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Thecorrect prediction of the deterioration of bituminous hot mix asphalt (HMA),which is a significant substructure investment for roads, is an indispensableelement in the correct sorting of the budget allocated for maintenance andrepair. This study has developed a pavement performance prediction model forstate roads and intercity roads hot mix asphalt (HMA) coated under theresponsibility of General Directorate of Highways (KGM) for capable ofperformance forecast for next year. International roughness index (IRI) wereused as a performance indicator of pavements. Pavement condition index (PCI) ofthe road sections (year of construction of the road known by KGM), usingexisting pavement distresses data by PAVER system, were obtained. Theprediction performance model was developed depending on the age and PCI of the roadsections using deterministic regression model technique. Then, mathematicalrelationship was determined between PCI and IRI on the same road section. Theobtained mathematical relationship is integrated into the predictionperformance model. A prediction model determines the pavements IRI values forthe coming years, based on independent variables of measured IRI value and year'sestimated performance, were obtained. The accuracy of the IRI deterioration predictionmodel was controlled by the data sets not used in the modeling studies., Önemlibir altyapı yatırımı olan bitümlü sıcak karışım (BSK) üstyapılarınbozulmalarının doğru tahmin edilmesi, bakım ve onarım için ayrılan bütçenindoğru olarak belirlenmesinde vazgeçilmez bir unsurdur. Çalışmada, KarayollarıGenel Müdürlüğünün (KGM) sorumluluğunda bulunan devlet yolu ve il yolustatüsündeki BSK kaplamalı yollarda, gelecek yıllara ait performans tahminiyapabilen bir üstyapı performans tahmin modeli geliştirilmiştir. Üstyapılarınperformans göstergesi olarak uluslararası düzgünsüzlük indeksi (IRI)kullanılmıştır. KGM tarafından yapım yılı bilinen yollarda mevcut üstyapıbozulma bilgileri kullanılarak PAVER sistemi yardımıyla kesimlerin üstyapıdurum indeksi (PCI) bilgileri elde edilmiştir. Kesimlerin PCI ve yaşlarınabağlı olarak deterministik regresyon modeli tekniği kullanılarak tahmin modelioluşturulmuştur. Sonrasında, aynı yol kesimlerinde PCI ve IRI arasındakimatematiksel ilişki ortaya konmuştur. Elde edilen bu matematiksel ilişki oluşturulangelecek tahmin modeline entegre edilerek, ölçülen IRI değeri ve performansıtahmin edilecek yıl bağımsız değişkenlerine bağlı olarak üstyapıların gelecekyıllara ait IRI değerlerini belirleyen bir tahmin modeli elde edilmiştir. IRIbozulma tahmin modelinin doğruluğu, modelleme çalışmalarında hiçbir şekildekullanılmayan veri setleri ile kontrol edilmiştir.

WOS:000410445200028 The 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.