Accuracy evaluation of DTM interpolated from data acquired with photogrammetric and GPS-RTK methods

W pracy przedstawiono analizę dokładności NMT zbudowanego na podstawie danych pozyskanych metodą fotogrametryczną i GPS-RTK. Obiektem badawczym był obszar o powierzchni ok. 50 km². Był to teren równinny, w przeważającej części użytkowany rolniczo. Dane GPS-RTK stanowił zbiór ponad 9 000 punktów. Czarnobiałe zdjęcia lotnicze w skalach 1:13 000 i 1:26 000 stanowiły podstawę do fotogrametrycznego opracowania NMT. W oparciu o pomierzone w terenie fotopunkty naturalne wyrównano na fotogrametrycznej stacji cyfrowej ImageStation blok 30 zdjęć - średni błąd pomiaru na zdjęciu po wyrównaniu wyniósł ±4.6 μm. Obszary leśne i porośnięte gęstymi zadrzewieniami wyłączono z pomiaru fotogrametrycznego. W oprogramowaniu IS Automatic Elevation określono w sposób automatyczny wysokości punktów w węzłach regularnej siatki o boku 15 m (punkty pomierzone wcześniej na zdjęciach półautomatycznie zostały włączone jako wartości inicjalne w trakcie automatycznego pomiaru). Powstały NMT został zweryfikowany. Punkty pomierzone bezpośrednio w terenie przyjęto jako bezbłędne i użyto do określenia dokładności NMT zbudowanego metodą fotogrametryczną. Z racji różnej dokładności pomiaru rzeźby terenu na zdjęciach w skalach 1:13 000 i 1:26 000 ocenę dokładności NMT wykonano oddzielnie w dwóch podobszarach. Porównując wysokości punktów interpolowanych z modelu z wysokościami punktów GPS-RTK, określono błąd NMT. W terenie odkrytym uzyskano dokładność NMT ±0.21 m (0.08‰ W) w obszarze pokrycia zdjęciami w skali 1:13 000 i ±0.28 m (0.07‰ W) w obszarze pokrycia zdjęciami w skali 1:26 000.Precise Digital Terrain Model (DTM), i.e., a model of high (20÷30 cm) accuracy can be built with data which come from miscellaneous sources, e.g., direct field measurements, measurements on photogrammetric images, cartographic data (large scale maps) or airborne laser scanning performed with the purpose of DTM building. In this work, analysis of DTM correctness is presented. The DTM was built based on photogrammetric and GPS-RTK data. The object of study was a terrain of an about 50 km² area. It was a flat rural area with maximum height difference of about 35 m. The GPS-RTK data formed a set of more than 9 000 points, collected using an Ashtech Z-Xtreme GPS receiver. Reference corrections were transmitted from a base station placed on the class II point of the national geodetic network. Black and white airborne photos taken in 2003-2004 to the scale of 1:13 000 (flight height of about 2 750 m) and to the scale of 1:26 000 (flight height of about 4 000 m) formed the basis for photogrammetric development of DTM. The images were acquired from CODGiK in Warsaw. The natural photogrammetric control points measured in the terrain, forming a set of 30 images, were adjusted using an ImageStation digital photogrammetric station. The RMS of the measurements taken from the images was, after adjustment, ±4.6 μm. Because the rural area photographed varied only little, semi-automatic measurements (9 205 points) were carried out prior to generation of full-automatic DTM points. A forested terrain and terrains covered by dense vegetation were excluded from photogrammetric measurements. At the next step, the heights of points in a regular 15 m GRID were automatically evaluated using the IS Automatic Elevation software (the points measured semi-automatically in the photos were included in the automatic process as the initial values). The DTM generated this way was manually verified: the wrong measured points which were not terrain points (roofs of buildings, high vegetation) were eliminated. The points acquired in field measurements were accepted as error-free points and used to evaluate the accuracy of the DTM built based on photogrammetric measurements. Evaluation of the DTM accuracy was carried out separately in two sub-areas because topographic measurements taken from images to the scales of 1:13 000 and 1:26 000 differed in accuracy. The DTM's RMS was determined by comparing point heights: points interpolated from the model were compared with the GPS-RTK points. In the uncovered terrain (without dense shrubbery and forest), the RMS of DTM was 0.21 m (0.08‰ W) and 0.28 m (0.07‰ W) in the area covered by photographs to the scales of 1:13 000 and 1:26 000, respectively

