Mustafa Dihkan

Spatio-temporal shoreline changes along the southern Black Sea coastal zone

The Black Sea is experiencing human-induced ecological degradation along its coastal zone. As part of the Coastal Zone Management strategy, regular monitoring of shoreline changes plays an important role. Growing population in coastal zones creates extra pressure on shores which leads to the creation of new land filling areas (accretion). Moreover, a recently completed highway construction caused a catastrophic impact on coastal areas of the southern part of the Black Sea and needs to be taken into account. The main purpose of this article is to determine the pattern of shoreline changes along the Turkish coast of the Black Sea. Remote sensing is used to identify and evaluate hot spots of shoreline changes. A developed algorithm automatically extracts the coast line position by processing satellite images covering the period of 1987 to 2001. The maximum and minimum shoreline changes in terms of erosion and accretion were 118 to 53 and 95 to 635 m, respectively. More significant changes have been determined in the eastern part than the western part of the Black Sea. The locations with higher changes were mainly accretion regions along the study area. It can be concluded that coastal movements mainly caused by humans induced impacts at the coasts of the Black Sea. Coastal accretion is significant at the most part of the Turkish Black Sea coast and might be related to a recently constructed international highway.

Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique

Tea (Camellia sp.) and its plantation are very important on a worldwide scale as it is the second-most consumed beverage after water. Therefore, it becomes necessary to map the widely distributed tea plantations under various geographies and conditions. Remote-sensing techniques are effective tools to map and monitor the impact of tea plantation on land-use/land-cover (LULC). Remote sensing of tea plantations suffers from spectral mixing as these plantation areas are generally surrounded by similar types of green vegetation such as orchards and bushes. This problem is mainly tied to planting style, topography, and spectral characteristics of tea plantations, and the side effects are observed as low classification accuracies after the classification process. In this study, to overcome this problem, a three-step approach was proposed and implemented on a test area with high slope. As a first step, spectral and multi-scale textural features based on Gabor filters were extracted from high resolution multispectral digital aerial images. Similarly, based on the wavelength range of the sensor, a modified normalized difference vegetation index (MNDVI) was applied to distinguish the green vegetation cover from other LULCs. The second step involves the classification of multidimensional textural and spectral feature combinations using a support vector machine (SVM) algorithm. As a final step, two different techniques were applied for evaluating classification accuracy. The first one is a traditional site-specific accuracy assessment based on a confusion matrix calculating statistical metrics for different feature combinations. The overall accuracy and kappa values were calculated as 93.68% and 0.92, 93.82% and 0.92, and 97.40% and 0.97 for LULC maps produced by red, green, and blue (RGB), RGB + MNDVI, and RGB + MNDVI + Gabor features, respectively. The second accuracy assessment technique was the pattern-based accuracy assessment. The technique involves polygon-based fuzzy local matching. Three comparison maps showing local matching indices were obtained and used to compute the global matching index (g) for LULC maps of each feature set combination. The g values were g(RGB) (0.745), g(RGB+MNDVI) (0.745), and g(RGB+MNDVI+Gabor) (0.765) for comparison maps. Finally, based on accuracy assessment metrics, the study area was successfully classified and tea plantation features were extracted with high accuracy.

Automatic detection of coastal plumes using Landsat TM/ETM+ images

Riverine fresh water outflows create coastal plumes that are distinguished from surrounding sea water by their specific spectral signature. Coastal waters are unique ecosystems, and they are very important in terms of living resources and oceanographic processes. River plumes and coastal turbid waters have important effects on coastal marine ecosystems, and they also influence marine life cycles, sediment distribution, and pollution. Remote sensing and digital image-processing techniques provide an effective tool to detect and monitor these plume zones over large areas. The primary goal of this study was automatic detection and monitoring of coastal plume zones using multispectral Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) imagery. For that purpose, the proposed algorithm exploits spectral features of the multispectral images by using feature extraction and decision-making steps. The procedure has two main stages: (1) some pre-processing operations were applied to the images in order to extract the plume core reflectance values with maximum turbidity and offshore water mass reflectance values; (2) a k-means algorithm was applied with initial seed values of reflectance computed from the pre-processing stage to classify coastal plume zones. Spatial pattern and variability of optical characteristics of coastal plume zones were then defined following the results of the classification process. The algorithm was automatically applied in three different regions with three multispectral Landsat images acquired on different dates, and yielded a very high classification accuracy in detecting coastal plume zones.

Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm

Recent developments in the field of remote sensing have introduced new sensor technologies in usage of LiDAR, SAR, and high-resolution optical data. Classification performance is expected to increase through combining these various data sources. The purpose of this study is to develop a new approach for automatic extraction of buildings in urbanized and suburbanized areas. For this purpose, multi-feature extraction process including the spatial, spectral, and textural features were conducted on the very high spatial resolution multispectral aerial images and the LiDAR data set. SVM algorithm was trained by using this multi-feature data, and the classification was performed. After the classification of building and non-building, objects were extracted with high accuracy for the test areas. As a result, it has been proven that multi-features derived from combination of optical and LiDAR data can be successfully applied to solve the problem of automatic detection of buildings by using the proposed approach.

Automatic detection of building roofs from point clouds produced by the dense image matching technique

ABSTRACT Three-dimensional (3D) spatial information of object points is a vital requirement for many disciplines. Laser scanning technology and techniques based on image matching have been used extensively to produce 3D dense point clouds. These data are used frequently in various applications, such as the generation of digital surface model (DSM)/digital terrain model (DTM), extracting objects (e.g., buildings, trees, and roads), 3D modelling, and detecting changes. The aim of this study was to extract the building roof points automatically from the 3D point cloud data created via the image matching techniques with optical aerial images (with red, green, and blue band (RGB) and infrared (IR)). In the first stage of the study, as an alternative to laser scanning technology, which is more expensive than optical imaging systems, the 3D point clouds were produced by matching high-resolution images using a Semi Global Matching algorithm. The normalized difference vegetation index (NDVI) values for each point were calculated using the spectral information (RGB + IR) in the 3D point cloud data, and the points that represented the vegetation cover were determined using these values. In the second stage, existing ground and non-ground points that were free of vegetation cover were determined within the point cloud. Subsequently, only the points on the roof of the building were detected automatically using the proposed algorithm. Thus, points of the roofs of buildings located in areas with different topographic characteristics were detected automatically detected using only images. It was determined that the average values of correctness (Corr), completeness (Comp), and quality (Q) of the pixel-based accuracy analysis metrics were 95%, 98%, and 93%, respectively, in the selected test areas. According to the results of the accuracy analysis, it is clear that the proposed algorithm is very successful in automatic extraction of building roof points.

Evaluation of urban heat island effect in Turkey

Recently, the rate of urbanisation has accelerated due to increasing population density which causes unexpected environmental disturbances and problems. One of the major problems encountered to date is the change in the land use/cover (LULC) structure. Increasing the impervious surface cover has changed microclimatic properties of urbanised areas by altering their thermal characteristics. One of the most important problems facing urban planning today is the phenomena known as the urban heat island (UHI), which is largely due to the changing the character of LULC. In this study, to create a national picture of the UHI structure in Turkey, seven cities, namely Istanbul, Bursa, Ankara, Izmir, Gaziantep, Erzurum and Trabzon, each located in different climatic regions of Turkey, were investigated. The Gaussian fitting technique was applied to characterise the UHI effect in the seven cities in the study. An original contribution of the current study was that the rural reference temperature surface was automatically determined by a proposed algorithm including the automatic masking of input data, as well as the application of iterative Gaussian low-pass filtering during the fitting procedure. Within this context, the daytime and nighttime surface urban heat island (SUHI) effect was modelled and temporally analysed from 1984 to 2011 for all the sub-regions using remote sensing techniques. Furthermore, atmospheric UHI was also investigated using the mobile transect method. The findings from this study suggest that UHI is a major environmental problem in urbanised areas, both atmospheric UHI and SUHI were detected in all cities in the study area, and this problem was found to have rapidly increased from 1984 to 2011. Finally, as inferred from the multiple regression results, it can be concluded that the UHI problem in Turkey might have resulted from the altered LULC structure, as well as anthropogenic pressure on and interference in city planning geometries.

An image analysis method to detect CSD on rocks with adjusted color images

Purpose – The purpose of this paper is to provide crystal size distribution (CSD) using photogrammetric and image analysis techniques. A new algorithm is proposed to detect CSDs and a comparison is carried out with conventional watershed segmentation algorithm. Design/methodology/approach – Polished granite plates were prepared to designate the metrics of CSD measurements. There are many important metrics for measurements on CSD. Some of them are orientation, size, position, area, aspect ratio, convexity, circularity, perimeter, convex hull, bounding box, eccentricity, shape, max-min length of CSDs fitted and corrected ellipse, and population density in a per unit area. Prior to image processing stage, camera calibration was performed to remove the image distortion errors. Image processing techniques were applied to corrected images for detecting the CSD parameters. Findings – The proposed algorithm showed the improved preservation of size and shape characteristics of the crystal material when compared t...