Uydu görüntüleri ile peyzaj tiplerinin belirlenmesinde mekansal çözünürlüğün etkisi
Effect of spatial resolution of satellite image defining landscape types
Yazar: BERK HACIAĞAOĞLU
Danışman: PROF. DR. AYŞE FİLİZ SUNAR
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2013
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry
Dizin: Coğrafi bilgi sistemleri = Geographical information systems ; Peyzaj = Landscape ; Peyzaj analizi = Landscape analysis ; Uzaktan algılama = Remote sensing ; Veri tabanı = Database ; Veri tabanı tasarımı = Database design

Özet:
Uzaktan algılama uyduları öncelikle askeri amaçlara yönelik fırlatılmış daha sonra sivil kullanıma açılmıştır. Uydular tarafından algılanan yeryüzü dijital uydu görüntüsü olarak elde edilir ve bu görüntüleri etkileyen birçok faktör vardır. Bunların en başında mekânsal çözünürlük gelmektedir. Mekânsal çözünürlük görüntüde ayırt edilebilir detay seviyesini gösteren bir özelliktir. Diğer bir ifade ile bir uydu görüntüsünde görünen detaylar, algılayıcının mekânsal çözünürlüğüne bağlı olup bu değer, görüntünün en küçük elemanı olan pikselin yeryüzünde kapladığı alana karşılık gelmektedir.
Avrupa'nın doğal ve kültürel peyzajlarının bir bütün olarak korunması, yönetilmesi ve planlanması konusunda bir çerçeve sözleşmesi olan Avrupa Peyzaj Sözleşmesi (APS) ülkemiz tarafından 2000 yılında imzalanmış, 2003 yılında 4881 sayılı Kanun ile onaylanarak yürürlüğe girmiştir.  Avrupa peyzaj sözleşmesi ile peyzaj tiplerinin belirlenmesinde uzaktan algılamanın ve Coğrafi Bilgi Sisteminin önemi vurgulanmıştır.
Bu tezin giriş kısmında uzaktan algılama, Coğrafi Bilgi Sistemi ve peyzaj hakkında genel bilgiler verilmiş ve tanımlamalar yapılmıştır. Birinci ve ikinci bölüm olan "Uzaktan algılamanın temelleri" ve "Peyzaj tipleri ve uzaktan algılama" kısmında ise uzaktan algılama hakkında daha ayrıntılı bilgiler ve tanımlamalar yapılmış, uzaktan algılama ile peyzaj ve peyzaj tiplerinin ilişkisinin öneminden bahsedilmiştir. Tezin uydu sistemleri bölümünde çalışmada kullanılan RapidEye uydusunun ve uydu görüntüsünün özellikleri anlatılmıştır. Ardından dijital veri işleme kısmında, uygulamada kullanılan raster ve vektör veri işleme ve düzenleme yöntemleri açıklanmıştır. Tezin uygulama kısmına, çalışmanın uygulama alanı ve kullanılan veriler hakkında bilgi verilerek giriş yapılmıştır. Ardından mekânsal çözünürlüğü 5 metre olan RapidEye uydu görüntüsünün mekânsal çözünürlüğü 30 metre ve 60 metreye yeniden örneklenmesi, bu verilerin kontrollü sınıflandırma yöntemi ile sınıflandırması alanın eşyükselti eğrileri yüzey modellenmesi yapılarak dijital yükseklik modelinin üretilmesi ve bu haritalardan yükseklik, bakı ve eğim haritalarının oluşturulması aşamaları detaylı bir şekilde açıklanmıştır. 
Son işleme adımı olarak, veriler raster formattan vektör formata çevrilmiştir ve tüm alan için oluşturulan 30 metre kenara sahip karelere (gridlere) mekânsal olarak işlenmiş ve belirlenen sınıf kodlarıyla kodlanmıştır. Bu işlem veri tabanında boş bir mekansal tablo oluşturulup tüm verilerin işlenmesi ile devam etmiştir. Diğer bir ifade ile çalışma alanının tüm bilgilerini içeren personal geodatabase formatında mekânsal veri tabanı oluşturulmuştur. Veri tabanında aynı koda sahip gridlerin olduğu veritabanı satırları her kod yanlızca bir satıra olacak şekilde gruplanıp birleştirilerek peyzaj karakter tipleri belirlenmiştir. Oluşturulan mekansal veritabanı ile sql sorguları yapılarak istenilen bilgiye ulaşılabilmektedir.
