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

The usage of modern remote sensing techniques in monitoring and mapping of landslide and snow related natural hazards

YÖK Tez No: 543065Uzaktan algılama tekniklerinin doğal afetlerde kullanımı teknolojik ve bilimsel gelişmelere bağlı olarak artmaktadır. Bu nedenle bu çalışmada insansız hava aracı (İHA) sistemleri, lazer tarama sistemleri (hava ve yersel), Sentetik Açıklıklı Radar (SAR) interferometri (InSAR) ve optik sayısal fotogrametri modern uzaktan algılama tekniklerinin üç ayrı ülkede (Türkiye, Avusturya ve İsviçre) yer alan beş ayrı çalışma alanında heyelan ve kar/çığ çalışmalarına yönelik uygulamaları yapılmıştır. Avusturya (Gallenzerkogel ve Gschliefgraben heyelanları) ve Türkiye'de (Himmetoğlu ve Devrek heyelanları) ikişer adet alanda heyelan çalışması ve İsviçre'de (Dischma vadisi) kar derinlik haritalama ve kar erimesinin izlenmesi çalışması yapılmıştır. Avusturya'daki heyelan sahalarında İHA ve hava lazer tarama verileri ile heyelan izleme, Türkiye'deki heyelan sahalarında InSAR zaman serileri (Persistent Scatterers Interferometry, PSI ve Small Baseline Subset, SBAS analizleri), İHA ve optik sayısal fotogrametri teknikleri ile heyelan deformasyon haritalama, İsviçre'de ise İHA ve yersel lazer tarama verileri ile kar derinlik haritalama ve kar örtüsü erimesinin izlenmesi çalışması yapılmıştır. Gschliefgraben heyelan alanında sadece hava lazer tarama verisi ile nokta bulutu karşılaştırma teknikleri ve sayısal görüntü korelasyon teknikleri ile heyelan deformasyonlarının ve yerdeğiştirme alanlarının belirlenmesi çalışması yapılmıştır. Gallenzerkogel heyelan alanında yaklaşık bir yıllık dönem için üç ayrı İHA verisi ve heyelan öncesi hava lazer tarama verisi kullanılarak hem Sayısal Yükseklik Modeli (DEM) farkları hem de modern nokta bulutu karşılaştırma algoritması olan M3C2 yöntemi kullanılarak heyelan izleme çalışması yapılmıştır. Dischma vadisinde ise yaklaşık bir aylık dönemde temin edilen beş ayrı İHA ve yersel lazer tarama (TLS) verisi ile kar erimesinin izlenmesi çalışması yapılmıştır. İHA ve TLS verileri zaman serileri kullanılarak kar erimesine ilişkin ilk örnek bir çalışma yapılmıştır. Zonguldak ili Devrek ilçesi Karşıyaka mahallesi heyelan alanında eski tarihli hava fotoğrafları ve İHA verileri ile birlikte ERS-1/2, Envisat ASAR ve Sentinel-1 C band SAR görüntüleri ile uzun dönemli (1992-2015) deformasyon haritalama için PSI analizi yapılmıştır. Bolu ili Göynük ilçesi Himmetoğlu köyü heyelan alanında ise İHA verisi ile birlikte, Sentinel-1 C band SAR görüntüleri ile PSI ve SBAS analizleri yapılarak yüzey deformasyonlarını izleme çalışması yapılmıştır. Gerçekleştirilen örnek uygulamalar ile doğal afetlerde gelişmiş uzaktan algılama tekniklerinin kullanım imkânları değerlendirilmiştir.The use of remote sensing techniques in natural hazards has been increasing due to technological and scientific developments. The present study examined the use of modern remote sensing techniques in landslide and snow/avalanche case applications within five study areas selected from three countries (i.e., Turkey, Austria and Switzerland). These selected modern remote sensing techniques included unmanned aerial vehicle (UAV) systems, laser scanning systems (aerial and terrestrial), Synthetic Aperture Radar (SAR) interferometry (InSAR), and optical aerial photogrammetry. The landslide studies were carried out within two study areas in Turkey (Himmetoğlu and Devrek) and two in Austria (Gallenzerkogel and Gschliefgraben), while the snow/avalanche study was conducted in one study area in Switzerland (Dischma valley). Both UAV and aerial laser scanning data were used for landslide monitoring in the study areas located in Austria and the InSAR time series together with UAV and optical aerial photogrammetry were used for landslide monitoring in the study areas in Turkey. However, only UAV and terrestrial laser scanning (TLS) were used for monitoring snow depth mapping and snow cover ablation in the study area in Switzerland. For the monitoring of the Gschliefgraben landslide, only techniques for comparison of point clouds and the digital image correlation method were used via the available aerial laser scanning data. However, both UAV and laser scanning data were used to monitor the Gallenzerkogel landslide using both the Digital Elevation Model (DEM) of difference and the M3C2 algorithm, an advanced point cloud comparison method. A case study was carried out for monitoring snow cover ablation with UAV and TLS data obtained as a five-time series over the period of a month in Dischma valley (Davos, Switzerland). This case study is one of the first in which snow cover ablation was monitored using both UAV and TLS. For a long-term period (1992-2015), the surface deformation due to the Karşıyaka Road landslide (Devrek, Zonguldak) was monitored and mapped via persistent scatterers interferometry (PSI) analysis carried out using C-band SAR images from ERS-1/2, Envisat ASAR, and Sentinel-1 satellites. In addition to data from SAR satellites, the aerial photographs and UAV data were also used. Both PSI and small baseline subset (SBAS) analysis were carried out together with UAV data in order to obtain C-band SAR images of the surface deformation caused by the Himmetoğlu Village landslide (Göynük, Bolu) from the Sentinel-1 satellite. The usage possibilities of advanced remote sensing techniques in natural hazards were evaluated based on the findings of these case studies

