Identification of Emergent and Floating Aquatic Vegetation Using an

Unsupervised Thresholding Approach: A Case Study of the Dniester

Delta in Ukraine

Ioannis Manakos

, Eleftherios Katsikis

, Sergiy Medinets

, Yevgen Gazyetov

Leonidas Alagialoglou

and Volodymyr Medinets

Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

Odesa National I.I. Mechnikov University, Odesa, Ukraine

Keywords: Wetland, Sentinel-2, Floating Vegetation, Emergent Vegetation, Thresholding, Multi-Class Segmentation.

Abstract: Monitoring of emergent and floating vegetation in freshwater ecosystems is of high importance for water

management in an area. This study proposes a methodology for the automatic monitoring of aquatic vegetation

using indicators estimated via remote sensing image analysis. The study area is located in the Lower Dniester

Basin in Southern Ukraine. The approach is developed using Sentinel-2 images and validated with field

measurements. The goal is to discriminate and map three classes of aquatic surface condition; namely, areas

covered with floating vegetation, or dominated by emergent vegetation, and open water. The approach is

transferable across different dates over a period of three years. Results are useful for governmental authorities

and natural/ national park administrations for near real-time monitoring of aquatic vegetation to mitigate the

impact of overgrowth on water quality, biodiversity, and ecosystem services.

1 INTRODUCTION

Freshwater ecosystems, being a valuable resource of

ecosystem services for local population wellbeing

and regional economy (e.g., drinking water

production, tourism, aquaculture, hydropower

generation), are vulnerable to anthropogenic impacts

(Sutton et al., 2011). The core driver of ecological

concerns in many transboundary river catchments,

including the Dniester, is excessive nutrients load of

anthropogenic origin as a result of agricultural,

industrial (via wastewater discharges and re-

deposition of gas emission), domestic (via sewage

discharges) and other anthropogenic activities (e.g.

Medinets et al., 2016, Medinets et al., 2020a,

Medinets et al., 2020b), which leads to a significant

increase of eutrophication in the river-deltas, their

lakes, and adjacent estuaries (Kovalova et al., 2021).

Moreover, temperature increase and precipitation

pattern alteration under changing climate, together

with fluvial water flow disturbance due to up-

regulation with hydro power constructions, often

enforce and intensify negative impacts on

biodiversity, biological resources, and ecosystem

services (Rouholahnejad et al., 2014). Along with

algal blooms, all this is also subjected to the

overgrowth of aquatic vegetation, which is often

observed in vulnerable deltaic areas.

Aquatic plants (emergent, floating and

submerged), being natural components of most water

bodies and playing an important role in aquatic

ecosystem functioning, when overgrown or bloomed,

often lead to harmful consequences for water quality,

biodiversity, ecosystem functioning and services

provision via

- decreasing dissolved oxygen level,

- increasing pH,

- reducing light penetration, slowing water velocity

(while increasing water temperature),

- increasing siltation rates (in slow streams),

- serving as mechanical substrates for filamentous

algae,

- clogging or hampering navigation channels/ areas

used for fishing and touristic purposes, and

- losing recreational/ touristic attractiveness

(Greenfield et al., 2007; Hussner et al., 2017).

Therefore, the near real time (semi-) automatic

monitoring of aquatic vegetation cover coupled with

the identification of its different types/ species is of

high value for authorities and natural/ national park

administrations. However, they are still a big

challenge in shallow water bodies.

Manakos, I., Katsikis, E., Medinets, S., Gazyetov, Y., Alagialoglou, L. and Medinets, V.

Identiﬁcation of Emergent and Floating Aquatic Vegetation Using an Unsupervised Thresholding Approach: A Case Study of the Dniester Delta in Ukraine.

