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Water resource management at catchment scales using
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lightweight UAVs: current capabilities and future perspectives
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DeBell L 1, Anderson, K.*1, Brazier, R.E.2, King, N3, and Jones, L.4
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Environment and Sustainability Institute, University of Exeter, Penryn Campus, UK
University of Exeter, College of Life and Environmental Sciences, Department of Geography, UK
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QuestUAV, Unit 7B Coquetdale Enterprise Park, Amble, Northumberland UK
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South West Water, Peninsula House, Rydon Lane, Exeter, Devon, UK
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*Corresponding author: Karen.Anderson@exeter.ac.uk
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Abstract: Lightweight, portable unmanned aerial vehicles (UAVs) or ‘drones’ are set to become a key
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component of a water resource management (WRM) toolkit, but are currently not widely used in this context. In
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practical WRM there is a growing need for fine-scale responsive data, which cannot be delivered from satellites
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or aircraft in a cost-effective way. Such a capability is needed where water supplies are located in spatially
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heterogeneous dynamic catchments. In this review, we demonstrate the step change in hydrological process
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understanding that could be delivered if WRM employed UAVs. The paper discusses a range of pragmatic
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concepts in UAV science for cost-effective and practical WRM, from choosing the right sensor and platform
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combination through to practical deployment and data processing challenges. The paper highlights that multi-
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sensor approaches, such as combining thermal imaging with fine-scale structure-from-motion topographic models
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are currently best placed to assist WRM decisions because they provide a means of monitoring the spatio-
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temporal distribution of sources, sinks and flows of water through landscapes. The manuscript highlights areas
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where research is needed to support the integration of UAVs into practical WRM – e.g. in improving positional
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accuracy through integration of differential global positioning system sensors, and developing intelligent control
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of UAV platforms to optimize the accuracy of spatial data capture.
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Keywords: UAV; drone; water resource management; catchment; hydrology; spatial; temporal; environmental
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monitoring
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1. Introduction
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1.1 Background
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Societal pressures on global water resources are higher than ever before (Rodda, 2001) and globally, society is
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approaching the “planetary boundary range” for consumptive freshwater use (Rockström et al., 2009). Oki and
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Kanae (2006) have highlighted that there will be severe problems for management of local to global scale water
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resources in the future (Gleick and Palaniappan 2010; Oelkers et al., 2011; Shiklomanov, 1998). Climate change
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projections suggest that 47% of the world’s population will live in areas of high water stress by 2030 (United
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Nations Water Portal, 2014) and in order to adapt, humanity will increasingly have to look towards currently
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under-valued or unquantified stores of water (e.g. soil water) to service this societal need. Responsive tools for
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providing scale-appropriate data in time and space are now required so that catchments can be managed
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sustainably (Macleod et al., 2007) and so that evidence-based solutions to societal problems of water supply can
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be found.
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1.2 The challenge
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Catchment management is now seen as a vital step in effective water resource management (WRM, UNEP, 2002;
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Kallis and Butler, 2001). Soil moisture is recognized as important in governing hydrological functioning at
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catchment scales (Brocca et al., 2012; Wainwright et al., 2011), and to improve understanding of this, new and
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improved methods for fine-scale monitoring at catchment and/or basin level are needed (Brocca et al., 2012;
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Ludwig et al., 2003; Mahmood, 1996). Remote sensing offers a familiar and mature scientific tool for monitoring
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water resources (Pultz et al., 2002; Schmugge et al., 2002; Jeniffer et al., 2010; Makhamreh.2011 and Alexakis et
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al., 2013). However, whilst the repeat coverage offered by Earth observation satellites is attractive for landscape
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monitoring over large spatial extents, data are frequently too coarse in either their spatial, temporal or spectral
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resolution (Gowda et al., 2008; Ludwig et al., 2003) to be useful for effective decision making about catchment-
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scale water management. Piloted aircraft surveys offer a viable alternative to satellite systems because they
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deliver finer spatial resolution data, but high costs can prohibit regular survey and deployments can rarely be
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commissioned at short notice. Consequently, there is a shortfall in current remote sensing data provision in
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relation to the following two challenges that cannot be met with current satellite or airborne imaging survey
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technologies:
1. Cost-effective capture of fine-scale spatial data describing the current hydrological condition and water
resource status of catchments at user-defined time-steps;
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2. Data capture at fine temporal resolution for describing water system dynamics in soil moisture,
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vegetation, and topography in catchments where there are important downstream effects on water
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resources (e.g. floods, erosion events or vegetation removal (Römkens et al., 2002)).
