9 research outputs found
Brief Communication: Mapping river ice using drones and structure from motion
In cold climate regions, the formation and break-up of river ice is important
for river morphology, winter water supply, and riparian and instream ecology
as well as for hydraulic engineering. Data on river ice is therefore
significant, both to understand river ice processes directly and to assess
ice effects on other systems. Ice measurement is complicated due to difficult
site access, the inherent complexity of ice formations, and the potential
danger involved in carrying out on-ice measurements. Remote sensing methods
are therefore highly useful, and data from satellite-based sensors and,
increasingly, aerial and terrestrial imagery are currently applied. Access to
low cost drone systems with quality cameras and structure from motion
software opens up a new possibility for mapping complex ice formations.
Through this method, a georeferenced surface model can be built and data on
ice thickness, spatial distribution, and volume can be extracted without
accessing the ice, and with considerably fewer measurement efforts compared to
traditional surveying methods. A methodology applied to ice mapping is
outlined here, and examples are shown of how to successfully derive
quantitative data on ice processes
CLASSIFICATION OF BENTHIC BIOCENOSES OF THE LOWLAND RIVER TUDOVKA (TVER REGION, RUSSIA) USING COMMUNITY FEATURES
Within the joint Russian-Austrian monitoring programme “REFCOND_VOLGA (2006 – 20XX)”, monitoring sites were established in the headwaters of the Volga (Tver Region). River Tudovka, a right tributary to the Volga River, was included within this monitoring programme as its catchment is partly protected and has only few anthropogenic activities. The monitoring activities include physico-chemical and hydraulic parameters as well as biota with a focus is on benthic organisms (diatoms and macrozoobenthos). In this work, the longitudinal patterns in community structure are classified in the lowland river Tudovka using a novel feature-based approach taken from signal processing theory. The method first clusters field sampling data into longitudinal classes (upper, middle, lower course). Community features based on the relative frequency of individual species occurring per class are then generated. We apply both generative and discriminative classification methods. The application of generative methods provides data models which predict the probability of a new sample to belong to an existing class. In contrast, discriminative approaches search for differences between classes and allocate new data accordingly. Leveraging both methods allows for the creation of stable classifications. On this basis we show how the community features can be used to predict the longitudinal class. The community features approach also allows for a robust cross-comparison of investigation reaches over time. In cases where suitable long-term data set are available, predictive models using this approach can also be developed