This work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH), an important 3D structural parameter in forest inventory and biomass estimation. The study areas were an agricultural field located in the province of Málaga, Spain, where a small group of olive trees was chosen for the UAV surveys, and an open woodland area in the outskirts of Sofia, the capital of Bulgaria, where a 10 ha area grove, composed mainly of birch trees, was overflown. A DJI Phantom 4 Pro quadcopter UAV was used for the image acquisition. We applied structure from motion (SfM) to generate 3D point clouds of individual trees, using Agisoft and Pix4D software packages. The estimation of DBH in the point clouds was made using a RANSAC-based circle fitting tool from the TreeLS R package. All trees modeled had their DBH tape-measured on the ground for accuracy assessment. In the first study site, we executed many diversely designed flights, to identify which parameters (flying altitude, camera tilt, and processing method) gave us the most accurate DBH estimations; then, the resulting best settings configuration was used to assess the replicability of the method in the forested area in Bulgaria. The best configuration tested (flight altitudes of about 25 m above tree canopies, camera tilt 60°, forward and side overlaps of 90%, Agisoft ultrahigh processing) resulted in root mean square errors (RMSEs; %) of below 5% of the tree diameters in the first site and below 12.5% in the forested area. We demonstrate that, when carefully designed methodologies are used, SfM can measure the DBH of single trees with very good accuracy, and to our knowledge, the results presented here are the best achieved so far using (above-canopy) UAV-based photogrammetry.

Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth’s heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring.

Global climate change is a phenomenon that seriously affects the balance of a wide variety of ecosystems and is the intense focus of climate scientists and environmental researchers. In this context, periodic monitoring of glacier areas in terms of a better understanding of atmosphere-ocean interactions; thus, predicting the effects of climate change and planning against future threats by evaluating environmental impacts is an important research area. Especially the polar regions, where the melting of glaciers and the rise of sea levels are visibly observed, are important for climate scientists in providing crucial observations to understand and predict global climate change. In this study, within the scope of the international bilateral cooperation project carried out in cooperation with Istanbul Technical University (ITU) and the Bulgarian Academy of Sciences (BAS) (Project No: 121N033), the spatial changes in snow/glacier areas obtained from UAV Photogrammetry products generated during the 6th and 7th Antarctic National Science Expeditions. Snow/glacier areas were segmented with the K-Net deep learning approach which has been previously tested for accuracy and provides glacier mapping with accuracy metrics over 99%, on the high spatial resolution orthophotos produced during the two periods. The snow/glacier areas difference between the two periods were calculated and compared and water bodies which are critical areas, were specifically examined. The result of this comparison shows that the glacier area decreased by approximately 11% in just 1 year. However, to better understand these changes in snow/glacier areas, the region needs to be observed closely for longer time periods. It is thought that future studies will contribute to efforts to manage global environmental impacts and cope with climate change by focusing on monitoring and better understanding changes in these critical regions.

Küresel iklim değişikliği süreçlerinin daha iyi anlaşılabilmesi ve insanlığın gelecekteki süreçlere uyum sağlayabilmesi açısından buzul alanlardaki değişikliklerin izlenmesi önem taşımaktadır. Buzul değişimlerinin takibi için kullanılan yöntemlerden birisi olan insansız hava aracı (İHA) fotogrametrisi ile kriyosferin ve buzul değişikliklerinin yüksek konumsal doğrulukla izlenebilmesi için küresel konum belirleme sistemlerine (Global Navigation Satellite System-GNSS) dayanan konum belirleme yöntemleri kullanılmaktadır. Bu çalışmada Antarktika'da, Türk Bilimsel Araştırma Kampı'na ev sahipliği yapan Horseshoe Adası'nda, buzul takibi amacıyla belirlenen bir test alanında kinematik ölçme sonrası değerlendirme (Post Processing Kinematic-PPK) yöntemi kullanılarak üretilen fotogrametrik ürünlerin konumsal doğruluğu araştırılmıştır. Hava fotoğraflarından hareket ile nesne oluşturma (Structure from Motion-SfM) yöntemine dayanarak üretilen ortofoto ve sayısal yükseklik modelinin (SYM) konum doğruluğu, arazide klasik gerçek zamanlı kinematik (Real Time Kinematic-RTK) konum belirleme yöntemiyle konumları belirlenmiş yer kontrol noktaları kullanılarak elde edilmiştir. Yatay ve düşey koordinatlar için hesaplanan karesel ortalama hata değerleri sırasıyla 1.05 cm, 0.52 cm ve 11.33 cm’dir.