Browsing by Author "Akturk, Emre"

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Habitat suitability model with maximum entropy approach for European roe deer (Capreolus capreolus) in the Black Sea Region.
(2019-10-24T00:00:00Z) Evcin, Ozkan; Kucuk, Omer; Akturk, Emre
Evaluating the relationships between wildlife species and their habitats helps to predict effects of habitat change for present and future management of wild animal populations. Building ecological models are good ways to understand and manage wildlife populations and to predict various environmental scenarios. Recently, management of ungulates is becoming more important in Europe due to a high demand of hunting and their role in biodiversity. European roe deer (Capreolus capreolus) is the smallest species of cervids and has a widespread distribution in Turkey. In this study, two habitat suitability models of roe deers, living in the Black Sea Region in Turkey, were created by using a maximum entropy (MaxEnt) approach. Two wildlife development areas, which have widely different habitat types, were selected as study sites. As a result of this study, area under the curve (AUC) values were found to be above 0.80. According to the modeling results, in two different habitat types, ecological variables are quite similar in general. This study is the first study on modeling European roe deers in Turkey.
ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform.
(2023-03-23T00:00:00Z) Akturk, Emre; Popescu, Sorin C; Malambo, Lonesome
Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams' segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale.
Modeling and monitoring riparian buffer zones using LiDAR data in South Carolina.
(2020-05-09T00:00:00Z) Akturk, Emre; Post, Christopher; Mikhailova, Elena A
Functional riparian areas protect water quality and conserve aquatic systems, plants, and wildlife. Laser-based remote sensing technology offers a high-resolution approach to both characterize and document changes in riparian buffer zones (RBZs). The objectives of this study were to demonstrate a rapid method and model to calculate riparian buffer widths on both sides of a stream using a LiDAR-derived slope variable, to classify riparian buffers and determine their quality, and to evaluate the appropriateness of using LiDAR in riparian buffer assessment. For this purpose, RBZs were delineated for Hunnicutt and King Creek, which are located in Oconee and Pickens counties, in South Carolina. Results show that LiDAR was effective in delineating required riparian buffer widths based on the topography slope of upstream areas, and in calculating the ratio of tree cover. This LiDAR-based assessment methodology could be applied to a wide-range of environments.