Bildiri Koleksiyonu
https://hdl.handle.net/11421/22619
2024-03-28T17:20:33ZUsing open source Geographic Information Systems and Remote Sensing softwares for geothermal explorations
https://hdl.handle.net/11421/22935
Using open source Geographic Information Systems and Remote Sensing softwares for geothermal explorations
Ayday, Can
Geothermal energy is defined as the internal temperature of the earth. The most important transportation agent which carries this internal energy to the surface is groundwater. Geothermal resources were created when the groundwater reach to the surface. Geothermal resources cannot exist obviously everywhere on the earth. In such places geothermal exploration should be considered first. Geothermal area is defined as the area located on the geothermal reservoir. Exploration of these area must come first for these studies. Use of satellite imagery of the region is the first step of the study. Lineament maps from satellite images of the region is obtained for subtracting the geothermal area where there is a high probability. Nowadays it is possible to distinguish geological units and soil temperature map which has the distinction of being the geothermal field by using satellite technology. Second step of the geothermal exploration is obtaining gas measurements and interpret these data for place likely to be the geothermal field. Beginning to geothermal drilling operation without adequate knowledge about the area without these steps often ends with a failure. Geographic Information System is the powerful method for the transformation of this field data into meaningful information. Large number of spatial data analysis and interpretation can be done by Geographic Information Systems (GIS) in a short time. Open source software is software used in Remote Sensing and Geographical Information Systems will assure the use of a large number of users of this method. The high side of this software allows many analysis capability of the data, it is also lack of license fees. In this study, the south of the Canakkale Province were interpreted according to geothermal field in terms of satellite imagery with remote sensing methods and gas measurement values obtained from the site with Geographical Information Systems. At the end, the possibility of geothermal field region was investigated. Open source Remote Sensing (RS) and Geographic Information System (GIS) software are used for this study.
et al.;Manila Observatory;National Mapping and Resource Information Authority (NAMRIA);Pacific Data Resources Asia;Philippine Institute of Volcanology and Seismology (PHIVOLCS);SRDP Consulting; 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 -- 24 October 2015 through 28 October 2015 -- -- 118634
2015-01-01T00:00:00ZRapid mapping of forested landslide from ultra-high resolution unmanned aerial vehicle data
https://hdl.handle.net/11421/22938
Rapid mapping of forested landslide from ultra-high resolution unmanned aerial vehicle data
Cömert, Resul; Avdan, Uğur; Gorum, T.
The Black Sea Region is one of the most landslide prone area due to the high slope gradients, heavy rainfall and highly weathered hillslope material conditions in Turkey. The landslide occurrences in this region are mainly controlled by the hydro-climatic conditions and anthropogenic activities. Rapid regional landslide inventory mapping after a major event is main difficulties encountered in this densely vegetated region. However, landslide inventories are first step and necessary for susceptibility assessment since considering the principle that the past is the key to the future thus, future landslides will be more likely to occur under similar conditions, which have led to past and present instability. In this respect, it is important to apply rapid mapping techniques to create regional landslide inventory maps of the area. This study presents the preliminary results of the semi-automated mapping of landslides from unmanned aerial vehicles (UAV) with object-based image analysis (OBIA) approach. Within the scope of the study, ultra-high resolution aerial photographs were taken with fixed wing UAV system on Aug 17, 2017 in the landslide zones which are triggered by the prolonged heavy rainfall event on August 12-13, 2016 at Bartin Kurucaşile province. 10 cm resolution orthomosaic and Digital Surface Model (DSM) data of the area were produced by processing the obtained photographs. A test area was selected from the overall research area and semi-automatic landslide detection was performed by applying object-based image analysis. OBIA has been implemented in three steps: image segmentation, image object metric calculation and classification. The accuracy of the resulting maps is assessed by comparisons with expert based landslide inventory map of the area. As a result of the comparison, 80% of the 240 landslides in the area were detected correctly
2018 Geoinformation for Disaster Management Conference, Gi4DM 2018 -- 18 March 2018 through 21 March 2018 -- -- 135177
2018-01-01T00:00:00ZSentinel-1 and Sentinel-2 data fusion for wetlands mapping: Balikdami, Turkey
https://hdl.handle.net/11421/22936
Sentinel-1 and Sentinel-2 data fusion for wetlands mapping: Balikdami, Turkey
Kaplan, Gordana; Avdan, Uğur
Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands' vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques
2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing -- 7 May 2018 through 10 May 2018 -- -- 136130
2018-01-01T00:00:00ZOBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA
https://hdl.handle.net/11421/22937
OBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA
Cömert, Resul; Kaplan, Onur
The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments
2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing -- 7 May 2018 through 10 May 2018 -- -- 136042
2018-01-01T00:00:00Z