Uzaktan algılama verileri ile taşkın alanlarının izlenmesi ve analizi
Monitoring and analysis of flood areas with remote sensing data
Yazar: RABİA EZGİ TEKİN ELMAS
Danışman: DOÇ. DR. FİLİZ BEKTAŞ BALÇIK
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry

Özet:
Taşkınlar, dünyada en sık görülen doğal afetlerden biridir. Genellikle şiddetli yağışlar, hızlı kar erimeleri, kıyı bölgelerde siklon veya tsunamiden kaynaklanan fırtına dalgası neden olur.Taşkınların tüm canlılar ve doğa için ağır yıkıcı, ekonomik sosyal ve çevresel etkileri olur. 1998 2017 yılları arasında meydana gelen ve kaydedilen tüm afetlerin %43.4 ile taşkın en sık görülen afet türüdür.
Taşkın, çarpık kentleşme sonucu dere yataklarının yapılaşması, doldurulması veya nehir yataklarının değiştirilmesi sonucu her yıl Türkiye'de büyük mal ve can kayıplarına neden olmaktadır.
Bu tez çalışmasında Türkiye'nin Edirne İli sınırları içerisinde bulunan Meriç Nehri ve çevresinde 2015-2021 yılları arasında meydana gelen taşkınların analizi Sentinel 1 (Sentetik Açıklıklı Radar) Sentinel 2 MSI (Multispectral Instrument) uydu görüntüleri kullanılarak uzaktan algılama teknikleri ile analizi yapılmıştır.
Meydana gelen üç taşkın olayı için Sentinel 1 ve Sentinel 2 uydu görüntüleri, dört taşkın olayı için Sentinel 1 uydu görüntüleri kullanılmıştır. Taşkınların analizi için uzaktan algılama teknikleri ile Sentinel 2 uydu görüntülerinden Normalize Edilmiş
Fark Su İndeksi (NDWI) ve Modifiye edilmiş Normalize Fark Su İndeksi (MNDWI), Sentinel 1 uydu görüntülerinden Otsu eşik değeri hesaplanmıştır. Hesaplanan indeks ve eşik değerleri kullanılarak taşkın haritaları oluşturulmuştur. 
NDWI ve MNDWI sonuçları değerlendirildiğinde NDWI'ten elde edilen genel doğruluk değerlerinin ve Kappa istatistiği değerlerinin daha yüksek olduğu gözlemlenmiştir. NDWI'ten elde edilen en yüksek genel doğruluk %98.97, Kappa istatistik değeri 0.94 olarak elde edilmiştir. MNDWI'ten elde edilen en yüksek genel doğruluk %98.83 Kappa istatistik değeri 0.91 olarak elde edilmiştir. Otsu eşikleme
yönteminden elde edilen genel doğruluk %99.34, Kappa istatistik değeri 0.94 olarak elde edilmiştir.
Otsu eşikleme yöntemi sonuçlarının MNDWI ve NDWI'ten hesaplanan değerlere göre daha yüksek doğrulukla elde edildiği görülmüştür. Bu nedenle taşkın analizi ve incelenmesi için Sentinel 1 görüntülerinden elde edilen sonuçlarla devam edilmiştir.
Ayrıca bölgede meydana gelen taşkınların etkilediği arazi yüzeylerinin hesaplanması için Rastgele Orman (RO) Sınıflandırma yöntemi ile 2017 ve 2020 yılları arasında üç adet Sentinel 2 uydu görüntüsü sınıflandırılmıştır. Otsu eşikleme yöntemi kullanılarak elde edilen taşkın alanlarının hangi arazi yüzeylerini etkilediği elde edilen sınıflandırılmış görüntüler kullanılarak belirlenmiştir. Sonuç olarak meydana gelen taşkınların en çok tarım alanlarını etkilediği görülmüştür.

Abstract:
Floods are one of the most frequent natural disasters in the world. They are usually caused by heavy rainfall, rapid snowmelt, and storm surge from cyclones or tsunamis in coastal areas. Floods have severe destructive, economic, social, and environmental
impacts on all living things and nature. Flooding is the most common type of disaster with 43.4% of all recorded disasters occurring between 1998-2017. At the same time, the frequency, magnitude, and total area affected by floods are increasing due to
climate change.
Floods cause great loss of property and live in Turkey every year as a result of the construction, filling, or alteration of river beds as a result of unplanned urbanization.
Flooding is the second most common meteorological natural disaster in Turkey
between 2010 and 2021. Floods have been occurring in Edirne, one of the largest cities in western Turkey,
since 1571. Within the scope of this thesis, the floods occurring in the Meriç River within the
borders of Edirne province are examined and analyzed with remote sensing data.
