Ormanların heyelan oluşumu üzerindeki etkileri

Özellikle dağlık bölgelerde ortaya çıkan stabilite problemlerinin olumsuz sonuçlarından dolayı, heyelanlar üzerindeki etkileri bakımından ormanların ve ormancılık faaliyetlerinin önemi ormanların koruma fonksiyonu ile birlikte giderek artmaktadır. Ormanlar ve ormancılık faaliyetleri (ağaç kesimi, yol inşası gibi) heyelan kaynaklı stabilite problemleri açısından literatürde çeşitli yönleriyle çalışılmıştır. Ancak orman örtüsünün mevcudiyetinin etkileri ile ormancılık faaliyetlerinin heyelanlar üzerindeki etkilerinin nasıl ve ne yönde olduğuna dair yapılan çalışmaların temel alınarak tartışıldığı bir derleme çalışmaya ihtiyaç olduğu dikkat çekmektedir. Bu makalede bu ihtiyaç göz önüne alınarak orman-heyelan ve ormancılık-heyelan konularında uluslararası düzeyde yapılan çalışmalar incelenerek tartışılmıştır. Anahtar kelimeler: Heyelan, Orman, Ormancılık, Vejetasyo

Use of UAV Data and HEC-RAS Model for Dimensioning of Hydraulic Structures on Forest Roads

forest roads should have the capability to drain the expected maximum discharge for a 50-year return period during their lifespan (i.e., 20 years). In Türkiye, Talbot’s formula, as empirical method, has commonly been used in determining the required cross-sectional area (CSA) of the structures. However, in practice, forest road engineers in Türkiye do not pay enough attention to their construction with required dimensions calculated by Talbot’s formula. In the present study, the Hydrological Engineering Centre – River Analysis System (HEC-RAS) model was used to evaluate the dimensions of installed structures in terms of their ability to drain maximum discharges, with the aim of determining the required dimensions for those that could not meet this requirement. To this purpose, the 6+000 km forest road No. 410 in Acısu Forest Enterprise, Gerede Forest Directorate (Bolu, Türkiye) was selected as the study area. In total, 15 small watersheds crossed by the forest road were delineated, with only six of them having cross-drainage structures. The HEC-RAS model geometry was generated by manual unmanned aerial vehicle (UAV) flights at altitudes of 5–15 m, providing very high spatial resolution (<1 cm). The maximum discharges of the watersheds were estimated for the HEC-RAS model using the Rational, Kürsteiner, and Soil Conservation Service-Curve Number (SCS-CN) methods. Maximum discharges of 0.18–6.03 were found for the Rational method, 0.45–4.46 for the Kürsteiner method, and 0.25–7.97 for the SCS-CN method. According to the HEC-RAS hydraulic model CSA simulations, most of the installed culvert CSAs calculated by Talbot’s formula were found to be incapable of draining maximum discharges. The study concluded that the HEC-RAS model can provide accurate and reliable results for determining the dimensions of such structures for forest roads

Using Machine Learning in Forestry

Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future

Forest mapping against rockfalls on a regional scale in Inebolu of Turkey

Determining areas where forest plantations provide protection against rockfall is significant in the prevention of disasters. In this paper, a case study is conducted in the Özlüce Forest District of İnebolu, Turkey. Potential rockfall source areas are firstly calculated and mapped via RollFree, which uses a digital elevation model as the only input. The rockfall travel distance is then identified using an empirical energy line angle to create propagation maps for different scenarios (using a set of four angles: 28°, 32°, 35°, and 38°). By marking the lower boundaries of propagation, the maximum run-out zone of a fallen block is determined as having a very low, low, medium, or high probability of occurrence (marking the lower boundaries of propagation). These propagation maps are then overlapped with a forest stand map to define areas where the forest provides a protective function against rockfall. According to propagation maps that indicate a high probability of occurrence, only 9% of the total forest area is found to be capable of playing a protective role, whereas for those determined as having a low probability of occurrence, 17% of the forest area provides a protective function

