Dissertations / Theses on the topic 'Multispectral aerial video imagery'

Video monitoring of beaches is becoming the preferred method for observing changes to nearshore morphology. Consequently this work investigates a new technique for predicting the probability of inundation that is based on measuring runup variation using video. Runup is defined as the water-level elevation maxima on the foreshore relative to the still water level and the waterline is defined as the position where the MWL intersects the beach face. Tairua, and Pauanui Beaches, on the north east coast of the North Island of New Zealand, were used as the field site in this study and represent two very different beaches with the same incoming wave and meteorological conditions. Tairua is most frequently in an intermediate beach state, whereas Pauanui is usually flatter in nature. In order to rectify runup observations, an estimate of the runup elevation was needed (Z). This was estimated by measuring the variation of the waterline over a tidal cycle from time-averaged video images during a storm event and provided beach morphology statistics (i.e. beach slope (α) and beach intercept (b)) used in the rectification process where Z=aX+b. The maximum swash excursions were digitized from time-stacks, and rectified to provide run-up timeseries with duration 20 minutes. Field calibrations revealed a videoed waterline that was seaward of the surveyed waterline. Quantification of this error gave a vertical offset of 0.33m at Tairua and 0.25m at Pauanui. At Tairua, incident wave energy was dominant in the swash zone, and the runup distributions followed a Rayleigh distribution. At Pauanui, the flatter beach, the runup distributions were approximately bimodal due to the dominance of infragravity energy in the swash signal. The slope of the beach was a major control on the runup elevation; runup at Pauanui was directly affected by the deepwater wave height and the tide, while at Tairua there was no correlation. Overall, the results of the study indicate realistic runup measurements, over a wide range of time scales and, importantly, during storm events. However, comparisons of videoed runup and empirical runup formulae revealed larger deviations as the beach steepness increased. Furthur tests need to be carried out to see if this is a limitation of this technique, used to measure runup. The runup statistics are consistently higher at Tairua and suggests that swash runs up higher on steeper beaches. However, because of the characteristics of flatter beaches (such as high water tables and low drainage efficiencies) the impact of extreme runup elevations on such beaches are more critical in regards to erosion and/ or inundation. The coastal environment is of great importance to Māori. Damage to the coast and coastal waahi tapu (places of spiritual importance) caused by erosion and inundation, adversely affects the spiritual and cultural well-being of Māori. For this reason, a chapter was dedicated to investigating the practices used by Māori to protect and preserve the coasts in accordance with tikanga Māori (Māori protocols). Mimicking nature was and still is a practice used by Māori to restore the beaches after erosive events, and includes replanting native dune plants and using natural materials on the beaches to stabilize the dunes. Tapu and rahui (the power and influence of the gods) were imposed on communities to prohibit and prevent people from free access to either food resources or to a particular place, in order to protect people and/ or resources. Interpretations of Māori oral histories provide insights into past local hazards and inform about the safety and viability of certain activities within an area. Environmental indicators were used to identify and forecast extreme weather conditions locally. Māori knowledge of past hazards, and the coastal environment as a whole, is a valuable resource and provides a unique source of expertise that can contribute to current coastal hazards management plans in New Zealand and provide insights about the areas that may again be impacted by natural hazards.

This is a master thesis of the Master of Science degree program in Applied Physics and Electrical Engineering (Y) at Linköping University. The goal of the projectis to develop an application for creating a map in real time from a video camera on a miniature unmanned aerial vehicle. This thesis project and report is a first exploratory study for this application. It implements a prototype method and evaluates it on sample sequences from an on-board video camera. The method first looks for good points to follow in the image and then tracks them in a sequence.The image is then pasted, or merged, together with previous images so that points from the different images align. Two methods to find good points to follow are examined with focus on real-time performance. The result is that the much faster FAST detector method yielded satisfactory results good enough to replace the slower standard method of the Harris-Stephens corner detector. It is also examined whether it is possible to assume that the ground is a flat surface in this application or if a computationally more expensive method estimating altitude information has to be used. The result is that at high altitudes or when the ground is close to flat in reality and the camera points straight downwards a two-dimensional method will do. If flying lower or with high objects in the picture, which is often the case in this application, it must to be taken into account that the points really are at different heights, hence the ground can not be assumed to be flat.

