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Fusun Balik Sanli

Yildiz Technical University, Geomatic Engineering, Faculty Member

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Fusun Balik Sanli received her MSc degree in ITC, The Netherlands, in 2000. She received a Ph.D. in Geomatics Engineering, Remote Sensing, and GIS program from YTU, Istanbul, Turkey, in2004. Currently, she is working as an academic staff in the Photogrammetry Division of the Geomatic Engineering Department, YTU. Her research includes optical and radar remote sensing, image fusion, information extraction from SAR and optical images.

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Papers by Fusun Balik Sanli

Spatio-temporal Analysis of Pollutant Gases using Sentinel-5P TROPOMI Data on the Google Earth Engine during the COVID-19 Pandemic in the Marmara Region, Türkiye

e-zbornik. Elektronički zbornik radova Građevinskog fakulteta, Jun 23, 2023

Aoutomatic Oil Spill Detection Using TerraSAR-X Data

41st COSPAR Scientific Assembly, Jul 1, 2016

Pléi̇ades Uydu Görüntüsünün Hava Lidar Verileri Entegrasyonu İle Nesne Tabanli Siniflandirilmasi Ve Doğruluk Analizi

Deri̇n Öğrenme Yöntemleri̇ İle Yüz Görüntüleri̇nden Yaş Kesti̇ri̇mi̇

Machine Learning Based Segmentation of Shoreline Using Mean-Shift, Random Forest and Support Vector Machine

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, Oct 17, 2022

Stereoskopik Radarsat F1-F5 Görüntülerinden Üretilen Sayısal Arazi Modellerinin Değerlendirilmesi

Jeodezi ve Jeoinformasyon Dergisi, Jun 1, 2006

Orman Ağaç Sinir Ekotonunun Deği̇şi̇mleri̇ni̇ Meşcere Hari̇talari İle Ve CBS Teni̇kleri̇ni̇ Ki̇llanilarak İzlenmesi̇

Evaluating training data for crop type classifıcation using support vector machine and random forests : Procjena klasifikacije testnih podataka za potrebe određivanja vrste kulture korištenjem metoda mašina vektora podrške i slučajnih šuma

DOAJ (DOAJ: Directory of Open Access Journals), Dec 1, 2017

Pamuk Ekili Alanların Nesne Tabanlı Sınıflandırma yöntemi ile Belirlenmesi: Menemen Örneği

Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey

Landscape and Ecological Engineering

Wildfires are regarded as one of the devastating natural disturbances to natural ecosystems, and ... more Wildfires are regarded as one of the devastating natural disturbances to natural ecosystems, and threatening the lives of many species. In July 2021, a wildfire took place in the Mediterranean region of Turkey in multiple areas. In Bodrum, a town with high touristic value and attraction, approximately 17,600 hectares of forest have been affected by the wildfire. In this study, the fire-affected areas were determined using an analytical hierarchy process (AHP) and geographical information system (GIS). Rainfall, slope, distance from the stream, pre-and post-fire land use and land cover, elevation, curvature, topographic wetness index, and lithology were selected as the governing variables for the AHP model. The contribution of each variable was determined from the literature. Based on the model, it was found that the area with a very high flood risk increased from 8.6 to 18.4%, implying flood risk in a particular region doubled following the wildfire. Immediately after the forest fire, floods occurred in Mazikoy in the region and its surroundings. The model accuracy was tested by using randomly selected 61 points in and around the flooded area. The model accuracy was quantified by the receiver operating characteristic (ROC) curves method. Pre-and post-fire areas under curve (AUC) values were found 0.925 and 0.933, respectively, which implies that the prediction ability of the model is acceptably accurate. The study revealed that the model could quantify the increased flood risk for vulnerable areas after a forest fire. Such knowledge may aid local authorities in determining the priorities of the precautions that need to be taken after a forest fire.