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1.3 The proposed solution
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A new emerging opportunity for on-demand, self-service, timely spatio-temporal water resource evaluation is
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offered by Unmanned Aerial Vehicles (UAVs or ‘drones’). With careful design, deployment and safe operation,
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UAV platforms could provide scale-appropriate data that are otherwise difficult to obtain from most other remote
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sensing platforms (Anderson and Gaston, 2013). Lightweight UAVs as presented in this paper, currently do not,
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and are unlikely to be able to offer the large scale synoptic monitoring capability for national or international
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coverage as provided by Earth observation satellites, but they look set to contribute to an enhanced spatial and
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temporal understanding of catchment management issues, through their ability to reveal local scale dynamics and
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structures, and for hydrological up-scaling. UAVs also look set to engage new markets in WRM– for example,
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Richard Allitt Associates Ltd (a UK consultancy company) have recently been granted a special license to fly
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UAVs to target flood monitoring within UK urban areas (Lewis, 2014). With WRM facing some of its biggest
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challenges in the developing world, there are major drivers for developing cost-effective UAV platforms for
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water resources research that can be rapidly deployed in challenging operational settings where aircraft surveys
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are not a viable survey option.
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In this manuscript we explore how UAVs could be adapted for water resource monitoring using various examples
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to show how catchment dynamics and their responses to management interventions could be better understood
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using the fine scale data that these platforms provide (Grand-Clement et al., 2013; Liu et al., 2008; van Dijk and
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Renzullo, 2011). There are also research challenges that need to be addressed if UAVs are to become an essential
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part of a WRM toolkit, and this paper will explore these and provide an operational demonstration of capability in
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a WRM context.
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2. UAVs as platforms for environmental research
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2.1 Historic setting and scientific uptake in the past decade
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The realization of UAV potential for civilian remote sensing and environmental monitoring started around 1993
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(Hutchison et al., 1993; Rango et al., 2006). However, this was still a time when the term ‘UAV’ directly referred
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to the military class of drone where retail and operational costs were too high for widespread academic use
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(Hutchison et al., 1993). However, the cost of UAV components has reduced in recent years and there is now a
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perceived cost-benefit to developing UAVs as platforms for monitoring landscape and catchment hydrology –
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lightweight airframes can be purchased ‘ready to fly’ for less than $1000 and this has created a large market for
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their development and use.
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The exponential growth in research involving UAVs in the past decade is reflected in the citation reports obtained
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from Web of Knowledge (Thomson Reuters, 2015) where Figure 1 shows the total citations and total papers for
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each year related to the key words “unmanned aerial vehicle”. We have only reported data to the end of 2014 so
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that 2015 trends are not mis-represented. Similar trends were found with search terms “unmanned aircraft systems
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(UAS)” and “remotely piloted vehicles (RPV)”, so this figure is representative of broader trends in the research
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literature.
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{figure 1 goes here}
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Amongst the papers included in these citation reports are several studies that report on the state-of the-art in UAV
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science for supporting watershed hydrology and ecohydrology studies (Anderson et al., 2012; Templeton et al.,
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2014; Vivoni, 2012). UAVs have been used at wetland sites, for example by Li et al. (2010) where fine-scale
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imaging data were used to map the distribution of terrestrial and aquatic ecosystems. Further work in rice paddies
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has shown that nitrogen status can be determined by comparing ground based multispectral chlorophyll retrieval
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techniques with UAV based photogrammetry, so that both nitrogen deficiency and over-application can be
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detected with consequences for run-off and nutrient pollution (Zhu et al., 2009). Such work provides indirect
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spatial proxies that deliver useful information to water resource managers. In addition there are a handful of
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practical pieces of research documenting UAV use for direct water and catchment management. For example,
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Zarco-Tejada et al (2012) used narrowband spectral measurements to determine canopy fluorescence to indicate
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crop water stress in vineyards whilst Baluja et al (2012) combined thermal with multispectral imaging approaches
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to address the same issue. UAVs have been used to monitor irrigation systems (Jimenez-Bello et al., 2013), and
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for near real-time water management (Chao et al., 2008). Additionally, Templeton et al (2014) have explored the
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use of UAV mounted sensors for spatial mapping of watershed evapotranspiration processes whilst Deitchman
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(2009) showed how UAV-mounted thermal sensors could provide useful data for the study of stream temperature
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regimes and groundwater discharge. Others have explored the use of collaborative robotic vehicles for monitoring
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water bodies (Pinto et al., 2013). In such settings, UAVs are the preferred remote sensing platform because data
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collected at close range allow individual plants or hydrological indicators to be detected and water management to
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be optimized at a fine scale. With developments of new lightweight sensors, open source control and data
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processing software, and new platforms for generating novel data products, UAV science is at a point where a
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wide range of new practical applications for WRM can be explored.
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2.2 Platform types and their operational capabilities
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There is ample literature showing the different types of lightweight UAVs that are available to scientists for use in
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a range of applications (Anderson and Gaston, 2013; Ollero and Merino, 2006) but there are currently no clear
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guidelines that would allow a water resource manager to choose the most effective platform and sensor
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combination for their required application. With the wealth of UAV platforms on the market, a summary of
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platform capabilities is therefore required with a view towards the types of data required for WRM. A review of
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available platforms and their relative merits is summarized in Table 1. In this review, we mainly focus on fixed
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wing and multi-rotor platforms because these offer the greatest opportunities for practical WRM and are where
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recent developments have delivered the biggest step-change in capability. For UAVs to deliver new data streams
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to water resource managers that are not available from other remote sensing systems, they must offer data that are
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more frequent in time and/or at finer spatial resolution. UAVs must therefore:
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be capable of delivering data at repeat survey times that adequately capture the dynamics of hydrological
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systems (e.g. ephemerality, seasonality and flashiness (e.g. in flood prone systems)) and offer a
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responsive or on-demand capability (e.g. within 2 days of a key ‘event’ occurring). This would allow
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event-based remote sensing data to be captured, which will offer improved insights into spatial
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hydrological processes not possible from current satellite technologies, and often prohibitively costly
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from piloted aircraft.
