Multi-Temporal Inundated Areas Monitoring Made Easy: The Case

of Kerkini Lake in Greece

Ioannis Manakos

, Malak Kanj

, Michail Sismanis

, Ioannis Tsolaikidis

and Chariton Kalaitzidis

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

Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania,

Chania, Greece

Lake Kerkini Management Authority, Kerkini, Serres, Greece

Keywords: Inundation Mapping, Automatic Thresholding, Sentinel-2, Sentinel-1, Kerkini Lake, Wetland.

Abstract: Satellite data may support management of wetland areas for monitoring of the inundation seasonality.

Previously successful in Doñana and Camargue Biosphere Reserves, this study examines the transferability

of unsupervised inundation mapping through automatic local thresholding in discriminating inundated areas

from non-inundated ones in Kerkini Lake. Nine different alternatives of this approach are employed on

Sentinel-2 (S2) Level-2A images (2016-2019). The best fit alternative was derived by the validation against

local and on-site registered attributes. To overcome unfavourable atmospheric conditions, Sentinel-1 (S1)

images were examined in tandem with derived S2 inundation maps (S2m), using the best fit alternative.

Two S2m, one preceding and one following a target S1 image, were used to train random forest models (per

pixel) to be applied to the target S1 image and derive the respective inundation map (S1m). S1m was

validated against a S2m for the same date; not previously used in the training process. Classification

performance reached k [0.77-0.94] and overall accuracy [88.05-97.16%] for the S2m. The evaluation of

S1m showed k of 0.99 and overall accuracy between 99.71-99.88%. Automation of the process and

minimum human interference supports its usage by non-specialists, e.g. for Protected Areas management.

https://orcid.org/0000-0001-6833-294X

https://orcid.org/0000-0002-6776-4692

https://orcid.org/0000-0001-6387-5849

https://orcid.org/0000-0001-6848-189X

https://orcid.org/0000-0001-5217-7164

1 INTRODUCTION

Wetlands are fundamental for maintaining life on

Earth and demonstrate high biodiversity. They

provide different ecosystem services that ultimately

affect human wellbeing (Finlayson & D’Cruz,

2010; Millennium Ecosystem Assessment, 2005).

They provide for food and shelter, flood control

and climate regulation, as well as for supporting

and maintaining biogeochemical cycles and soil

formation. Nowadays, they are seen as having a

cultural role to visitors, too, as they provide a good

source of income from tourism and recreation.

These ecosystem services along with their rapid

decline as a result of human pressures and climate

change urge for capacity improvement in

monitoring status. In this context, water presence

and extent across time is as seriously treated as

water quality maintenance. The variability of water

extent is vital for any decision to tackle any

misbalances in ecosystem services (e.g. cattle

feeding vs. bird nesting). Spaceborne Earth

Observation monitoring can be a powerful

approach for accurate and cost-effecting frequent

monitoring of open water surfaces.

Numerous approaches, utilizing optical and

radar data for water surface area estimation, can be

used to rapidly generate flood extent maps in real

time, with no additional need for supplementary

data (Cohen et al., 2019; Marti-Cardona et al.,

Manakos, I., Kanj, M., Sismanis, M., Tsolaikidis, I. and Kalaitzidis, C.

Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece.

DOI: 10.5220/0010555700480055

In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021), pages 48-55

ISBN: 978-989-758-503-6

2013). Radar data-based approaches have an

advantage over optical data ones by operating

under nearly all weather and day-night conditions.

However, emergent vegetation, waves, sand, and

radar shadows produced by terrain features hinder

the efficient delineation between water and land

(Kordelas et al., 2019; Manakos et al., 2019).

