IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS

In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions

Upstream structural management measures for an urban area flooding in Turkey

In recent years, flooding has become an increasing concern across many parts of the world of both the general public and their governments. The climate change inducing more intense rainfall events occurring in short period of time lead flooding in rural and urban areas. In this study the flood modelling in an urbanized area, namely Samsun-Terme in Blacksea region of Turkey is performed. MIKE21 with flexible grid is used in 2-dimensional shallow water flow modelling. 1 × 1000−1 scaled maps with the buildings for the urbanized area and 1 × 5000−1 scaled maps for the rural parts are used to obtain DTM needed in the flood modelling. The bathymetry of the river is obtained from additional surveys. The main river passing through the urbanized area has a capacity of 500 m3 s−1 according to the design discharge obtained by simple ungauged discharge estimation depending on catchment area only. The upstream structural base precautions against flooding are modelled. The effect of four main upstream catchments on the flooding in the downstream urban area are modelled as different scenarios. It is observed that if the flow from the upstream catchments can be retarded through a detention pond constructed in one of the upstream catchments, estimated Q100 flood can be conveyed by the river without overtopping from the river channel. The operation of the upstream detention ponds and the scenarios to convey Q500 without causing flooding are also presented. Structural management measures to address changes in flood characteristics in water management planning are discussed

Object-Based Classification of Multi-temporal Images for Agricultural Crop Mapping in Karacabey Plain, Turkey

The objective of this research is to classify major crop types cultivated in Karacabey Plain of north western Turkey using multitemporal Kompsat-2 and Envisat ASAR data with an object-based methodology. First a pansharpening algorithm is applied to each panchromatic and multispectral Kompsat-2 data to produce colour images having 1m spatial resolution. Next, Mean-Shift image segmentation procedure is applied to the pansharpened Kompsat-2 data with multiple parameter combinations. Multiple goodness measures are utilized to evaluate the object-based results. The optimum objects are then employed in object-based classifications of the single-date images. Next, single-date multispectral (MS) Kompsat-2 images and Kompsat-2 images along with the Envisat ASAR data are classified with the Support Vector Machines (SVMs) method. The training samples are provided automatically by the selected objects based on spatial statistical properties. Next, probability maps are generated for each image in pixel-based manner during the image classification operations. The maximum probabilities are then assigned to the pixels as class labels and the combined thematic maps (June-July, June-August, June-July-August) are generated in pixel-based and object-based manners. The produced thematic maps are evaluated through the confusion matrices and compared also with the results of parcel-based classifications using original agricultural parcels. Results indicate that the combined thematic maps of June-August and June-July- August provide the highest overall accuracy and kappa value approximately 92 % and 0.90, respectively

AUTOMATIC TRAINING SITE SELECTION FOR AGRICULTURAL CROP CLASSIFICATION: A CASE STUDY ON KARACABEY PLAIN, TURKEY

This study implements a traditional supervised classification method to an optical image composed of agricultural crops by means of a unique way, selecting the training samples automatically. Panchromatic (1m) and multispectral (4m) Kompsat-2 images (July 2008) of Karacabey Plain (~100km2), located in Marmara region, are used to evaluate the proposed approach. Due to the characteristic of rich, loamy soils combined with reasonable weather conditions, the Karacabey Plain is one of the most valuable agricultural regions of Turkey. Analyses start with applying an image fusion algorithm on the panchromatic and multispectral image. As a result of this process, 1m spatial resolution colour image is produced. In the next step, the four-band fused (1m) image and multispectral (4m) image are orthorectified. Next, the fused image (1m) is segmented using a popular segmentation method, Mean- Shift. The Mean-Shift is originally a method based on kernel density estimation and it shifts each pixel to the mode of clusters. In the segmentation procedure, three parameters must be defined: (i) spatial domain (hs), (ii) range domain (hr), and (iii) minimum region (MR). In this study, in total, 176 parameter combinations (hs, hr, and MR) are tested on a small part of the area (~10km2) to find an optimum segmentation result, and a final parameter combination (hs=18, hr=20, and MR=1000) is determined after evaluating multiple goodness measures. The final segmentation output is then utilized to the classification framework. The classification operation is applied on the four-band multispectral image (4m) to minimize the mixed pixel effect. Before the image classification, each segment is overlaid with the bands of the image fused, and several descriptive statistics of each segment are computed for each band. To select the potential homogeneous regions that are eligible for the selection of training samples, a user-defined threshold is applied. After finding those potential regions, the training pixels are automatically selected and labelled. Thereafter, those training pixels are utilized in a traditional Maximum Likelihood Classification to classify five crop types namely; corn, tomato/pepper, rice, sugar beet, and wheat. The accuracy of the classification is evaluated in pixel-based manner with the help of a reference map including crop information of the area. Promising results are achieved for pixel-based approach. Based on the error matrices used in the evaluation, overall accuracy of the pixel-based analysis is computed as 89.31%. Similar to the overall accuracies, high individual class accuracies are obtained as well. The results point out that automatically collecting the training samples by extracting representative homogenous areas significantly increases the speed of the classification and minimizes the human interaction. The results also confirm that the proposed approach is highly appropriate for the extraction of representative homogenous training areas

Validation of the operational MSG-SEVIRI snow cover product over Austria

The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is aboard the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day, with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS-combined (Terra + Aqua) snow cover product for the period April 2008–June 2012. <br><br> The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ greatly from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9 and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in Alpine valleys and flatland areas in the spring and winter months, respectively

Modelling the temporal variation in snow-covered area derived from satellite images for simulating/forecasting of snowmelt runoff in Turkey

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 km(2)). 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

Results of Stereotactic Radiation Therapy (SABR) in Early Stage Lung Cancer: Turkish Radiation Oncology Group (TROG) Study

WOS: 000454014503151

Medically Inoperable Early-Stage Lung Cancer Treated with Stereotactic Ablative Radiation Therapy (SABR): Multicenter Study of Turkish Radiation Oncology Group (TROG)

60th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) -- OCT 21-24, 2018 -- San Antonio, TXWOS: 000447811602068…Amer Soc Radiat Onco