This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m 2 in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m 2 after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m 2 . The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.
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.
Seker, D.Z.;Yuksel, Y.; Guner, H.A.A., and Cetin, I., 2013. An integrated approach to temporal monitoring of the shoreline and basin of Terkos Lake.In this study, the combinatorial shoreline and land-use/cover (LULC) changes in the shoreline and basin of Terkos Lake were examined using Landsat satellite images taken in 1986, 2001, and 2009. Terkos Lake is one of seven freshwatersupplying reservoirs of Istanbul, and its borders are very close to the Black Sea. Terkos Lake is in danger because of the approach of its borders to the Black Sea. Changes in the lake's shoreline have been measured using an algorithm based on a hybrid region growing image-segmentation method. The LULC changes have been monitored using object-oriented image-processing software to provide understanding of the impact of these changes on the shoreline. Overall accuracy of the classification reached 92% for 1986, 94% for 2001, and 93% for 2009. The maximum shoreline change measured was 280 m in 23 years. Also, the obtained shoreline and LULC changes have been integrated into the long-term analysis of wave and wind characteristics and sediment-transport calculations. The calculations have been validated with shoreline changes, which have been automatically extracted from Landsat satellite images. The basic outcomes and proposals have been suggested to deal with uncontrolled human activities in the study area.
Coastal management requires rapid, up-to-date, and correct information. Thus, coastal movements have primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data are some of the most important information and are utilized for differentiating any detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from satellite images. In the literature most of the algorithms developed for optical images have been discussed in detail. In this study, an algorithm which extracts coastlines efficiently and automatically by processing SAR (Synthetic Aperture Radar) satellite images has been developed. A data set of ALOS Palsar image of Fine Beam Double (FBD) HH-HV polarized data has been used. PALSAR image has L-band data, and has a 14 MHz bandwidth and 34.3 degrees look angle. Data were acquired in ascending geometry. Ground resolution of PALSAR image was resampled to 15m to amplitude image. Zonguldak city, lies on the northwest costs of Turkey, has been selected as the test area. An algorithm was developed for automatic coastline extraction from SAR images. The algorithm is encoded in a C__ environment. To verify the results the algorithm was applied on two PALSAR images gathered in two different date as 2007 and 2010. The results of automatic coastline extraction obtained from SAR images were compared to the results derived from manual digitizing. Random control points which are seen on each image were used. The average differences of selected points were calculated.
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