Occupational Exposure to Impulse Noise Associated With Shooting

Shooting training is associated with exposure to a considerable amount of unique noise. We wanted to evaluate noise exposure during such training. Our observations especially apply to professional sport shooters, but they are also valid for shooting coaches/instructors. We collected acoustic signals in 10-, 25- and 50-m as well as open-air shooting ranges. The recorded material was analysed with orthogonal, adaptive parameterization by Shur. The mean duration of a single acoustic signal was 250–800 ms with the C-weighted sound peak pressure level of 138.2–165.2 dB. Shooters may be exposed to as many as 600–1350 acoustic impulses during a training unit. The actual load for the hearing organ of a professional shooter or a shooting coach is ~200 000 acoustic stimuli in a year-long training macrocycle. Orthogonal, adaptive parameterization by Shur makes safe scheduling of shooters’ training possible

Study of accuracy of DTM inerpolated from airborne laser scanning data of ScaLARS System

Dokładność Numerycznego Modelu Terenu (NMT), interpolowanego na podstawie danych lotniczego skaningu laserowego, zależy od wielu czynników, m.in. od ukształtowania terenu, pokrycia terenu, stabilności nalotu fotogrametrycznego, jakości danych nawigacyjnych i dokładności kalibracji, terenowej wielkości śladu plamki promienia lasera (wysokości lotu i zbieżności wiązki), gęstości pozyskanych punktów, zastosowanej metody filtracji danych. W pracy przedstawiono ocenę dokładności NMT zrealizowanego dla 20-kilometrowego odcinka doliny rzeki Widawy na potrzeby modelowania hydrodynamicznego. Skaning laserowy wykonany został prototypowym skanerem ScaLARS, skonstruowanym w Instytucie Nawigacji Uniwersytetu w Stuttgarcie. Do rejestracji sygnału INS i GPS wykorzystano system Applanix POS/AV 510. Nalot wykonano samolotem AN-2, z wysokości 550 m. Terenowa wielkość śladu plamki lasera to około 0.6 m. Kalibrację systemu wykonano semi-automatycznie, uzyskując błąd bezwzględny w odniesieniu do obszarów kontrolnych, pomierzonych techniką GPS na poziomie 0.3 m wzdłuż i w poprzek do kierunku lotu oraz błąd wysokości 0.1 m. Badanie dokładności zbudowanego NMT przeprowadzono w oparciu o dane pozyskane z pomiaru terenowego technikami GPS i tachimetryczną. Wykonano pomiar na czterech reprezentatywnych obszarach obiektu badawczego. Filtrację danych przeprowadzono automatycznie z wykorzystaniem własnych algorytmów, bazujących na odpornej aproksymacji danych ruchomą powierzchnią wielomianową. W zależności od ukształtowania i pokrycia terenu uzyskano dokładności wysokościowe NMT od 0.17 m do 0.46 m.Accuracy of Digital Terrain Model (DTM) generated from airborne laser scanning data depends on many factors, e.g. terrain structures, landcover, stability of photogrammetric flight, quality of navigation data, accuracy of calibration, size of laser footprint on terrain (height of flight and convergence of laser beam), density of captured points, method of raw ALS data filtering. In this work the accuracy determination of DTM generated for 20-kilometer part of valley of Widawa river was presented. This DTM was used in hydrodynamic modelling. Airborne laser scanning was carried out using prototypic ScaLARS scanner (developed in Institute of Navigation of Stuttgart University). INS and GPS signals were registered by Applanix POS/AV 510 system. Photogrammetric flight using AN-2 aeroplane was made from height of 550 m. Footprint of laser beam had on the terrain size of about 0.6 m. Calibration of system was carried out semiautomatically. In the reference of GPS measured control fields relative error was estimated on the level about 0.3 m (along and across the flight direction) and error of height was about 0.1 m. Research of accuracy determination of generated DTM was carried out based upon fields measurements using GPS and tacheometric techniques. The measurements were made for four representative fields of study area. Data filtering was carried out using own algorithms based upon robust estimation of moving polynomial surface to scanning data. Depending on the terrain landscape and landcover DTM accuracy was evaluated from value 0.17 m to 0.46 m