DOI: 10.5220/0012024000003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 98-103

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Various histogram-based methods for automatic

earth observation features’ estimation exist in the

literature, which are based on satellite imagery

(Kordelas et al., 2018). Furthermore, based on the

success of machine learning methods in other

applications, (Chen et al., 2018) utilized decision

trees for mapping underwater vegetation, while the

study of Espel et al. (2020) compared the Random

Forest and the Support Vector Regression algorithms

for estimating submerged macrophyte cover from

very fine-scale resolution (50 cm) multispectral

Pléiades satellite imagery, and showed that both

algorithms have promising performance metrics.

Further studies aimed at classifying floating

vegetation in various water bodies using Sentinel-2

images. The results showed that the classification

accuracy was dependent on the density (Midwood et

al., 2010; Valta-Hulkkonen et al., 2004), and the

species (Ade et al., 2022) of the floating vegetation.

In this study, an approach is developed for

automatic monitoring of aquatic vegetation, by

discriminating and mapping three main classes

usually met in freshwater ecosystems (floating,

emergent vegetation, and open water-no vegetation).

Several indicators, which are derived by algebraic

combinations of the satellite bands, are exploited

within a multicriteria hierarchical analysis approach

on top of a verified unsupervised thresholding

approach (Kordelas et al., 2018, 2019; Manakos et

al., 2019). The proposed approach has been

developed and validated within the WQeMS H2020

project (Grant Agreement No. 101004157) using

reference and satellite data of the Dniester River,

which were initially registered by the authors for the

needs of ENI CBC BSB PONTOS project (Grant

Agreement No. BSB 889).

2 MATERIALS AND METHODS

2.1 Study Area

The study area is located in the Lower Dniester Basin,

covering the Dniester Delta and the adjacent Dniester

Estuary (Southern Ukraine) with a total area of

roughly 1800 km

(Fig. 1), including the Lower

Dniester National Nature Park (LDNNP). The

Dniester is the largest transboundary river in the

Western Ukraine and Moldova, discharging to the

Black Sea. The Lower Dniester Basin is located

within the Black Sea lowland, consisting of steppe

plains. The topography is a gently dipping plain,

which contributed to the development of extensive

wetland area in the floodplain of the river, dissected

Figure 1: Study area of the Dniester Delta (red boundaries)

and the territory occupied by the Lower Dniester National

Nature Park (dashed area), overlaid on a Google Earth

image snapshot.

by branches, ancient riverbeds that are often flooded

(OSCE, 2005).

The pilot area has a temperate continental climate.

Annual mean air temperature is 10.5°C (period of

2000-2014) varying from 8.4°C to 12.5°C (Medinets

et al., 2016). The long-term average annual

precipitation sum was 464 mm (2000-2014) but

varied substantially over the last years from 420 mm

(in 2020) to 771 mm (in 2021). The atmospheric total

N (TN) deposition rate is moderate at ca. 11.4 kg N

-1

(Medinets et al., 2020b) with around 67%

contribution from organic constituents. Such large

contribution is also observed for open waters in the

northwestern part of the Black Sea (Medinets and

Medinets, 2012; Medinets, 2014).

2.2 Satellite Imagery

Sentinel-2 (Level 2A) products are downloaded from

the Copernicus European Space Agency (ESA) hub

for the dates 11/08/2018, 05/08/2020, 30/08/2020,

05/08/2021. The acquired products refer to the tile

T35TQM.

2.3 Validation Data

Direct measurements of aquatic vegetation

boundaries were performed by field GPS tracking

using a boat in the north part of the Dniester Estuary

by the Odesa National I.I. Mechnikov University

(ONU) on an annual basis (in July) over 2010-2021

Identiﬁcation of Emergent and Floating Aquatic Vegetation Using an Unsupervised Thresholding Approach: A Case Study of the Dniester

Delta in Ukraine

within the national projects studying Dniester

ecosystems funded by the Ministry of Education and

Science of Ukraine. This approach included the

following stages:

• Tracking of the boundaries of emergent and

floating vegetation with the boat-mounted GPS

device of Eagle SeaCharter 640CDF GPS with

horizontal accuracy of 3-5 m (when it was

impossible to distinguish floating vs. dense semi-

submerged vegetation, a sum of both was indicated

as a floating vegetation).

