Flood Analysis With Remote Sensing Data –
A Case Study: Maritsa River, Edirne

Sunar, A. F.; Yagmur, Nur; Dervisoglu, Adalet

doi:10.5194/isprs-archives-xlii-3-w8-497-2019

Cited by 7 publications

(5 citation statements)

References 3 publications

(3 reference statements)

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“…Several pre-processing steps have been already applied to the datasets and, thus, users can use corrected data besides raw data. For instance, the orthorectified, atmospherically corrected, and Calibrated Top of Atmosphere (ToA) Landsat data are easily accessible apart from the raw data [44], [45]. Analysis-ready SAR datasets on GEE represent a significant step forward because SAR preprocessing is relatively complex (especially for regular users).…”

Section: ) Datasetsmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

597

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: ) Datasetsmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

597

216

View full text Add to dashboard Cite

show abstract

“…Due to the cloudy weather conditions, each month of the year could not analyzed with Sentinel-2 optic images. Therefore, the water surface areas of the months without a clear cloudless image were determined by Sentinel-1 SAR images, with ascending tracking mode and VV polarization, utilized to separate water and non-water areas [27]. With the averages of the five years, monthly water surface area graphs were created.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine

Dervisoglu

2022

IJGI

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Deltas and lagoons, which contain many flora and fauna, have rich coastal ecological and biological environments, and are wetlands of vital importance for humans. In this study, the current problems in all coastal Ramsar sites in Turkey are summarized, and changes in water surface areas are investigated using Landsat and Sentinel 1/2 satellite images on the Google Earth Engine (GEE) cloud computing platform. Landsat TM and OLI images were used in the long-term analysis, and time series were created by taking annual and July to September averages between 1985 and 2020. In the short-term analysis, monthly averages were determined using Sentinel 2 images between 2016 and 2020. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in the months when optical data were not suitable for use in monthly analysis. The Normalized Difference Water Index (NDWI) was used to extract water surface areas from the optical images. Afterwards, a thresholding process was used for both optical and radar images to determine the changes. The changes were analyzed together with the meteorological data and the information obtained from the management plans and related studies in the literature. Changes in the water surface areas of all coastal Ramsar sites in Turkey were determined from 1985 to 2020 at different rates. There was a decreasing trend in the Goksu and Kızılırmak Deltas, which also have inland wetlands. The decreasing rates from 1985 to 2020 were −24.52% and −2.86%, for annual average water surfaces for the Goksu and Kızılırmak Deltas, respectively, and −21.64% and −6.34% for the dry season averages, respectively. However, Akyatan Lagoon, which also has inland wetlands, showed an increasing trend. Observing the annual average surface area from 1985 to 2020, an increase of 438 ha was seen, corresponding to 7.65%. Every year, there was an increasing trend in the Gediz Delta and Yumurtalık Lagoons, that do not have inland wetlands. The increasing rates from 1985 to 2020 were 46.01% and 17.31% for the annual average surface area, for the Gediz Delta and Yumurtalık Lagoons, respectively, and 38.34% and 21.04% for the dry season average, respectively. The obtained results reveal the importance of using remote sensing methods in formulating strategies for the sustainable management of wetlands.

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“…In-situ measurements and laboratory analysis of water samples are point based monitoring methods and it is difficult to consistently collect in-situ data due to the need of time, labour, and financial support. However, remote sensing provides a cost-effective and non-invasive method for measuring water quality parameters such as chlorophyll-a, turbidity, and dissolved organic matter (Sunar et al, 2022;Sunar et al, 2023). Additionally, they offer a synoptic view of water bodies, enabling the detection of variations in water quality across wide areas, and frequent measurements over time help to monitor temporal changes in water quality.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Retrieval Accuracy of Optically Active Water Components From Satellite Data Under Varying Image Resolutions

Sunar¹,

Dervisoglu²,

Yagmur

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Water quality monitoring has a key role in maintaining a sustainable ecosystem and environmental health. To ensure consistent monitoring, remote sensing provides regular data acquisition with varying spatial resolutions. However, more accurate, and effective solutions can be achieved by integrating remote sensing data with in-situ measurements. This study investigates the integration of in-situ measurements with satellite data, which have different spectral and spatial resolutions, using linear and exponential regression models for four optically active components in the Gulf of Izmit. In this context, Sentinel-2 (S2) and PlanetScope SuperDove (PS) multispectral images, which were acquired on the same date, were used for the comparative analysis of the accurate mapping of chlorophyll-a (Chl-a), turbidity, Secchi disk depth (SDD) and total suspended matter (TSM) water quality parameters combined with simultaneously collected in-situ measurements. The models were evaluated using validation data, along with visual comparison, to assess their accuracy. The results indicate that, overall, exponential models provide more accurate results than linear models, except for the SDD parameter. Furthermore, models created with S2 data demonstrate better performance in retrieving water quality parameters for Chl-a, turbidity, and TSM, with R2 values of 0.71, 0.84, and 0.91, respectively. The linear model created with PS data stands out in the accurately mapping of SDD parameter. Nevertheless, the spatial distribution of these parameters using both satellite dataset exhibits a similar pattern throughout the gulf, which is under threat from significant terrestrial pollution sources, particularly in the eastern part.

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Flood Analysis With Remote Sensing Data – A Case Study: Maritsa River, Edirne

Cited by 7 publications

References 3 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine

Analyzing the Retrieval Accuracy of Optically Active Water Components From Satellite Data Under Varying Image Resolutions

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