This study tested the potential of a short time series of very high spatial resolution (cm to dm) remote sensing datasets obtained from unmanned aerial system (UAS)-based photogrammetry and terrestrial laser scanning (TLS) to monitor snow cover ablation in the upper Dischma valley (Davos, Switzerland). Five flight missions (for UAS) and five scans (for TLS) were carried out simultaneously: Four during the snow-covered period (9, 10, 11, and 27 May 2016) and one during the snow-free period (24 June 2016 for UAS and 31 May 2016 for TLS). The changes in both the areal extent of the snow cover and the snow depth (HS) were assessed together in the same case study. The areal extent of the snow cover was estimated from both UAS- and TLS-based orthophotos by classifying pixels as snow-covered and snow-free based on a threshold value applied to the blue band information of the orthophotos. Also, the usage possibility of TLS-based orthophotos for mapping snow cover was investigated in this study. The UAS-based orthophotos provided higher overall classification accuracy (97%) than the TLS-based orthophotos (86%) and allowed for mapping snow cover in larger areas than the ones from TLS scans by preventing the occurrence of gaps in the orthophotos. The UAS-based HS were evaluated and compared to TLS-based HS. Initially, the CANUPO (CAractérisation de NUages de POints) binary classification method, a proposed approach for improving the quality of models to obtain more accurate HS values, was applied to the TLS 3D raw point clouds. In this study, the use of additional artificial ground control points (GCPs) was also proposed to improve the quality of UAS-based digital elevation models (DEMs). The UAS-based HS values were mapped with an error of around 0.1 m during the time series. Most pixels representing change in the HS derived from the UAS data were consistent with the TLS data. The time series used in this study allowed for testing of the significance of the data acquisition interval in the monitoring of snow ablation. Accordingly, this study concluded that both the UAS- and TLS-based high-resolution DSMs were biased in detecting change in HS, particularly for short time spans, such as a few days, where only a few centimeters in HS change occur. On the other hand, UAS proved to be a valuable tool for monitoring snow ablation if longer time intervals are chosen.

Forest mapping against rockfalls on a regional scale in Inebolu-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. Keywords: Energy line angle, protection forest, rockfall, rockfall propagation map Kaya yuvarlanmalarından etkilenen orman alanlarının belirlenmesi: İnebolu örneği Özet: Kaya yuvarlanmalarına karşı potansiyel koruyucu ormanların belirlenmesi, önemli ölçüde koruma sağladıkları için son derece önemlidir. Bu amaç doğrultusunda Özlüce Orman İşletme Şefliği’nde (İnebolu, Türkiye) örnek bir çalışma gerçekleştirilmiştir. İlk olarak potansiyel kaya yuvarlanması kaynak bölgeleri (PKYKB), tek bir girdi olarak Sayısal Yükseklik Modeli (SYM) kullanan RollFree yazılımı ile belirlenerek haritalanmıştır. Daha sonra, çok düşük (düşük yayılım sınırlarını gösteren), düşük, orta ve yüksek şeklinde sınıflandırılan maksimum birikme bölgesi olası sınırlarını elde etmek için farklı senaryolarda, yani dört farklı enerji hattı açısı (28°, 32°, 35° and 38°) için kaya blokları yayılım haritalarının oluşturulmasında ampirik enerji hattı açısı (EHA) yaklaşımı ile düşen kaya bloklarının hareket mesafelerin tespiti ile elde edilmiştir. Kaya yuvarlanmasına karşı koruyucu fonksiyona sahip orman alanlarının belirlenmesi için üretilen kaya yuvarlanması yayılım haritaları meşcere haritası ile çakıştırılmıştır. Yüksek olasılıklı yayılım haritalarına göre toplam ormanlık alanın sadece %9’u kaya yuvarlanmasına karşı koruyucu fonksiyon görürken, çok düşük olasılıklı yayılım haritasına göre ormanlık alanların %17’si koruyucu fonksiyon görmektedir. Anahtar kelimeler: Enerji hattı açısı, koruma ormanı, kaya yuvarlanması, kaya yuvarlanması yayılım haritası Received (Geliş): 27.12.2016 - Revised (Düzeltme): 08.03.2017 - Accepted (Kabul): 28.03.2017 Cite (Atıf): Aydin, A., Eker, R., 2017. Forest mapping against rockfalls on a regional scale in Inebolu of Turkey. Journal of the Faculty of Forestry Istanbul University 67(2): 136-149. DOI: 10.17099/jffiu.281710

The rising floodwaters of the Maritza River, originating in the Balkans, affect those living in adjacent areas. The lower regions of the Maritza River are especially vulnerable to floods. The city of Edirne and the surrounding region in Turkey located downstream of the Maritza River are frequently affected by flooding. The section of the river inside the Turkish border is short; therefore, there is not adequate warning time for Turkey to alert the population against flash floods. For this reason, it is essential that Turkey acquire current flow information from Bulgarian sites for use in current flow prediction for Turkey's section of the river. To this aim, in order to predict the current flow of the Kirişhane station (Turkey) from the transboundary data of Plovdiv and Svilengrad stations (Bulgaria), four different models (M1‒M4) were developed by using the fuzzy inference system (FIS). Flow data from the Plovdiv, Svilengrad and Kirişhane stations were gauged every two hours covering the period from 9 February 2010 00:00:00 to 21 February 2010 22:00:00. In the first model, estimation was made using the current flows of the Plovdiv and Svilengrad stations. In the second model, estimation was made based on a two hour ahead prediction of the Svilengrad station and a four hour ahead prediction of the Plovdiv station. In the third model, calculations were based on predictions of four hours ahead of the Svilengrad station and eight hours ahead of the Plovdiv station. In the last model, estimation was based on predictions of six hours ahead of the Svilengrad station and twelve hours ahead of the Plovdiv station. The prediction ability of the FIS was evaluated by using the determination coefficient (R²), the Nach–Sutcliffe sufficiency score (NSSS), the normalized root mean square error (NRMSE), and the correlation coefficient (CORR). According to their performance criteria, all developed models produced highly satisfactory results. The M₂ model gave the best results according to both NSSS and R² values, whereas M₄ had the lowest NSSS and R² values. This study demonstrated that developed fuzzy rule-based models can satisfactorily predict flood waves and thus can be used for flood forecasting and warning systems.