• Visual assessment of emergent and floating vegeta-

tion, its types and covered areas with a photo report.

• Post-expeditionary processing of geolocation data

was carried out. GPS data was downloaded and

converted into a coordinate system suitable for the

Geographical Information Systems (GIS) software.

• In a GIS software, the position of the aquatic

vegetation boundaries was checked and manually

corrected (where required) using available

spaceborne images (LandSat 5, 7, 8 and Sentinel-

2), since in some areas it was not possible to bypass

the aquatic vegetation polygons on a boat (small

vessel), because of dense vegetation cover or the

presence of other difficulties.

• Spatial analysis of aquatic vegetation polygons was

performed using a GIS software, which included

the corrections for boat indentation from the

vegetation boundaries, the production of digital

maps of emergent and floating vegetation cover,

and the analysis of spatiotemporal variations of

emergent and floating aquatic vegetation in certain

sectors of the Dniester Delta (Fig. 2). The studied

area was divided into 5 sectors according to the

geohydromorphological characteristics: (i) Sector

A: a north part of Dniester estuary with extensive

wetland area on the right bank of the river (76.3

); (ii) Sector B: the territory between two

branches (Deep Turunchuk and Dniester) of the

Dniester river (81.2 km

); (iii) Sector D: the

territory of the Dniester branch mouth with

adjacent area (20.3 km

); (iv) Sector E: the territory

of the left bank of the Dniester branch and the

Karaholsky bay (26.9 km

); (v) Sector F: an open

water central part of the Dniester Estuary (51.6

The ground reference data, which were used in this

study, were collected on 22/07/2018, 05/08/2020 and

26/07/2021, and utilized for validation as follows:

• Ground data on 22/07/2018 were used to assess the

validity of the predictions on 11/08/2018;

• Ground data on 05/08/2020 were used to assess the

validity of the predictions on 05/08/2020 and

30/08/2020;

• Ground data on 26/07/2021 were used to assess the

validity of the predictions on 05/08/2021.

Due to cloud conditions some dates of the ground

and the satellite data acquisitions are zero (0) to

twenty-five (25) days apart. The effect is considered

negligible for the development of the plant

communities during this period; however, the effect

is visible in the results and discussed, accordingly.

Figure 2: Location of sectors used for spatiotemporal

analysis of the emergent and floating vegetation cover in

the deltaic part of the Lower Dniester.

2.4 Methodology

An unsupervised approach was applied to map the

study area in three aquatic vegetation classes: namely,

i) open water, ii) emergent vegetation, and iii)

floating vegetation. The workflow is broken down in

three phases.

In the first phase the Sentinel-2 bands B04 (red),

B08 (near infrared; NIR), and B11 (shortwave

infrared; SWIR) are initially utilized to classify the

area in the land, open water and emergent vegetation

classes following the thresholding method suggested

in Kordelas et al., 2018 (Fig. 3).

All pixels with value smaller than the value of the

first deep valley (left Fig. 3 – left arrow) are classified

as open water. The emergent vegetation is identified

in the area, where following conditions apply:

a) the pixel value of the SWIR’s band histogram is

between the value of the first and the second deep

valley (Fig. 3 left – between left and right arrow),

and

b) the pixel value of the Normalized Difference

Vegetation Index (NDVI; (B08+B04)/(B08-B04))

histogram is after the first deep valley which is

greater than the value 0.3 (Fig. 3 right – after the

arrow).

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

100

The rest area is classified as land, and renamed to

unclassified, as this category is not of direct interest

for the aquatic vegetation mapping.

Figure 3: Open water (left Figure - left arrow) and emergent

vegetation (left Figure – right arrow & right figure arrow)

thresholds, estimated on the SWIR band (left Figure) and

the NDVI (right Figure), respectively (image acquisition

date: 30/08/2020).