PREDICTION OF DAILY STREAMFLOW USING JORDAN-ELMAN NETWORKS

WOS: 000305777000002The prediction of daily streamflow is required for future planning in water resource activities. This study presents the application of the Jordan-Elman network with the Levenberg-Marquardt algorithm. Prediction was made by using flow data of gauging station no. 2122 on Birs River, Switzerland between 2000 and 2010. The data, 4018 days in total, were used as calibration and validation sets for the chosen Jordan-Elman Neural Network architecture. Of the data obtained, 2922 days (1st January 2000 - 31st December 2007) were reserved for calibration, and remaining data were used for validation. In total, six different models were developed, based on the prediction of current flow from up to six-days-ahead flows. Mean square error (MSE), Nash-Sutcliffe Sufficiency Score (NSSS) and coefficient of correlation (R-value) were used as performance criteria. Model M-6 (six-days- ahead flows) gave the best results, with respect to all prediction performance criteria

Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yığılca Forest Directorate (Turkey)

Forest roads are one of the biggest investments in forest management. Teir possible adverse efect on the environment is becoming an important issue for administrators due to a recent increase in public awareness. Especially in the Black Sea Region of Turkey, road-related landslides are common in forested areas because the roads are located in hilly regions with steep slopes. In addition to their impact on forests, landslides can cause damage to roadbeds which requires immediate maintenance. Landslide-susceptibility maps are widely used for diferent purposes such as reducing the efects of landslides, decision making, and planning. Tese maps can easily be generated by utilizing the advanced features of Geographical Information Systems (GIS) and computer technologies. Logistic regression (LR) is a widely used technique for mapping landslide susceptibility; landslide conditioning parameters such as topography, lithology, land use, distance to streams and roads, and curvature can be mapped by GIS tools. In this study a feldwork- generated inventory of 288 landslides was used to produce a landslide-susceptibility map for the Yığılca Forest Directorate (Turkey). Tis map was generated by applying a GIS-based LR method. Land use, lithology, elevation, slope, aspect, distance to streams, distance to roads, and plan curvature were considered as the landslide conditioning parameters. Afer the landslide-susceptibility map was divided into 5 classes of susceptibility (very low, low, moderate, high, and very high), it was overlapped with a road network map in order to evaluate forest road conditions in terms of landslide susceptibility. For a quantitative analysis of forest road-landslide interaction, 2 new parameters were determined: a landslide frequency index (divided into general and real) and a road-landslide index (divided into general and real). Real landslide frequency and general landslide frequency on the roads were found to be 0.42 and 0.18, respectively. Te results showed that the real road-landslide index and the general road-landslide index in the area were 0.10 and 0.04, respectively

Prediction of temperature variation within a snowpack in open areas and under different canopy covers

WOS: 000312548800007Snow temperature is a major component of many physical processes in a snowpack. The temperature and the change in temperature across a layer have a dominant effect on physical properties of snow grains as well as its hardness, strength, and failure resistance. In this study, temperature and snow cover thickness were measured during the snow season of 20072008 in 11 elevation classes and in three different sampling locations, one in an open area and two under different forest canopy covers for each class along Kartalkaya road, Bolu. Each sampling site was visited 44 times to collect data including snow depth, snow surface temperature, ground temperature, and temperature within snowpack at 20-cm intervals. Seven different models are developed to determine snowpack temperature variations under forest canopy covers and in an open area with different leaf area index values. All models were performed using a multilayer perceptron (MP) method for the BoluKartalkaya area, Turkey. MP approach constitutes a standard form of neural network modeling and can modify two-layer linear perceptron methods using three and more layers. The ability of MP is to handle complex nonlinear interactions, which ease the natural process of modeling. This method can overcome complex computations using neuron networks, and they can easily nonlinearly link input and output variables. The predictive errors are determined on the basis of mean absolute error and mean square error criteria. The NashSutcliffe sufficiency score showing compliance between observed and predicted values is also calculated. According to the mean absolute error, the mean square error, and the NashSutcliffe sufficiency score criteria, the predictive errors are within reasonable error intervals, justifying the use of the developed MP models for engineering applications. Copyright (c) 2012 John Wiley & Sons, Ltd.Western Black Sea Forestry Research InstituteThe authors gratefully acknowledge the support of The Western Black Sea Forestry Research Institute. They thank Ahmet Duyar for assistance in the field

