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10The complex multi-directional interactions between hydrological, biological and fluvial processes govern the formation and evolution of river landscapes. In this context, as key geomorphological agents, riparian trees are particularly important in trapping sediment and constructing distinct landforms, which subsequently evolve to larger ones. The primary objective of this paper is to experimentally investigate the scour/deposition patterns around different forms of individual vegetation elements. Flume experiments were conducted in which the scour patterns around different representative forms of individual in-stream obstructions (solid cylinder, hexagonal array of circular cylinders, several forms of emergent and submerged vegetation) were monitored by means of a high-resolution laser scanner. The three dimensional scour geometry around the simulated vegetation elements was quantified and discussed based on the introduced dimensionless morphometric characteristics. The findings reveal that the intact vegetation forms generated two elongated scour holes at the downstream with a pronounced ridge. For the impermeable form of the plant, the scour got localized, more deposition was detected within the monitoring zone, and the distance between the obstruction and deposition zone became shorter. It is also shown that with the effect of bending and the subsequent decrease of the projected area of the plant and the increase of bulk volume, the characteristic scour values decrease compared to the intact version, and the scour zone obtains a more elongated form and expands in the downstream direction.

Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (R2) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.

Özİnsansız Hava Araçları (İHA) otomatik veya yarı otomatik uçuş prensibine sahip başlangıçta askeri amaçlar için kullanılan motorlu veya motorsuz hava araçlarıdır. Son yıllarda meydana gelen gelişmelerle birlikte İHA'lar, afet yönetimi ve planlama, ormancılık, fotogrametrik değerlendirme, yol ve nehir gözlemleri, arazilerin üç boyutlu (3B) modellerinin üretilmesi gibi birçok ticari ve akademik çalışmalarda yaygın olarak kullanılmaya başlanmıştır. İHA'lara digital kameralarının entegre edilmesi yüksek çözünürlükte görüntülerin elde edilmesini sağlamıştır. Bu görüntülerin değerlendirilmesi zor ve zaman alıcı olmasına rağmen dijital fotogrametri ile birlikte bu sorun ortadan kalkmakta ve objenin veya arazinin 3 boyutlu(3B) yoğun nokta bulutu, sayısal yüzey modeli (SYM) ve ortogörüntüleri üretilebilmektedir. Bu çalışmada İTÜ Ayazağa Kampüsünde, 60m, 80m ve 100m yüksekliklerinde uçuşlar gerçekleştirilerek 60m ve 80m yükseklikte 90°, 100m yükseklikte 45°, 60° ve 90° kamera açıları ile yüksek çözünürlüklü sayısal görüntüler elde edilerek alanın ortogörüntüleri üretilmiştir. Daha sonra çalışma alanında jeodezik yöntemler kullanılarak önceden tesis edilmiş 5 adet yer kontrol noktası ile ortogörüntülerin doğrulukları test edilmiştir. Ayrıca, farklı yüksekliklerin ve aynı yükseklikte farklı kamera açılarından üretilen ortogörüntünün doğruluk irdelemesi gerçekleştirilmiştir. Anahtar Kelimeler: İnsansız Hava Aracı (İHA), OrtoGörüntü, Eğik Fotogrametri, Dijital Fotogrametri The Investigation of The Height and The Camera Angle in The Production of Orthoimage with Images of Unmanned Aerial Vehicle (UAV)Abstract Unmanned Aerial Vehicles (UAV) are motorized or non-motorized aerial vehicle with an automatic or semiautomatic flight principle. Firstly, UAV's were used for military purposes. Along with the developments in recent years, UAV's have been widely used in many commercial and scientific studies such as disaster management and planning, forestry, photogrammetric assessment, road and river observations and the production of 3D models of land or object. High-resolution images can be obtained with the integration of digital cameras into UAV's. Although image processing is difficult and time-consuming, digital photogrammetry alleviates this problem and can produce 3D dense point cloud, Digital Surface Model (DSM) and orthoimages. In this study, ortoimage was obtained from high resolution digital images which taken using 90° camera angles at 60 and 80-meter altitude and 45°, 60° and 90° camera angles at 100-meter altitude in Istanbul Technical University Ayazağa campus. Subsequently, using geodetic methods, accuracy of ortoimages were tested via 5 ground control points which established before study. Also, effect of different altitude and camera angles at same altitude on accuracy of ortoimages were invastigated.

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Two of the very basic forestry parameters, the Breast Height Diameter (DBH) and Tree Height (TH) are very effective when characterizing forest stands and individual trees. The traditional measurement process of these parameters takes a lot of time and consumes human power. However, because of the development of PC power and digital storage in recent years, 3D Point Cloud (PC) gains quickly provide a very detailed view of forestry parameters. PC data sources include Airborne LiDAR Systems (ALS), Terrestrial Laser Scanning (TLS) and finally, the Unmanned Air Vehicle (UAV) for forestry applications. In this study, the PC datasets from these sources were used to study the feasibility of the DBH and TH values of a D-stage oak stand. The DBH and TH estimates are compared with the onsite measurements, which are considered to be fundamental truths, to their performance due to overall error statistics, as well as the cost of calculation and the difficulties in data collection. The results show that the computer data obtained by TLS has the best average square error (0.22 cm for DBH and 0,051 m for TH) compared to other computer data. The size of Pearson correlation between TLS-based and on-site-based measurements has reached 0.97 and 0.99 for DBH, respectively.

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