Spatio-temporal assessment of natural lakes in Turkey

Firatli, Ertug; Dervisoglu, Adalet; Yagmur, Nur; Musaoğlu, Nebiye; Tanık, Ayşegül

doi:10.1007/s12145-022-00778-8

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…The evaluations of the size of Çivril Lake show a downward trend, which is in line with the literature [27,[29][30][31]. In one of these studies, spatiotemporal statistical analyses of water area changes of ten lakes in Turkey, including Çivril Lake, were performed using Landsat satellite imagery, considering the meteorological variables.…”

Section: Discussionsupporting

confidence: 83%

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Karakus

2023

Sustainability

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Lakes and reservoirs, comprising surface water bodies that vary significantly seasonally, play an essential role in the global water cycle due to their ability to hold, store, and clean water. They are crucial to our planet’s ecology and climate systems. This study analyzed Harmonized Sentinel-2 images using the Google Earth Engine (GEE) cloud platform to examine the short-term changes in the surface water bodies of Çivril Lake from March 2018 to March 2023 with meteorological data and lake surface water temperature (LSWT). This study used the Sentinel-2 Level-2A archive, a cloud filter, the NDVI (normalized difference vegetation index), NDWI (normalized difference water index), MNDWI (modified NDWI), and SWI (Sentinel water index) methods on lake surfaces utilizing the GEE platform and the random forests (RFs) method to calculate the water surface areas. The information on the water surfaces collected between March 2018 and March 2023 was used to track the trend of changes in the lake’s area. The seasonal (spring, summer, autumn, and winter) yearly and monthly changes in water areas were identified. Precipitation, evaporation, and temperature are gathered meteorological parameters that impact the observed variation in surface water bodies for the same area. The correlations between the lake area reduction and the chosen meteorological parameters revealed a strong positive or negative significant association. Meteorological parameters and human activities selected during different seasons, months, and years have directly affected the shrinkage of the lake area.

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Section: Discussionsupporting

confidence: 83%

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Karakus

2023

Sustainability

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“…In this research, all 606 accessible imageries between 2000 and 2021 were used to examine the variations in lake extents at finer temporal scales (Figure 2), except for 2012 due to no images being available in that year. Previous studies [30,33,35,41] have used short-and long-term data in monitoring the changes that occurred in the water surface area of the lakes. Due to describing long-term patterns of surface water fluctuation, images of different years must be captured at the same time zone (the same period of a year), which might lead to inconsistency in surface water extraction and uncertainty in the results.…”

Section: Methodsmentioning

confidence: 99%

“…Türkiye's 200 natural lakes have a combined surface area of about 906,000 hectares, representing over 1.6% of the country's total land area administered by the General Directorate of Nature Conservation and National Parks (GDNCNP), which is under the Ministry of Agriculture and Forestry (MoAF). The national inventory includes 302 distinct lakes and wetlands with surface areas ranging from 8.0 to 357,269 hectares [35]. The selected study area is in the Lakes Region which is situated in Türkiye's Mediterranean region between latitudes of 37 • 01 N and 38 • 30 N, and longitudes of 29 • 33 E and 32 • 21 E. It is composed of basins and 16 lakes, the majority of which are tectonic in origin.…”

Section: Study Areamentioning

confidence: 99%

Assessment of Spatio-Temporal Changes in Water Surface Extents and Lake Surface Temperatures Using Google Earth Engine for Lakes Region, Türkiye

Albarqouni

Yagmur

Balçık

et al. 2022

IJGI

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This study aims to extract water surface area and lake surface water temperature (LSWT), and to present long-term spatio-temporal analysis of these variables together with meteorological parameters. Three lakes in Türkiye’s Lakes Region, namely, Lake Burdur, Egirdir, and Beysehir, were considered as test sites. The normalized difference water index (NDWI) was applied to Landsat 5 and 8 data from 2000 to 2021 to extract the water extent in the Google Earth Engine (GEE) cloud-based platform. In addition to the lake surface area, Landsat thermal images were used to examine the LSWT. The findings indicated that water pixels could be extracted rather accurately using NDWI, with an overall accuracy of 98%. Between 2000 and 2021, the water surface area value of Lake Burdur decreased by more than 22%, while Lake Egirdir has dropped by less than 4%, and Lake Beysehir has not changed noticeably. LSWT of Burdur and Egirdir Lakes increased by more than 2.13 °C and 0.32 °C, respectively, while it decreased about 1.5 °C for Beysehir Lake. The obtained results were evaluated with meteorological parameters and our findings indicated that human-induced activities were more dominant than climate effects over Lake Burdur, unlike the others.

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“…Characterizing how and where lakes are changing on broad scales is, therefore, critical to understanding the drivers of change. However, studies that have investigated patterns of waterbody surface area change, rather than quantifying variability or linear change, are typically performed on short-term time series (∼2 years), long-term but low-resolution time series (i.e., only five measurements in 22 years), or on few (<30) waterbodies. − Recently, the Reservoir and Lake Surface Area Time series (ReaLSAT) data set, a long-term, spatially extensive water body data set, has become available which makes possible, using new analytic tools, a comprehensive analysis of long-term patterns of change across broad spatial scales.…”

Section: Introductionmentioning

confidence: 99%

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Wander

Farruggia

Fuente

et al. 2024

Environ. Sci. Technol.

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Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

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Spatio-temporal assessment of natural lakes in Turkey

Cited by 13 publications

References 30 publications

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Assessment of Spatio-Temporal Changes in Water Surface Extents and Lake Surface Temperatures Using Google Earth Engine for Lakes Region, Türkiye

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

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