Quantifying the effect of aerial imagery resolution in automated hydromorphological river characterisation

Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment

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

Mapping islands, reefs and shoals in the oceans surrounding Australia

The author has identified the following significant results. Comparisons of the imagery with aerial photography of areas of reefs and island and with 1:250,000 maps of coastlines indicate that the MSS imagery depicts detail to an extent which is satisfactory for 1:250,000 mapping. As the imagery does not have some of the disadvantages of aerial photography, the former should be valuable for mapping reefs, islands, and shoals. The water discoloration problem is significant as the discolored water appears to occur near shallow depths, so that confusion could arise through the misinterpretation of discolored water, when it exists, as shallow water

Aerial imagery for yield prediction

UAVs enable fast, high resolution image capture of cotton fields. These images are typically assessed manually to identify areas of stress or reduced productivity. However, these assessments are not currently linked directly with on-farm management decisions. NCEA has developed software that determines yield prediction and irrigation requirements from: (i) UAV images; (ii) automated image analysis that extract cotton growth rates; and (iii) biophysical cotton model. CottonInfo extension officers and agronomists collected imagery in three regions in the 2016/17 and 2017/18 cotton seasons. Yield predictions from the evaluations in the 2016/17 season were within 5% of the final yield

Radar images - Meteor Crater, Arizona

Aerial radar imagery of Meteor Crater, Arizon

Evaluation of radar imagery of highly faulted volcanic terrain in southeast Oregon

Aerial radar imagery of highly faulted volcanic terrain in southeast Orego

The usefulness of thermal infrared and related imagery in the evaluation of agricultural resources. An exploratory study, volume I Final report

Multispectral imagery for aerial inventory and analysis of cultivated crop resource