management decisions influence land use. A forest area, for example, could have the same land cover but different land uses such as timber production, recreation, or conservation. Land use refers to how humans use and manage land, whereas land cover refers to the physical characteristics of the land surface [
139]. The above protocols detect the land cover for modeling purposes, and they discuss the land usage in their conclusions concerning human activity.
The aquatic state is a term used in environmental regulations to describe the state and quality of water resources in a particular ecosystem, including rivers, lakes, wetlands, and coastal areas. Water chemistry, physical habitat characteristics, aquatic biota, and ecosystem processes are used to determine the aquatic status. The aquatic condition is a critical indicator for assessing aquatic ecosystem health and recognizing potential risks to their integrity [
140]. The goal of aquatic environmental protocols is to develop rules and standards for water quality and management techniques in order to maintain or improve the ecological health of aquatic ecosystems.
5. Conclusions
Temporary streams are an important component of the hydrological cycle in arid and semi-arid regions, but their flow is highly variable and difficult to quantify due to their intermittent character. Conventional flow measurement tools, like as current meters and stream gauges, might be difficult to utilize in these conditions. Unmanned aerial vehicles (UAVs) offer a promising alternative for measuring flow in temporary streams.
Climate change, physical and biological changes, and anthropogenic litter, particularly macro and micro-plastic pollution, make IRES vulnerable. Monitoring, evaluating, and categorizing their environmental status is critical. Recent advances in photogrammetry and image-based velocimetry monitoring techniques hold considerable promise for IRES monitoring, allowing for low-cost real-time observations with excellent spatial resolution. Natural river monitoring techniques, such as IV-UAV, are frequently employed, and they involve tracer seeding and picture recording.
In conclusion, accurate river discharge data is crucial for hydrological processes and various practical applications, especially in arid or semi-arid regions where flash flood warning systems are limited. As discussed above, the successful implementation of IV-UAV methodology on different IRES cross-sections in Lesvos Island using the simple RGB camera of the UAV platform resulted in a good water visibility pattern in all case studies except for one cross section where the depth was higher and water was flowing. Fauna was also recorded in some cases. Overall, this methodology could be incorporated in UAV-based optical protocols. This would help estimating flow in IRES and evaluating their environmental status quick, safe and easy.
Figure 1.
The location of Lesvos Island in relation to Greece.
Figure 1.
The location of Lesvos Island in relation to Greece.
Figure 2.
The sites of Lesvos Island belonging to the Natura 2000 Network.
Figure 2.
The sites of Lesvos Island belonging to the Natura 2000 Network.
Figure 3.
The digital elevation model and the hydrographic network of Lesvos Island.
Figure 3.
The digital elevation model and the hydrographic network of Lesvos Island.
Figure 4.
Representative UAV-captured images of the stream network in Lesvos (a) Vatera beach, (b) Evergetoulas – Kerameia, (c) Pterounta – Vathilimno, (b) Eresos, (e) Kremasti Bridge – Agia Paraskevi and (f) Prinis Bridge – Agia Paraskevi.
Figure 4.
Representative UAV-captured images of the stream network in Lesvos (a) Vatera beach, (b) Evergetoulas – Kerameia, (c) Pterounta – Vathilimno, (b) Eresos, (e) Kremasti Bridge – Agia Paraskevi and (f) Prinis Bridge – Agia Paraskevi.
Figure 5.
The studied section of Agia Anna – Polichnitos captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >2 m/s.
Figure 5.
The studied section of Agia Anna – Polichnitos captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >2 m/s.
Figure 6.
The studied section of Kalloni captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 0.8 m/s.
Figure 6.
The studied section of Kalloni captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 0.8 m/s.
Figure 7.
The studied section of Evergetoulas – Kerameia captured by the UAV (a) the vectors of velocity are depicted in green color and the red area was excluded from analysis; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 1.8 m/s.
Figure 7.
The studied section of Evergetoulas – Kerameia captured by the UAV (a) the vectors of velocity are depicted in green color and the red area was excluded from analysis; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 1.8 m/s.
Figure 8.
The studied section of Pterounta – Vathilimno captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >0.8 m/s.
Figure 8.
The studied section of Pterounta – Vathilimno captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >0.8 m/s.