The Performance Analysis of Different Water Indices and Algorithms Using Sentinel-2 and Landsat-8 Images in Determining Water Surface: Demirkopru Dam Case Study

Arabian Journal for Science and Engineering

Küresel İkli̇m Deği̇şi̇kli̇ği̇ni̇n Etki̇leri̇ Ve Bu Etki̇leri̇ Önlemeye Yöneli̇k Uluslararasi Gi̇ri̇şi̇mler

Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Jan 30, 2017

Assessment of vegetation indices for the determination of agricultural crop types

Journal of Environmental Protection and Ecology, 2018

Karyotype Characteristics of Chalcalburnus mossulensis (Heckel, 1843) from Karasu River, Erzurum

Quantification and visualization of flood-induced morphological changes in meander structures by UAV-based monitoring

Engineering Science and Technology, an International Journal, 2021

Analysis of Multi Temporal Satellite Imagery for Total Suspended Sediments in a Wave-Active Coastal Area-Gaza Strip Coastal Water, Palestine

International Journal of Environment and Geoinformatics, 2016

Sediment load materials is one of the key factors that determine the surface water quality, both ... more Sediment load materials is one of the key factors that determine the surface water quality, both of oceanic and river water, and it specifies water optical properties. Thus it provides a background for a plenty of applications and projects in the water and oceanography community. Landsat detects and classifies reflected solar energy from bodies on the earth's surface. Suspended sediments existing in water column have an optical influences. So that, Landsat images could detect suspended sediments concentration in such a water surface. In this study we have three main objectives to be achieved as; TSS Concentration maps generation in the Gaza Strip coastal zone, achieving analysis processes on TSS trend itself and TSS related coastal phenomenon, and investigation of the ability of Landsat images to detect TSS comprehensively in a wavy coastal zone. For this purpose two landsat TM5 images acquired in 1999 and 2010, one Landsat TM7 images acquired in 2003, and 2 Landsat Oli 8 ima...

Investigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods

Survey Review, 2017

GPS ve Ps-Insar Yöntemleri Kullanılarak Koyulhisar (Sivas) Heyelanlarının İzlenmesi: İlk Sonuçlar

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2014

Testing the Generalization Efficiency of Oil Slick Classification Algorithm Using Multiple Sar Data for Deepwater Horizon Oil Spill

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012

Stereo Foto Yorumlama Teknikleri İle Toprak Etüd ve Haritalama Çalışmalarında Multispektral Verilerin Katkısı

Ege Üniversitesi Ziraat Fakültesi Dergisi, 2020

Amaç: Araştırmada stereo foto yorum yöntemiyle yapılan toprak etüd ve haritalama çalışmaları süre... more Amaç: Araştırmada stereo foto yorum yöntemiyle yapılan toprak etüd ve haritalama çalışmaları sürecinde, çok bantlı uydu görüntülerinden de yararlanma olanağının sağlanması ve bu kazanımın toprak etüd ve haritalama çalışmalarına yapacağı katkı düzeyinin araştırılması amaçlanmıştır.Materyal ve Metot: Çalışma, iki ana bölümden oluşmaktır. Birinci bölümde, geleneksel toprak etüd ve haritalama çalışmaları, ikinci bölümde ise gerçekleştirilen seri bazındaki etüd çalışmalarına birleştirilmiş uydu görüntülerinin katkısının araştırılması şeklinde gerçekleştirilmiştir.Bulgular: Bu araştırma ile çalışma alanı içerisinde kalan topraklar seri bazında tanımlanmış, haritalanmış ve toprak sınıflandırma sistemlerine göre taksonomik sınıfının da belirlenmesiyle birlikte toprak etüd ve haritalama çalışmaları tamamlanmıştır. Bu amaçla, toprakların morfolojik özellikleri yanı sıra laboratuvarda belirlenen ve toprak sınıflandırmada kriter olarak kullanılan fiziksel ve kimyasal analizleri de yapılmıştır. ...

