Hikmet Gunal

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Dr. Gunal graduated with a Bachelor of Science in Soil Science from the University of Cukurova in 1991. He continued his education when he completed a Masters of Soil Science from the University of Cukurova in 1995. Dr. Gunal began his professional experience in the Yuzuncu Yil University as a research assistant. In 2001 Dr. Gunal completed his Ph.D. work in Soil Genesis, Classification and Mineralogy from Kansas State University under Prof. Dr. M.D. Ransom’s supervision. After gaining his PhD, Dr. Gunal returned back to Turkey and joined the Agricultural faculty at Gaziosmanpasa University.I am a soil scientist who work on a range of projects spanning both agricultural and suburban environments.During my professional career, I learned the importance of keeping an open mind and being flexible. These skills have proved vital in my position. I do not only train students in the faculty but also closely interreact with producers in the field. I monitor the problems in the field and actively seek solutions.Outside the research and teaching, I enjoy spending time with my wife and three children.EducationPh.D. Soil Genesis and Mineralogy, The Kansas State University, 2001M.S. Soil Genesis and Classification, University of Cukurova, 1995B.S. Soil Science, Faculty of Agriculture, University of Cukurova, 1991ResearchMy research involves pedology, soil mineralogy, soil quality and land degradation.

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An integrated framework to identify and map gullies in a Mediterranean region of Turkey

This research introduces a scientific methodology to identify areas affected by gully erosion usi... more This research introduces a scientific methodology to identify areas affected by gully erosion using Geographic Object Based Image Analysis (GEOBIA) and Random Forest (RF) supervised machine learning. The GEOBIA and RF were applied in Besni district, which has a Mediterranean climate, of Adiyaman province in Turkey by including many factors in the model. Estimation Scale Parameter (ESPII) algorithm was used in the segmentation phase. The novelty of this study is the implementation of RF supervised classification algorithm to classify a large number of objects determined after the segmentation process, due to the large size of the study area. Therefore, open access data has been evaluated with high classification accuracy without the need for labor. Precision, Recall and F1-Score values were calculated using true positive (TP), true negative (TN), false positive (FP) and false negative (FN) values based on field observations and Google Earth images of the study area. The TP, TN, FP and FN values were 0.90, 0.95 and 0.92, respectively. In addition, a Kappa-index was calculated as 0.88. The gully erosion map obtained using aforementioned methodology can be used to take necessary measures to prevent further degradation and plan sustainable land uses.

Hikmet Gunal

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