Figure 9.
The studied section of Eresos captured by the UAV (a) the vectors of velocity are depicted in green color and the red area was excluded from analysis; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 0.45 m/s.
Figure 9.
The studied section of Eresos captured by the UAV (a) the vectors of velocity are depicted in green color and the red area was excluded from analysis; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 0.45 m/s.
Figure 10.
The studied section of Agra captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >0.8 m/s.
Figure 10.
The studied section of Agra captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >0.8 m/s.
Figure 11.
The studied section of Kremasti Bridge – Agia Paraskevi captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to 2 m/s.
Figure 11.
The studied section of Kremasti Bridge – Agia Paraskevi captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to 2 m/s.
Figure 12.
The studied section of West of Kremasti Bridge captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >1.8 m/s.
Figure 12.
The studied section of West of Kremasti Bridge captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow to >1.8 m/s.
Figure 13.
The studied section of Prinis Bridge – Agia Paraskevi captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 5.5 m/s.
Figure 13.
The studied section of Prinis Bridge – Agia Paraskevi captured by the UAV (a) the vectors of velocity are depicted in green color; (b) the vectors of velocity are depicted in a colorized scale where dark blue corresponds to 0 and yellow up to 5.5 m/s.
Figure 14.
Examples of no IV-UAV implementation due to (a) high vegetation; (b) frequently in coastal areas wind creates surface waves against the real water flow; (c) very limited water existence, not enough width to be recorded by UAVs; (d) not flowing water + sun reflectance; (e) shadows from the tree canopy; (f) very limited access; (g) good water visibility but water quality was poor - dead animal bodies or carcasses; (h) good water visibility but water quality was poor - anthropogenic litter and algae bloom; (i) no water flow; (j) contaminated water – black color – intense odor.
Figure 14.
Examples of no IV-UAV implementation due to (a) high vegetation; (b) frequently in coastal areas wind creates surface waves against the real water flow; (c) very limited water existence, not enough width to be recorded by UAVs; (d) not flowing water + sun reflectance; (e) shadows from the tree canopy; (f) very limited access; (g) good water visibility but water quality was poor - dead animal bodies or carcasses; (h) good water visibility but water quality was poor - anthropogenic litter and algae bloom; (i) no water flow; (j) contaminated water – black color – intense odor.
Table 1.
The studied cross sections at IRES of Lesvos Island.
Table 1.
The studied cross sections at IRES of Lesvos Island.
Location (cross-section) |
Date |
X |
Y |
Z |
UAV height |
Water Conditions |
Agia Anna – Polichnitos |
12-5-2021 |
39°03'28.8"N |
26°11'34.8"E |
28.0 |
20 |
good visibility – flowing water – shallow depth |
Kalloni |
13-5-2021 |
39°12'16.9"N |
26°10'39.4"E |
8.40 |
10 |
good visibility – flowing water – shallow depth |
Evergetoulas – Kerameia |
15-5-2021 |
39°07'43.7"N |
26°25'35.1"E |
44.30 |
10 |
good visibility – flowing water – shallow depth |
Pterounta – Vathilimno |
16-5-2021 |
39°12'41.6"N |
26°03'06.5"E |
238.01 |
5 |
good visibility – flowing water – shallow depth |
Eresos |
17-5-2021 |
39°08'38.3"N |
25°59'14.0"E |
74.39 |
7 |
good visibility – flowing water – shallow depth |
Agra |
18-5-2021 |
39°09'02.5"N |
26°04'15.3"E |
185.86 |
10 |
good visibility – flowing water – shallow depth |
Kremasti Bridge – Agia Paraskevi |
19-5-2021 |
39°16'13.3"N |
26°15'10.5"E |
70.34 |
15 |
good visibility – flowing water – shallow depth |
West of Kremasti Bridge |
19-5-2021 |
39°16'29.0"N |
26°14'38.4"E |
66.21 |
20 |
good visibility – flowing water – shallow depth |
Prinis Bridge – Agia Paraskevi |
19-5-2021 |
39°15'10.1"N |
26°15'05.0"E |
44.03 |
30 |
low visibility – flowing water – significant depth |
Drone Specifications |
Camera Specifications |
Takeoff weight: 907 g |
Sensor: 1” CMOS |
Dimensions (length × width × height): 322×242×84 mm |
Effective pixels: 20 million |
Max flight time: 29-31’ |
Shooting range: 1 m to ∞ |
Battery capacity: 3850 mAh Battery Voltage: 15.4 V |
ISO range Photo: 100–3200 (auto) |
Max speed (sport): 72 km/h |
Image size: 5472×3648 |
Satellite positioning: GPS/GLONASS |
Format: JPEG / PEG / DNG (RAW) |
Hover accuracy range: Vertical ±0.1 m Horizontal ±0.3 m |
Gimbal Stabilization: 3-axis (tilt, roll, pan) |
Max transmission: 6000 m |
Lens FOV: about 77° 35 mm Format Equivalent: 28 mm |
Table 3.