Evidently, extraction of the water surface from

optical imagery is generally more straightforward

than radar imagery. The rich spectral information

of optical data allows for the reliable detection of

the water presence by utilizing various indices and

bands; especially, when applying thresholds to

them. Commonly used thresholding approaches to

indices, include Normalized Difference Water

Index (NDWI) (Du et al., 2016; McFeeters, 1996;

Zhang et al., 2018), Modified NDWI (Xu, 2006),

and the Automated Water Extraction Index (Feyisa

et al., 2014; Guo et al., 2017; Rokni et al., 2014;

Zhang et al., 2018). Numerous classification

methods, supervised and unsupervised, have been

applied in detecting water bodies and their extent

from multispectral imagery (Kordelas et al., 2019;

Thenkabail, 2015). Furthermore, machine learning

algorithms (MLA) have been employed for remote

sensing image analysis and demonstrated improved

accuracies for inundation map derivation.

Commonly used MLAs include k-Means

(Yousefi et al., 2018), Artificial Neural Networks

(Skakun, 2010), Support Vector Machine

(Nandi et al., 2017; Sarp & Ozcelik, 2017),

Decision Trees (Acharya et al., 2019), and Random

Forest (Feng et al., 2015; Ko et al., 2015).

These classification-based approaches may achieve

higher accuracy than thresholding; however,

ground truth data are required to select appropriate

training samples. This, in turn, requires skilled

personnel or enhanced human interference with the

process.

Focusing on non-supervised inundation

mapping, automatic local thresholding methods

have been successfully applied to Doñana and

Camargue Biosphere Reserves; both with optical

data alone or by fusing radar and optical data

sources by means of machine learning, to increase

information retrieval capacity (Kordelas et al.

2018, 2019; Manakos et al, 2019). This study

examines the transferability of the methods to

monitor inundation seasonality of a river deltaic

system entering an inland lake at the foothills of

Kerkini Mountain in North Greece.

2 MATERIALS AND METHODS

2.1 Study Area

Lake Kerkini (41°13’N, 23°08’E) refers to the

artificial lake (reservoir) created in 1932 and the

surrounding wetland area. Its surface area of 70-76

lies at the transboundary of Strymonas River in

northern Greece close to the border with Bulgaria.

Its drainage area extends over 11,600km

, with the

Hellenic sub-basin making up to 803 km

(Ovakoglou et al., 2016; Psilovikos & Margoni,

2010). Kerkini climate is an intermediate between

Mediterranean and Mid-European, with hot

summers and cold winters. The average annual

rainfall reaches 463.5 mm and occurs in two peaks,

one during the cold period of the year and the other

during May-June (Gerakes, 1989).

Lake Kerkini has developed into one of the most

popular stops for migratory bird populations in

Europe, as well a wetland of international

significance; established as a Natura2000 protected

area and a RAMSAR wetland of international

importance. Kerkini accommodates over 300 bird

species; with at least 1300 plant species; including

indigenous and rare species, as well as Greece’s

largest water buffalo population (Bubalus bubalis).

Thus, understanding inundation seasonality is

crucial for the Lake Kerkini Management Authority

to balance nesting and feeding needs of the

migrating birds, feeding needs of buffalos and

irrigation needs of the Serres plain.

2.2 Satellite Imagery

Sentinel-2 Level-2A (L2A) products were

downloaded from the Copernicus European Space

Agency (ESA) hub between 6 September 2017 and

27 August 2019 (52 products), as well as 24 S2

Level-1C (L1C) products between 16 November

2015 and 23 July 2017. The acquired products

comprise the tile 34TFL. S2 L1C Top-of-

Atmosphere (TOA) products were processed to L2A

Bottom-of-Atmosphere (BOA) products using

Sentinel-2 Level2A Prototype Processor (Sen2Cor)

software downloaded in the ESA SNAP Desktop

third-party plugin of the Sentinel-2 Toolbox.

Sen2Cor has a high performance in generating the

Scene Classification layer (SCL) (92 ± 4%) (Main-

Knorn et al., 2017).

To assess the application of the unsupervised

automatic local thresholding during unfavourable

atmospheric conditions for S2 image acquisition 11

S1 Ground Range Detected (GRD) images were

Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece

downloaded from the Copernicus Open Access Hub

for the periods between 24 February 2019 to 26

March 2019 and 30 July 2019 to 29 August 2019.