3D Modeling of the Prussian Fortress in Nysa Using Laser Scanning Data

Laser scanning data, both airborne and terrestrial, are increasingly being used for 3D modeling. This is a particularly effective measurement technology for historic fortresses that are a combination of stone and earthen structures and that are usually covered by dense vegetation. This paper presents a methodology for constructing a realistic 3D model using the example of the Prussian Fortress in Nysa. The data used for modeling were collected by airborne and terrestrial laser scanning and supplemented with digital photos. Scanning was performed with a resolution of 12 points per m2 for the airborne platform and about 2 cm for the terrestrial one. The steps and requirements involved in modeling are presented in detail. The algorithms and software that were developed for this work highlight the potential that would be available by automating this process. The specifics of the model are discussed for this type of military structure on a combination of airborne and terrestrial laser scanning data. The issues of the level of detail and accuracy of the modeling are discussed, while emphasizing the opportunities for the use of laser scanning in landscape architecture

Sclerostin as a novel marker of bone turnover in athletes

Sclerostin is a protein secreted by osteocytes that acts as an inhibitor of bone formation. It has been shown that physical activity affects sclerostin concentration and thus bone remodelling. The aim of the study was to evaluate serum concentrations of sclerostin, selected bone turnover markers (PTH, P1NP), 25(OH) D3 and the intake of calcium and vitamin D in physically active versus sedentary men. A total of 59 healthy men aged 17-37 were enrolled in the study (43 athletes and 16 non-athletes). The mean sclerostin concentration in the group of athletes (A) was significantly higher than in non-athletes (NA) (35.3±8.9 vs 28.0±5.6 pmol• l-1, p= 0.004). A compared with NA had higher concentrations of P1NP (145.6±77.5 vs 61.2±22.3 ng• ml-1, p= <0.0001) and 25(OH)D3 (16.9±8.4 vs 10.3±4.3 ng • ml-1, p= 0.004) and lower concentrations of PTH (25.8±8.3 vs 38.2±11.5 pg• ml-1, p= <0.0001). Vitamin D deficiency was found in 77% of A and 100% of NA. A and NA had similar daily energy intake. They did not differ as to the intake of calcium and vitamin D. We observed a negative correlation between the serum concentrations of sclerostin and calcium in the studied subjects. Our results suggest that regular, long-lasting physical training may be associated with higher concentration of sclerostin. It seems that increased sclerostin is not related to other bone turnover markers (PTH, P1NP)

Sex Hormones Response to Physical Hyperoxic and Hyperbaric Stress in Male Scuba Divers: A Pilot Study

The use of hyperbaric oxygen plays a significant role in many aspects of medicine. However, there are few studies that analyzed the role of hyperbaric oxygen, in addition to physical exercise, on the endocrine profile. The aim of this study was to compare changes in plasma male sex hormones after hyperbaric physical exercise with different hyperbaric oxygen pre-conditionings. We recruited six healthy, well-trained recreational male divers. Concentrations of prolactin (PRL), follicle-stimulating hormone (FSH), luteotrophic hormone (LH), cortisol, 17-β estradiol (E2), and total testosterone (TT) were measured in venous blood immediately after four different study conditions. Exercise increased PRL and hyperbaric oxygen potentiated this effect. Hyperbaria stimulated the E2 reduction and hyperoxia partially inhibited this reduction. Hyperbaria, but not hyperoxia, stimulated the TT reduction. There were no changes in FSH, LH, and cortisol. The increase in PRL likely reflects a stress response after physical exercise, amplified by hyperbaric oxygen. TT reduction may be interpreted as an acute and transient fertility impairment. Age, blood pressure, and BMI were taken into account as covariates for statistical analyses, and they significantly affected the results, in particular TT. These data open new insight into the role of E2 and PRL in male endocrine adaptive responses