In the second phase additionally Sentinel-2 bands

B05 (red edge; RE) and B12 (shortwave infrared;

SWIR2) are exploited. Three conditions are used to

determine the range of values that are most likely to

correspond to floating vegetation for the study area.

These conditions were identified using histogram

analysis based on the knowledge about the spectral

behavior of water and plants. Discriminating

thresholds are set accordingly. Specifically, for the

study area it is experimentally identified that a) the

B05/B11 ratio has to be between 0.6 and 1.5, b) the

Normalized Difference Water Index (NDWI) ((B08-

B11)/(B08+B11)) has to be between 0.2 and 0.45, and

c) the B12 band value has to be between 100 and 900.

In the third phase the results from the second

phase about the presence of floating vegetation are

superimposed over the previous results of the first

phase and the areas found as floating vegetation

replace any other underlying class. At the end a map

is produced, where all three classes are evident.

Accuracy assessment was performed with the

help of overall, user’s accuracy (UA) and producer’s

accuracy (PA) metrics. The overall accuracy (OA) is

calculated from the division of the number of the

correctly classified pixels by the total number of the

sampled pixels. The PA of each class, also called

precision, is the number of the correctly classified

pixels in this class divided by the number of reference

pixels in this class. The PA shows the false negative

predictions and compares the classified map with the

producers’ expectations. The UA highlights the false

positives, and it is calculated from the number of the

correctly predicted pixels of each class, divided by the

number of the pixels that have been classified in this

class and indicates how each classified pixel on the

map represents the class on the ground.

3 RESULTS AND DISCUSSION

In Fig. 4 and 5 high OA for all classes is observed

ranging from ~92% to 97%. The OA appears not to

be influenced by the day difference between the

spaceborne and ground data acquisition dates, since

even when taken 25 days apart, the OA remains

relatively stable (Fig. 4, 5). However, this is not a

consistent remark, as lower OA appears in the year

2018 (11/08/2018), where the time interval is 20 days.

This is to be accounted merely to the emergent and

floating vegetation detection performance, which

appears to drop further when two datasets are timely

apart (see specifically the PA chart – Fig. 4).

Figure 4: Producer's accuracy (in parenthesis the day

difference from the acquisition date of the ground reference

data).

Figure 5: User's accuracy (in parenthesis the day difference

from the acquisition date of the ground reference data).

Overall high OA (> 91%) at all dates is in this case

misleading for the performance of the approach in

each class, as the assessed dataset is imbalanced.

Namely, the average reference area over all dates of

the class ‘Open Water’ was 247.87 km

(71.86% of

the area), followed by 89.01 km

(25.80% of the area),

which were covered with ‘Emergent Vegetation’, and

8.07 km

(2.34% of the area) dominated by ‘Floating

Vegetation’ (Fig. 6). Thus, the detailed analysis with

the support of PA and UA is required.

91%

92%

93%

94%

95%

96%

97%

98%

60%

65%

70%

75%

80%

85%

90%

95%

100%

05/08/2020

(same day)

05/08/2021

(10 days)

11/08/2018

(20 days)

30/08/2020

(25 days)

Overall accuracy (%)

Producer's accuracy (%)

Open Water Emergent Vegetation

Floating Vegetation OA

91%

92%

93%

94%

95%

96%

97%

98%

60%

65%

70%

75%

80%

85%

90%

95%

100%

05/08/2020

(same day)

05/08/2021

(10 days)

11/08/2018

(20 days)

30/08/2020

(25 days)

Overall accuracy (%)

User's accuracy (%)

Open Water Emergent Vegetation

Floating Vegetation OA

Identiﬁcation of Emergent and Floating Aquatic Vegetation Using an Unsupervised Thresholding Approach: A Case Study of the Dniester

Delta in Ukraine

101

Figure 6: Aquatic vegetation maps on different dates (on the

left) juxtaposed against the ground reference data (overlaid

on Google Earth image snapshot) (on the right). Snapshot

maps are arranged from top to bottom according to the

timeliness of image vs. ground data acquisition dates.