Mapping landslide susceptibility using geographical information systems and its evaluation for forest roads in the Yigilca Forest Directorate

YÖK Tez No: 344055Yığılca Orman ??letme Müdürlüğü, Türkiye'de heyelanların en fazla görüldüğü Karadeniz Bölgesi'nin Batı bölümünde yer almaktadır. Alan 499 km2büyüklüğündedir. Alandaki en önemli yerle?im Yığılca (Düzce) ilçesidir. Alanda heyelanlar özellikle yerle?im ve ziraat alanlarına yakın alanlarda görülmektedir. Ayrıca alandaki yollar heyelan olu?umunu tetiklemekte ve meydana gelen heyelanlardan etkilenmektedir. Bu yüzden alana ait heyelan duyarlılık haritası, Coğrafi Bilgi Sistemleri (CBS) tabanlı olarak olu?turulmu?tur ve orman yolları açısından değerlendirilmi?tir. Heyelan duyarlılık haritasının olu?turulmasında temel altlık olan heyelan envanteri, yoğun arazi çalı?ması ile elde edilmi?tir. Ayrıca, 2005 yılında MTA (Maden Tetkik Arama)tarafından üretilen 1/500000 ölçekli heyelan envanterinden de yararlanılmı?tır. Alanda envanter çalı?ması ile 288 adet heyelan belirlenmi?tir.Heyelan duyarlılık haritasının üretilmesinde girdi parametre olarak, heyelan olu?umunda etkili olduğu arazi çalı?maları sırasında gözlenen sekiz parametre kullanılmı?tır. Bu parametreler; arazi kullanımı, litoloji, yükselti, eğim, bakı, akarsuyauzaklık, yola uzaklık ve plan eğriselliktir. Heyelan duyarlılık haritası, sayısalla?tırılan envanter haritası ve girdi parametreler kullanılarak Lojistik Regresyon (LR) yöntemi ile üretilmi?tir. Üretilen duyarlılık haritasının doğrulanmasında ROC (Relative Operating Curve) eğrisi altında kalan alan olan AUC (Area of Under the Curve ) kullanılmı?tır. Modelin AUC değeri 0,905 olarak elde edilmi?tir. Üretilen harita, çok dü?ük (0-0,2), dü?ük (0,2-0,4), orta (0,4-0,6), yüksek (0,6-0,8) ve çok yüksek (0,8-1) olmak üzere be? duyarlılık sınıfına ayrılmı?tır. Buna göre, 8439,9 ha alan çok dü?ük duyarlılık sınıfında, 20191,7 ha alan dü?ük duyarlılık sınıfında, 14211,6 ha alan orta duyarlılık sınıfında, 6659,0 ha alan yüksek duyarlılık sınıfında ve 371,8 ha alan çok yüksek duyarlılık sınıfında yer almaktadır. Çalı?ma alanında yol ağı planına göre orman yolu, köy yolu ve karayolu olmak üzere 931,9 km (866,3 km'si in?a edilmi?) yol bulunmaktadır. Bu yolların heyelanlar açısından değerlendirilmesi amacıyla çakı?tırma analizi yapılmı?tır. Yapılan analize göre plandaki yolların 84,1 km'si çok dü?ük duyarlılık sınıfı üzerinde, 413,5 km'si dü?ük duyarlılık sınıfı üzerinde, 232,1 km'si orta duyarlılık sınıfı üzerinde, 174,6 km'si yüksek duyarlılık sınıfı üzerinde ve 22,7 km'si çok yüksek duyarlılık sınıfı üzerinde yer almaktadır. Yol ağı planında bulunan ancak henüz in?a edilmemi? yolların ise 25,2km'si çok dü?ük duyarlılık sınıfında, 29,5 km'si dü?ük duyarlılık sınıfında, 9,9 km'si orta duyarlılık sınıfında ve 1 km'si ise yüksek duyarlılık sınıfında yer almaktadır. Çok 2yüksek duyarlılık sınıfında ise bulunmamaktadır. Çalı?ma alanındaki yollar üzerindeki gerçek heyelan frekans değeri 0,42 ve genel heyelan frekans değeri 0,18'dir. Alanda gerçek yol-heyelan indeks değeri 0,10 olarak ve genel yol-heyelan indeks değeri 0,04 olarak belirlenmi?tir. Anahtar sözcükler: Coğrafi Bilgi Sistemleri, Heyelan Duyarlılık Haritası, Lojistik Regresyon, Orman