Sonuç kısmında, farklı mekânsal çözünürlüklü uydu görüntüsü sınıflandırması ile belirlenen peyzaj karakter tipleri alansal, şekilsel ve konumsal karşılaştırılarak çözünürlüğün etkisi irdelenmiştir.

Abstract:
Firstly, remote sensing satellites were using just for military purposes. After that, remote sensing satellites were opened for use for civil applications. Remote sensing is the sensing technique of integration sensors on planes and satellites without physical touch. Remote sensing senses that human being can't see a large spectrum of electromagnetic waves with their eyes and also senses large areas depending on satellites specifications. With this technique, remote sensing has lot of benefits such as distinguish objects more than human being. 
Electromagnetic waves have a large scale spectrum with nanometers to meters, kilometers. Human eyes can only senses optical electromagnetic spectrum area that is starting with 400 nanometers to 700 nanometers. On the other hand, remote sensing senses nearly all electromagnetic spectrum depending on sensor types. For example, with infrared region of electromagnetic spectrum, thermal objects can be distinguished with their temperature and with microwave of electromagnetic spectrum, objects that under 1,2 centimeters of soil can be detectable. Remote sensing satellites convert earth sensing to digital data. There are lots of factors that affect digital images.
 Satellites can senses the earth and there are so many factor of affecting satellite images such as spatial resolution, spectral resolution and radiometric resolution. The first and the most important factor is spatial resolution. Spatial resolution shows recognizable objects size. In remote sensing, spatial resolution is typically limited by diffraction, as well as by aberrations, imperfect focus, and atmospheric distortion. The ground sample distance (GSD) of an image, the pixel spacing on the Earth's surface, is typically considerably smaller than the resolvable spot size.
Europe's protection, manage and plan  of natural and cultural landscape is begun with European Landscape Convention is signed by Turkey in 2000 and with number of low 4881 European Landscape Convention is came in force in 2003. Geographic Information System and remote sensing is getting more and more important with European Landscape Convention.
In this thesis, general information about Geographic Information System and landscape are given and thesis consists of some of definition about them. Second of thesis consists of remote sensing fundamentals and some important definitions about remote sensing regarding landscape and geographic information system.  Electromagnetic spectrum, atmosphere and energy relations such as absorbing and scattering energy, earth and energy relations such as albedo, spectral reflection, object and spectral reflection relations. Third part of thesis is about landscape definitions regarding the remotes sensing and Geographic Information System.
 Landscape types and its importance is given in this part. In this thesis, remote sensing Geographic Information Systems benefits are mentioned by giving examples. Also there is an explanation about remote sensing importance of specifying landscape types with giving two example. 
Landscape character shows visual landscape analyses and gives natural and cultural landscape elements for defining characters. Natural landscape elements are topography, vegetation, geologic formations and water surfaces and cultural landscape elements are human based elements mass-space relations, circulation networks such as railways, architecture styles and other all superstructure objects such as electric poles.
Fourth part of thesis consists of definition of remote sensing satellite systems. There are general definitions about remote sensing satellites and their specifications such as spatial, spectral and radiometric resolution and satellite orbits. Detailed definition of satellites (RapidEye and Landsat) which is used in this thesis is given for the understanding effects of satellites clearly. There are two main satellite orbit such as sun synchronised and earth synchronised. RapidEye satellite is sun synchronised satellite that provides monitoring all of world. 
Fifth part of thesis explains digital image processing such as raster image data and spatial, spectral, radiometric and temporal resolution definition. This part includes digital satellite image processing levels and levels' definitions. These levels are defined by satellite image processings. Level 1 satellite image data is raw data and eliminated geometric and radiometric errors. Level 2 satellite image data is rectified data with control points. Level 3 satellite image data is orthorectified data that consist of elevation of ground control points or digital elevation model.
In this project, RapidEye satellite image is used with digital satellite image processing level. Level 3. Orthorectify satellite image is used with digital elevation model that generates from radar satellite data. Also normalized difference vegetation index is generated from RapidEye satellite image for differentiate vegetation clearly. There are steps of satellite image processing such as classification types and their methods and classification accuracy. Fifth part also includes vector data type and vector data definitions such as topology, spatial database system.
