Yazar "Gundogan, Recep" seçeneğine göre listele

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    Importance of geotechnical soil properties for precision Forest activities in a karst area
    (Elsevier, 2024) Dindaroglu, Turgay; Vermez, Yasin; Gundogan, Recep; Yakupoglu, Tugrul; Gunal, Hikmet; Akay, Abdullah E.
    Some soil behaviors change significantly as water content varies over time. The particle size distribution of soils has a direct impact on mechanical properties such as soil water content, resistance to dispersion, swelling-shrinkage, fluidity, plasticity, and stickiness. This study was conducted to investigate the usability of Atterberg limits, consistency index and coefficient of linear extensibility (COLE) in the temporal planning of ecosystem restoration activities such as silvicultural interventions, tillage, afforestation, and the construction of forest roads, etc. Surface soil samples were collected from the sections numbered 263, 264, 266, 268, 317, 318, 319, 323, 324,325 and 366 of the degraded forest of the And & imath;r & imath;n forestry operations department. The COLE, liquid limit (LL), plastic limit (PL), plasticity index and consistency index values of soil samples were determined. The LL values ranged from 17.5 to 62.4%, the PL values from 8.2 to 46.8% and the PI values from 6.4 to 15.5. The highest COLE value (0.13) was recorded in the section 318, while the lowest COLE value (0.03) was in section 325.The LL and PL values have a positive linear relationship with clay and organic matter content. All sections have karstic characteristics, but the mechanical characteristics of the soils differ significantly between the sections. Silvicultural interventions carried out especially in section 318, which had the lowest consistency index (Ic = 0.70), and sections 263, 264, 317 and 319, where the consistency index is >2,should take mechanical properties into account, and the planting time intervals should be determined, accordingly for sustainable forestry.
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    Multispectral UAV and satellite images for digital soil modeling with gradient descent boosting and artificial neural network
    (Springer Heidelberg, 2022) Dindaroglu, Turgay; Kilic, Mirac; Gunal, Elif; Gundogan, Recep; Akay, Abdullah E.; Seleiman, Mahmoud
    Sensor technology and machine learning (ML) enable rapid and accurate estimation of soil properties. This study aimed to estimate some soil characteristics with different ML algorithms using unmanned aerial vehicle (UAV) and Sentinel 2A optical satellite images. Four spectral indices and soil data were statistically compared to assess the performance of estimation. The ML algorithms including Multi-Layer Perception Artificial Neural Network (MLP-ANN) and Gradient Descent Boosting Tree (GDBT)ML were employed to improve the estimation. Bayesian optimization was used to optimize the hyperparameters of the GDBT ML algorithm. The relationships between vegetation indices calculated using the UAV and Sentinel 2A (S2A)satellite images were examined. Total of 122images were taken for 1.66 ha land with a spatial resolution of 3.99 cm. The Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Transformed Soil Adjusted Vegetation Index (TSAVI) from UAV in rangeland and olive orchards were highly correlated with the same vegetation indices calculated using the S2A image. The RMSE values improved between 23.23 and 35.66% for sand, silt and soil organic matter content in MLP-UAV networks, while the MLP-S2A networks provided 9.73 to 19.85% improvement for pH, clay and soil moisture (SM). The RMSE values in UAV-based GBDT ML algorithms were more successful in estimation of pH, sand, silt, CaCO3, and SM than the S2A models and the relative improvement was between 12.16 and 93.66%. The results showed that (i) estimation success is affected by the spectral response of the soil property as well as statistical characteristics of the observation values, (ii) different optimization techniques as well as the estimation algorithm affect the estimation accuracy, (iii) land use types play an important role in the estimation variance, and (iv) the estimation performance of UAV based models is compatible with the S2A.
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    The effect of traditional and reduced tillage systems on the sediment yield of plots constructed in the Mediterranean climate zone caused by natural rainfall
    (Inderscience Enterprises Ltd, 2020) Yakupoglu, Tugrul; Dindaroglu, Turgay; Akay, Abdullah Emin; Kusvuran, Kadir; Alma, Veysel; Gundogan, Recep
    The objective of this study was to investigate the effects of different types of tillage systems and different crops on the sediment yield under natural rainfalls from agricultural parcels established in two Mediterranean cities of Turkey. The study was carried out in two consecutive hydrological years, in which both total precipitation and precipitation pattern were different. The results indicated that the soil tillage system (P <= 0.05) and whether or not the plant production was implemented in the parcels (P <= 0.01) had statistically significant effect on the sediment yield of the parcels. Although the total sediment yield was not statistically different for both hydrological years, there was a statistical difference between two years in terms of sediment yield per unit rainfall (P <= 0.01). The applied tillage system and the rainfall pattern are considerably effective on the erosion.