In general, for all four validation dates, the highest

PA is shown in the class of ‘Open Water’, followed

by the class ‘Emergent Vegetation’ and the ‘Floating

Vegetation’ with the lowest PA. Latter demonstrates

the challenges that are posed for this class to be

accurately discriminated from the surrounding

environment. Furthermore, the PA is higher for the

‘Floating Vegetation’ class, when the reference and

classification dates are timely close. This observation

might be attributed to (i) the actual change of floating

vegetation distribution, and/ or (ii) wind-induced

floating vegetation polygon’s density/ geometry

change/ shift, and/ or (iii) wave- or water-level-

induced floating plant leaves moistening/ partial

flooding in the estuary during these intervals.

In contrary to the PA, where both emergent and

floating vegetation showcase an undulated pattern

through time, the UA seems not influenced for the

emergent vegetation and remains stable through time,

which however is not the case for the floating

vegetation (Fig. 5).

In addition to the aforementioned (see PA results

explanation) possible reasons for the lower

performance identifying water lilies and chestnuts

(floating vegetation in our study area), it is registered

that this type of classification error depends also on

the floating vegetation species and the density-level.

In Midwood et al. (2010) different wetlands in the

lake have been tested and the PA was higher in high-

density floating vegetation and the UA was higher in

low-density floating vegetation. Similar results are

reached in the study of the lake Luupuvesi in Finland

(Valta-Hulkkonen et al., 2004), where the dense

floating vegetation has higher PA, and the sparse

floating vegetation has higher UA. This seems to be

true for the study area and time of data acquisition, as

well. In July/August (until there is no strong flooding)

wind may substantially change the geometry and

density of floating vegetation appearance within

hours-to-days, as well as even move the water lilies

and water chestnut formations (polygons). High

waves (occurring in large-scale shallow water bodies)

may also break the polygons, by uprooting floating

rooted plant and move them.

4 CONCLUSIONS

The proposed unsupervised approach showed high

overall accuracy ranging from 92% to 97% on various

dates between 2018 and 2021, when classifying the

study area into three classes: open water, emergent

vegetation, and floating vegetation. It is found that

among the four validation dates, the open water class

had the highest OA, PA and UA, followed by the

emergent vegetation class, while the floating

vegetation class had the lowest performance (PA

between 67% - 81%, and UA between 61% – 84%),

indicating challenges in the discrimination and

monitoring of this class from space. The PA for the

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

102

floating vegetation class improved and the UA got

lower, when the reference and classification dates

were timely closer. This may be accounted to (i)

floating vegetation formation density/ geometry (in

line with international literature findings), (ii) floating

vegetation formation density/ geometry alterations

due to hydrometeorological disturbance with time,

and/ or (iii) changes in the distribution of floating

vegetation in the estuary through time (for timely

more apart reference and classification dates).

Further experimentation is required, where ground

reference data allow, to enhance the transferability of

the approach. Reference data acquisition across

additional sites may allow testing strict thresholding

performance and possibly evolving adaptive

thresholding techniques; thus, leading to

generalization of the approach. Ground data may also

support augmenting the suggested approach by

encompassing submerged aquatic vegetation

mapping. This is still a challenge for Earth

Observation due to the influence of the water column

on the reflected signal.

ACKNOWLEDGEMENTS

This research has received funding from the European

Union’s Horizon 2020 Research and Innovation

Action programme under Grant Agreement

101004157 – WQeMS, and was partially supported

by the GEF-UNEP funded ‘Towards INMS’ project

(www.inms.international). Ground reference data

were acquired within the projects NDR#602 funded

by the Ministry of Education and Science of Ukraine

(2020-2022) and ENI CBC BSB PONTOS (Grant

Agreement: BSB 889).

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Delta in Ukraine

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