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be capable of operating at costs that are lower than those charged by commercial aerial operators
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(typically ~£10K per commissioned survey in the UK, for example) to include end-to-end processing and
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delivery of a final product capable of ease-of-use within GIS.
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The fine spatial resolution nature of UAV data (in both space and time) and the self-service capability is what sets
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these systems apart from those products currently offered by commercial satellite systems and commonly
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available aerial photography and other remote sensing data repositories. For understanding WRM issues, we
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argue that UAV data are central for supporting enhanced scientific understanding of catchment systems for a
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range of pertinent reasons. There is a growing consensus in hydrology that fine spatial resolution data are
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important for understanding processes and connectivity. Blöschl (2001) argues that local-scale observations of
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hydrologically-relevant patterns, both qualitative and/or quantitative, should be used in upscaled models due to
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the improved relationship of space-time dynamics that can be achieved within flow systems. Importantly,
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Bachmair and Weiler (2013)’s paper highlights some of the spatio-temporal complexities in hydrological
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processes and their findings explicitly point to the need for cross-scale approaches for catchment monitoring and
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a consideration of the high spatial variability in sub-surface hydrological connectivity. In offering fine-scale data
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at the sub-pixel level of many other airborne or satellite imaging systems, UAV-based investigations over small
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reaches or in sub-catchments or headwater areas (McGlynn et al, 2004; Uchida et al, 2005) will be very useful in
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addressing this need. Furthermore there is evidence that points to the need for very fine spatial resolution image
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data for optimising landscape management approaches in aquatic ecosystems – Tormos et al. (2014) for example,
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show how ‘highly resolved’ (finer than 20 m resolution) spatial data improve spatial understanding of processes
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in riparian zones, particularly in headwater catchments.. In the temporal domain, the simple argument in favour of
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UAV-based remote sensing approaches is the responsiveness with which event-based data can be captured, which
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is particularly important for hydrological surveys due to the temporal dynamics of rainfall, runoff events. UAV
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survey readiness and responsiveness is potentially much greater than with other remote sensing systems, and as
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such, UAVs offer the means of capturing data that other systems would miss by virtue of their orbital
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characteristics, intervening cloud cover (satellites) or lack of availability (piloted aircraft). This is particularly
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important for capturing data about events such as floods, droughts and leakages in a WRM context.
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Operational deployment issues and technological trade-offs must also be considered when deciding which UAV
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platform to use (Table 1). For example, platform stability in pitch, roll, yaw and height, is a major consideration if
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collecting aerial photographs for generating landscape mosaics. Here, a multi-rotor platform may be better than a
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fixed wing, as its stability in flight can be controlled more precisely. Conversely, multi-rotor flight endurance is
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typically poorer than for fixed wing systems, reducing the areal coverage that can be achieved in a single flight
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mission. On the other hand, if data are being collected with structure-from-motion processing in mind (see section
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3.2 for further details), it is better if the flight plan is designed to incorporate off-nadir view angles deliberately
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(e.g. oblique image capture) or cross-strips to enhance surface reconstruction and point cloud derivation (Dellaert
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et al., 2000; James and Robson, 2014). For applications where the endurance of a fixed wing system is required,
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but the resource manager does not have access to a suitable take-off and landing space within working distance of
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the target area, a blimp, kite or balloon may be more suitable as a platform. Users should also consider the
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difficulty of flying particular types of UAVs – with tethered kites, balloons and free flying blimps being the
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easiest to deploy (but generally offering only static images or limited spatial coverage), and multi-rotors and fixed
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wing systems requiring more expertise. Whilst UAVs are predominantly designed for autonomous flight along a
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pre-determined GPS-guided waypoint path (UAVs.org, 2014), there are scenarios where the operator could be
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required to take manual control of the aircraft (e.g. during landing, or if the auto-pilot malfunctions) and thus, this
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is a consideration if deployment in difficult terrain is required. Resource managers should also consider the
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implications of the modifiability of UAVs. Multi-rotors are probably the easiest platforms to modify so that they
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can carry a range of sensors interchangeably, because the sensor payload is attached externally to the underside of
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the UAV. Conversely, fixed wing systems are harder to modify due to sensors being housed internally, but in the
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event of a crash the internal devices in fixed wing systems are better protected and more likely to survive than in
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the exposed underside of a multi-rotor (especially because multi-rotors will not glide if motors fail). Power-to-
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weight ratios are limited by battery technology and construction materials. The endurance of particular systems in
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flight is also affected by the weight of payload being carried. This is especially noticeable in multi-rotor systems
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where flight duration is usually less than 20 minutes. An alternative is to use a gas or petrol engine with higher
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endurance due to improved energy capacity – but these systems introduce more vibration with effects on data
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quality. Finally, key personnel who may wish to deploy this technology such as scientists or water resource
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managers may not have previous experience with UAVs or even with hobby radio controlled aircraft, and
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subsequently may not know or understand the questions to ask that would promote an effective decision making
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process.
