Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Solomon Islands: Malaita Hub scoping report

The CGIAR Research Program (CRP) Aquatic Agricultural Systems (AAS) will target five countries, including Solomon Islands. The proposed hubs for Solomon Islands were to cover most provinces, referencing the Western, Central and Eastern regions. Scoping of the initial ‘Central’ hub was undertaken in Guadalcanal, Malaita and Central Islands provinces and this report details findings from all three. As scoping progressed however, it was agreed that, based on the AAS context and priority needs of each province and the Program’s capacity for full implementation, the Central Hub would be restricted to Malaita Province only and renamed “Malaita Hub”. Consistent in each AAS country, there are four steps in the program rollout: planning, scoping, diagnosis and design. Rollout of the Program in Solomon Islands began with a five month planning phase between August and December 2011, and scoping of the first hub began in January 2012. This report, the second to be produced during rollout, describes the findings from the scoping process between January and June 2012. This report marks the transition from the scoping phase to the diagnosis phase in which output from scoping was used to develop a hub level theory of change for identifying research opportunities. Subsequent reports detail in-depth analyses of gender, governance, nutrition and partner activities and discuss Program engagement with community members to identify grass-roots demand for research

Use of baited remote underwater video (BRUV) and motion analysis for studying the impacts of underwater noise upon free ranging fish and implications for marine energy management

© 2016 Elsevier Ltd Free-ranging individual fish were observed using a baited remote underwater video (BRUV) system during sound playback experiments. This paper reports on test trials exploring BRUV design parameters, image analysis and practical experimental designs. Three marine species were exposed to playback noise, provided as examples of behavioural responses to impulsive sound at 163–171 dB re 1 μPa (peak-to-peak SPL) and continuous sound of 142.7 dB re 1 μPa (RMS, SPL), exhibiting directional changes and accelerations. The methods described here indicate the efficacy of BRUV to examine behaviour of free-ranging species to noise playback, rather than using confinement. Given the increasing concern about the effects of water-borne noise, for example its inclusion within the EU Marine Strategy Framework Directive, and the lack of empirical evidence in setting thresholds, this paper discusses the use of BRUV, and short term behavioural changes, in supporting population level marine noise management

INTELLIGENT VISION-BASED NAVIGATION SYSTEM

This thesis presents a complete vision-based navigation system that can plan and follow an obstacle-avoiding path to a desired destination on the basis of an internal map updated with information gathered from its visual sensor. For vision-based self-localization, the system uses new floor-edges-specific filters for detecting floor edges and their pose, a new algorithm for determining the orientation of the robot, and a new procedure for selecting the initial positions in the self-localization procedure. Self-localization is based on matching visually detected features with those stored in a prior map. For planning, the system demonstrates for the first time a real-world application of the neural-resistive grid method to robot navigation. The neural-resistive grid is modified with a new connectivity scheme that allows the representation of the collision-free space of a robot with finite dimensions via divergent connections between the spatial memory layer and the neuro-resistive grid layer. A new control system is proposed. It uses a Smith Predictor architecture that has been modified for navigation applications and for intermittent delayed feedback typical of artificial vision. A receding horizon control strategy is implemented using Normalised Radial Basis Function nets as path encoders, to ensure continuous motion during the delay between measurements. The system is tested in a simplified environment where an obstacle placed anywhere is detected visually and is integrated in the path planning process. The results show the validity of the control concept and the crucial importance of a robust vision-based self-localization process

Integrative framework and methods for coastal area management: proceedings

Coastal zone management, ASEAN,

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone