Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Hacettepe Üniversitesi, Mühendislik Fakültesi, Geomatik Mühendisliği Bölümü, Türkiye
Tezin Dili: İngilizce
Öğrenci: TUĞÇE GÜLSEVEN
Danışman: Mustafa Türker
Özet:
Inland water bodies are among the most important
natural resources on Earth due to their ecological, economic, and social
importance, supporting life, agriculture, biodiversity, and disaster management.
Monitoring and mapping inland water bodies from remote sensing imagery has
become critical for the sustainability of ecosystems. Traditional water index methods
require spatially variable threshold values to obtain accurate results, which
represent a limiting factor in their applicability. On the other hand, machine
learning techniques achieve highly successful results in detecting water
surfaces from remote sensing imagery. In this study, an approach is presented
to extract inland water bodies from Sentinel-2 imagery using object-based machine
learning classification. The approach was implemented on a study area located
in the Lakes Region of Türkiye using a single date Sentinel-2 imagery. The
image segmentation process necessary for object-based classification was
carried out using the Simple Linear Iterative Clustering (SLIC) superpixel
segmentation algorithm. Training
samples were selected automatically with the help of the Global Surface Water (GSW) dataset. Image classification
was performed in R Studio using the random forest (RF) algorithm. In
addition to 10m and 20m bands of Sentinel-2 data, the Normalized Difference
Water Index (NDWI) was calculated and used as an additional band in
classification. A comprehensive evaluation based on validation samples revealed
overall accuracy higher than 98.4% and Kappa value higher than 95.7 %. The
achieved results suggest that the presented approach is promissing in water
body mapping from Sentinel-2 imagery with very high accuracy.