Remote sensing is an important technology for disaster management and risk assessment due to its wide applicability. Using remote sensing methods, 7 different flood events that occurred between 2015- 2021 in and around the Meriç River were
analyzed.
This study aims to analyze and investigate flood areas using different sensors and methods. Both Sentinel 1 SAR (Synthetic Aperture Radar) and Sentinel 2 MSI (Multispectral Instrument) satellite images were used for 3 different flood events.
Sentinel 1 SAR satellite images were used for 4 different flood events. For each flood event, pre-flood and post-flood dated images were used.
Image indices were used to analyze flooding from Sentinel 2 satellite imagery. Any remote sensing index usually utilizes the two most contrasting bands in terms of the reflectance of the feature in question. The feature has maximum reflectance in one
band and minimum reflectance in the other band. In this study, 2 different image indices were used.
The first one is NDWI (Normalized Difference Water Index). NDWI maximizes the reflectance of water using the green wavelength and minimizes the reflectance of soil and vegetation using the Near-Infrared (NIR) wavelength. It was developed
specifically to depict open water features and enhance their presence in remotely sensed imagery while eliminating soil and vegetation features. 
Water features have positive values and other areas have negative values. The second of these is MNDWI (Modified Normalized Difference Water Index).
MNDWI maximizes the reflectance of water using green wavelength and minimizes the reflectance of soil and vegetation using Short-wave Infrared (SWIR) wavelength.
After applying the image indices, the satellite image is categorized into two classes: water-covered and non-water covered. NDWI and MNDWI take values between +1 and -1. Water features have positive values, and soil and vegetation features have zero or negative values.
The Otsu thresholding method was used for flood analysis from Sentinel 1 satellite images. Otsu thresholding method is a thresholding method that can be applied to gray-level images. It is assumed that the images consist of two different color
classes, background and foreground, and the intraclass variance of these two color classes is calculated for all threshold values. The threshold value that minimizes this value is the optimum threshold value. According to the threshold values, Sentinel 1
satellite images are categorized into two classes: water-covered areas and non-watercovered areas.
These methods have been implemented on the Google Earth Engine (GEE) cloud platform. GEE stores a publicly available data archive of historical earth imagery dating back more than four decades. Images taken daily are then made available for
data mining on a global scale.
GEE data has many uses such as agriculture, forestry, wetland and hydrology, map production, and change detection.
An accuracy assessment of the methods used was then performed. Accuracy assessment is a general measure of the quality of thematic maps produced and needed by users to help assess the suitability of a map for a particular purpose. It is based on evaluating the validation dataset obtained from classified images against a reference dataset representing the same classes. Discrepancies between these two datasets are interpreted as errors in maps derived from remotely sensed data.
To calculate the accuracy of classification, a k x k error matrix is constructed. The overall accuracy is calculated by dividing the number of correctly classified pixels by the total number of reference pixels. Also, Cohen's Kappa statistic value ' κ ' is
another measure of classification accuracy.
The overall accuracy of the areas covered by water calculated from NDWI ranges between 98.97% and 95.60%, with Kappa statistical values between 0.74 and 0.94. The overall accuracy of the areas covered by water calculated from MNDWI ranges
between 95.76% and 98.83% and Kappa statistical values between 0.75 and 0.91.
The overall accuracy of the areas covered by water calculated from the Otsu thresholding method ranged between 99.34% and 94.84%, and Kappa statistical values ranged between 0.71 and 0.94.
When the accuracy assessment results of NDWI and MNDWI indices are compared, it is seen that the overall accuracy and Kappa statistic values of the flood maps obtained with NDWI are higher.
When the results of the Otsu thresholding method and image indices are compared, it
is seen that higher overall accuracy is obtained from the Otsu thresholding method.
Sentinel 1 SAR data played a major role in the investigation and analysis of flood
areas due to the high accuracy of the applied Otsu thresholding method and the
availability of more data for the floods studied for 7 different flood events, the flood coverage area was obtained from the difference of the areas covered by water obtained from Sentinel 1 satellite images before and after the flood.
As the last stage of the study, 3 different Sentinel 2 MSI satellite images were classified between 2017-2020 to calculate the land surfaces affected by floods occurring in the region. Random Forest Classification method was used to determine
the land cover and land use classes of Sentinel 2 MSI satellite images.
Classification is an important step in terms of data extraction from satellite images.
Classification can be defined as the process of assigning all pixels in the image to
certain classes based on spectral information.
Random Forest Classification (RFC) method provides many advantages such as high
classification accuracy, identification of essential variables, and missing value
estimation. The overall accuracies of the images classified using the Random Forest
Classification method were 0.98 and 0.97. The land cover and land use types affected by flooding areas of different dates
obtained from Sentinel 1 satellite images were calculated using the classified images. It was determined by the analysis that the most affected land use type by floods was
agricultural land.