Ugur Alganci

Parcel-Level Identifi cation of Crop Types Using Different Classifi cation Algorithms and Multi-Resolution Imagery in Southeastern Turkey

This research investigates the accuracy of pixel- and object-based classifi cation techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to fithe optimum data source for the identifi cation of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classifi cation (OBC). Post-classifi cation methods were applied to the output of pixel-based classifi cation to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classifi cation. OBC results provided comparatively the best performance for both parcel identifi cation and area estimation at 10 m and fi ner spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identifi cation and area estimation. In general, parcel identifi cation effi ciency was strongly correlated with spatial resolution while the classifi cation algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classifi cation algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns.

Water Quality Determination of Küçükçekmece Lake, Turkey by Using Multispectral Satellite Data

This study focuses on the analysis of the Landsat-5 TM + SPOT-Pan (1992), IRS-1C/D LISS + Pan (2000), and Landsat-5 TM (2006) satellite images that reflect the drastic land use/land cover changes in the Küçükçekmece Lake region, Istanbul. Landsat-5 TM satellite data dated 2006 was used for mapping water quality. A multiple regression analysis was carried out between the unitless planetary reflectance values derived from the satellite image and in situ water quality parameters chlorophyll a, total phosphorus, total nitrogen, turbidity, and biological and chemical oxygen demand measured at a number of stations homogenously distributed over the lake surface. The results of this study provided valuable information to local administrators on the water quality of Küçükçekmece Lake, which is a large water resource of the Istanbul Metropolitan Area. Results also show that such a methodology structured by use of reflectance values provided from satellite imagery, in situ water quality measurements, and basin land use/land cover characteristics obtained from images can serve as a powerful and rapid monitoring tool for the drinking water basins that suffer from rapid urbanization and pollution, all around the world.

An investigation on water quality of Darlik Dam drinking water using satellite images.

Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ) in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Küçükçekmece water reservoir, also in Istanbul City.

Comparison of pixel and object-based classification for burned area mapping using SPOT-6 images

On 30 May 2013, a forest fire occurred in Izmir, Turkey causing damage to both forest and fruit trees within the region. In this research, pre- and post-fire SPOT-6 images obtained on 30 April 2013 and 31 May 2013 were used to identify the extent of forest fire within the region. SPOT-6 images of the study region were orthorectified and classified using pixel and object-based classification (OBC) algorithms to accurately delineate the boundaries of burned areas. The present results show that for OBC using only normalized difference vegetation index (NDVI) thresholds is not sufficient enough to map the burn scars; however, creating a new and simple rule set that included mean brightness values of near infrared and red channels in addition to mean NDVI values of segments considerably improved the accuracy of classification. According to the accuracy assessment results, the burned area was mapped with a 0.9322 kappa value in OBC, while a 0.7433 kappa value was observed in pixel-based classification. Lastly, classification results were integrated with the forest management map to determine the effected forest types after the fire to be used by the National Forest Directorate for their operational activities to effectively manage the fire, response and recovery processes.

Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

Accuracy Assessment of Different Digital Surface Models

Digital elevation models (DEMs), which can occur in the form of digital surface models (DSMs) or digital terrain models (DTMs), are widely used as important geospatial information sources for various remote sensing applications, including the precise orthorectification of high-resolution satellite images, 3D spatial analyses, multi-criteria decision support systems, and deformation monitoring. The accuracy of DEMs has direct impacts on specific calculations and process chains; therefore, it is important to select the most appropriate DEM by considering the aim, accuracy requirement, and scale of each study. In this research, DSMs obtained from a variety of satellite sensors were compared to analyze their accuracy and performance. For this purpose, freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m, and Advanced Land Observing Satellite (ALOS) 30 m resolution DSM data were obtained. Additionally, 3 m and 1 m resolution DSMs were produced from tri-stereo images from the SPOT 6 and Pleiades high-resolution (PHR) 1A satellites, respectively. Elevation reference data provided by the General Command of Mapping, the national mapping agency of Turkey—produced from 30 cm spatial resolution stereo aerial photos, with a 5 m grid spacing and ±3 m or better overall vertical accuracy at the 90% confidence interval (CI)—were used to perform accuracy assessments. Gross errors and water surfaces were removed from the reference DSM. The relative accuracies of the different DSMs were tested using a different number of checkpoints determined by different methods. In the first method, 25 checkpoints were selected from bare lands to evaluate the accuracies of the DSMs on terrain surfaces. In the second method, 1000 randomly selected checkpoints were used to evaluate the methods’ accuracies for the whole study area. In addition to the control point approach, vertical cross-sections were extracted from the DSMs to evaluate the accuracies related to land cover. The PHR and SPOT DSMs had the highest accuracies of all of the testing methods, followed by the ALOS DSM, which had very promising results. Comparatively, the SRTM and ASTER DSMs had the worst accuracies. Additionally, the PHR and SPOT DSMs captured man-made objects and above-terrain structures, which indicated the need for post-processing to attain better representations.

Vineyard site suitability analysis by use of multicriteria approach applied on geo-spatial data

Abstract In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation.

A comparative analysis of gridding systems for point-based land cover/use analysis

Abstract For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria.

Comperative analysis of different geometric correction methods for very high resolution pleiades images

This research aims to analyze geometric correction accuracy of empirical Rational Function Model (RFM), empirical Rational Polynomial Coefficient (RPC) Refinement Model and physical Toutin Model applied to Very High Resolution (VHR) Pleiades satellite images. Two different pilot regions located in Turkey with different topographic characteristics were selected and analysis were conducted for these regions using ground control points obtained from 1:5000 scale aerial ortho-photos. Further analysis was conducted to analyze positional accuracy of resulting images using independent check points collected from aerial ortho-photos and Google Earth separately. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer Global Digital Elevation Model (ASTER GDEM) was used for all different geometric correction processes for both regions. The accuracy of each geometric correction model was presented by root mean square error (RMSE) metric. Results showed that first and second order RPC refinement models together with Toutin Model provided highly accurate results for both regions with RMSE between 3–6 meters while rational function model with three and four RPCs provided comparatively lower horizontal accuracy. Applying the rational function model with the 20 RPCs, generated from ground control points using PCI Geomatica, reduced the RMSE comparatively to a better level but increased the number of ground control points required to perform geometric correction with this model. Moreover, results indicated a possible effect of terrain structure and land use /cover types on the accuracy of geometric correction.