ESA SNAP was used for preprocessing the Sentinel-

1 GRD using the command line graph processing

framework, to (i) apply orbit file, (ii) remove

thermal and border noise, (iii) calibration, (iv)

speckle filtering using Lee Signa filter with a

window-size of 5x5, (v) Range-Doppler Terrain

Correction. The unitless backscatter coefficient is

converted then to dB using a logarithmic

transformation (Filipponi, 2019).

2.3 Validation Data

Acquisition dates (22 dates) coinciding with

recurring water level measurements (2017-2019),

provided by the Lake Kerkini Management

Authority, are considered in the unsupervised

inundation mapping by automatic local thresholding.

Validation maps were delineated taking into

consideration actual water level measurements (10-

minute interval), bathymetry map with 10-m pixel

resolution (Tsolakidis & Vafiadis, 2019), water class

derived from the Copernicus SCL and expert local

knowledge about the maximum expected annual

fluctuation across decades. Specifically, in situ water

level gauge measurements were combined with the

bathymetry map (i.e. all pixels under the gauge level

in the bathymetry map without barriers in-between

are considered inundated). Then the expert

knowledge across decades for the maximum flood

elevation level ever reached was superimposed. In

addition, information from the water class of the

SCL was considered at positions, where

sedimentation of the delta might have influenced

information derived from existing but older

bathymetry map.

2.4 Methodology

2.4.1 Local Automatic Thresholding of

Sentinel-2

The work presented by Kordelas et al. (2018, 2019)

introduced unsupervised approach in discriminating

between inundated and non-inundated areas, through

detecting automatic thresholds. The pre-processed

S2 L2A image is segmented into non-overlapping

regions to select segments with high percentage of

inundated pixels. Then an expanding patch based

approach, taking into consideration medians of

percentages of inundated/ non inundated areas, is

followed based on the centroids of the segments.

The open water subclass is examined by estimating

the initial threshold with the ability to separate

inundated pixels from non-inundated ones through

the use of: (i) SWIR-1 Band (Alt1), (ii) product of

SWIR-2 and NIR (Alt2) and (iii) product of SWIR-1

and NIR (Alt3). The initial threshold is calculated as

the first deep valley the histogram can detect. The

final threshold is calculated based on (i) the

minimum cross entropy thresholding algorithm

(MCET), (ii) Otsu’s algorithm or (iii) the average

between them, resulting in nine different alternatives

from all possible combinations of data and

thresholding method taken into consideration. The

performance of each of the alternatives is assessed

by its ability to accurately distinguish between

inundated and non-inundated pixels, against the

validation data, using the overall accuracy of the

validation dates and the overall Kappa coefficients

(Congalton & Green, 2009; Whitten et al., 2011).

2.4.2 Pixel-centric Classification of

Sentinel-1

Under the unavailability of data or unfavourable

weather conditions S2m cannot be generated, hence,

producing a gap in the monitoring capacity. To

counterbalance this, Manakos et al. (2019) proposed

the use of multiple local random forest classifiers’

estimation per pixel, based on S1 images timely

close or coinciding with S2m dates. The training set

is created for 3x3 pixel samples, with the features

being the pixel’s backscatter coefficients for bands

VH and VV, algebraic combinations of the same

bands, and the season of the year, while the

reference class for each pixel is derived from the

closest S2m. Pixel-centric classification is

performed on the S1 target date data using the

trained classifiers, based on the location in the

image, to delineate the required inundation map

(S1m). The method used in this work is abbreviated

as TIM (after Manakos et al. (2019)), where two

S2m, one preceding and one following the

acquisition date of the target S1 image are used.

Furthermore, the TIM method was modified in order

to produce results using only one S2m, either

proceeding or following. The accuracy of the

classification of the target S1 image was evaluated

against the best fit alternative result used to produce

the timely coinciding S2m; not previously used in

the training process.

