The impact of impervious surface, vegetation, and soil areas on land surface temperatures in a semi-arid region using Landsat satellite images enriched with Ndaisi method data.

Impervious surfaces are a significant issue of both urbanization and environmental assessment. However, it is a problem to classify impervious surface (IS) and soil areas as separate classes in land cover classification. The objectives of this study are to obtain impervious surface, vegetation, and soil areas clearly of an urban complex with a semi-arid climate and to better determine the relationships of IS, vegetation, and soil areas with land surface temperatures (LSTs). For this purpose, IS, vegetation, and soil areas in a semi-arid city of Turkey-Kayseri city were identified by using Normalized Difference Anthropogenic Impervious Surface Index (NDAISI) data and support vector machine (SVM) method together in the classification of different areas. Landsat 5, 7, and 8 satellite images of 1987, 2000, and 2013 were used, respectively, in this study. Afterward, the effects of these areas on LSTs were analyzed. Regression analysis was used to determine the relationships between land cover areas and surface temperatures. To better demonstrate these relationships, besides common pixel-based and classical regional-based approaches, a new density-based regional analysis approach was proposed. This study is an innovative one in terms of detecting IS and indicating relationships between land cover areas and surface temperatures in semi-arid regions. Another innovation of the study is related to the results produced. The results showed that decreasing LST values were observed with increasing IS and vegetation cover values and increasing LST values were observed with increasing soil areas. The present findings may provide significant contributions to the literature and will facilitate the development of urban planning strategies in semi-arid regions.

A Framework to Prioritize the Public Expectations from Water Treatment Plants based on Trapezoidal Type-2 Fuzzy Ahp Method.

Water treatment plants play a major role in the cycle of water recovery and reuse. Besides the benefits of water treatment plants, they have a great impact on the environment, social life, economy, and natural habitats. In this sense, decision-makers should effectively plan the construction and operational activities of plants, taking into account the expectations of users. Growing public expectations about water treatment plants increase the pressures on investors and government managers. In this study, we focus on defining and determining the weights of public expectations from water treatment plants and handle as a multi-criteria decision-making problem. A two-level hierarchical model is structured to evaluate public expectations from water treatment plants as model criteria. For the problem, a literature review is performed to search the main criteria. The most suitable criteria for the problem are determined using experts' opinions. Then, the sub-criteria are determined. Experts' evaluations are collected by face to face interviews. These evaluations are consolidated and finalized via the modified Delphi method. Trapezoidal Type-2 Fuzzy Analytic Hierarchy Process (T2F-AHP) is employed to determine criteria weights using results obtained by the modified Delphi method. A sensitivity analysis is performed to show the reliability of the proposed methodology. A comparison is also performed between the Analytic Hierarchy Process (AHP) and the proposed methodology. The results of this study can be used as a guide to develop public strategies about water treatment plants. Finally, conclusions and future directions of this work are given.

An Integrated Methodology Using Extended Swara and Dea for the Performance Analysis of Wastewater Treatment Plants: Turkey Case.

Public and private companies make significant water infrastructure investments to meet increasing water demand. In this context, investments in wastewater treatment plants (WWTPs), which play an important role in recycling of used water, are also increasing. This study investigates determination of the efficiency scores of WWTPs considering each metropolitan municipality as a decision-making unit (DMU). In this study, a two-step methodology is established to determine efficiency scores of WWTPs. In the first step, the input and output parameters are searched by a literature review for the performance evaluation, and candidate parameters are determined. Then, to determine the most appropriate and related parameters, the importance weights of all candidate inputs and outputs are computed using the extended stepwise weight assessment ratio analysis (SWARA) method. Next, the inputs and outputs are chosen according to their importance weights. In the second step, efficiency scores of WWTPs are calculated using output-oriented data envelopment analysis (DEA) models. Based on the expert opinions, the parameters used as input variables are as follows: Daily Wastewater Amount per Person Discharged in Municipalities, WWTP Capacity, and Number of WWTPs; and the parameters used as output variables are as follows; Amount of Wastewater Treated in WWTPs and Municipal Population Served by WWTPs. The results are presented and discussed by sensitivity analysis. Results show that 14 metropolitan municipalities have total efficiency, 19 metropolitan municipalities have technical efficiency, and 21 metropolitan municipalities have scale efficiency.

Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey.

Wetlands are among the most productive ecosystems that provide services ranging from flood control to climate change mitigation. Wetlands are also critical habitats for the survival of numerous plant and animal species. In this study, we used satellite remote sensing techniques for classification and change detection at an internationally important wetland (Ramsar Site) in Turkey. Sultan Marshes is located at the center of semi-arid Develi closed basin. The wetlands have undergone significant changes since the 1980s due to changes in water flow regimes, but changes in recent years have not been sufficiently explored yet. In this study, we focused on the changes from 2005 to 2012. Two multispectral ASTER images with spatial resolution of 15 m, acquired on June 11, 2005 and May 20, 2012, were used in the analyses. After geometric correction, the images were classified into four information classes, namely water, marsh, agriculture, and steppe. The applicability of three classification methods (i.e. maximum likelihood (MLH), multi-layer perceptron type artificial neural networks (ANN) and support vector machines (SVM)) was assessed. The differences in classification accuracies were evaluated by the McNemar's test. The changes in the Sultan Marshes were determined by the post classification comparison method using the most accurate classified images. The results showed that the highest overall accuracy in image classifications was achieved with the SVM method. It was observed that marshes and steppe areas decreased while water and agricultural areas expanded from 2005 to 2012. These changes could be the results of water transfers to the marshes from neighboring watershed.

Modeling Forest Productivity Using Envisat MERIS Data.

The aim of this study was to derive land cover products with a 300-m pixelresolution of Envisat MERIS (Medium Resolution Imaging Spectrometer) to quantify netprimary productivity (NPP) of conifer forests of Taurus Mountain range along the EasternMediterranean coast of Turkey. The Carnegie-Ames-Stanford approach (CASA) was usedto predict annual and monthly regional NPP as modified by temperature, precipitation,solar radiation, soil texture, fractional tree cover, land cover type, and normalizeddifference vegetation index (NDVI). Fractional tree cover was estimated using continuoustraining data and multi-temporal metrics of 47 Envisat MERIS images of March 2003 toSeptember 2005 and was derived by aggregating tree cover estimates made from high-resolution IKONOS imagery to coarser Landsat ETM imagery. A regression tree algorithmwas used to estimate response variables of fractional tree cover based on the multi-temporal metrics. This study showed that Envisat MERIS data yield a greater spatial detailin the quantification of NPP over a topographically complex terrain at the regional scalethan those used at the global scale such as AVHRR.