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3. Operational UAV deployment for water resources management
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3.1 Sensor payloads
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The challenge in the imaging domain is to produce scientifically robust sensors that can deliver data of
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comparable type and quality to those collected by piloted aircraft or satellites, but which are light enough to be
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deployed on UAVs with limited payload capacity. A typical lightweight UAV (with sub 7kg – take-off weight -
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TOW) will be limited to a sensor payload of between 0.5 and 2 kg. There are hobbyists that have built heavy lift
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multi-rotors which far exceed this (e.g. Blueray450, 2012), but the practicality of using such a system in WRM is
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very limited due to the impacts of weight on flight endurance. For these reasons, there are decisions facing water
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resource managers in choosing the most appropriate sensor suite to deploy on any given UAV. The following
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sections summarize the state-of-the-art in UAV suited sensors for deployment on sub-7kg TOW UAVs and
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provide an overview of the potential ways in which these sensors could be utilized in operational WRM.
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3.1.1 Optical sensors
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Optical sensors are both the easiest and cheapest of those available to deploy and can produce good quality data
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for WRM if operated carefully. From single lens reflex (SLR) cameras equipped with global positioning system
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(GPS) capabilities to lower-cost ‘point and shoot’ cameras it is possible to acquire good quality aerial
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photographs from all of the UAV platforms listed in table 1. The resulting pixel size in the captured images will
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be determined by the flying height, focal length and camera detector resolution. At the top end of the optical
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range, cameras such as the Nikon D800 DSLR and the Sony a7R mirrorless model now offer the capability to
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reproduce a medium format image equivalent (once only available from optical images captured from light
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aircraft). These offer the potential for gathering data with a sub 1 cm spatial resolution from a typical flying
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height of 100 m, however with body and lens combinations being larger and heavier than typical ‘point and shoot’
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camera models (e.g. Nikon has dimensions of 146 x 123 x 81.5 mm body with a weight of 1.28kg (including
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battery, memory card and 50mm prime lens); Sony has dimensions of 126.9 x 94.4 x 48.2 mm and a weight of
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0.6kg (including battery, pro duo memory stick and 35mm lens)), the choice of UAV platform will be limited to
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high specification multi-rotors such as the Droidworx AD-8-HL ‘heavy lifting’ octocopter (Anderson and Gaston,
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2013), or a larger bodied fixed wing such as the QuestUAV (King, 2013). The weight of these cameras also
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presents problems if there is a requirement for multi-sensor deployments on each flight. There is evidence from
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some studies that the improved spatial resolution offered by these systems can be applied to describe surface
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water distribution and/or vegetation type in great detail, and these are useful proxies for soil moisture distribution
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– a useful parameter for monitoring within WRM studies (Rango et al., 2009).
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At the lower-cost end of the range smaller cameras have the advantage of being compact and light enough to be
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flexibly deployed on most UAVs. For example, a Panasonic Lumix LX5 model (110 x 65 x 43 mm, 0.27kg, 10
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megapixel resolution and costing £450 in July 2010) deployed on a QuestUAV fixed wing aircraft flying at 100m
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captured the images shown in figure 2a and 2b (camera setting: 24mm equivalent, at f5.1, ISO 200, aperture
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priority setting, and exposure at 1/1600). Figure 2c was captured from a Sony NEX7 camera (120 x 43 x 67 mm ,
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290 g , 24.7mp and £762 in Aug 2013 body only; 16mm prime lens – 24mm equivalent – at f3.2, ISO100,
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aperture priority and 1/360 sec) using the same QuestUAV fixed wing platform and at the same altitude. The
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white line visible in figure 2a was the launch cable measuring 1 cm diameter, whilst figure 2b shows tussocky
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vegetation at a culm grassland site in South West England, where Molinea caerulea tussocks were being mapped
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and monitored for a WRM project. Figure 2c shows an area of upland catchment that is subject to heavy water
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erosion, where drainage channels have been incised by water to form gullies, resulting in a flashy downstream
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hydrological response. The UAV image data were being used here to inform a UK project concerned with
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improving spatial understanding of catchments for quantifying flash-flooding and soil erosion risk. These data
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illustrate what can be achieved using a relatively low cost, lightweight camera system on board a fixed wing
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UAV, operating within normal limits.