In spatial database, Esri Shapefile is used for data storage and all sql queries are created from Esri, Arcgis, Arcmap software. The database is esri geodatabase that is the common data storage and management framework for ArcGIS. It combines spatial data with data repository to create a central data repository for spatial data storage and management. With this database system, data stores centralized location, roles, relationships and topologies are defined and multiusers can reach at same time. Each data model uses commonly adopted points, lines, and polygons (spatial representations), classifications, and map layer specifications that can be implemented in any GIS. Each data model specifies the commonly used integrity rules for key data layers and feature classes.
Sixth part is application of thesis. Beginning of application, 5 meters spatial resolution RapidEye satellite image is used for artificially produce different spatial resolutions of satellite image such as 30 and 60 meters for effect of spatial resolution defining landscape types. 
Working area is Kapısuyu Havzası 41°50'57.29"-41°44'39.22" North parallel and 32°51'44.74"-32°39'30.18" East meridians. Working area consists of Kömeç, Yeniköy, Kapısuyu, Kirlikmüslim, Hacıköy, İlyasgeçidi, Başköy, Kaleköy, Sarıderesi, Ziyaret, Kavaklı and Aydınlar villages with 51.997 km2. There are many canyons, valleys, caves, waterfalls in this working area. Working area has many elevation differences from sea level to 1381 meters. Kapısuyu Basin is between the mountains that are perpendicular to Black Sea. Kapısuyu Basin also has different valleys and hills. Elevation changes low to high from sea to Küre Mountains National Park. Hills have valley between each other.
Resampling of RapidEye images is done using average value method from 5-meter spatial resolution to 30 and 60 meters spatial resolution. There are 3 methods of interpolation such as bilineer interpolation, cubic interpolation and nearest neighbor resambling for higher spatial resolution and combination methods for producing lower spatial resolution. In this thesis, new pixels (30, 60 meters) are larger than original pixels (5 meters), so mean combination method is applied for resampling the satellite image. 
Classification methods depends on reflecting or emitting electromagnetic energy from objects. With this method, the different reflecting electromagnetic energy can be detected to distinguish objects to other. There are 2 main classification methods such as supervised classification and unsupervised classification. In this thesis, supervised classification is applied with sample pixels that we know from the field work or the other maps that are produced with different methods.
Classification is done with maximum likelihood method with 8 classes with 62960 sample pixels (5 meters spatial resolution data). Sample areas are same spatial areas for 3 different satellite images for the best results. Supervised classification is done with all different spatial resolution of satellite images for comparison results. 
Classification accuracy is calculated for all different spatial resolution of satellite images. After the classification, all classes in raster format is converted to vector format to work with the other vector format together. 
Digital elevation model that includes contour lines 10 meters by 10 meters is used for elevation, aspect and slope analyses. Elevation, aspect, slope analyses is used for classifying by intervals and they are coded. Geology and Hydrogeology maps are prepared from other foundations for this application. These maps are classified by intervals they are coded. After arranging these vector format maps, spatial geodatabase is created with spatial tables and indexes (for sql queries). Database format is personal geodatabase and all tables have poly column.
For base plate, 30 meters by 30 meters squares are created for all work area and squares shows in database with one line. All classified maps are spatially joined with these squares for defining landscape types. After spatial join, all lines that have same coded columns are merge. After merging, all lines show different landscape types in database. This method is applied for all 3 different spatial resolution satellite images with same other vector data.
Results are examined for comparison, 1582 landscape types with 5 meter spatial resolution satellite image, 1547 landscape types with 30 meter spatial resolution satellite image and 1595 landscape types with 30 meter spatial resolution satellite image is defined. On the other hand, a landscape type area is digitalize from satellite image in vector format and this vector data is compared with the other 3 landscape types with their shape and total area. Within the shape and total area, 30 meters spatial resolution satellite image classification gives the best result for this work.
In conclusion, for this work 30 meters spatial resolution satellite image gives best result because the other data resolutions are closer to 30 meters. While choosing satellite image, the other data have to consider with spatial resolution and their scale.