QuickBird

Günümüzde, zengin bilgi içeriği sunan uydu görüntülerinin varlığı ve teknolojik gelişmelere bağlı olarak bu görüntülerin
işlenebilmesi, uzaktan algılama teknolojilerinin sahadaki kullanım yerini sağlamlaştırmaktadır. Uydu görüntüleri, uygun ölçekte
harita yapımı ve güncellenmesi, planlama, değişim ve gelişimin izlenmesi, risk yönetimi, analiz ve benzeri birçok alandaki
uygulamalara altlık teşkil edebilmektedir. Bu tür uygulamalardaki kritik kararların alınabilmesi için güncel, hızlı ve düşük
maliyetlerle elde edilebilen verilere ihtiyaç duyulması, uydu görüntülerinin bu alanlarda kullanılmasında tercih sebebidir. Bu
çalışmada, uzaktan algılama teknolojisi kullanılarak Zonguldak ili merkezindeki yerleşim bölgesinin zamansal gelişiminin izlenmesi
ve bu açıdan analizlerin ve gerekli değerlendirilmelerin yapılması amaçlanmaktadır. Bunun yanında, yerleşim bölgesi olarak sınırlı sayılabilecek olan Zonguldak ili merkezine alternatif olabilecek yeni yerleşim alanları araştırılacaktır. Böylelikle, Zonguldak gibi karışık bir jeolojik yapıya sahip bir kentsel alan için özellikle bölgedeki kamu-kurum ve kuruluşların, yerel yönetimlerin; yeni yerleşim alanlarının belirlenmesi, acil eylem planlaması vb. konularda ileriye yönelik projelerinin oluşumunda fayda sağlayacaktır.
Zonguldak ilinin değişik yıllara ait düşük, orta ve yüksek çözünürlüklü uydu görüntüleri, hava fotoğrafları, ortofotoları, vektör haritaları vb. çeşitli verileri araştırılacak ve uygun veri üzerinden alınabilecek bilgiler dâhilinde istenilen kentsel detayların çıkarılması sağlanacaktır. İlgili yazılımlar yardımıyla detaylar, vektörleştirme ve görüntü değerlendirme adımlarından olan sınıflandırma işlemi ile çıkarılacak ve alternatif yerleşim alanları belirlenerek yeni alanlar için öneriler verilecektir.

Aerial photography is an important source of high resolution remotely sensed data. Before 1970, aerial photographs were the only remote sensing data source for land use and land cover classification. Using these old aerial photographs improve the final output of land use and land cover change detection. However, classic techniques of aerial photographs classification like manual interpretation or screen digitization require great experience, long processing time and vast effort. A new technique needs to be developed in order to reduce processing time and effort and to give better results. Geographic object based image analysis (GEOBIA) is a newly developed area of Geographic In-formation Science and remote sensing in which automatic segmentation of images into objects of similar spectral, temporal and spatial characteristics is undertaken. Unlike pixel-based technique, GEOBIA deals with the object properties such as texture, square fit, roundness and many other properties that can improve classification results. GEOBIA technique can be divided into two main steps; segmentation and classification. Segmentation process is grouping adjacent pixels into objects of similar spectral and spatial characteristics. Classification process is assigning classes to the generated objects based on the characteristics of the individual objects. This study aimed to use GEOBIA technique to develop a novel approach for land use and land cover classification of aerial photographs that saves time and effort and gives improved results. Aerial photographs from 1954 of Valle Telesina in Italy were used in this study. Images were rectified and georeferenced in Arcmap using topographic maps. Images were then processed in eCognition software to generate land use and land cover map of 1954. A decision tree rule set was developed in eCognition to classify images and finally nine classes of general land use and land cover in the study area were recognized (forest, trees stripes, agricultural crops fields, olive tree fields, vineyards, mixed vineyards and olive trees, roads, rivers and urban areas). Results showed that in the segmentation process, grater values of scale, shape and compactness parameters are more suitable for classifying forest and river objects. While lower values of scale, shape and compactness parameters are more suitable for classifying objects of agricultural crops fields, olive tree fields, vineyards, mixed vineyards and olive trees, roads and urban areas. Applying map accuracy assessment test revealed high accuracy of the generated map of approximately 87%. Forest and river objects were recognized through the main spectral signal value of the object. Tree stripes were recognized through the object’s main spectral signal value, shape and size. Many different features were used for classifying the other classes’ objects. Among others, features like object’s density, texture, relative border to other classes, width, contrast and standard deviation were used in the classification process. The results showed that after developing the rule set process tree, classifying an aerial photograph can be faster by more than thirty times the classical techniques based on the computer machine speed. Generally, GEOBIA is a promising technique for classifying and interpreting gray scale aerial photographs.

Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images,
an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies.
Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.

Human- driven alteration of the Chaco strongly affects ecological patterns and associated processes at all spatial scales. To understand these modifications, sufficient methods for describing and quantifying high levels of landscape complexity caused by human activities in the region are urgently needed. Most methods involve the use of passive remote sensors, which capture complexity in only two dimensions (2D). A common 2D approach has been to calculate landscape metrics, such as Shannon’s Landscape Diversity Index. But, it is not clear what aspects of three dimensional (3D) vegetation structure are being captured by these metrics. 3D structure is known to be as important as or more important than 2D structure in determining landscape patterns of biodiversity of many groups of organisms. In addition, studies have used a limited number of coarsely defined land-cover classes to calculate metrics. Our question was: how is vegetation structure related to remote sensing attributes in an agricultural frontier in the subtropical dry Chaco, NW Argentina? A secondary question was to explore the relationships between traditional landscape metrics and the semivariogram, a geostatistical tool used to describe 2D complexity. We described landscape complexity from the panchromatic QuickBird band and measured vegetation structure in 22-1 ha plots across an agricultural frontier in the subtropical dry Chaco, northern Argentina. A total of 2,683 individual trees in 51 plant species and 21 families were measured in the field and 25,665 points were recorded to estimate foliage height diversity. Four landscape complexity groups were identified by a two-way cluster analysis using the 2D metrics. Four vegetation variables differed significantly among the 2D complexity groups: the standard deviation of the Enhanced Vegetation Index, the coefficient of variation of density per transect (CV density), mean tree diameter (DBH), and foliage height diversity (FHD). Largest patch index and semivariogram range were negatively related to CV density, mean DBH and FHD, while semivariogram sill, mean shape index, landscape shape index and number of patches were positively related to all three vegetation variables. Landscape metrics were not related to tree species diversity or density as previously shown, probably as a result of structural similarity among the dominant tree species in the Chaco biome.