Levent Genç

Using satellite imagery and LIDAR data to corroborate an adjudicated ordinary high water line

Determination of the ordinary high water line (OHWL) has been and continues to be an important issue. The OHWL defines the separation of sovereignty lands and private ownership on non‐tidal water bodies. Determination of OHWL is conducted on a case‐by‐case basis in Florida through court challenges. A judge makes the decision on where the line exists based upon several criteria—including remote sensing data. This study investigated the possibility of using various remote sensing technologies to provide an efficient and accurate means of determining OHWL for a lake in central Florida. Landsat Enhanced Thematic Mapper (ETM) satellite imagery was compared with the higher resolution imagery IKONOS and Light Detection And Ranging (LIDAR) imagery in order to determine the waters edge and location of vegetation communities that may be correlated with OHWL. It was found that ETM imagery could be used only for mapping vegetation community transition zones and that this zone provided limited insight to OHWL. IKONOS imagery, on the other hand, was more promising for land cover mapping, but requires further study in order to draw general conclusions regarding its application to OHWL. LIDAR data provided the best results for determining OHWL, but also need further study over a larger area in order to draw final conclusions.

Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper

Abstract Thermal and hyperspectral data can be used to determine non-destructive water stress in agriculture. The objective of this study was to determine water stress not visible to the naked eye, the required threshold to start irrigation, and also to estimate the yield and some physiological traits of pepper (Capsicum annuum L.) using thermal imaging and hyperspectral data at different water stress levels. A field experiment was conducted in Çanakkale, Turkey in 2013 consisting of four irrigation treatments: full irrigation (100% (non water-stressed)) and three stress levels applying 25, 50, and 75% of full irrigation. The results indicated that pepper yield, chlorophyll content (ChlR), relative water content, and thermal indices and spectral indices were affected adversely by water stress. While none of the spectral indices were able to distinguish statistically the difference between non water-stressed (I-100) and mildly water-stressed (I-75) pepper, all the thermal indices provided acceptable results. According to single variable regression and stepwise multiple linear regression analysis, significant relationships were determined between the investigated traits and indices. It was found that pepper is very susceptible to water stress and also that thermal and spectral indices can be used successfully in the determination of water stress.

A journey to children's perceptions on forest fire through drawings in Canakkale Province, Turkey: Exploring the needs for alternative educative approaches

ABSTRACT The study aimed to statistically analyze forest fire perceptions and cognitive deficits of children through drawings. Results showed that childrens perceptions on forest fire were under desirable levels. Also, perception levels were significantly impacted by gender and grade level, whereas impact of school type was not significant. Since childrens imaginations are found to be limited and most of forest fires are known to arise from human-induced activities, applied approaches are suggested to enlarge their vision on nature and forest fire with the purpose of raising more responsible, aware, and environmentally-conscious children, as a contributor to prevention of future forest fires.

E-nose identification of Salmonella enterica in poultry manure

Abstract A DiagNose II electronic nose (e-nose) system was tested to evaluate the performance of such systems in the detection of the Salmonella enterica pathogen in poultry manure. To build a database, poultry manure samples were collected from 7 broiler houses, samples were homogenised, and subdivided into 4 portions. One portion was left as is; the other three portions were artificially infected with S. enterica. An artificial neural network (ANN) model was developed and validated using the developed database. In order to test the performance of DiagNose II and the ANN model, 16 manure samples were collected from 6 different broiler houses and tested using these two systems. The results showed that DiagNose II was able to classify manure samples correctly as infected or non-infected based on the ANN model developed with a 94% level of accuracy.