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

3 RESULTS AND DISCUSSION

3.1 Inundation Maps Derived by

Sentinel-2 Images

Accuracy assessment took into consideration all

pixels present in the area with excellent results as

indicated in Figure 1. In Table 1 is shown that the

overall k ranged from 0.77 to 0.94, ‘substantial’ but

mostly ‘almost perfect’ agreement according to

Landis and Koch (1977) and overall accuracy ranged

from 88.05 to 97.16%. Using Alt1, Band 11 (SWIR-

1) as an initial threshold and applying MCET

algorithm to find the final threshold, achieved the

best classification performance.

Table 1: Overall accuracy assessment shows the use of the

nine alternatives of local automatic thresholding in

distinguishing between inundated and non-inundated

pixels, averaged over all 22 images/ dates.

Alternatives Overall

Accuracy (%)

Overall

kappa

Alt1 and MCET 97.16 0.94

Alt1 and Otsu’s 96.82 0.93

Alt1 and average 97.08 0.94

Alt2 and MCET 91.32 0.83

Alt2 and Otsu’s 89.77 0.79

Alt2 and average 91.03 0.82

Alt3 and MCET 89.06 0.79

Alt3 and Otsu’s 88.05 0.77

Alt3 and average 90.22 0.81

3.2 Inundation Maps Derived

Synergistically by Sentinel-2 and

Sentinel-1 Images

For the two target dates examined with the TIM

method, the S1m produced an overall accuracy over

99.71% in all cases, when compared with the S2m

reference. The overall kappa values were all over

0.99 (Table 2).

(a)

(b)

(c)

Figure 1: (a) Inundation map (example of 17 August 2019)

derived Sentinel-2 image. (b) Validation layer. (c)

Accuracy assessment (a) against (b).

Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece

(a)

(b)

Figure 2: (a) Inundation map (example: 17 August 2019)

based on pixel centric classification using TIM (07 August

2019 and 27 August 2019). (b) Accuracy assessment on

the inundation map S1m (17 August 2019) validated

against the timely coinciding S2-derived inundation map

(S2m) produced by best fit alternative. Training dates

used: 07 August 2019 and 27 August 2019.

For the same target dates and by using the

modified TIM method with reduced number of

training S1 images (Table 3), 1 S2m was used to

train the target S1 dates, and achieved lower

accuracies than in Table 2, with overall accuracy of

83.11 to 99.78%, when compared to the reference

S2m. The overall kappa values ranged from 0.62 to

0.98. It becomes clear that the method may be

successfully applied with less S1 images and in

various time intervals away from the target date;

however, results are not as credible.

Table 2: Accuracy assessment of pixel-centric

classification method done using TIM method applied to

S1 images acquired on 08 March 2019 and 17 August

2019.

Target

Training dates Accuracy

(%)

kappa

S2 images S1 images

08.03 28.02; 20.03 24.02; 02.03

20.03; 26.03

99.78 0.99

17.08 12.08; 22.08 11.08; 23.08 99.88 0.99

17.08 07.08; 27.08 05.08; 11.08

23.08; 29.08

99.77 0.99

17.08 02.08; 27.08 30.07; 05.08

23.08; 29.08

99.71 0.99

Table 3: Accuracy assessment of pixel-centric

classification method done using the modified TIM

method with reduced number of training S1 images

acquired on 08 March 2019 and 17 August 2019.

Target

Training dates Accuracy

(%)

kappa

S2 images S1 images

08.03 28.02 24.02; 02.03 99.78 0.98

08.03 25.03 20.03; 26.03 98.55 0.96

17.08 02.08 30.07; 05.08 83.11 0.62

17.08 07.08 05.08; 11.08 98.03 0.95

17.08 12.08 11.08 98.58 0.96

17.08 22.08 23.08 98.94 0.97

17.08 27.08 23.08; 29.08 98.26 0.95

3.3 Applicability of the Methods

The aim of this work was to assess the performance

of unsupervised methods applied to Camargue and

Doñana Biosphere Reverses, and its applicability for

Kerkini Lake, an inland reservoir whose intense use

across the years has suffered from a changing water

extent due to the human pressures, and uncontrolled

frequent extreme flooding events.