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{figure 2 goes here}
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Small digital cameras currently retailing at less than £100 can also be modified and optimized for UAV
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deployment (i.e. the Canon A2400IS 94.4 x 54.2 x 20.1 mm in size, weighs 0.12kg, has a 16 megapixel resolution
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and retails for £113 in April 2012). With many such cameras weighing less than 200 grams the range of UAV
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platforms that can then be used for deployment is large, giving the water resource manager a wide range of
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potential platform and sensor combinations for acquisition of aerial photography. The main challenge with all of
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these optical camera systems for the non-UAV expert is setting up an automatic trigger system – which in some
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models requires a physical servo arm to press the trigger button in flight, or in other cases triggering from the
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flight controller, either by way of a connected IR trigger or modified USB cable (Gentles, 2015). Establishing this
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capability requires personnel with electronics expertise. Tools such as the Canon Hack Development Kit – CHDK
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(CHDK, 2014) can be helpful – this can be loaded onto the camera and used to trigger data collection and apply
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specific camera settings (e.g. forcing use of particular exposure settings), reducing the need for specific wiring or
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electronic expertise.
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There is also a great opportunity to develop mobile ‘apps’ for UAV use – e.g. most smartphones are now
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equipped with optical cameras with good image quality, and if combined with other sensors in the phone
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(accelerometers, compass and GPS), these could revolutionize low-cost GIS-ready image capture from
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lightweight UAVs (e.g. see Teacher et al. 2013 for other examples). Such developments could be particularly
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pertinent for supporting water resource managers in developing countries where funding for monitoring is limited,
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and could support networks of low-cost remote sensing UAVs for distributed monitoring across complex
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landscapes, because unit costs would be low. Very low cost platforms, such as Bixler powered gliders (ready to
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fly for less than £100 excluding an autopilot (HobbyKing, 2014)) could be adapted to carry smartphone sensors
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offering a very cost-effective and rapid way of data collection. Such capabilities could prove very useful in areas
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where rapid assessment of water resources is required – e.g. in humanitarian disaster relief zones, or in the event
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of a flood where impact assessment is required to direct resources and aid.
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3.1.2 Laser scanning
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Laser scanning (referred to as Light Detection and Ranging or LiDAR) has become a popular tool for generating
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digital elevation models (DEMs), digital surface models (DSMs) or terrain maps for modeling hydrological flow
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path networks (Goulden et al., 2014). LiDAR data can also be very useful for describing fine-scale vegetation
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structure which is important in describing patterns of near-surface water storage or hydrological connectivity in a
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variety of landscapes (for examples see (Luscombe et al., 2014a) and (Jones et al., 2008)). However, the size and
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weight of regularly used LiDAR sensors has, until recently, limited their use to either terrestrial laser scanning or
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airborne laser scanning, with the latter deployed in light aircraft or helicopters, and thus being an expensive
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method for data collection. UAV capable LiDARs are now in development (Lin et al., 2011). Custom research
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LiDAR platforms such as those developed by TerraLuma (e.g. Wallace et al.,2012; Wallace et al.,2014), and the
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more recent release of several new commercial lightweight LiDAR systems from Velodyne, Yellowscan and
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Riegl suggest that UAV-LiDAR will be commonplace in the future. In the commercial domain, Velodyne LiDAR
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were the first to release a commercial system targeted at the UAV market (144mm x 85mm in size, and weighing
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less than 2 kg; (Velodyne, 2014)). More recently the Yellowscan LiDAR (dimensions 200 x 170 x 150 mm and
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weighing 2 kg as a standalone system (Yellowscan, 2014)) and the slightly heavier commercial grade system by
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Riegl (227 x 180 x 125 mm, weight 3.6 kg; (Riegl LMS, 2014)) are further offerings in this field. With studies
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such as those by Kenward et al. (2000) highlighting the effect that DEM accuracy has upon the ability to predict
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hydrological outcomes and Liu (2005) demonstrating the impact and change that using high resolution LiDAR
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data can have upon catchment boundaries and drainage basin maps, this is an important technological step that
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will improve WRM by enhancing understanding of the spatially distributed nature of water supply and flow
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parameters.
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UAV-based LiDAR surveys are particularly attractive for use in settings where there is regular land surface
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modification (e.g. in highly eroded environments) and thus where airborne LiDAR or other DSM archives
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regularly require updating (e.g. agricultural areas), or where landscape complexity occurs at a scale that is too fine
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to be captured accurately by other airborne or satellite topographic sensors. The ability of UAVs to fly at close
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range, and with greater maneuverability than manned aircraft will also confer a finer spatial resolution in the
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resulting DSMs and DTMs. Furthermore with intelligent flight planning, UAV-LiDAR will allow higher point
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cloud densities to be collected over key areas of landscape complexity (thus addressing shortfalls of airborne
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LiDAR in complex catchments as highlighted by James et al (2007)). UAV-LiDAR will thus allow new questions
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concerning fine-scale hydrological connectivity in complex landscapes to be addressed with strong relevance for
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WRM.