Spatio-temporal Analysis of Pollutant Gases using Sentinel-5P TROPOMI Data on the Google Earth Engine during the COVID-19 Pandemic in the Marmara Region, Türkiye

e-zbornik. Elektronički zbornik radova Građevinskog fakulteta, Jun 23, 2023

Aoutomatic Oil Spill Detection Using TerraSAR-X Data

41st COSPAR Scientific Assembly, Jul 1, 2016

Pléi̇ades Uydu Görüntüsünün Hava Lidar Verileri Entegrasyonu İle Nesne Tabanli Siniflandirilmasi Ve Doğruluk Analizi

Deri̇n Öğrenme Yöntemleri̇ İle Yüz Görüntüleri̇nden Yaş Kesti̇ri̇mi̇

Machine Learning Based Segmentation of Shoreline Using Mean-Shift, Random Forest and Support Vector Machine

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, Oct 17, 2022

Stereoskopik Radarsat F1-F5 Görüntülerinden Üretilen Sayısal Arazi Modellerinin Değerlendirilmesi

Jeodezi ve Jeoinformasyon Dergisi, Jun 1, 2006

Orman Ağaç Sinir Ekotonunun Deği̇şi̇mleri̇ni̇ Meşcere Hari̇talari İle Ve CBS Teni̇kleri̇ni̇ Ki̇llanilarak İzlenmesi̇

Evaluating training data for crop type classifıcation using support vector machine and random forests : Procjena klasifikacije testnih podataka za potrebe određivanja vrste kulture korištenjem metoda mašina vektora podrške i slučajnih šuma

DOAJ (DOAJ: Directory of Open Access Journals), Dec 1, 2017

Pamuk Ekili Alanların Nesne Tabanlı Sınıflandırma yöntemi ile Belirlenmesi: Menemen Örneği

Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey

Landscape and Ecological Engineering

Wildfires are regarded as one of the devastating natural disturbances to natural ecosystems, and ... more Wildfires are regarded as one of the devastating natural disturbances to natural ecosystems, and threatening the lives of many species. In July 2021, a wildfire took place in the Mediterranean region of Turkey in multiple areas. In Bodrum, a town with high touristic value and attraction, approximately 17,600 hectares of forest have been affected by the wildfire. In this study, the fire-affected areas were determined using an analytical hierarchy process (AHP) and geographical information system (GIS). Rainfall, slope, distance from the stream, pre-and post-fire land use and land cover, elevation, curvature, topographic wetness index, and lithology were selected as the governing variables for the AHP model. The contribution of each variable was determined from the literature. Based on the model, it was found that the area with a very high flood risk increased from 8.6 to 18.4%, implying flood risk in a particular region doubled following the wildfire. Immediately after the forest fire, floods occurred in Mazikoy in the region and its surroundings. The model accuracy was tested by using randomly selected 61 points in and around the flooded area. The model accuracy was quantified by the receiver operating characteristic (ROC) curves method. Pre-and post-fire areas under curve (AUC) values were found 0.925 and 0.933, respectively, which implies that the prediction ability of the model is acceptably accurate. The study revealed that the model could quantify the increased flood risk for vulnerable areas after a forest fire. Such knowledge may aid local authorities in determining the priorities of the precautions that need to be taken after a forest fire.

The Performance Analysis of Different Water Indices and Algorithms Using Sentinel-2 and Landsat-8 Images in Determining Water Surface: Demirkopru Dam Case Study

Arabian Journal for Science and Engineering

Küresel İkli̇m Deği̇şi̇kli̇ği̇ni̇n Etki̇leri̇ Ve Bu Etki̇leri̇ Önlemeye Yöneli̇k Uluslararasi Gi̇ri̇şi̇mler

Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Jan 30, 2017

Assessment of vegetation indices for the determination of agricultural crop types

Journal of Environmental Protection and Ecology, 2018

Karyotype Characteristics of Chalcalburnus mossulensis (Heckel, 1843) from Karasu River, Erzurum

