Yazar "Aksoy, H." seçeneğine göre listele

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    Estimation of soil erodability parameters based on different machine algorithms integrated with remote sensing techniques
    (Springer, 2024) Saygin, F.; Aksoy, H.; Alaboz, P.; Birol, M.; Dengiz, O.
    Erosion causes significant damage to life and nature every year; therefore, controlling erosion is of great importance. Therefore, maintaining the balance between soil, plants, and water plays a vital role in controlling erosion. Aim of this study was to estimate some erodability parameters (structural stability index-SSI, aggregate stability-AS, and erosion ratio-ER) with indices and reflectance obtained via TripleSat satellite imagery using machine learning algorithms (support vector regression-SVR, artificial neural network-ANN, and K-nearest neighbors-KNN) in Samsun Province, Vezirkopru, Turkiye. Various interpolation methods (inverse distance weighting-IDW, radial basis function-RBF, and kriging) were also used to create spatial distribution maps of the study area for observed and predicted values. Estimates were made using NDVI, SAVI, and ASVI indices obtained from satellite images and NIR reflectance. Accordingly, the ANN algorithm yielded the lowest MAE (2.86%), MAPE (9.46%), and highest R2 (0.82) for SSI estimation. For AS and ER estimation, SVR had the highest predictive accuracy. Given the RMSE values in spatial distribution maps for observed and estimated values (SSI 7.861-7.248%, AS 14.485-14.536%, and ER 4.919-3.742%), the highest predictive accuracy was obtained with kriging. Thus, it was concluded that erosion parameters can be successfully estimated with reflectance and index values obtained from satellite images using SVR and ANN algorithms, and low-error distribution maps can be created using the kriging method.
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    Öğe
    Exploring land use/land cover change by using density analysis method in yenice
    (Springer, 2022) Aksoy, H.; Kaptan, S.; Varol, T.; Cetin, M.; Ozel, H. B.
    In this study, the changes in forest areas and land cover in the period of 2001-2011 and 2011-2021 in the Yenice Forestry Operation Directorate of Turkey were examined in time interval, category and transition levels by using the density analysis method. Landsat images of the study area for the relevant years were classified according to the supervised classification method, land cover change maps and matrices were produced. Method of Intensity Analysis dissects the transition matrices of 2001-2011 and 2011-2021 time period for six categories: Coniferous, Broad-Leaved, Mixed, Settlement, Agriculture and Water. Interval results of the analysis show that the accelerated change in the second period resulted in a decrease in mixed forest and agricultural areas (2%, 8%, respectively) and an increase in residential and pure forests (1% and 9%, respectively) in 20 years. According to the category level results, Broad-Leaved category is active in gain in the first time interval and dormant in terms of loss and gain in the second time interval. Mixed, on the other hand, is an active loser in the first timeframe and active in terms of gains in the second timeframe. According to the Transition level results, the gains of Coniferous and Mixed categories showed that they targeted each other's losses in both time intervals. While Broad-Leaved targeted Mixed losses in the first timeframe, Agriculture losses were targeted as gains in the second timeframe.