Yolları, YığılcaThe Yığılca Forest Directorate is located in the Western Black Sea Region of Turkey, which has the highest occurrence of landslides in the country. The most important settlements in the 499-square-km study area are in Yığılca County of Düzce Province. Landslides are especially prevalent in areas near settlements and agricultural land. In addition, roads in the area trigger landslides and are generally affected by their occurrence. Thus, a landslide susceptibility map covering the study area has been generated based on the Geographical Information Systems (GIS) and evaluated in terms of forest roads. A landslide inventory based on susceptibility mapping was produced as a result of intensive fieldwork. In addition, a landslide inventory of the area created in 2005 by the MTA (Mineral Research and Exploration), scaled at 1/500000, was used for comparison. This inventory, generated through fieldwork, included 288 landslides.Eight parameters that have been observed in fieldwork as being effective for landslide occurrence were used for mapping landslide susceptibility. These are: land use, lithology, elevation, slope, aspect, distance to streams, distance to roads and plan curvature. Using these eight parameters, a landslide susceptibility map was created, as well as a digitized inventory map using the Logistic Regression (LR) method. For validation, the AUC (Area Under the Curve), which is the area under the ROC (Relative Operating Curve), was used to create the susceptibility map. The AUC value of the model was 0.905. The resulting map included five susceptibility classifications: very low susceptibility (0-0.2), low susceptibility (0.2-0.4), moderate susceptibility (0.4-0.6), high susceptibility (0.6-0.8) and very high susceptibility (0.8-1). According to these criteria, an area of 8439.9 ha was located in the very low-susceptibility class, an area of 20191.7 ha in the low-susceptibility class, an area of 14211.6 ha in the moderatesusceptibility class, an area of 6659.0 ha in the high-susceptibility class, and an area of 371.8 ha in the very high-susceptibility class. According to the road network plans, 931.9 km of roads, including forest roads, village roads and motorways, are to be found in the study area, 866.3 km of which have been constructed to date. An overlay analysis was made for evaluating these roads in terms of landslides. According to the results, of the roads in the network plan, 84.1 km were located over the very low-susceptibility areas, 413.5 km over the low-susceptibilityareas, 232.1 km over the moderate-susceptibility areas, 174.6 km over the highsusceptibility areas, and 22.7 km over the high-susceptibility areas. Of those roads which have not yet been constructed, 25.2 km are located over very low-susceptibility4class areas, 29.5 km over low-susceptibility areas, 9.9 km over moderate-susceptibility areas, and 1 km over high-susceptibility class areas. There are no planned roads over very high-susceptibility class areas. In the study area, real landslide frequency and general landslide frequency on the roads are 0.42 and 0.18, respectively. The real roadlandslide index and the general road-landslide index in the area are 0.10 and 0.04, respectively

