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Öğe A Comparison of Two Felling Techniques Considering Stump-Height-Related Timber Value Loss(Zagreb Univ, Fac Forestry, 2023) Gulci, Nese; Gulci, Sercan; Akay, Abdullah E.; Sessions, JohnHarvest from plantations can provide both industrial wood and forest residues for bioenergy, including stumps. The literature suggests that the choice of cutting system can affect the division between industrial wood recovery and remaining stump volume. In this study, two felling techniques - motor-manual chainsaw and feller-buncher, were compared based on stump-height-related timber value loss for four ground slope classes: high, medium, low, and flat. The economic value loss of wood material for three products - sawlogs, pulpwood, and fiber-chip wood, was determined based on the estimated volume of stumps left in the woods. The results indicated that the average stump height for the motor-manual chainsaw and feller-buncher was 17.16 cm and 8.69 cm. The economic value loss of wood material per stump was higher in felling by manual chainsaw as compared to the feller-buncher operation (log: (sic)0.60 up arrow, paper wood: (sic)0.29 up arrow, fiber-chip: (sic)0.15 up arrow). However, volume loss due to high stumps could contribute to wood for bioenergy if stumps are subsequently removed. Additional research is needed to evaluate the benefits and costs of stump removal for bioenergy as part of a total supply chain to provide both industrial wood and wood for bioenergy.Öğe ASSESSING THE EXPOSURE OF CHIPPER OPERATORS TO WOOD DUST IN A ROADSIDE LANDING AREA(Parlar Scientific Publications (P S P), 2018) Gulci, Sercan; Akay, Abdullah Emin; Spinelli, Raffaele; Magagnotti, NatasciaDue to recent high emission values and increasing public demands for renewable energy, many countries have promoted biomass use instead of fossil originated fuel consumption. Wood chipping operation is one of the most popular biomass processing techniques. In recent years, there has been an interest in using mechanization in forestry, especially in wood chipping in Turkey. There are very limited number of studies on the productivity of wood chipping operations in Turkey, and the potential effects of operation and work environment on chipper operators have not been studied properly. In particular, wood dust exposure may result in serious occupational illness such as lung cancer, asthma, skin and eye irritations. This study investigated exposure of a wood chipper operator to wood dust during a chipping operation at the roadside landing. To measure dust exposure, a low-cost dust sensor mounted on the operator helmet was used, and real-time recorded measurements were examined statistically. The study showed that during the chipping operation in the open area, the operator was exposed to a dust density of 6.04 mg/m(3) over the 8 hours time average, which was above the legal limits of 5mg/m(3). Inhalable dust particle density averaged 0.055 mg/m(3) for each truck load produced. Although these figures were within the range reported by previous wood dust exposure studies conducted on chipper operators, exposure was still above the legal limit and may cause serious health problems. Thus, chipper operators should be instructed to use personnel protection equipment in order to prevent occupational disease.Öğe Assessment of ecological passages along road networks within the Mediterranean forest using GIS-based multi criteria evaluation approach(Springer, 2015) Gulci, Sercan; Akay, Abdullah EminMajor roads cause barrier effect and fragmentation on wildlife habitats that are suitable places for feeding, mating, socializing, and hiding. Due to wildlife collisions (Wc), human-wildlife conflicts result in lost lives and loss of biodiversity. Geographical information system (GIS)-based multi criteria evaluation (MCE) methods have been successfully used in short-term planning of road networks considering wild animals. Recently, wildlife passages have been effectively utilized as road engineering structures provide quick and certain solutions for traffic safety and wildlife conservation problems. GIS-based MCE methods provide decision makers with optimum location for ecological passages based on habitat suitability models (HSMs) that classify the areas based on ecological requirements of target species. In this study, ecological passages along Motorway 52 within forested areas in Mediterranean city of Osmaniye in Turkey were evaluated. Firstly, HSM coupled with nine eco-geographic decision variables were developed based on ecological requirements of roe deer (Capreolus capreolus) that were chosen as target species. Then specified decision variables were evaluated using GIS-based weighted linear combination (WLC) method to estimate movement corridors and mitigation points along the motorway. In the solution process, two linkage nodes were evaluated for eco-passages which were determined based on the leastcost movement corridor intersecting with the motorway. One of the passages was identified as a natural wildlife overpass while the other was suggested as underpass construction. The results indicated that computer-based models provide accurate and quick solutions for positioning ecological passages to reduce environmental effects of road networks on wild animals.