In relation with the use of the multispectral

information, Kordelas et al. (2018, 2019) applied

threshold techniques, which have been usually

employed for radar images to quantify flood water

extent (Grimaldi et al., 2016), on multispectral

images and led to high mapping accuracy of the

water extent in Kerkini Lake, as well. Minimum

cross entropy thresholding (MCET) for the

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

estimation of the final threshold had consistent

results with Camargue and Doñana marine coastal

areas. The results from this work prove the efficacy

of the methods in an inland water body and wetland.

The alternative approaches Alt2 (MCET) and Alt3

(MCET) demonstrated also similarly good results as

for Camargue and Doñana complete areas (Kordelas

et al., 2019).

In relation with cases when unfavourable

atmospheric conditions prevail, the sole use of radar

images proves to fail achieving high accuracy due to

backscatter confusion among landscape features,

such as water with emergent vegetation, shadow

effects, sandy areas, which may be classified either

as belonging to the water or land class. The use of

the pixel-centric classification has the ability to

capture the pixel-related fluctuation of the

backscatter across a time period, which in one case

might mean inundation and in a neighbouring one no

inundation. As a result the application of the pixel-

centric classification with the use of one or two

Sentinel-2 inundation maps up to a 30-day time

interval from the targeted Sentinel-1 image, has

achieved accurate results. The utilization of two

Sentinel-2 inundation maps provided the best results

in this study and is consistent with the results from

its application at the Doñana Biosphere Reserve

(Manakos et al., 2019).

The validation of the automation techniques

provides consistent results for managing water use.

In the case of Lake Kerkini the hydroperiods

generated using the S2m and S1m throughout the

years, revealed the intense reservoir use for flood

control due to frequent extreme events, which assists

in retaining a lower level of the lake. Seasonal

patterns could be identified for various subareas

within the delta and beyond.

4 CONCLUSIONS

This research contributes to the studies conducted by

Kordelas et al. (2018, 2019) and Manakos et al.

(2019) on the evaluation of the credibility and

applicability of the developed methods for

inundation mapping to other protected areas than

coastal marine ones. It became evident that methods

apply also at Lake Kerkini, a protected area and an

artificially generated inland water body for flood

mitigation in the plain of Serres, by achieving high

accuracy.

High inundation mapping accuracy is achieved

without the need for simultaneous ground truth data

or user’s intervention. Employing machine learning

through fusion of S-1 and S-2 data, allows the

consistent delivery of products, overcoming the

limitation of weather conditions and optical data.

Further steps may utilize DEM or additional post-

processing techniques to correct for hillshade or

aspects. Additional index optimization could be

applicable for areas with different types of

vegetation. Automation of the process and minimum

human interference further supports the

implementation of the verified workflow as an

effective service (even transformed to an online one)

for Protected Areas management.

ACKNOWLEDGEMENTS

This study has been partially funded and supported

by the European Union's Horizon 2020 innovation

program under Grant Agreement No. 820852, e-

shape (https://e-shape.eu/).

REFERENCES

Acharya, T. D., Subedi, A., & Lee, D. H. (2019).

Evaluation of Machine Learning Algorithms for

Surface Water Extraction in a Landsat 8 Scene of

Nepal. Sensors, 19(12), 2769.

https://doi.org/10.3390/s19122769

Cohen, S., Raney, A., Munasinghe, D., Loftis, D.,

Molthan, A., Bell, J., Rogers, L., Galantowicz, J.,

Brakenridge, G. R., Kettner, A. J., Huang, Y.-F., &

Tsang, Y.-P. (2019). The Floodwater Depth

Estimation Tool (FwDET v2.0) for Improved Remote

Sensing Analysis of Coastal Flooding. Natural

Hazards and Earth System Sciences Discussions,

2017, 1–15. https://doi.org/10.5194/nhess-2019-78

Congalton, R. G., & Green, K. (2009). Assessing the

Accuracy of Remotely Sensed Data: Principles and

Practices. In Psychology Applied to Work: An

Introduction to Industrial and Organizational

Psychology, Tenth Edition Paul (Second, Vol. 53,

Issue 9). CRC Press Taylor & Francis group.

Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X.

(2016). Water Bodies’ Mapping from Sentinel-2

Imagery with Modified Normalized Difference Water

Index at 10-m Spatial Resolution Produced by

Sharpening the SWIR Band. Remote Sensing, 8(4),

354. https://doi.org/10.3390/rs8040354

Feng, Q., Gong, J., Liu, J., & Li, Y. (2015). Flood

Mapping Based on Multiple Endmember Spectral

Mixture Analysis and Random Forest Classifier—The

Case of Yuyao, China. Remote Sensing, 7(9), 12539–

12562. https://doi.org/10.3390/rs70912539

Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R.

(2014). Automated Water Extraction Index: A new

technique for surface water mapping using Landsat

Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece

imagery. Remote Sensing of Environment, 140, 23–35.

https://doi.org/10.1016/j.rse.2013.08.029

Filipponi, F. (2019). Sentinel-1 GRD Preprocessing

Workflow. Proceedings, 18(11), 1–4.

https://doi.org/10.3390/ECRS-3-06201

Finlayson, C. M., & D’Cruz, R. (2010). Inland Water

Systems. In Ecosystems and Human Well-being:

Current State and Trends (Vol. 1, Issue January, pp.

551–583). http://www.millenniumassessment.org/en/

Condition.html

Gerakes, P. A. (Ed.). (1989). Conservation and

Management of Greek Wetlands. In Proceedings of a

Workshop on Greek Wetlands (p. 493). IUCN, 1992.

Grimaldi, S., Li, Y., Pauwels, V. R. N., & Walker, J. P.

(2016). Remote Sensing-Derived Water Extent and

Level to Constrain Hydraulic Flood Forecasting

Models: Opportunities and Challenges. Surveys in

Geophysics, 37(5), 977–1034. https://doi.org/10.1007/

s10712-016-9378-y

Guo, M., Li, J., Sheng, C., Xu, J., & Wu, L. (2017). A

Review of Wetland Remote Sensing. Sensors, 17(4),

777. https://doi.org/10.3390/s17040777

Ko, B., Kim, H., & Nam, J. (2015). Classification of

Potential Water Bodies Using Landsat 8 OLI and a

Combination of Two Boosted Random Forest

Classifiers. Sensors, 15(6), 13763–13777.

https://doi.org/10.3390/s150613763

Kordelas, G., Manakos, I., Aragonés, D., Díaz-Delgado,

R., & Bustamante, J. (2018). Fast and Automatic Data-

Driven Thresholding for Inundation Mapping with

Sentinel-2 Data. Remote Sensing, 10(6), 910.

https://doi.org/10.3390/rs10060910

Kordelas, G., Manakos, I., Lefebvre, G., & Poulin, B.

(2019). Automatic Inundation Mapping Using

Sentinel-2 Data Applicable to Both Camargue and

Doñana Biosphere Reserves. Remote Sensing, 11(19),

20. https://doi.org/10.3390/rs11192251

Landis, J., & Koch, G. (1977). The Measurement of

Observer Agreement for Categorical Data. Biometrics,

33(1), 159-174. https://doi.org/10.2307/2529310

Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V.,

Müller-Wilm, U., & Gascon, F. (2017). Sen2Cor for

Sentinel-2. In L. Bruzzone, F. Bovolo, & J. A.