Quantification and visualization of flood-induced morphological changes in meander structures by UAV-based monitoring

Engineering Science and Technology, an International Journal, 2021

Analysis of Multi Temporal Satellite Imagery for Total Suspended Sediments in a Wave-Active Coastal Area-Gaza Strip Coastal Water, Palestine

International Journal of Environment and Geoinformatics, 2016

Sediment load materials is one of the key factors that determine the surface water quality, both ... more Sediment load materials is one of the key factors that determine the surface water quality, both of oceanic and river water, and it specifies water optical properties. Thus it provides a background for a plenty of applications and projects in the water and oceanography community. Landsat detects and classifies reflected solar energy from bodies on the earth's surface. Suspended sediments existing in water column have an optical influences. So that, Landsat images could detect suspended sediments concentration in such a water surface. In this study we have three main objectives to be achieved as; TSS Concentration maps generation in the Gaza Strip coastal zone, achieving analysis processes on TSS trend itself and TSS related coastal phenomenon, and investigation of the ability of Landsat images to detect TSS comprehensively in a wavy coastal zone. For this purpose two landsat TM5 images acquired in 1999 and 2010, one Landsat TM7 images acquired in 2003, and 2 Landsat Oli 8 ima...

Investigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods

Survey Review, 2017

GPS ve Ps-Insar Yöntemleri Kullanılarak Koyulhisar (Sivas) Heyelanlarının İzlenmesi: İlk Sonuçlar

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2014

Testing the Generalization Efficiency of Oil Slick Classification Algorithm Using Multiple Sar Data for Deepwater Horizon Oil Spill

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012

Stereo Foto Yorumlama Teknikleri İle Toprak Etüd ve Haritalama Çalışmalarında Multispektral Verilerin Katkısı

Ege Üniversitesi Ziraat Fakültesi Dergisi, 2020

Amaç: Araştırmada stereo foto yorum yöntemiyle yapılan toprak etüd ve haritalama çalışmaları süre... more Amaç: Araştırmada stereo foto yorum yöntemiyle yapılan toprak etüd ve haritalama çalışmaları sürecinde, çok bantlı uydu görüntülerinden de yararlanma olanağının sağlanması ve bu kazanımın toprak etüd ve haritalama çalışmalarına yapacağı katkı düzeyinin araştırılması amaçlanmıştır.Materyal ve Metot: Çalışma, iki ana bölümden oluşmaktır. Birinci bölümde, geleneksel toprak etüd ve haritalama çalışmaları, ikinci bölümde ise gerçekleştirilen seri bazındaki etüd çalışmalarına birleştirilmiş uydu görüntülerinin katkısının araştırılması şeklinde gerçekleştirilmiştir.Bulgular: Bu araştırma ile çalışma alanı içerisinde kalan topraklar seri bazında tanımlanmış, haritalanmış ve toprak sınıflandırma sistemlerine göre taksonomik sınıfının da belirlenmesiyle birlikte toprak etüd ve haritalama çalışmaları tamamlanmıştır. Bu amaçla, toprakların morfolojik özellikleri yanı sıra laboratuvarda belirlenen ve toprak sınıflandırmada kriter olarak kullanılan fiziksel ve kimyasal analizleri de yapılmıştır. ...

Land Use and Cover Classification of Sentinel-1A SAR Imagery: A Case Study of Istanbul

