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Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artifi cial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer -one hidden layer -output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices Journal of Marketing and Consumer Behaviour in Emerging Markets 1(5)2017Olgun Kitapci, Ömür Tosun, Murat Fatih Tuna, is relatively new in the fi eld of housing economics. Moreover, this is the fi rst study that uses the artifi cial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey. JEL classifi cation: C15, D14, R31

Lumpy skin disease (LSD) is caused by the virus of the same name and has major economic impacts on cattle breeding. In Turkey, frequent cases of cattle LSD have been reported over the last years. The present study aimed to analyze potential risk factors for LSD and provide information for controlling the spread of infectious diseases by a geographic information system (GIS). The research included cross-sectional and retrospective studies with active disease follow-up and semi-structured interviews (SSI) from August 2013 to December 2014 in Turkey. Potential risk factors for LSD were evaluated based on environmental conditions and provincial demographic and epidemiological data. Of the total of 562 observed animals, 27.22% and 2.67% of cattle were sick and died due to LSD, respectively. The morbidity rate was 26.04% in mixed and 38.18% in local breeds. The animal-level prevalence significantly differed among animals of different age, sex, and with different vaccination status (P<0.05). It was more serious in younger animals and females and during drier weather conditions. A trend of seasonality was observed in LSD occurrence. Significant risk factors affecting the prevalence of LSD were proximity to the southern border of Turkey, animal movements, and animal markets. In this process, geographical query, analysis, and thematic map production were performed by GIS.

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