Airborne multispectral video was evaluated as a tool for obtaining urban land cover information for hydrological simulations. Land cover data was obtained for a small urban watershed in Tucson, Arizona using four methods: multispectral aerial video (2 meter and 4 meter pixel resolution), National High Altitude Photography (NHAP), multispectral satellite imagery from Systeme Pour l'Observation de la Terre (SPOT), and by conventional survey. A semi-automated land cover classification produced four classes: vegetation, buildings, pavement, and bare soil. The land cover data from each classification was used as input to a runoff simulation model. Runoff values generate by each simulation were compared to observed runoff. A chi-square goodness-of-fit test indicated that SPOT produced landcover data most similar to the conventional classification. In the curve number model, the SPOT data produced simulated runoff values most similar to observed runoff.

Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. This contrasts with the current availability of affordable and efficient snow monitoring methods to estimate such an extremely variable parameter in both space and time. In this thesis, a novel approach to determine spatial snow depth distribution in challenging alpine terrain is presented and tested during a field campaign performed in Tarfala, Sweden in April 2019. A multispectral camera capturing five spectral bands in wavelengths between 470 and 860 nanometers on board of a small Unmanned Aerial Vehicle is deployed to derive 3D snow surface models via photogrammetric image processing techniques. The main advantage over conventional photogrammetric surveys is the utilization of accurate RTK positioning technology that enables direct georeferencing of the images, and thus eliminates the need for ground control points and dangerous and time-consuming fieldwork. The continuous snow depth distribution is retrieved by differencing two digital surface models corresponding to the snow-free and snow-covered study areas. An extensive error assessment based on ground measurements is performed including an analysis of the impact of multispectral imagery. Uncertainties and non-transparencies due to a black-box environment in the photogrammetric processing are, however, present, but accounted for during the error source analysis. The results of this project demonstrate that the proposed methodology is capable of producing high-resolution 3D snow-covered surface models (&lt; 7 cm/pixel) of alpine areas up to 8 hectares in a fast, reliable and cost-efficient way. The overall RMSE of the snow depth estimates is 7.5 cm for data acquired in ideal survey conditions. The proposed method furthermore assists in closing the scale gap between discrete point measurements and regional-scale remote sensing, and in complementing large-scale remote sensing data by providing an adequate validation source. As part of the Swedish cooperation project ’Snow4all’, the findings of this project are used to support and validate large-scale snow models for improved snow quality prediction in northern Sweden.<br>På grund av klimatförändringar och naturliga meteorologiska händelser i arktis behövs mer exakta snökvalitetsprognoser för att stödja samernas rensköttsamhällen i norra Sverige som har problem med att anpassa sig till det snabbt föränderliga arktiska klimatet. Rumslig snödjupsfördelning är en avgörande parameter för att inte bara bedöma snökvaliteten utan även för flera miljöforskning och sociala markanvändningsändamål. Detta står i motsats till den nuvarande tillgången till överkomliga och effektiva metoder för snöövervakning för att uppskatta sådan extremt varierande parameter i tid och rum. I detta arbete presenteras och testas en ny metod för att bestämma rumslig snödjupssdistribution i utmanande alpin terräng under en fältstudie som genomfördes i Tarfala i norra Sverige i april 2019. Via fotogrammetrisk bildbehandlingsteknik hämtades snöytemodeller i 3D med hjälp av en multispektral kamera monterad på en liten obemannad drönare. En viktig fördel, i jämförelse med konventionella fotogrammetriska undersökningar, är användningen av exakt RTK-positioneringsteknik som möjliggör direkt georeferencing och eliminerar behovet av markkontrollpunkter. Den kontinuerliga snödjupfördelningen hämtas genom att ytmodellerna delas upp i snöfria respektive snötäckta undersökningsområden. En omfattande felsökning som baseras på markmätningar utförs, inklusive en analys av effekten av multispektrala bilder. Resultaten från denna studie visar att den famtagna metoden kan producera högupplösta snötäckta höjdmodeller i 3D (&lt; 7 cm/pixel) av alpina områden på upp till 8 hektar på ett snabbt, pålitligt och kostnadseffektivt sätt. Den övergripande RMSE för det beräknade snödjupet är 7,5 cm för data som förvärvats under idealiska undersökningsförhållanden. Som ett led i det svenska projektet “Snow4all” används resultaten från projektet för att förbättra och validera storskaliga snömodeller för att bättre förutse snökvaliteten i norra Sverige.