Coastline Difference Measurement (CDM) Method

Çelik, Özlem; Gazioğlu, Cem

doi:10.30897/ijegeo.706792

Cited by 13 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each index is principally a definite mixture of the sensor-measured reflectance properties at 2 or more wavelengths that reveals specific characteristics of vegetation (Çelik and Gazioğlu, 2020). In this study different types of spectral indices were generated for mapping and monitoring the changes in water spread and aquatic vegetation in the Nalsarovar using Landsat TM and Sentinel-2 multi-spectral data.…”

Section: Generation Of Spectral Indicesmentioning

confidence: 99%

Monitoring Water Spread and Aquatic Vegetation using Spectral Indices in Nalsarovar, Gujarat State-India

Chauhan¹,

Patel²,

Shukla³

et al. 2021

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

Nalsarovar Bird Sanctuary, a natural freshwater lake (a relict sea) that is the largest natural wetland in the Thar Desert Biogeographic Province and represents a dynamic environment with salinity and depth varying depending on rainfall. For maintaining ecological balance in coastal and inland environments, wetlands play an important role and the changes in wetland environment can be monitored using satellite remote sensing technique. The present study was carried out using Landsat-7 TM (Feb-2002), Landsat-5 TM (Feb-2009) and Sentinel-2 multi-spectral data (Feb-2018) covering Nalsarovar area in Ahmedabad district. The study area of Nalsarovar with 10km buffer was extracted from the satellite digital data. Various spectral indices like Normalized difference Vegetation Index (NDVI), Normalized difference Water Index (NDWI) and Normalized difference Turbidity Index (NDTI) were generated for better identification and delineation of water body and aquatic vegetation in the Nalsarovar. The composite images of these indices were also generated to map and monitor changes in water spread and aquatic vegetation in the Nalsarovar. The water spread and aquatic vegetation in the Nalsarovar along with the land use classes were mapped using False Colour Composite (FCC) images of differed years.The result indicates that the water spread in Nalsarovar has changed from 2002, 2009 and 2018 depending on the monsoon pattern during these years. During drought year of 2002 total precipitation was very less resulting drastic reduction in the water spread in Nalsarovar. The aquatic vegetation has changed from 146.81 ha during 2002 to 510.93 ha during 2018. The land use change indicates that agriculture land, built-up, water body & wetland has increased and barren land, salt affected area has decreased. The aquatic vegetation in Nalsarovar was clearly visible on composite images generated using NDVI, NDWI and NDTI of different years. This study gives clear idea for land use change detection and water spread monitoring using two different techniques using Composite image of NDVI, NDWI and NDTI.

show abstract

Section: Generation Of Spectral Indicesmentioning

confidence: 99%

Monitoring Water Spread and Aquatic Vegetation using Spectral Indices in Nalsarovar, Gujarat State-India

Chauhan¹,

Patel²,

Shukla³

et al. 2021

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

show abstract

“…Machine learning, deep learning, and artificial intelligence have been the subject of lots of different studies, such as intelligent cities, meteorological estimates, and change detection analysis. In Figure 1, the relationship among artificial intelligence, machine learning, and deep learning (Çelik and Gazioğlu, 2020).…”

Section: Deep Learningmentioning

confidence: 99%

Comparison of Fully Convolutional Networks (FCN) and U-Net for Road Segmentation from High Resolution Imageries

Öztürk

Sarıtürk

Şeker

2020

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

Segmentation is one of the most popular classification techniques which still have semantic labels. In this context, the segmentation of different objects such as cars, airplanes, ships, and buildings that are independent of background and objects such as land use and vegetation classes, which are difficult to discriminate from the background is considered. However, in image segmentation studies, various difficulties such as shadow, image blockage, a disorder of background, lighting, shading that cause fundamental modifications in the appearance of features are often encountered. With the development of technology, obtaining high spatial resolution satellite imageries and aerial photographs contain detailed texture information have been facilitated easily. Parallel to these improvements, deep learning architectures have widely been used to solved several computer vision tasks with an increasing level of difficulty. Thus, the regional characteristics, artificial and natural objects, can be perceived and interpreted precisely. In this study, two different subset data that were produced from a great open-source labeled image sets were used to segmentation of roads. The used labeled data set consists of 150 satellite images of size 1500 x 1500 pixels at a 1.2 m resolution, which was not efficient for training. In order to avoid any problem, the imageries were divided into smaller dimensions. Selected images from the data set divided into small patches of 256 x 256 pixels and 512 x 512 pixels to train the system, and comparisons between them were carried out. To train the system using these datasets, two different artificial neural network architectures U-Net and Fully Convolutional Networks (FCN), which are used for object segmentation on high-resolution images, were selected. When the test data with the same size as the training data set were analyzed, approximately 97% extraction accuracy was obtained from high-resolution imageries trained by FCN in 512 x 512 dimensions.

show abstract

“…It is a very practical method to use remote sensing (RS) technology to observe possible changes in sea waters from the very beginning (Ateş et al, 2020). RS techniques are a unique method for monitoring possible changes in the world, thanks to the satellites providing (Haque and Basak, 2017;Wang et al, 2018) for supervised classification and K-Means algorithm and Iterative Self-Organizing data analysis algorithm (ISODATA) are the most preferred algorithms for unsupervised classification (Martinez, 2003;Çelik and Gazioğlu, 2020). In addition to the classification process, various indexes are used to distinguish the target class from other classes.…”

Section: Introductionmentioning

confidence: 99%

Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine

Acar

Yılmaz

Çelen

et al. 2021

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

In this study, a methodology has been developed for the detection of mucilage with the help of remote sensing (UA) techniques by considering the current mucilage formation in the Sea of Marmara. For this purpose, mucilage formation from10.03.2021 to 06.06.2021 was determined by classification of Sentinel-2 (MSI) satellite images using Random Forest (RF) algorithm on Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index (AWEI) indexes were used for classification. In the classification study, 5 different date ranges were determined by considering the availability of satellite images and cloud ratio. In the first date range (10.03.2021-30.03.2021), the first mucilage image was detected in the Dardanelles Strait. In the following dates, the spread of mucilage towards the Gulf of Izmit and the Gulf of Gemlik in addition to the Dardanelles was determined. Finally, in the images dated between 17.05.2021-06.06.2021, it was seen that the density of mucilage increased in the Dardanelles Strait, Izmit Gulf, Gemlik Gulf, Erdek Kapıdağ Peninsula and the north of the Marmara Island. The area covered by mucilage as of the last date range was calculated as 12,741.94 ha, and this value shows that 1.07% of the Sea of Marmara is covered with mucilage. With this developed methodology, it has been seen that mucilage formation can be detected quickly within minutes and with high accuracy from satellite images anywhere in the world.

show abstract

Coastline Difference Measurement (CDM) Method

Cited by 13 publications

References 9 publications

Monitoring Water Spread and Aquatic Vegetation using Spectral Indices in Nalsarovar, Gujarat State-India

Monitoring Water Spread and Aquatic Vegetation using Spectral Indices in Nalsarovar, Gujarat State-India

Comparison of Fully Convolutional Networks (FCN) and U-Net for Road Segmentation from High Resolution Imageries

Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine

Contact Info

Product

Resources

About