Browsing by Author "Makineci, H.B."

Now showing 1 - 4 of 4

Citation - Scopus: 2
Agricultural Land Suitability Analysis
(Springer International Publishing, 2023) Orhan, O.; Makineci, H.B.
Citation - Scopus: 5
Comparison Of Dem Based On Geodetic Methods And Photogrammetric Usage Of Uav
(Osman Orhan, 2020) Makineci, H.B.; Karabörk, H.; Durdu, A.
Unmanned Aerial Vehicles (UAV) use in the production of the map for photogrammetric purposes. Unlike aerial photogrammetry, UAV cameras are non-metric amateur cameras. Therefore, they need some operations to use in photogrammetry. Structure from Motion (SfM) algorithms prefers for processing images because of the usage of the non-metric cameras. These algorithms generally identify key-points (via feature extraction) on the photos and match tie-points (via feature point matching) in overlap images. SfM is a photogrammetric technique that produces keypoint to match by identifying key points, such as edge-to-corner points, through high-resolution RGB photos. The scope of this study was to compare the results obtained by UAVs and the results acquired by ground truth data. In this comparison, SfM algorithm performance, the effects of flight height, overlap rate, and UAV-type on the model investigated, and significant results achieved. Additionally, the models obtained from the UAV photographs with different flight heights and overlaps in the areas with varying characteristics of the slope compared. Consequently, it determined the difference between around 20 cm (Z value), comparing the flight height of 80 m and the flight height of 120 m. Since it is observed that the flight height does not have a significant effect. © Author(s) 2021.
Citation - Scopus: 1
Devastating Natural Hazard Observation With the Combination of Optical and Microwave Remote Sensing Datasets, Valencia 2025 Flood
(International Society for Photogrammetry and Remote Sensing, 2025) Makineci, H.B.
Floods represent some of the most catastrophic natural hazards, impacting infrastructure, ecosystems, and human lives significantly. The flood event in Valencia in 2025 serves as a critical case for investigating flood dynamics and developing disaster preparedness strategies. In response, remote sensing datasets, including Sentinel-1 SAR and PlanetScope MSI, provide invaluable insights by capturing changes in land cover and fluctuations in water extent both before and after flood occurrences. This study employs a multi-sensor approach to analyze the 2025 Valencia flood, integrating optical and microwave datasets to produce comprehensive and precise observations of the flood's impact, spatial patterns, and potential causal factors. Furthermore, assessments based on the Normalized Difference Water Index (NDWI) further illustrate the limitations associated with optical methods in flood mapping, thereby reinforcing the indispensable role of SAR in crisis management. The findings highlight the critical importance of flexible urban planning, including creating flood protection zones to prevent loss of life and reduce structural damage in cities vulnerable to flooding. A comparison with previous flood incidents indicates rising extreme weather, reinforcing the need for proactive government measures. This study confirms the essential role of remote sensing in contemporary disaster management, providing essential, large-scale, real-time data for informed policymaking, effective emergency response, and building long-term resilience. By incorporating advanced satellite technologies, this research establishes a new standard for flood evaluation and early warning systems, with far-reaching effects on climate adaptation and strategies for risk reduction. © 2025 Hasan Bilgehan Makineci.
Citation - Scopus: 1
Evaluation of Classification Differences Occurring Between 2019-2020 in Trabzon Province With Sentinel-2a Data Using Different Algorithms
(Osman Orhan, 2023) Makineci, H.B.; Akosman, E.N.
Environmental status reports are important documents that reveal the situation of the relevant city and its surroundings as of the years they were published. Based on the 2021 environmental status report covering Trabzon Province and its surroundings, it has been noticed that green areas are decreasing in the 1/100000 scale environmental arrangement plan (EAP) classification. This study examined the change in the green regions in Trabzon Province with different controlled-uncontrolled classification methods and random forest (RF) algorithm, one of the machine learning algorithms, between 2019 and 2020. In monitoring the change, classification processes were carried out with Normalized Difference Vegetation Index (NDVI), controlled, uncontrolled, and RO algorithm. Classification results were evaluated using the Sentinel-2A satellite data sets of the study area between May 2019 and May 2020, using band composites with 10 m spatial resolution and performing NDVI processing. As a result, as a result of the controlled classification process, the general accuracy rate for 2019 was 100.0%, while the general accuracy rate for 2020 was 81.31%. Classification of satellite images was also carried out with the RO algorithm, one of the machine learning algorithms, and the overall accuracy rate was obtained as 97.6%. © Author(s) 2023.