Uğur Avdan

Object-based water body extraction model using Sentinel-2 satellite imagery

ABSTRACT Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas. Since their spectral differences cannot be separated, in this paper a method that combines a pixel-based index and object-based method has been used on a Sentinel-2 satellite image with a resolution of 10 m. The method uses image segmentation on a multispectral image containing 13 bands. It also uses indexes used for extracting water bodies, such as the Normalized Difference Water Index (NDWI). Two study areas with different characteristics have been chosen, one mountainous and one urban region, both of them located in Macedonia. Using object-based techniques and pixel-based indexes, such as NDWI, the results from the NDWI have been improved by a kappa value of more than 0.5.

DETERMINATION OF BUILDING MATERIALS WITH IR-THERMOGRAPHY IN HISTORICAL BUILDINGS

Lately, IR thermography is frequently applied for important purposes in monitoring buildings such as energy leaking, ventilating and air-conditioning installations, electrical and mechanical installations, moisture detection etc. Nondestructive monitoring methods are essential in case of monitoring historical buildings. It is curtail not to destruct any part of the buildings, which are historically important. Therefore, nondestructive methods need to be used while monitoring this kind of buildings. In this study, IR-Thermography was used in order to distinguish different construction materials that were used to build the walls of a historical building. The thermal monitoring was made with Optris PI-450 long wave infrared camera. The results showed that IR monitoring can be useful to distinguish the building materials without destroying the investigated buildings’ plaster or facing. It is observed that the best results can be achieved on higher temperatures at IR-Thermography.

Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.

Address standardization using the natural language process for improving geocoding results

Abstract Geocoding is a tool that can be used in many areas such as the development of disaster prevention systems, crime mapping and the monitoring of communicable diseases, and which has gradually gained importance. However, the use of geocoding is not yet possible in some areas where it could serve as an effective tool, for various reasons such as inconsistencies in address formats, including inaccurate numbering systems, misspellings, the use of abbreviations and a lack of data that refers to the geocoding process. This study seeks to address these problems by way of a standardization process. To that end, it employs a method that decomposes addresses used as input data in geocoding, identifies spelling mistakes and abbreviations, and reorganizes the addresses through the Natural Language Process (NLP). As test data, the addresses of primary schools in the district of Eskisehir are taken. First the geocoding process is performed on the data set, using both Google geocoding API and ArcGIS geocoding API. Then, the addresses are reformatted into three address formats by applying standardization processes. Geocoding is performed on the re-formatted addresses and the results compared to the non-standardized results. The standardization used is shown to make a significant improvement in the accuracy of the geocoding results. The method used in this study is significant not only in increasing the accuracy of the geocoding process, but also in sustaining its wider use.

Geomorphology of the Mount Akdag landslide, Western Taurus range (SW Turkey)

ABSTRACT This paper presents the results of a geomorphological investigation of Mount Akdag landslide complex, located on the Western Taurus range, SW Turkey. The landslide, resulting in the collapse of a 5 km segment of Mount Akdag, covers an area of 9.8 km2 and has a volume of about 3 × 108 m3. The 1:15,000 scale geomorphological map (Main Map) of the Akdag landslide presented here was produced from a combination of field studies and unmanned aerial vehicle-based DSM, including aerial-photo interpretation and geomorphometric analysis of landforms. The map shows contemporary and past geomorphological hillslope processes and landforms associated with the landslide complex. Our new map suggests that the Akdag landslide was a complex rockslide consisting of multiple landslide types that developed through various movements. We found that 43 highly developed and active mass movements on the main body and side slopes of the landslide made a major contribution to the sediment input. The sediment produced in the upstream severely threatens the touristic site of Saklikent Canyon in the lower catchment via an increasing magnitude of torrents. This map will provide important data for local and national administrators regarding monitoring up-to-date mass-wasting processes, understanding the landslide and its related sedimentary dynamics, and implementing risk assessment and necessary precautions.

Water extraction technique in mountainous areas from satellite images

Abstract. Water monitoring is an important part of water resource management and has become an essential aspect of remote sensing. A number of indices have been developed for water extraction using satellite images. Even though all indices can extract the extent of a water body, none can do so without including a noise component, such as topographic shadows, cloud shadows, snow, ice, and buildup areas, all of which have spectrally similar characteristics under certain circumstances. In order to select the best index for water body extraction, several water indices have been compared. This paper proposes a method for extracting water bodies called the water extraction surface temperature index (WESTI). This method uses normalized difference water index (NDWI) and land surface temperature to eliminate the noise components, especially in mountainous and cold areas where other indices have very low accuracy. The results have shown that WESTI improves the NDWI results by removing more than 80% of topographic shadows, with an overall accuracy of 99% in all cases.

ALGORITHM FOR SNOW MONITORING USING REMOTE SENSING DATA

Snow cover is an important part of the Earth`s climate system so its continuous monitoring is necessary to map snow cover in high resolution. Satellite remote sensing is a science that successfully can monitor land cover and land cover changes. Although indexes such as Normalized Difference Snow Index (NDSI) has quite good accuracy, sometimes topography shadow, water bodies and clouds can be easily misplaced as snow. Using Landsat TM, Landsat +ETM and Landsat TIRS/OLI satellite images, the NDSI was modified for more accurate snow mapping. In this paper, elimination of the misplaced water bodies was made using the high reflectance of the snow in the 0.45 – 0.52 µm wavelength. Afterwards, the modified NDSI (MNDSI) was used for estimating snow cover through the years on the one of the highest mountains in Republic of Macedonia. The results from this study shows that the MNDSI accuracy is higher than the NDSI`s, totally eliminating the misplaced water bodies, and partly the one caused from topography and clouds. Also, it was noticed that the snow cover in the study area has not been changed drastically through the years. For future studies, the MNDSI should be validated on different study areas with different characteristics.

SPATIAL CHANGE ANALISYS BASED ON NDVI VALUES USING LANDSAT DATA: CASE STUDY IN TETOVO, MACEDONIA

The rapid urban growth of the cities is a result of the population growth and the negative effects on human health and quality life are obvious which makes the monitoring of the urban growth very important. In this study monitoring over the city Tetovo in the northwestern part of Republic of Macedonia was made using remote sensed satellite images from Landsat archive that is widely used for monitoring the Earth’s surface. An attempt of mapping the urban area on an annual basis from the period of 1984 to 2015 was made. Change detection in rapidly growing areas was made using the NDVI (Normalized Difference Vegetation Index) values that were classified for distinguishing the differences between urban and vegetated areas. The study area covers nine cadaster municipalities with 14.6 km. The results showed significant urban growth of 21.3% and 17.7% vegetation loss in the study area from 1984 to 2015.

LAND SURFACE TEMPERATURE CHANGE AFTER CONSTRUCTION OF THE KOZJAK DAM BASED ON REMOTE SENSING DATA

Remotely sensed images are a resource for researches in wide study area. Based on thermal remote sensing technique, Land Surface Temperature (LST) changes analyses were made after construction the Dam of Kozjak near the capital of Republic of Macedonia. Two Landsat 5 remotely sensed images from 1984 and 2011, were used before and after construction of the dam, respectively. The LST of the images was automatically calculated by using LST retrieving tool in Erdas IMAGINE software. The analyses were made using zonal statistics within the boundaries of the previous created buffers of 100 meters, up to 1 km around the dam. The results showed that, the surroundings Land Surface Temperature values has decreased maximum 8.5 °C and has also lowered the boundaries of the minimum, maximum and mean values of the region after the dam construction.