Cankut Ormeci

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.

Edge and fine detail preservation in SAR images through speckle reduction with an adaptive mean filter

A speckle reduction algorithm, the Edge Map‐Directed Adaptive Mean (EMDAM) filter, is studied in this paper. It adapts the ordinary mean filter according to the scene heterogeneity. Edge‐crossing maps determined by an edge detector are used to find the largest homogeneous subregion in the moving filter window. Then, the mean filter is adapted only to this homogeneous part of the moving filter window and applied if no edge crossing is found. We compared some filters in the literature to the EMDAM filter using two examples: a 1997 JERS‐1 synthetic aperture radar (SAR) image of Tuzla, Istanbul and a computer‐simulated SAR image. The filter performance was assessed both quantitatively and qualitatively. We found that the EMDAM filter preserves textures and details while reducing speckle to a desired level. A new testing quantity, the Quality Factor (Q), is also introduced.

Modelling land cover change impact on the summer climate of the Marmara Region, Turkey

Landscape characteristics of the Marmara Region, Turkey, changed significantly after the 1980s as a result of rapid industrialisation and population increase. To investigate the effects of these land cover changes on the summer regional climate, we implemented 1975 and 2005 land cover maps of the region produced from Landsat images into the Weather Research and Forecasting (WRF) regional climate model. Urbanisation and conversion from forest to barren areas increased average temperatures by 0.5-1.5°C. Significant precipitation changes could not be detected. The average wind magnitude decreased by 0.3-0.9 m/s over the city and surrounding areas.

Determination of chlorophyll-a amount in Golden Horn, Istanbul, Turkey using IKONOS and in situ data

The objective of this research was to explore an accurate and fast way of estimating chlorophyll-a amount, a water quality parameter (WQP), using IKONOS satellite sensor image and in situ measurements. Since the in situ data of WQPs are limited with the number of sampling locations, deriving a correlation between these measurements and remotely sensed image allows synoptic estimates of the related parameter over large areas even if the areas are in remote and inaccessible locations. In this study, simultaneously collected satellite image data and in situ measurements of chlorophyll-a were correlated using multivariate regression model. Different experiments were designed by changing the numbers and distributions of in situ measurements. Regression coefficients of each design and differences between model-derived data and in situ measurements were calculated to find out the optimum design to produce chlorophyll-a map of study region. Results illustrated that both the number and distribution of in situ measurements have impact on regression analysis, therefore should be selected attentively. Also, it is found that IKONOS imagery is an efficient and effective source to derive chlorophyll-a map of the large areas using limited number of ground measurements.

Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

Accurate spatial interpolation (SI) of climate data is vital for the management and supervision of natural resources and agriculture. Owing to the lack of an adequate number of meteorological stations, meteorological-station-data-based SI methods may not always reflect the real climatic conditions of an interpolated point. Land surface temperature (LST) data obtained from satellite sensors enable the characterization of meteorological conditions of areas without meteorological stations. The aim of this article is to present a new modified inverse distance weighting (M-IDW) SI method for air temperature (Ta), total precipitation (Pt), and relative humidity (RH) by integrating Landsat LST data with meteorological station data for the interpolation process. The M-IDW approach is based on the correlation relationship between the climate data and LST at each meteorological station, which is incorporated into the traditional IDW to improve the estimation of the climate data at an interpolation location of interest. The proposed method, M-IDW, is applied for the interpolation of long years’ (i.e. long term) monthly average (LYMA) Ta, Pt, and RH climate data from meteorological stations in the Eastern Thrace region, which is 23,764 km2, located in southeast Europe. The LYMA of the Ta, Pt, and RH has been constructed using data obtained from 27 meteorological stations that had functioned at least 10 years between 2000 and 2012 and from the corresponding satellite data. The outputs of the interpolation are in the form of LYMA, so are the analysed climate data. The spatial resolution of the predicted surface was taken as 30 m, similar to the original data presented by United States Geological Survey. The results were compared with those of the standard IDW, ordinary kriging (OK), and ordinary cokriging (OCK) methods to analyse the performance and accuracy of the proposed method. The results show that the proposed M-IDW method has the potential for SI of climate data, if enough number of images and cloudless pixels are incorporated in the LYMA LST computation. The proposed method, in general, yields better results than standard IDW and OK methods, especially during spring, summer, and partially in autumn for the interpolation of Ta (with 0.72°C, 0.53°C, and 0.66°C root mean square error (RMSE) values, respectively) and Pt (with 11.07 mm, 7.64 mm, and 4.85 mm RMSE values, respectively). OCK and M-IDW results were comparable in spring, summer, and autumn where M-IDW was slightly better for Ta in autumn and spring and was slightly better for Pt in summer. For the RH interpolation, although M-IDW results were found to be close to the results of IDW, OK, and OCK in spring, summer, and autumn, for the overall seasonal interpretation, the RMSE values of M-IDW were worse than the others. In general, M-IDW yields worse results for the winter months, which in turn is related to cloudiness and availability of satellite images.

Channel Regulation Monitoring along the Lower Meric River, Turkey, Using Landsat-7 Enhanced Thematic Mapper Data

Abstract River channelization is a widespread practice including river bed regulation worldwide. A primary goal of channelization is to rehabilitate the floodplain of rivers, modifying the energy regime and sediment transport capacity. This paper presents an application of this process carried out in the floodplain of the Lower Meric River, Turkey. The application was performed by constructing a new channel in two regions along the river in the 1970s. Thus, the river was straightened, isolating its meanders to reduce the floods. These channel regulations are summarized in the paper by using satellite data and historical maps. The Landsat-7 enhanced thematic mapper (2001) image is the base data for interpretation of the present condition of the river channel, and the historical geomorphologic maps are used for the detection of the river bed prior to channelization.

Water quality monitoring using satellite image data: a case study around the Salt Lake in Turkey

Rapid urbanization and increasing agricultural activities raises risks of surface water impoundment pollution at the Salt Lake which is one of the most important natural sources in Turkey. The Lake is a hazardous region for surface measurements especially in spring months due to its bottom covered with a 1 to 30 cm. salt layer. It is, therefore, necessary to make water quality assessment using remote sensing data. The applications of remote sensing techniques have been in use over the years for this purpose. In order to evaluate changes, in this paper, the water quality at the Salt Lake was examined by using remote sensing data. Firstly, Landsat (MSS, TM and ETM) and ASTER images were collected for providing multitemporal satellite data. After the image processing operations such as geometric correction, image classification, all resulting data were evaluated together. It is obvious from the remotely sensed and treated data that the water quality has retrogressed during the last two decades due to uncontrolled domestic, agricultural and industrial wastewaters. It is suggested that the waste discharges should be controlled and the surface water pollution monitoring should be carried out using satellite technology in addition to ground measurements.