Benediktsson (Eds.), Image and Signal Processing for

Remote Sensing XXIII (p. 3). SPIE.

https://doi.org/10.1117/12.2278218

Manakos, I., Kordelas, G. A., & Marini, K. (2019). Fusion

of Sentinel-1 data with Sentinel-2 products to

overcome non-favourable atmospheric conditions for

the delineation of inundation maps. European Journal

of Remote Sensing, 1–14. https://doi.org/10.1080/22

797254.2019.1596757

Marti-Cardona, B., Dolz-Ripolles, J., & Lopez-Martinez,

C. (2013). Wetland inundation monitoring by the

synergistic use of ENVISAT/ASAR imagery and

ancilliary spatial data. Remote Sensing of

Environment, 139(June 2018), 171–184.

https://doi.org/10.1016/j.rse.2013.07.028

McFeeters, S. K. (1996). The use of the Normalized

Difference Water Index (NDWI) in the delineation of

open water features. International Journal of Remote

Sensing, 17(7), 1425–1432. https://doi.org/10.1080/

01431169608948714

Millennium Ecosystem Assessment. (2005). Ecosystems

and Human Well-being: Synthesis. Island Press.

Nandi, I., Srivastava, P. K., & Shah, K. (2017). Floodplain

Mapping through Support Vector Machine and

Optical/Infrared Images from Landsat 8 OLI/TIRS

Sensors: Case Study from Varanasi. Water Resources

Management, 31(4), 1157–1171. https://doi.org/

10.1007/s11269-017-1568-y

Ovakoglou, G., Alexandridis, T. K., Crisman, T. L.,

Skoulikaris, C., & Vergos, G. S. (2016). Use of

MODIS satellite images for detailed lake

morphometry: Application to basins with large water

level fluctuations. International Journal of Applied

Earth Observation and Geoinformation, 51, 37–46.

https://doi.org/10.1016/j.jag.2016.04.007

Psilovikos, A., & Margoni, S. (2010). An empirical model

of sediment deposition processes in Lake Kerkini,

Central Macedonia Greece. Environmental Monitoring

and Assessment, 164(1–4), 573–592.

https://doi.org/10.1007/s10661-009-0914-9

Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014).

Water Feature Extraction and Change Detection Using

Multitemporal Landsat Imagery. Remote Sensing, 6(5),

4173–4189. https://doi.org/10.3390/rs6054173

Sarp, G., & Ozcelik, M. (2017). Water body extraction

and change detection using time series: A case study

of Lake Burdur, Turkey. Journal of Taibah University

for Science, 11(3), 381–391. https://doi.org/10.1016/

j.jtusci.2016.04.005

Skakun, S. (2010). A neural network approach to flood

mapping using satellite imagery. Computing and

Informatics, 29, 1013–1024.

Thenkabail, P. S. (2015). Remotely Sensed Data

Characterization, Classification, and Accuracies. In

Remotely Sensed Data Characterization,

Classification, and Accuracies (Vol. 1). CRC Press.

https://doi.org/10.1201/b19294

Tsolakidis, I., & Vafiadis, M. (2019). Comparison of

Hydrographic Survey and Satellite Bathymetry in

Monitoring Kerkini Reservoir Storage. Environmental

Processes, 6(4), 1031–1049. https://doi.org/10.1007/

s40710-019-00394-7

Whitten, I. H., Frank, E., & Hall, M. A. (2011). Data

Mining: Practical Machine Learning Tools and

Techniques (Third). Elsevier. https://doi.org/10.1016/

C2009-0-19715-5

Xu, H. (2006). Modification of normalised difference

water index (NDWI) to enhance open water features in

remotely sensed imagery. International Journal of

Remote Sensing, 27(14), 3025–3033.

https://doi.org/10.1080/01431160600589179

Yousefi, P., Jalab, H. A., W. Ibrahim, R., Mohd Noor, N.

F., Ayub, M. N., & Gani, A. (2018). Water-Body

Segmentation in Satellite Imagery Applying Modified

Kernel K-Means. Malaysian Journal of Computer

Science, 31(2), 143–154. https://doi.org/10.22452/

mjcs.vol31no2.4

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

Zhang, F., Li, J., Zhang, B., Shen, Q., Ye, H., Wang, S., &

Lu, Z. (2018). A simple automated dynamic threshold

extraction method for the classification of large water

bodies from landsat-8 OLI water index images.

International Journal of Remote Sensing, 39(11),

3429–3451. https://doi.org/10.1080/01431161.2018.14

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