Results of the IV-UAV method on IRES cross sections of Lesvos Island during the dates that measurements taken.
Table 3.
Results of the IV-UAV method on IRES cross sections of Lesvos Island during the dates that measurements taken.
Location (Date) |
Channel Width (m) |
Water Depth (m) |
Surface Velocity Range (m/s) |
Maximum Water Discharge (m3/s) |
Agia Anna – Polichnitos (12-5-2021) |
1.50 |
0.05 |
0 to 2.0 |
≤0.15 |
Kalloni (13-5-2021) |
4.00 |
0.02 |
0 to 0.8 |
0.07 |
Evergetoulas – Kerameia (15-5-2021) |
3.70 |
0.04 |
0 to 1.6 |
0.25 |
Pterounta – Vathilimno (16-5-2021) |
1.25 |
0.04 |
0 to 0.8 |
0.04 |
Eresos (17-5-2021) |
2.50 |
0.09 |
0 to 0.4 |
0.09 |
Agra (18-5-2021) |
1.30 |
0.03 |
0 to 1.8 |
0.05 |
Kremasti Bridge – Agia Paraskevi (19-5-2021) |
0.60 |
0.28 |
0 to 2.0 |
0.34 |
West of Kremasti Bridge (19-5-2021) |
1.50 |
0.035 |
0 to 1.6 |
0.08 |
Prinis Bridge – Agia Paraskevi (19-5-2021) |
3.65 |
0.03 |
0 to 5.5 |
0.60 |
Table 4.
Different parameters included in environmental protocols to classify the environmental status of streams.
Table 4.
Different parameters included in environmental protocols to classify the environmental status of streams.
Environmental Protocol [Reference] |
[117] |
[118] |
[119] |
[120] |
[121] |
[122] |
[123] |
[124] |
[125] |
[126] |
[127] |
Salinity/ Chemical Analysis |
X |
X |
X |
X |
X |
X |
X |
⋁ |
X |
⋁ |
X |
Substrate |
⋁ |
⋁ |
⋁ |
⋁ |
X |
⋁ |
⋁ |
⋁ |
X |
⋁ |
X |
Vegetation |
⋁ |
⋁ |
⋁ |
⋁ |
⋁ |
⋁ |
⋁ |
X |
X |
⋁ |
⋁ |
Land cover/use |
⋁ |
X |
⋁ |
X |
⋁ |
X |
⋁ |
X |
X |
⋁ |
⋁ |
Slope |
X |
V |
X |
X |
X |
X |
⋁ |
⋁ |
X |
⋁ |
⋁ |
Morphology |
⋁ |
⋁ |
X |
X |
⋁ |
⋁ |
⋁ |
X |
⋁ |
⋁ |
⋁ |
Flow type |
⋁ |
⋁ |
X |
X |
X |
⋁ |
X |
⋁ |
⋁ |
⋁ |
⋁ |
Aquatic State |
X |
⋁ |
X |
X |
X |
⋁ |
X |
X |
X |
X |
X |
Bathymetry |
⋁ |
⋁ |
⋁ |
⋁ |
⋁ |
X |
X |
X |
X |
X |
X |
Water Velocity |
⋁ |
⋁ |
X |
⋁ |
X |
⋁ |
⋁ |
X |
⋁ |
⋁ |
⋁ |
Tools |
Field |
Field/UAV/satellite |
UAV |
Field/ UAV |
Field/ UAV |
Field |
Field |
Field |
Field |
Field |
Field |