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3.1.3 Thermal imaging
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Thermal imaging has long been recognized as providing a useful proxy for landscape water resource assessments:
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early thermal data captured from satellites have demonstrated their regional and national scale relevance for
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evapotranspiration assessments for example (Price, 1982). However their limited spatial resolution has proven
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prohibitive for resolving key hydrological features within small catchments. Thermal sensors such as those by
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Optris (Optris GmbH, 2014), FLIR (Flir Systems, Inc., 2014) and Thermoteknix (Thermoteknix, 2014) are now at
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a size and weight point that makes them a viable option for UAV systems. Of particular relevance to water
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resources research, Optris have developed a ready to fly, lightweight thermal system specifically for UAVs,
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called the “PI Lightweight” weighing 0.38kg for both sensor and micro-computer (dimensions 111 x 55 x 45
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mm). These systems, when operated from a UAV can provide products with spatial resolutions of approximately
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20 cm when flown at 100 m altitude. This provides an innovative way of capturing information about near surface
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moisture via algorithms that model the relationship between surface temperature or emissivity and near-surface
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soil moisture (as has been shown to work from thermal imaging sensors mounted on piloted aircraft by Luscombe
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et al., 2014a).
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3.1.4 Hyperspectral and multispectral measurements
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In the hyperspectral domain, one technique used with UAVs is to adapt lightweight ground-based non-imaging
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spectroradiometers to build a spatial picture of hyper- or multi-spectral reflectance properties over land surfaces
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as the UAV moves in the along-track direction (Hakala et al., 2013). For WRM, these data provide useful
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information about water quality, flood detection and monitoring, structure and physiology of plants, wetland
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mapping, evapotranspiration, and vegetation and land-use classification (Govender et al., 2007) because a spatial
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model of hyperspectral reflectance or radiance can be built up from a hovering multi-rotor where each location is
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logged in turn. There are some developments towards production of lightweight imaging spectrometers (e.g. the
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Microhyperspec instrument by Headwall Photonics (Headwall, 2014)) which show promise for some WRM
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applications (Zarco-Tejada et al., 2012; Lucieer et al., 2014b). Hyperspectral imaging can also potentially be
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delivered from consumer grade cameras – through the use of specialised using lens adapters, achieving up to 0.8
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nm spectral resolution and 120×120 pixel spatial resolution (Habel et al., 2012), a development that would be
339
advantageous for lower cost hyperspectral monitoring to support WRM. Zhu et al (2009) have used hyperspectral
340
sensors to assess nitrogen status and application within rice fields, which could be adapted for assessment of
341
nitrogen pollution within freshwater systems, for example.
342
343
Standard digital cameras can also be modified to measure infrared (IR) radiation, and thus capture multi-spectral
344
visible-IR image data, if the internal filter is removed from the sensor array (e.g. see Peauproductions (2015) and
345
Maxmax (2015)), and users can do this quite easily themselves (Milton 2002; Publiclabs 2015). The addition of
346
an infrared remote sensing capability can expand the potential questions that can be answered in a WRM context
347
– for example, Ballesteros et al. (2014) show various advantages in practical agronomy applications, specifically
348
in guiding sustainable water usage. Well-calibrated multispectral data can be useful for WRM, for example Berni
349
et al (2009) have used multispectral image data from an ‘MCA6 Tetracam’ system (a multispectral imager
350
comprising a series of individual radiometric sensors configured to measure in specific parts of the spectrum) to
351
assess water stress within vegetation, to detect areas experiencing drought or lowered water table conditions with
352
a view to targeting watering schemes in agricultural settings. Knoth et al (2013) have used near-infrared data
353
acquired from UAV platforms at degraded bog sites, so that restoration can be targeted optimally to promote
354
future water storage. The latter technique could be readily applied to other restoration projects where information
355
on the relationship between habitat condition and hydrological function was required.
356
357
3.2 Data processing
358
Once data have been collected by UAVs there is a demand for a software workflow for handling the high data
359
volumes and producing outputs that are ready-to-use within WRM. Perhaps the most interesting and relevant area
360
of data processing for water resource managers is the emergence of ‘structure from motion’ (SfM) processing.
