Zuhal Akyurek

Identification and mapping of submerged plants in a shallow lake using quickbird satellite data

Turkey is a country rich in lakes and wetlands--monitoring of all these will require advances in technology such as remote sensing. In this study, the aquatic plants of the large and shallow Lake Mogan, located in Central Anatolia were identified and mapped using high spatial resolution Quickbird imagery. As Lake Mogan is an important bird area the assessment of submerged plant species is of great value for ecosystem conservation and management. Quickbird multispectral image acquired on August 6, 2005 was geometrically corrected and a water mask was used based on strong absorption of Near Infrared (NIR) wavelengths by calm, clear and deep water. The water mask was applied using band reflectance values for a specific pixel satisfying the conditions of band decreasing property (Green>Red>NIR) and NIR<NIR(threshold). Unsupervised classification was applied to the wetland-only image to identify submerged plant vegetation classes. Spectral similarity among the isodata classes was used to decrease the number of the classes to the available species in the lake. Classification of Quickbird satellite data with an unsupervised classification technique provided high accuracy for identification and mapping of submerged plant coverage and of different submerged plant species and water classes (83.02% and 71.69%). Quickbird sensor data were found to be very useful for classifying submerged plants in a large and shallow lake. However, the closeness of the dates of field data collection to that of the sensor overpass and mixed pixel problem are the main limitations in the near-ideal conditions for submerged vegetation classification with satellite data having high spatial resolution.

The enhancement of the cell-based GIS analyses with fuzzy processing capabilities

In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS softwares to model uncertain real world, the natural variability in the environmental phenomena cannot be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used for modeling. An alternative approach is the fuzzy set theory, which provides a formal framework to represent and reason with uncertain information. In addition, linguistic variable concept in a fuzzy logic system is useful for communicating concepts and knowledge with human beings. FuzzyCell is a system designed and implemented to enhance commercial GIS software, namely ArcMap^(R) with fuzzy set theory. FuzzyCell allows users to (a) incorporate human knowledge and experience in the form of linguistically defined variables into GIS-based spatial analyses, (b) handle imprecision in the decision-making processes, and (c) approximate complex ill-defined problems in decision-making processes and classification. It provides eight membership functions, inference methods, methods for rule aggregation, operators for set operations and methods for defuzzification. The operation of FuzzyCell is presented through case studies, which demonstrate its application for classification and decision-making processes. This paper shows how fuzzy logic approach may contribute to a better representation and reasoning with imprecise concepts, which are inherent characteristics of geographic data stored and processed in GIS.

Monitoring snow-covered areas using NOAA-AVHRR data in the eastern part of Turkey

Abstract Monitoring snow-covered areas and estimating the snow water equivalent play an important role in predicting discharges during spring months, especially in regions where snow is an important resource. This study has been conducted in the Upper Euphrates River basin, of 10 200 km2 area, and elevation range of 1125–3500 m. In estimating snow-covered areas, besides semi-supervised multispectral classification of NOAA-AVHRR data, a theta algorithm, developed by the US National Weather Service, has been used. The two classification techniques were applied to the Advanced Very High Resolution Radiometer (AVHRR) data obtained directly from the satellite receiver located at the university campus in Ankara, Turkey. The corrected images were rectified according to the UTM coordinate system. Snow-covered areas were obtained for cloud-free and partial cloudy images for April 1998 within the project area located in the eastern part of Turkey. Furthermore, the threshold to separate clouds from cloud-free areas is determined. The effects of elevation, aspect, slope and prevailing winds in determining the snow-covered areas for April 1998 are explained and the changes in the snow line are determined considering the effects of these topographic and meteorological factors. Snow depletion curves were obtained by using the proper classification technique for all the other cloud-free and partial cloudy images for 1998. These curves were used with other meteorological parameters as input to a snowmelt runoff model in order to predict the daily discharges, which were compared with the records at the streamgauge of the basin. The effects of aspect and slope on the snow depletion curves for different elevation zones are also shown and, considering this effect, the depletion curves are improved.

