Aydin Abdurrahim, Prof

Protected areas play an active role in protecting natural resources and wildlife habitat. These areas must be accessible within protection-use balance. For this reason, road networks in protected areas are one of the main functions of sustainable infrastructure services. The construction phases of road networks in these sensitive areas should be considered in planning within the balance of protection-use with interdisciplinary studies. Especially during the construction of the road network, it is necessary to pay attention to the construction machinery used, geotextile materials, hydraulic and ecological road structures, plantation of the slopes, fences that increase the visual quality and work schedule. Based on a related literature survey, the issues to be considered during the construction phases of road networks (i.e. road planning, tree felling and removing, excavation and embankment, subgrade finishing, road structures and surfacing) in protected areas were evaluated under nine headings. The implementation phases of these issues are important in reducing the adverse effects that will occur in protected areas. In this regard, during the construction phases of road networks, the issues to be considered were evaluated together with the conceptual indicators in terms of management, technique, economy, ecology, and aesthetics. Matters needing attention according to the sensitivity of conceptual indicators during the construction phases of road networks in and around protected areas that contain sensitive ecosystems have been identified and presented in a framework to further the discussions on this issue. Accordingly, the use of the issues to be considered in the planning and construction of road networks with conceptual indicators will help evaluate the planning phase before and after construction. In particular, it can be expected to lead to the creation of a checklist after the planning phase. Thus, the continuity of the issues to be considered during the maintenance, repair, and construction phases of the new road networks or existing road networks planned to be built in a protected area and surrounding areas will provide significant contributions to the functions of the protected areas. The main contributions may include increasing the number of visitors to the protected areas, reducing impacts on wildlife in protected areas by implementing innovative technologies, and developing alternative modes in tourism industry.

In terms of engineering standards, the dimensions of hydraulic structures such as culverts on 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 Turkey, 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 Turkey 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, Turkey) 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.

Differentiating areas of insect damage in forests from areas of healthy vegetation and predicting the future spread of damage increase are an important part of forest health monitoring. Thanks to the wide coverage and temporal observation advantage of remote sensing data, predicting the future direction of insect damage spread can enable accurate and uninterrupted management and operational control to minimize damage. However, due to the large amount of remotely sensed data, it is difficult to process the data and to identify damage distinctions. Therefore, this paper proposes a spatio-temporal Autoregressive Integrated Moving-Average (ARIMA) prediction model based on the Machine Learning technique for processing big data by monitoring oak lace bug (Corythucha arcuata (Heteroptera: Tingidae)) damage with remote sensing data. The advantage of this model is the automatic selection of optimal parameters to provide better forecasting with univariate time series. Thus, multiple spatio-temporal warning levels are distinguished according to the damage growth trend in the series, and the network is constructed with improved time series to better predict future insect damage spread. In the proposed model, the historical Red (R) – Green (G) – Blue (B) bands of the Sentinel-2 (GSD 10 m) satellite were tested as a dataset for the oak lace bug damage in the oak forest situated in the campus of Düzce University, Turkey. The dataset, which contained 38 images for each of the RGB bands, was modeled using the open source R programming language for the peak damage period in 2021. As a result of the test, significant correlations were found between the synthetic and true images (True and synthetic band 2: r=0.960, p<0.001; True and synthetic band 3: r=0.945, p<0.001; True and synthetic band 4: r=0.962, p<0.001). Then, the 48-month time series bands were modeled, and the band estimates were made to predict the August 2023 spread. Finally, a synthetic composite image was created for future prediction using the predicted bands. The tests showed that the model had a good performance in insect damage monitoring. With open access Sentinel-2 images, the proposed model achieved the highest prediction accuracy with a rate of 96%, and had a small prediction error.