361
This is a computer-vision technique for 3D surface reconstruction through optimised pixel matching, whereby
362
overlapping images of landscapes can be used to generate point clouds. SfM products are useful in studies
363
requiring models of elevation or vegetation structure and as such can be very useful in WRM because these
364
variables modulate landscape water storage and flow. Dandois and Ellis (2010) first demonstrated how vegetation
365
structure could be monitored using this approach from kite-based aerial photography and have extended this to
366
include models that show 3D canopy attributes and utilize colour from R,G,B brightness attributes (Dandois and
367
Ellis, 2013). Further to this, Mancini et al (2013) and Lucieer et al (2013) have presented topographic and digital
368
terrain modelling (DTM) using SfM approaches, useful in flow connectivity mapping/modelling in a WRM sense
369
(Bates et al., 1998). SfM approaches offer a low-cost technique for landscape structure description and UAVs
370
provide flexible platforms from which to carry out SfM research. A recent study by Castillo et al. (2012) has
371
suggested that data quality from SfM outputs is equivalent to that gained from traditional laser scanning
372
approaches when ground-based photography is used – however, there is still work to be done when considering
373
applications from UAVs. For example, McShane et al. (2014) showed that SfM products derived from a fixed
374
wing UAV over an eroded gully provided the least accurate depiction of hydrologically-relevant landscape
375
structures (compared to ground-based SfM and terrestrial laser scanning data). Of great importance is the
376
distinction between an optically-based SfM product and a laser-scanning dataset – SfM can only describe the top
377
‘visible’ part of the vegetation and while it may be possible through high overlaps and oblique imagery to derive
378
more points relating to different parts of the vegetation, the product will not be the same as that obtained from an
379
airborne LiDAR or terrestrial laser scanning system, because these offer high powered lasers which can penetrate
380
the canopy and thus potentially provide more detailed information on within-canopy structure. Flight planning for
381
SfM is essential for ensuring a high quality output, as suggested by James and Robson (2014) who report on the
382
importance of having oblique view angles in addition to nadir photographs to improve point cloud
383
parameterization. Further research is needed towards intelligent optimization of UAV flight plans so as to
384
improve image overlap in areas of more complex terrain. Most UAV systems allow the GPS locations of the
385
aircraft, as reported by the flight controller, to be inserted into the EXIF data of each image collected. Such data
386
can be utilized to generate a basic ortho-photo build with a resultant accuracy of +/-5 to 10 m (as dictated by the
387
selective availability of civilian GPS). Additionally, the emerging capability offered by lightweight real time
388
kinematic (RTK) and dual-frequency GPS require testing in a WRM setting to determine their utility in
389
comparison to existing GPS solutions. What remains is the need for a robust field-based method to collect
390
location data with which to drive and validate the subsequent stitching of image data. A robust method is
391
described by Puttock et al (2015) who used a series of highly visible distributed markers which were surveyed in
392
the field using a differential GPS to inform the orthorectification of data (flying a multirotor at an average height
393
of 25 m they produced a 0.01 m resolution orthophoto with a 3D root mean squared error of 0.49 m which was
394
dominated by a z (height) error).
395
396
Software innovations now allow users to generate the SfM products efficiently, alongside more traditional
397
georeferenced, orthorectified images. Agisoft's PhotoScan (Agisoft, 2006) a cross-platform (MS Windows, OS X
398
and Linux) software package, which can handle hundreds of photographs at a time to align and stitch together
399
images, has been successfully used in archaeology for several years (Verhoeven, 2011). PhotoScan does not
400
require any external information to align the image data but can also be used with ground control points which
401
can be added at a later stage to achieve absolute positioning in a real-world coordinate frame. Alongside
402
Photoscan, there are a number of proprietary and open source software options to aid in mosaicking,
403
orthorectification and SfM building. Open source, cross-platform options include Visual Structure from Motion
404
(Wu, 2014) and Bundler (Snavely, et al., 2006) both of which can image stitch and produce SfM data, whilst
405
Meshlab (Visual Computing Lab, 2007) and CloudCompare (Girardeau-Montaut, 2011) are both able to build
406
texture from point cloud data or post-process SfM data into DEMs or analyse density, curvature and roughness of
407
point clouds. Microsoft’s Photosynth and Image Composite Editor for the Windows platform (Microsoft, 2011,
408
2012) are proprietary but free, and can be used for image mosaicking and simple 3D reconstruction, mainly for
409
input into Bing Maps. Further paid for, proprietary software (MS Windows only) such as Pix4D has similar
410
advanced functions to Photoscan for ortho-photo generation and SfM (Pix4D, 2014).
411
412
A typical workflow showing the steps taken to post-process data using these techniques with an example WRM
413
dataset is shown in Figure 3. During this survey 225 aerial images were collected using a Sony NEX7 camera (the
414
same system as described for figure 2(c)) over a degraded upland peatland site in the Forest of Bowland
415
(Lancashire, UK) using a QuestUAV fixed wing platform flying at 100 m altitude. The rationale for this survey
416
was to collect data so that hydrological response times in the catchment and rates of soil erosion could be
417
understood. Data were mosaicked to produce an ortho-mosaic using Agisoft Photoscan (version 1.0.4) on a
418
desktop PC (Figure 3a), and a point cloud describing the topographic structure of the landscape using SfM
419
approaches (Figure 3b). By selecting a smaller batch of 25 photos from the original 225 that were collected, so
420
that the modeling focused only on the main gully of interest, the resultant file was 891MB in size, with the
421
processing time taking 20 minutes on a medium specification desktop workstation (i.e. Intel I5 4960K processor,
422
16Gb DDR3 RAM, 120Gb SSD hard drive, 2Gb NVidia Geforce 750ti graphics card). Subsequently the point
423
cloud (as an ascii format text file) was exported to CloudCompare for sub-sampling and for cropping a region of
424
interest (yellow box, figure 3b). Further processing involved gridding and graphing within Matlab (Mathworks,
425
2014). The resultant elevation models derived (Figures 3c and 3d) show clearly the relative changes in
426
topography across this small example sub-catchment – cross sectional diagrams in Figure 3e further evidence the
427
quality of the topographic data that can be rapidly captured to support hydrological modeling and catchment
428
management.