Fuzzy rule-based landslide susceptibility mapping in Yığılca Forest District (Northwest of Turkey)

Landslide susceptibility map of Yığılca Forest District was formed based on developed fuzzy rules using GIS-based FuzzyCell software. An inventory of 315 landslides was updated through fieldworks after inventory map previously generated by the authors. Based on the landslide susceptibility mapping study previously made in the same area, for the comparison of two maps, same 8 landslide conditioning parameters were selected and then fuzzified for the landslide susceptibility mapping: land use, lithology, elevation, slope, aspect, distance to streams, distance to roads, and plan curvature. Mamdani model was selected as fuzzy inference system. After fuzzy rules definition, Center of Area (COA) was selected as defuzzification method in model. The output of developed model was normalized between 0 and 1, and then divided five classes such as very low, low, moderate, high, and very high. According to developed model based 8 conditioning parameters, landslide susceptibility in Yığılca Forest District varies between 32 and 67 (in range of 0-100) with 0.703 Area Under the Curve (AUC) value. According to classified landslide susceptibility map, in Yığılca Forest District, 32.89% of the total area has high and very high susceptibility while 29.59% of the area has low and very low susceptibility and the rest located in moderate susceptibility. The result of developed fuzzy rule based model compared with previously generated landslide map with logistic regression (LR). According to comparison of the results of two studies, higher differences exist in terms of AUC value and dispersion of susceptibility classes. This is because fuzzy rule based model completely depends on how parameters are classified and fuzzified and also depends on how truly the expert composed the rules. Even so, GIS-based fuzzy applications provide very valuable facilities for reasoning, which makes it possible to take into account inaccuracies and uncertainties

Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yigilca Forest Directorate (Turkey)

WOS: 000329965800014Forest roads are one of the biggest investments in forest management. Their possible adverse effect on the environment is becoming an important issue for administrators due to a recent increase in public awareness. Especially in the Black Sea Region of Turkey, road-related landslides are common in forested areas because the roads are located in hilly regions with steep slopes. In addition to their impact on forests, landslides can cause damage to roadbeds which requires immediate maintenance. Landslide-susceptibility maps are widely used for different purposes such as reducing the effects of landslides, decision making, and planning. These maps can easily be generated by utilizing the advanced features of Geographical Information Systems (GIS) and computer technologies. Logistic regression (LR) is a widely used technique for mapping landslide susceptibility; landslide conditioning parameters such as topography, lithology, land use, distance to streams and roads, and curvature can be mapped by GIS tools. In this study a fieldwork-generated inventory of 288 landslides was used to produce a landslide-susceptibility map for the Yigilca Forest Directorate (Turkey). This map was generated by applying a GIS-based LR method. Land use, lithology, elevation, slope, aspect, distance to streams, distance to roads, and plan curvature were considered as the landslide conditioning parameters. After the landslide-susceptibility map was divided into 5 classes of susceptibility (very low, low, moderate, high, and very high), it was overlapped with a road network map in order to evaluate forest road conditions in terms of landslide susceptibility. For a quantitative analysis of forest road-landslide interaction, 2 new parameters were determined: a landslide frequency index (divided into general and real) and a road-landslide index (divided into general and real). Real landslide frequency and general landslide frequency on the roads were found to be 0.42 and 0.18, respectively. The results showed that the real road-landslide index and the general road-landslide index in the area were 0.10 and 0.04, respectively