Öğe ASSESSMENT OF THE ROAD IMPACTS ON CONIFEROUS SPECIES WITHIN THE ROAD-EFFECT ZONE USING NDVI ANALYSIS APPROACH(Parlar Scientific Publications (P S P), 2017) Gulci, Sercan; Akay, Abdullah Emin; Oguz, Hakan; Gulci, NeseRemote sensing (RS) techniques and Geographical information system (GIS) applications, which provide more economical and time saving methods than ground-based measurements, have been widely used for earth observation and environmental assessments. Thus, consider Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, the present and past conditions of the coniferous species and land change that are within 100 meters away from the road alignment were evaluated to explain the changes in the road effect zone. Normalized Difference Vegetation Index (NDVI) obtained from Landsat images of the years between 2000 and 2015 were performed by using thresholds to estimate temporal and spatial changes of the coniferous species. The value of thresholds (0.45Öğe Evaluation of Automatic Prediction of Small Horizontal Curve Attributes of Mountain Roads in GIS Environments(Mdpi, 2022) Gulci, Sercan; Acar, Hafiz Hulusi; Akay, Abdullah E.; Gulci, NeseRoad curve attributes can be determined by using Geographic Information System (GIS) to be used in road vehicle traffic safety and planning studies. This study involves analyzing the GIS-based estimation accuracy in the length, radius and the number of small horizontal road curves on a two-lane rural road and a forest road. The prediction success of horizontal curve attributes was investigated using digitized raw and generalized/simplified road segments. Two different roads were examined, involving 20 test groups and two control groups, using 22 datasets obtained from digitized and surveyed roads based on satellite imagery, GIS estimates, and field measurements. Confusion matrix tables were also used to evaluate the prediction accuracy of horizontal curve geometry. F-score, Mathews Correlation Coefficient, Bookmaker Informedness and Balanced Accuracy were used to investigate the performance of test groups. The Kruskal-Wallis test was used to analyze the statistical relationships between the data. Compared to the Bezier generalization algorithm, the Douglas-Peucker algorithm showed the most accurate horizontal curve predictions at generalization tolerances of 0.8 m and 1 m. The results show that the generalization tolerance level contributes to the prediction accuracy of the number, curve radius, and length of the horizontal curves, which vary with the tolerance value. Thus, this study underlined the importance of calculating generalizations and tolerances following a manual road digitization.Öğe Evaluation of eco-friendly soil slope stabilization techniques for forest roads by using an Artificial Neural Network (ANN)(Keai Publishing Ltd, 2025) Yuksel, Kivanc; Gulci, Nese; Akay, Abdullah Emin; Gulci, SercanIn this study, the effectiveness of different stabilization techniques implemented on the forest road cut slopes was investigated in terms of controlling erosion and runoff. Wood production residues, hydro-seeding, and jute geotextile treatments were applied on study plots located on the example road. The amount of erosion and runoff were measured on the study plots which were established for different slope grades of 20 degrees, 30 degrees, and 40 degrees. Then, the amount of erosion and runoff measured from the plots were compared to determine the performance of stabilization techniques on the cut slope. In the solution process, an Artificial Neural Network (ANN) model, which is one of the machine learning algorithms, was used to predict sediment yield from forest road cut slopes. The sediment yields averaged over the three slope grades from highest to lowest were measured as 6.41, 1.16, 0.65, and 0.45 g/m2 in the control plot with no treatment, jute geotextile, hydroseeding, and wood production residues, respectively. The averaged over the three runoff amounts slope grades from the highest to the lowest were determined as 6.82, 3.71, 1.64, and 1.30 mm/m2 in control the plot, jute geotextile, hydroseeding, and wood production residues, respectively. Comparing to the control plot, wood production residues, hydroseeding, and jute geotextile treatments reduced the sediment yields by 14,10, and 5 times, respectively. On the other hand, wood production residues, hydroseeding, and jute geotextile applications reduced the runoff amount by 5, 4, and 2 times, respectively. As a result, it was found that wood production residues and hydroseeding treatment can be more efficient in reducing the amount of runoff and sediment yield compared to the jute geotextile treatment. The ANN method achieved high accuracy in predicting sediment yield and it was concluded that the ANN can be used as an effective method to evaluate soil slope stabilization techniques. (c) 2025 International Research and Training Centre on Erosion and Sedimentation. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Öğe Evaluation of UAV- and GNSS-Based DEMs for Earthwork Volume(Springer Heidelberg, 2018) Akgul, Mustafa; Yurtseven, Huseyin; Gulci, Sercan; Akay, Abdullah EminRoad planning and construction is a complex and time consuming process. One of the most important components in this process is estimating earthwork. Resolution of DEM, which is commonly used in road planning stage, directly affects success of earthworks estimation and construction costs. Within the scope of this study, capabilities of two different data collection systems (UAV and GNSS) were compared for DEM generation. In the study, two sets of DEMs of Bursa Technical University Kestel campus area were produced using both UAV- and GNSS-based methods. Then, cut and fill volumes were compared with considering UAV-based DEM and GNSS-based DEM depending on reference plane for three different roads. According to NRTK-GNSS-based surveying results, point density was obtained as 35 point/ha, while UAV-based surveying point density was computed as point/ha. Using UAV-based DEM as a reference plane, it was found that the volumes of excavations and embankments were very close to each other when the average excavation per unit (i.e., 1 m) road length was calculated.Öğe Land Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers(Mdpi, 2025) Gulci, Sercan; Wing, Michael; Akay, Abdullah EminThe use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas.Öğe Usage opportunities of generating digital elevation model with unmanned aerial vehicles on forestry(Istanbul Univ, 2016) Akgul, Mustafa; Yurtseven, Huseyin; Demir, Murat; Akay, Abdullah Emin; Gulci, Sercan; Ozturk, TolgaUnmanned Aerial Vehicles (UAVs) are sustained in flight by aerodynamic lift and guided without an onboard crew, they may be expandeble or recoverable and can fly autonomously or semiautonomously. Within the scope of study, new generation series autonomous UAV brand which is Trimble UX5 is used for generating high accuracy digital model model and obtaining high accuracy image in Istanbul University research and application forest. These obtained images are evaluated with photogrammetry software Trimble Business Center (TBC) v3. 1. In this study it was determined that we can obtan high accuracy data image resolution from 2.4 cm to 24 cm depending on the flight altitude with UAV. It was concluded that UAV systems can contribute in forestry work yo obtain sensitive data because of there is no other high accuracy data such as LIDAR. And lack of trained personnel in UAV flights is disadvantages. In this study, UAV and it's systems were evaluated and tested in all steps. It was expected that geographic information data which requiered forestry applications, can be easly be obtain with UAV. When digital surface model (DSM) data was assessed comprehensively, it was concluded that the data which obtained from UAV systems are more cheaper, productive and from LIDAR and IFSAR data. At the same time UAV data are relatively sensitive such LIDAR and IFSAR.Öğe Using thermal infrared imagery produced by unmanned air vehicles to evaluate locations of ecological road structures(Istanbul Univ, 2016) Gulci, Sercan; Akay, Abdullah EminThe aerial photos and satellite images are widely used and cost efficient data for monitoring and analysis of large areas in forestry activities. Nowadays, accurate and high resolution remote sensing data can be generated for large areas by using Unmanned Aerial Vehicles (UAV) integrated with sensors working in various spectral bands. Besides, the UAV systems (UAVs) have been used in interdisciplinary studies to produce data of large scale forested areas for desired time periods (i.e. in different seasons or different times of a day). In recent years, it has become more important to conduct studies on determination of wildlife corridors for controlling and planning of habitat fragmentation of wild animals that need vast living areas. The wildlife corridors are a very important base for the determination of a road network planning and placement of ecological road structures (passages), as well as for the assessment of special and sensitive areas such as riparian zones within the forest. It is possible to evaluate wildlife corridors for large areas within a shorter time by using data produced by ground measurements, and remote sensing and viewer systems (i.e. photo-trap, radar and etc.), as well as by using remote sensing data generated by UAVs. Ecological behaviors and activities (i.e. sheltering, feeding, mating, etc.) of wild animals vary spatially and temporally. Some species are active in their habitats at day time, while some species are active during the night time. One of the most effective methods for evaluation of night time animals is utilizing heat sensitive thermal cameras that can be used to collect thermal infrared images with the night vision feature. When the weather conditions are suitable for a flight, UAVs assist for determining location of corridors effectively and accurately for moving wild animals at any time of the day. Then, the most suitable locations for ecological road structures can be determined based on wildlife corridor data. In this study, the possibilities of using remote sensing data within thermal band produced by UAVs were investigated for positioning of ecological road structures.
