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

Sensors for deformation monitoring of large civil infrastructures

In the maintenance of large infrastructures such as dams, bridges, railways, underground structures (tunnels, mines) and others, monitoring of deformations plays a key role in maintaining the safety serviceability conditions and for mitigating any consequences due to ageing factors and possible structural failures. [...]

The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at Cwm Idwal, North Wales

This paper was accepted for publication in the journal Geomorphology and the definitive published version is available at http://dx.doi.org/10.1016/j.geomorph.2014.07.021Novel topographic survey methods that integrate both structure-from-motion (SfM) photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving investigative technique. Due to the diverse range of survey configurations available and the infancy of these new methods, further research is required. Here, the accuracy, precision and potential applications of this approach are investigated. A total of 543 images of the Cwm Idwal moraine–mound complex were captured from a light (b5 kg) semi-autonomous multi-rotor unmanned aircraft system using a consumer-grade 18 MP compact digital camera. The imageswere used to produce a DSM(digital surfacemodel) of themoraines. The DSMis in good agreement with 7761 total station survey points providing a total verticalRMSE value of 0.517mand verticalRMSE values as lowas 0.200mfor less densely vegetated areas of the DSM. High-precision topographic data can be acquired rapidly using this technique with the resulting DSMs and orthorectified aerial imagery at sub-decimetre resolutions. Positional errors on the total station dataset, vegetation and steep terrain are identified as the causes of vertical disagreement. Whilst this aerial survey approach is advocated for use in a range of geomorphological settings, care must be taken to ensure that adequate ground control is applied to give a high degree of accuracy

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Images from unmanned aircraft systems for surveying aquatic and riparian vegetation

Aquatic and riparian vegetation in lakes, streams, and wetlands has important ecological and regulatory functions and should be monitored to detect ecosystem changes. Field surveys are often tedious and in countries with numerous lakes and streams a nationwide assessment is difficult to achieve. Remote sensing with unmanned aircraft systems (UASs) provides aerial images with high spatial resolution and offers a potential data source for detailed vegetation surveys. The overall objective of this thesis was to evaluate the potential of sub-decimetre resolution true-colour digital images acquired with a UAS for surveying non-submerged (i.e., floating-leaved and emergent) aquatic and riparian vegetation at a high level of thematic detail. At two streams and three lakes in northern Sweden we applied several image analysis methods: Visual interpretation, manual mapping, manual mapping in combination with GPS-based field surveys, and automated object-based image analysis and classification of both 2D images and 3D point data. The UAS-images allowed for high taxonomic resolution, mostly at the species level, with high taxa identification accuracy (>80%) also in mixed-taxa stands. UAS-images in combination with ground-based vegetation surveys allowed for the extrapolation of field sampling results, like biomass measurement, to areas larger than the sampled sites. In automatically produced vegetation maps some fine-scale information detectable with visual interpretation was lost, but time-efficiency increased which is important when larger areas need to be covered. Based on spectral and textural features and height data the automated classification accuracy of non-submerged aquatic vegetation was ~80% for all test sites at the growth-form level and for four out of five test sites at the dominant-taxon level. The results indicate good potential of UAS-images for operative mapping and monitoring of aquatic, riparian, and wetland vegetation. More case studies are needed to fully assess the added value of UAS-technology in terms of invested labour and costs compared to other survey methods. Especially the rapid technical development of multi- and hyperspectral lightweight sensors needs to be taken into account

Capturing and analyzing multispectral UAV imagery to delineate submerged aquatic vegetation on a small urban stream

For decades, remote sensing has been used by scientists and planners to make detailed observations and decisions on areas with industrial problems, remediation and development sites, and resource management. It is challenging to make high spatial and temporal resolution observations along headwater and small streams using traditional remote sensing methods, due to their high spatial variability and tendency for rapidly changing water quality and discharge. With improved technology in sensors and launching platforms, remote sensing via Unoccupied Aerial Vehicles (UAVs) now allows for imagery to be collected at high spatial and temporal resolution, with the goal of providing a deeper analysis of these intricate and difficult to access regions. One recent area of interest is the use of UAVs to delineate land and water cover. While recent innovations in low-altitude multispectral and hyperspectral imagery have been used extensively for tracking land cover, it has been used less frequently to detect changes within the water column through space and time. In addition, it is unclear whether classification methods applied to headwater systems are translatable across adjacent stream reaches or across flights on different days, as well as how much information is needed to perform such classifications. This study demonstrates that UAV multispectral imagery can be used to classify land cover as well as uniquely identify submerged aquatic vegetation by combining methods of remote sensing, image processing and machine learning. A linear discriminant analysis (LDA) model was developed to provide land and water cover classification maps (with statistical analysis of error) using training data from hand delineated multispectral shapefiles. This method proved to be robust when classifying land cover along a single reach, even when using a very small proportion of the training data. Through attempts to transfer data through space and time, this exercise highlights the shortcomings in multispectral imagery and the dependence on lighting conditions, reach orientation and shading from nearby structures such as vegetation. Therefore, this approach is likely most beneficial for classifying land cover and submerged aquatic vegetation at a single reach for a single time, but more work must be done to further identify physical limitations of multispectral imagery and calibration methods which might allow for an “absolute” measure of reflectance

Towards UAV Assisted Monitoring Of An Aquatic Vegetation Within The Large Rivers – The Middle Danube

UAV technologies provide a time- and cost-efficient framework for a variety of environmental monitoring domains. It also increases data resolution and provides new insights into observed objects and phenomena, especially within the difficult-to-access and complex for monitoring aquatic habitats. The objective of this study was to develop UAV-based acquisition and GIS-based image processing guidelines for aquatic macrophyte detection and monitoring in large temperate rivers. According to the European standard CEN EN -14184:2014, the assessment of aquatic macrophytes should be performed using the transect approach. Large rivers, such as the Danube, represent an exception and should be evaluated using 1km transects. Therefore, seven transects of the Middle Danube in Serbia were simultaneously surveyed using traditional field methods and novel UAV technology. UAV images were acquired using RGB and multispectral cameras carried by a fixed-wing drone. The images were processed and orthomosaics were classified using Object Based Image Analysis (OBIA), to create digital GIS maps of the river transects. During the traditional monitoring approach, the relative abundance of 22 macrophyte species was recorded along the transects. Using the UAV technology and OBIA approach eight macrophyte classes were distinguished based on dominant macrophyte taxa or plant life form traits. Aquatic macrophytes were 'almost perfectly' distinguished from the orthomosaics, achieving a high classification accuracy of 96 % / 88 % / 0.84 for RGB and 94 % / 97 % / 0.95 Producers /Users accuracy/Kappa index for the multispectral approach. Individual macrophyte classes accuracy varied between 0.5 and 1 Kappa and were generally higher for the multispectral imagery approach. Although the resolution of the taxonomic data is lower, UAV monitoring provided the necessary spatial context of macrophytes distribution and absolute area occupied by macrophytes. It also provided information on the diversity and distribution of habitats along the river. Therefore, the UAV-assisted monitoring approach described in this study can be effectively integrated into macrophyte monitoring during large river expeditions such as the JDS