Browsing by Author "Berberoğlu, S."

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Evaluation of Land Use Suitability for Wheat Cultivation Considering Geo-Environmental Factors by Data Dependent Approaches
(Centenary University, 2021) Şatir, O.; Berberoğlu, S.
Two techniques were investigated to be standard deviation based weighting Multi Criteria Assessment (MCA), and Artificial Neural Network (ANN) considering base environmental factors to define wheat cultivation suitability in Van region. Climate data (long term annual, maximum and minimum temperature, total mean precipitation and solar radiation), physical factors such as elevation, hillshade, slope, soil depth, accessibility to the fields and land use cover were used to produce wheat suitability map. All inputs were weighted with reference to existing wheat areas. MCA and ANN approaches were applied using same dataset to compare the performance of the two techniques. In total, 228 wheat parcels were used as training (171 parcels) and testing (57 parcels) data. Relative Operational Characteristic (ROC) was applied for accuracy assessment. ROC values of the MCA technique which was depended on existing wheat lands, and ANN techniques were derived to be 0.875 and 0.71 respectively. Results showed that 15% of the research area was very suitable for wheat farm, and today, only 67% of very suitable areas were used to be agriculture. Other areas were currently used as grassland (28%), bare ground (4%), and other (1%). © 2021, Centenary University. All rights reserved.
Object Based Classification of Crop Pattern Using Multi-Temporal Satellite Dataset in Multi-Cropped Agricultural Areas: Lower Seyhan Plane Case Study
(Centenary University, 2017) Yeler, O.; Şatir, O.; Berberoğlu, S.
Lower Seyhan Plane (LSP) is one of the most productive agricultural basins of Turkey and it covers main part of the Çukurova Region. The crop productivity in the study area is much more than most developed countries and Turkey’s average productions. Ideal spatial conditions such as climate, soil and transportation for agriculture creates these productive lands. The aim of this research was to define winter and summer crop pattern using multi-temporal Landsat satellite dataset applying object based classification technique. Crop pattern was detected according to 2013 hydrological term (October 2012 – September 2013) as winter and summer. Landsat dataset was defined according to the greenest and cloud free times of the crops. Object based classification was applied because of regular parcel distribution of the crops. As a result of the study; general kappa coefficiency of LSP was obtained as 0.9. According to the results, it was found that while wheat, potato and onion for winter crops were determined as areal distribution, corn and cotton as first crop and corn as second crop in summer season. © 2017, Centenary University. All rights reserved.