Evaluating the utility of the ANSA blended snow cover product in the mountains of eastern Turkey

Snow-covered area depletion curves represent a key input for snow run-off melting models, e.g. the snowmelt run-off model (SRM). SRM is a degree-day-based model for daily run-off simulations and forecasts in mountainous areas in which snowmelt is the major run-off contributor. Satellite images and aerial photographs are valuable sources for retrieving snow-covered area. The accuracy of snow cover mapping studies in the optical wavebands is highly dependent upon the algorithms ability to detect clouds. On very cloudy days it is not possible to map snow cover using only optical sensors; however, microwave sensors can be used to obtain snow information on cloudy days. The snow-water equivalent (SWE) of a dry snowpack can be estimated with passive-microwave sensors such as Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E). Development of snow cover products based on multi-sensor data sources is needed for continuous regional and global snow cover mapping for climate, hydrological and weather applications. A preliminary blended snow product has been developed jointly by the US Air Force Weather Agency (AFWA) and NASA/Goddard Space Flight Center. The AFWA–NASA Snow Algorithm, or ANSA, blended snow product is an all-weather product that utilizes both visible and near-infrared (Moderate Resolution Imaging Spectroradiometer, MODIS) and microwave (Advanced Microwave Scanning Radiometer-Earth, AMSR-E) data. In this study the validation of the ANSA blended snow cover product, having 25 and 5 km resolution, respectively, was performed for the eastern part of Turkey for five months in the winter of 2007–2008. This is the first time that the ANSA snow cover product has been evaluated in a mountainous area, where the elevation ranges between 850 and 3000 m. Daily snow data collected at 36 meteorological stations were used in the analysis. Use of the ANSA snow products was found to improve the mapping of snow cover extent relative to using either MODIS or AMSR-E products alone, for the 2007–2008 winter in the eastern part of Turkey. 91% agreement was obtained between the ANSA snow maps and in situ observations for February. The lowest agreement percentage of 68% was obtained for March due to shallow snow depth and wetness of the snow. Change in the spatial resolution of the ANSA product from 25 km to 5 km increased the agreement percentages from 68% to 74% for March. ANSA prototype maps of 5 km resolution from February and March 2008 were used to derive snow cover depletion curves for the upper Euphrates basin located in the eastern part of Turkey. The results were compared with the curves obtained from MODIS daily snow products, and found to provide an improvement over using MODIS daily maps alone. This is because the ability of the microwave sensors to map snow through clouds provides snow cover information on cloudy days when the MODIS maps cannot, though at a coarser spatial resolution than can be obtained using MODIS.

Modelling the temporal variation in snow-covered area derived from satellite images for simulating/forecasting of snowmelt runoff in Turkey/Modélisation de la variation temporelle de la surface enneigée à partir d'images satellitaires pour la simulation/prévision de l'écoulement de fonte nivale en Turquie

Abstract Monitoring the change of snow-covered area (SCA) in a basin is vitally important for optimum operation of water resources, where the main contribution comes from snowmelt. A methodology for obtaining the depletion pattern of SCA, which is based on satellite image observations where mean daily air temperature is used, is applied for the 1997 water year and tested for the 1998 water year. The study is performed at the Upper Euphrates River basin in Turkey (10 216 km2). The major melting period in this basin starts in early April. The cumulated mean daily air temperature (CMAT) is correlated to the depletion of snow-covered area with the start of melting. The analysis revealed that SCA values obtained from NOAA-AVHRR satellite images are exponentially correlated to CMAT for the whole basin in a lumped manner, where R 2 values of 0.98 and 0.99 were obtained for the water years 1997 and 1998, respectively. The applied methodology enables the interpolation between the SCA observations and extrapolation. Such a procedure reduces the number of satellite images required for analysis and provides solution for the cloud-obscured images. Based on the image availability, the effect of the number of images on the quality of snowmelt runoff simulations is also discussed. In deriving the depletion curve for SCA, if the number of images is reduced, the timing of image analysis within the snowmelt period is found very important. Analysis of the timing of satellite images indicated that images from the early and middle parts of the melt period are more important.