by Mustafa Ustuner and Fusun Balik Sanli

[English] In this study, Sentinel-1A SAR imagery for land use/cover classification and its impact... more [English] In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and KNN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy. .................................................................................................................................................................................... [Türkçe] Bu çalışmada, Sentinel-1A SAR (Sentetik Açıklıklı Radar) görüntüsünün arazi örtüsü ve kullanımı sınıflandırmasında kullanılabilirliği ve sınıflandırma algoritmaları üzerindeki etkileri araştırılmıştır. İkili/çift polarizasyona (DD, DY; D:Düşey, Y:Yatay) sahip Sentinel-1A görüntüsü Avrupa Uzay Ajansı’ndan ücretsiz olarak elde edilmiştir. Çalışma alanı olarak İstanbul seçilmiştir. Elde edilen SAR görüntüsünün, yörünge düzeltmesi, veri kalibrasyonu, multilooking işlemi, benek (speckle) filtrelemesi ve arazi düzeltmesi (terrain correction) işlemlerinin ardından sınıflandırma işlemine geçilmiştir. Çalışma kapsamında üç farklı sınıflandırma tekniği (Destek Vektör Makineleri (DVM), Rastgele Orman(RO) ve K-En Yakın Komşuluk(K-EYK)) kullanılmış ve orijinal iki banttan oluşturulmuş diğer bantların (DD-DY, DD+DY vb.) etkisi incelenmiştir. Çalışmada en yüksek sınıflandırma doğruluğu (%81.90) orijinal iki bandın DVM yöntemi ile sınıflandırılması sonucu elde edilmiştir. Diğer bantların sınıflandırma doğruluğuna olan etkisinin seçilen algoritmaya göre farklılık gösterdiği gözlenmiştir.

Comparison of rededge texture features of RapidEye imagery for crop classification

by Fusun Balik Sanli and Mustafa Ustuner

Since the rapid population growth and climate change over the world, the need of the food and wat... more Since the rapid population growth and climate change over the world, the need of the food and water for human life have been increasing hence the sustainable management of agricultural resources has been becoming crucially important for increased agricultural production as well as food security measures. New earth observation satellites offers high spatial resolution with different variety of spectral features and fast revisit rates of the same location, and this provide an advantage to manage the natural resources. In this study, we examined the impacts of texture features of red-edge spectral band for classifying crops in Aydin Province, located in Turkey. The study area is located in Aegean region of Turkey and comprised of intensive cultivation areas. Different texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) from Gray Level Co-occurrence Matrix (GLCM) using different window sizes have been computed. Support Vector Machines (SVM), is one of the machine learning algorithms and recently preferred since its superior image classification performance, has been carried out here for the classification step. Radial Basis Function (RBF) has been selected here as a kernel type for SVM classification. We also compared the classification results of texture features with original RapidEye imagery. Our experimental results indicate that the size of processing window play an important role on the classification accuracy of the red-edge band texture features and the classification accuracy of RapidEye original bands outperformed the others as 89.90% overall accuracy.

INFORMATION EXTRACTION WITH OBJECT BASED SUPPORT VECTOR MACHINES AND VEGETATION INDICES

by Fusun Balik Sanli, Mustafa Ustuner, and Saygin Abdikan

Information extraction through remote sensing data is important for policy and decision makers as... more Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage

by Fusun Balik Sanli and Aliihsan Sekertekin

APPLIED SCIENCES, 2019

The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challe... more The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R 2) of 0.82 and 0.81 indicate that there is a strong relationship between the maize height and SAR parameters including VV + VH and VV, respectively. The maize CC is well correlated with VV parameter (R 2 = 0.73), but it is observed that at the later growing stage the correlation became weaker. This means that the sensitivity decreases with increasing vegetation cover growth. Compared to maize, the sensitivity of SAR parameters to wheat variables is often good at the early stage. However, the highest correlation with wheat height represented by Alpha (α) decomposition parameter (R 2 = 0.67). The sunflower height has an insignificant correlation with the majority of SAR parameters and only VH polarization shows low sensitivity (R 2 = 0.31). The sunflower CC shows relatively higher correlation with VV polarization (R 2 = 0.46) at the early stage while no considerable correlation is observed at the later stage. It is found that Sentinel-1 has a high potential for estimation of crop height and CC of the maize as a broad-leaf crop. The same is not true for sunflower as another broad-leaf crop.