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

Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey)

This study presents a successful combination of different remote sensing data used in a long-term retrospective investigation of a large and destructive deep-seated, slow-moving landslide reactivated on 16 July 2015 in Devrek District (Zonguldak, Turkey). To this aim, Synthetic Aperture Radar (SAR) data were used for Interferometric SAR (InSAR) time-series analysis together with unmanned aerial vehicle (UAV) images and aerial photographs for digital photogrammetric analysis. The SAR dataset was divided into three sub-periods: 1) 1992-2001 for ERS-1 and ERS-2 satellites; 2) 2003-2010 for Envisat ASAR; and 3) 2014-2015 for Sentinel-1. Persistent Scatterers Interferometry (PSI) was applied for each sub-period. In total, 20 aerial photographs, dating from as early as 1944, were obtained, along with data from a UAV flight mission conducted on 23 June 2018. The historical aerial photographs revealed that the region has had a landslide problem since the 1940s. Between 1944 and 2018, a noticeable expansion of the settlement area towards the toe of the landslide was also observed. Aerial photographs (1984 and 2011) and UAV images (2018) were used to map landslide deformations using the M3C2 algorithm. Due to the high number of modelling errors, the 1984 and 2011 aerial photographs did not allow mapping of the landslide deformations. However, it was possible to determine them for the periods of 2011 and 2018. The M3C2 results between 2011 and 2018 were also compared to the PSI results, which were quite compatible with those obtained via photogrammetric methods. Moreover, two orthophotos belonging to 2011 and 2018 were used to reveal the horizontal displacement of buildings caused by the landslide. As a result, the complete investigation of the landslide performed in this study may serve to facilitate additional plans and strategies for prevention and mitigation of potential reactivations in the future.Restrained Dataset Project [31237]The authors are grateful to ESA for providing ERS-1/2 and Envisat ASAR satellite data with the Restrained Dataset Project (Proposal Number: 31237).WOS:0005839552000532-s2.0-8509033501

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

The effects of forests on landslides

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.Especially because of adverse results of stability problems in mountainous regions, in point of their effects on the landslides, the importance of forests and forestry activities as well as their protection function has been increased. Forests and forestry activities such as logging and road construction in terms of landslide related problems have been studied in literature with different aspects. However, it attracts the attention that is needed to a review article which discuss why and how forest and forestry activities affect the landslide occurrence. In this article, studies made in international levels were discussed by analyzing based on the subjects of forest-landslide and forestry-landslide