A comparative study of the surface level changes of Urmia Lake and Aral Lake during the period of 1988 to 2018 using satellite images

Khorshiddoust, Ali Mohammad; Patel, Nilanchal; Khalilzadeh, Elnaz; Bostanabad, Saeed Armaghan; Tajbar, Sapna

doi:10.1007/s11707-022-1010-5

Cited by 5 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some previous studies have obtained different data on lake surface area and shoreline (Sakaoğlu & Çepni, 2022). In obtaining these data, the resolution of the data source and satellite image, the techniques and methods used may cause differences in the change results (Himmelstoss et al, 2018;Khorshiddoust et al, 2022). For this reason, in order to ensure that coastal flora is taken into account in wetland lakes and to determine the water surface and shoreline more accurately, a formula that provides water-land separation specific to the study was produced by using 3 different water indices and 4 different vegetation indices.…”

Section: Discussionmentioning

confidence: 99%

“…Before the analyses in the research, a systematic process of determining the shoreline in the most accurate way was established. Due to the presence of reeds on the shores of wetland lakes such as Lake Manyas, it is more difficult to determine the shoreline compared to other shores (Gao, 1996;Khorshiddoust et al, 2022;Şenol et al, 2023). For this reason, both water indices and green area and vegetation indices were used for shoreline determination (Table 2).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Manyas Lake Surface Area and Shoreline Change Over Various Periods with DSAS Tool

Uzun

2024

TUZAL

View full text Add to dashboard Cite

In this study, the shoreline and lake surface area changes of Lake Manyas were analysed by using Geographical Information Systems (GIS) and Remote Sensing (RS) techniques for long term (1980-2020) and annual (2022) with DSAS tool. In the study, a formula was created using NDWI, MDWI, WRI water indices and NDVI, RVI, NDMI, GCI vegetation indices over Landsat satellite images of 1980, 1985, 1990, 1995, 2000, 2000, 2005, 2005, 2010, 2015, 2020 and all months of 2022, and shoreline extraction was performed. Then, shoreline and lake surface area change were analysed over different periods with NSM, EPR, SCE, LRR statistics in DSAS tool. According to the results of the analyses, the average shoreline changes between 1980 and 2020 was 139 m according to NSM statistics, 3,5 m/year according to EPR, 243.1 m according to SCE and 3.4 m/year according to LRR. While the shoreline extended a maximum of 1599 m, the minimum value was -403 m. From 1980 to 2020, 5.85 km2 coastal accumulation, 1.03 km2 coastal erosion and 146.5 km2 permanent lake surface area data were determined on the surface area and shores of Lake Manyas. According to the monthly data of Lake Manyas for 2022, the shoreline is advancing by 18 m on average. Due to the natural dynamic process and the productive structure of the wetland system, the lake surface area reaches its widest size in April with 149.01 km2 and its narrowest area is 146.05 km2 in August. On the southern shores of Lake Manyas, reedbed development and coastal accumulation are intensely experienced with the progression of the Manyas Stream delta, while coastal erosion is observed on the northern shores.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis of Manyas Lake Surface Area and Shoreline Change Over Various Periods with DSAS Tool

Uzun

2024

TUZAL

View full text Add to dashboard Cite

show abstract

Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia lake basin

Solaimani,

Darvishi

2024

Advances in Space Research

View full text Add to dashboard Cite

Uluabat Gölü yüzey alanının zamansal değişim analizi üzerinden DSAS ve yapay sinir ağları modellerine göre gelecek tahminleri

Uzun

2024

Türk Coğrafya Dergisi

View full text Add to dashboard Cite

Göller jeomorfolojik, hidrografik, klimatolojik ve antropojenik etkenlerle birçok özellik açısından değişimlere uğramaktadır. Bu çalışmanın amacı Uluabat Gölü’nün yüzey alanı ve kıyı çizgisi değişimini zamansal, mekânsal açıdan saptamak ve çeşitli tekniklerle farklı senaryolar kapsamında geleceğe dair öngörüler üretmektir. Çalışmada 1975-2020 arasında 5’er yıllık ile 2023 yılı ve 2022 yılındaki her aya ait birer Landsat uydu görüntüsü kullanılmıştır. Multispektral görüntüler üzerinden su indis analizleri ve eşik yöntemi ile göl yüzey alanı ve kıyı çizgisi çıkarımı yapılmıştır. Daha sonra gölde farklı değişimleri ve geleceğe dönük senaryoları üretmek için 1975-2023, 1985-2023 ve 2005-2023 dönemlerine Sayısal Kıyı Çizgisi Analiz Sisteminde (DSAS) yer alan NSM, SCE, EPR ve LLR istatistik analizleri uygulanmıştır. Dönem verileri temel alınarak Kalman Filter yöntemi ile 10 ve 20 yıllık göl yüzey alanı tahminleri 3 farklı senaryo kapsamında yapılmıştır. Uzun dönem ve aylık kıyı değişimi, DSAS analizleri, batimetri verisi ve geçiş olasılıklarıyla toplamda 8 farklı kriter, Yapay Sinir Ağları (YSA) yönteminde analiz edilmiş ve göl yüzey alanı tahminleri tekrar üretilmiştir. Bulgular, gölün yüzey alanının 1975’den 2023’e % 20 azaldığı ve güney kesimdeki deltada 3000 m’lik kıyı ilerlemesinin olduğunu göstermektedir. YSA modelinde 2023’e göre göl yüzey alanının 2033 yılında % 8, 2043 yılında ise % 13,6 küçüleceği tahmin edilmiştir.

show abstract

A comparative study of the surface level changes of Urmia Lake and Aral Lake during the period of 1988 to 2018 using satellite images

Cited by 5 publications

References 30 publications

Analysis of Manyas Lake Surface Area and Shoreline Change Over Various Periods with DSAS Tool

Analysis of Manyas Lake Surface Area and Shoreline Change Over Various Periods with DSAS Tool

Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia lake basin

Uluabat Gölü yüzey alanının zamansal değişim analizi üzerinden DSAS ve yapay sinir ağları modellerine göre gelecek tahminleri

Contact Info

Product

Resources

About