Elif Sertel

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Use of Semivariograms to Identify Earthquake Damage in an Urban Area

The 1999 Izmit earthquake (Mw 7.4) on the North Anatolian Fault Zone resulted in severe damage to the urban areas of Izmit, Adapazari (Sakarya), Golcuk, and Yalova. A semivariogram approach was used to quantify earthquake-induced spatial variation and thereby the degree of damage in the Adapazari inner city. Semivariograms were calculated for 24 transects on SPOT high resolution visible infrared (HRVIR) panchromatic images obtained before and after the earthquake. The differences between the pre- and postearthquake semivariogram shape and measures of shape, range, nugget, and sill were related to the severity of earthquake damage

Geometric correction accuracy of different satellite sensor images: application of figure condition

In this study, the figure condition method was introduced to analyse the accuracy of geometric correction. Figure condition denotes the transformation ability of estimated model parameters for a given transformation model, and it can be used in a geometric correction procedure. To study the figure condition, multisensor satellite images were geometrically corrected using ground control points obtained by different methods. The accuracy of each geometric model was analysed by means of the root mean square error of unit weight and variance–covariance matrix of unknown parameters. Then, an error propagation law was applied to the geometric model in order to investigate the transformation ability of the model parameters and estimate error values of geometric correction for the whole image surface. The results of the research demonstrated that the figure condition can be applied to geometric correction, and error values of the whole study area can be obtained with this new approach without using check points.

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.

Estimating Daily Mean Sea Level Heights Using Artificial Neural Networks

Abstract The main purpose of this study is to estimate daily mean sea level heights using five different methods, namely the least squares estimation of sea level model, the multilinear regression (MLR) model, and three artificial neural network (ANN) algorithms. Feed forward back propagation (FFBP), radial basis function (RBF), and generalized regression neural network (GRNN) algorithms were used as ANN algorithms. Each method was applied to a data set to investigate the best method for the estimation of daily mean sea level. The measurements from a single tide gauge at Newlyn, obtained between January 1991 and December 2005, were used in the study. Daily mean sea level estimation was carried out considering the precedent 8-day mean sea level data of the same station, the average and standard deviation of each day for a 15-year period, and 6 monthly and yearly periodicities in tidal variations. Results of the study illustrated that the ANN and MLR models provided comparatively better results than the conventional model used for estimating sea level, least squares estimation. FFBP, RBF, and MLR algorithms produced significantly better results than the GRNN method, and the best performance was obtained using the FFBP algorithm. From the graphs and statistics, it is apparent that neural networks and MLR solution can provide reliable results for estimating daily mean sea level.

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.

Multi-Temporal Analysis and Mapping of Coastal Erosion Caused by Open-Mining Areas

Coastal zones are exposed to erosion due to natural and human-induced activities around the world. The land use of the coastal zone in the northern part of Istanbul, Turkey, has been changing due to open-pit coal mining begun in 1980. The objective of this study is to determine the changes that occurred in a selected coastal zone by utilizing interpretations of multi-temporal LANDSAT satellite data. Satellite images of the zone taken during the years 1984, 1992, and 2001 were transformed to the universal transverse mercator (UTM) coordinate system, and 17 bands of images for each of these years were interpreted using layer-stack method. A new red, green, and blue (RGB) image including infrared band of each year was created. These findings show that 304.7 ha area of sea was filled with soil between years 1984 and 1992. However, the total area filled between 1984 and 2001 was only 67.7 ha, due to the fact that 237.0 ha was removed by coastal erosion after year 1992.

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.

Vineyard parcel identification from Worldview-2 images using object-based classification model

Abstract Accurate identification of spatial distribution and characteristics of vineyard parcels is an important task for the effective management of vineyard areas, precision viticulture, and farmer registries. This study aimed to develop rule sets to be used in object-based classification of Worldview-2 satellite images to accurately delineate the boundaries of vineyards having different plantation styles. Multilevel segmentation was applied to Worldview-2 images to create different sizes of image objects representing different land cover categories with respect to scale parameter. Texture analysis and several new spectral indices were applied to objects at different segmentation levels to accurately classify land cover classes of forest, cultivated areas, harvested areas, impervious, bareland, and vineyards. A specific attention was given to vineyard class to identify vine areas at the parcel level considering their different plantation styles. The results illustrated that the combined usage of a newly developed decision tree and image segmentation during the object-based classification process could provide highly accurate results for the identification of vineyard parcels. Linearly planted vineyards could be classified with 100% producer’s accuracy due to their regular textural characteristics, whereas regular gridwise and irregular gridwise (distributed) vineyard parcels could be classified with 94.87% producer’s accuracy in this research.