A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data

This research study aims to classify crop diversity in agricultural land with a segment-based approach using multi-temporal Kompsat-2 and Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) data acquired in June, July and August on Karacabey Plain, Turkey. Analyses start with the image segmentation process applied to the fused optical images to search homogenous objects. The segmentation outputs are evaluated using multiple goodness measures, which take into consideration area and location similarities. Image classifications are performed on each multispectral (MS) single date image. In order to combine the most probable classes of the thematic maps, distance maps are generated. Evaluations of the thematic maps are performed through confusion matrices based on pixel-based and segment-based approaches. The results indicate that the highest overall accuracy of 88.71% and a kappa result of 0.86 are provided for the segment-based approach of the combined thematic map along with the microwave data, which is around 10% higher than the related pixel-based results.This research study aims to classify crop diversity in agricultural land with a segment-based approach using multi-temporal Kompsat-2 and Environmental Satellite Envisat advanced synthetic aperture radar ASAR data acquired in June, July and August on Karacabey Plain, Turkey. Analyses start with the image segmentation process applied to the fused optical images to search homogenous objects. The segmentation outputs are evaluated using multiple goodness measures, which take into consideration area and location similarities. Image classifications are performed on each multispectral MS single date image. In order to combine the most probable classes of the thematic maps, distance maps are generated. Evaluations of the thematic maps are performed through confusion matrices based on pixel-based and segment-based approaches. The results indicate that the highest overall accuracy of 88.71% and a kappa result of 0.86 are provided for the segment-based approach of the combined thematic map along with the microwave data, which is around 10% higher than the related pixel-based results.

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method.

Modern Applied Mathematics for Alternative Modeling of the Atmospheric Effects on Satellite Images

Nonparametric regression and classification techniques are mostly the key data mining tools in explaining real life problems and natural phenomena where many effects often exhibit nonlinear behavior. The remotely sensed earth data collected by earth-observing satellites is degraded due to the absorption and scattering of solar radiation by atmospheric gases and aerosols. In order to use these data for information extraction, they must first be corrected for the atmospheric effects. Recent methods based on radiative transfer modelling still have many challenges including achieving high accuracy and developing real-time processing capability of large numbers of satellite images acquired with high temporal resolution and Large Field of View instruments. In this chapter, two state-of-the-art nonparametric tools, Multivariate Adaptive Regression Splines (MARS) and its successor Conic Multivariate Adaptive Regression Splines (CMARS), are reviewed within the frame of an earth science example. Both methods are utilized for the atmospheric correction of five sets of MODIS images taken over European Alps. The Simplified Method for Atmospheric Correction (SMAC), a simplified version of 6S radiative transfer model, is also applied on the image data sets for the removal of atmospheric effects. The performance of the models was evaluated by comparing their results with the MODIS atmospherically corrected surface reflectance product in terms of RMSE. Although MARS and CMARS approaches produce similar results on the data sets, they both outperform SMAC.

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.

Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey

Abstract Monitoring snow parameters (e.g. snow-cover area, snow water equivalent) is challenging work. Because of its natural physical properties, snow strongly affects the evolution of weather on a daily basis and climate on a longer time scale. In this paper, the snow recognition product generated from the MSG-SEVIRI images within the framework of the Hydrological Satellite Facility (HSAF) Project of EUMETSAT is presented. Validation of the snow recognition product H10 was done for the snow season (from 1 January to 31 March) of the water year 2009. The MOD10A1 and MOD10C2 snow products were also used in the validation studies. Ground truth of the products was obtained by using 1890 snow depth observations from 20 meteorological stations, which are mainly located in mountainous areas and are distributed across the eastern part of Turkey. The possibility of 37% cloud cover reduction was obtained by merging 15-min observations from MSG-SEVIRI as opposed to using only one daily observation from MODIS. The ...Abstract Monitoring snow parameters (e.g. snow-cover area, snow water equivalent) is challenging work. Because of its natural physical properties, snow strongly affects the evolution of weather on a daily basis and climate on a longer time scale. In this paper, the snow recognition product generated from the MSG-SEVIRI images within the framework of the Hydrological Satellite Facility (HSAF) Project of EUMETSAT is presented. Validation of the snow recognition product H10 was done for the snow season (from 1 January to 31 March) of the water year 2009. The MOD10A1 and MOD10C2 snow products were also used in the validation studies. Ground truth of the products was obtained by using 1890 snow depth observations from 20 meteorological stations, which are mainly located in mountainous areas and are distributed across the eastern part of Turkey. The possibility of 37% cloud cover reduction was obtained by merging 15-min observations from MSG-SEVIRI as opposed to using only one daily observation from MODIS. The coarse spatial resolution of the H10 product gave higher commission errors compared to the MOD10A1 product. Snow depletion curves obtained from the HSAF snow recognition product were compared with those derived from the MODIS 8-day snow cover product. The preliminary results show that the HSAF snow recognition product, taking advantage of using high temporal frequency measurement with spectral information required for snow mapping, significantly improves the mapping of regional snow-cover extent over mountainous areas. Citation Surer, S. and Akyurek, Z., 2012. Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey. Hydrological Sciences Journal, 57 (8), 1–11.