429
430
(Figure 3 goes here)
431
432
There is no doubt that the data volumes that can potentially be produced by UAV platforms are challenging to
433
handle with standard desktop computing facilities. For example, fifteen minutes of data capture with a fixed wing
434
UAV over a small catchment (~20 ha) can produce up to 400 individual aerial photographs, requiring over 24
435
hours of processing time on a high-specification desktop PC to build a dense point cloud and ortho-mosaic (using
436
Agisoft Photoscan; Author’s own work). Processing times will be reduced as computer capabilities improve and
437
with advancements in the efficiency of the software. Additionally, GPS geo-tagging of photographs can reduce
438
pair selection time and speed up processing times.
439
440
4. Flying UAVs for WRM
441
The preceding sections have demonstrated how UAV platforms and sensors and accompanying methods to
442
process data are now reasonably robust and well-developed. However, there are still areas requiring development
443
and consideration. For example, in some settings, there is a necessity for research that improves UAV flight
444
planning and operations so that data quality and consistency can be improved for WRM. The following sections
445
present and discuss these various issues in turn.
446
447
4.1 The legal framework governing UAV deployment
448
When considering use of UAVs for WRM it is very important to understand the rapidly developing legal
449
framework governing operational deployment. There is a huge diversity in the legislative framework governing
450
UAV use globally, and coupled with diverse cultural attitudes to UAVs this can make the decision of where and
451
how to fly quite difficult. Globally there is some consensus about where it is legal and safe to fly. Broadly
452
speaking, flights over built up areas such as cities, housing estates and ‘congested zones’ are not permitted by
453
non-licensed pilots, and due to safety considerations would not be advisable by an amateur pilot even if local laws
454
permitted it. This would therefore limit WRM survey flights to ‘uncongested’ areas unless pilots have special
455
licenses or permissions. This restriction goes some way towards explaining why there are so many published
456
papers on UAV use for WRM in agricultural or semi-natural settings where access and flights are legal and
457
unrestricted, whilst there is conversely very little work published in urbanized systems. In the context of WRM,
458
there is great potential for future work to be undertaken in assessing civil fluvial or coastal flood risk using fine-
459
grained data from UAVs (e.g. building very high resolution DEMs for flood modeling using SfM) but to do this,
460
pilots must first navigate the legislation to ensure that they are operating within legal limits. There are a few
461
countries in the world where such ‘congested zone’ restrictions do not apply – an example is Peru – and here
462
there are examples beginning to emerge that demonstrate how UAV data can transform understanding of fine-
463
scale topography in dynamic systems, with high relevance for WRM (Remap Lima Project, 2015). UAVs, if used
464
safely could democratize the capture of time series data in similar systems globally, improving scientific
465
understanding of risk in hydrologically prone areas.
466
467
There are usually civil aviation laws that dictate what constitutes ‘safe operations’ – for example, in the UK,
468
flights are only permitted within 500 m visual line of sight (VLOS) to the operator (this will of course also
469
depend on platform visibility), and aircraft must be at least 150m from congested areas, at maximum altitudes of
470
400ft (121.92 m) for non-professional pilots (Haddon and Whittaker, 2004). As in most countries, different
471
conditions apply to certified operators that present an adequate safety case. In addition, depending on location,
472
topography, and obscuring vegetation there may be physical limits to the distance that can be safely and legally
473
flown with lightweight UAVs. This further restricts UAV applications to proximal studies at very fine spatial
474
resolution over small areas – restricting focus to small catchments rather than large watersheds. To cover large
475
areas one must seek special permission, or use multiple flights from several take-off and landing zones to achieve
476
good spatial coverage. A further consideration is platform size and weight – there are different legal requirements
477
that come into play with larger UAVs – e.g. in the UK, 7 kg is the upper limit beyond which special permission
478
and additional ‘airworthiness tests’ are required. In Australia the distinction between ‘small’ and ‘large’ UAVs is
479
made at the 2 kg weight limit (including payload), so operators must pay attention to local guidelines.
480
481
Globally, there is a large variability with regards to use of lightweight UAVs and there is very little in the way of
482
published literature outlining the complexities in international legislation that applies. As part of this research, we
483
compiled a brief summary of UAV flight restrictions for major areas of the world (Table 2). This information has
484
not previously been compiled elsewhere, largely because the sources of information are varied and often buried in
485
grey literature and in online sources. Some further useful information on European flying legislation is covered by
486
Nex and Remondino (2013), and Colomina and Molina (2014) who provide a useful overview of regulatory
487
bodies and regulations governing international use of UAVs.
488
489
{Table 2 goes here}
490
491
The lack of a harmonized international agreement for UAV operations can create challenges for deployment for
492
WRM. Many catchments that would benefit from UAV surveys are much larger than a standard maximum line-
493
of-sight survey limit, thus requiring multiple take-off sites and operational costs, along with extra data processing.
494
With sense-and-avoid systems (where intelligent control within the platform’s flying systems can be used to avoid
495
scenarios of collision with objects or other aircraft) some of these issues could be overcome (Melnyk et al., 2014).
496
In fact, there is much to be gained if real-time adjustments to flight plans could be achieved according to data
497
from on-board sensors (i.e. intelligent control). Additionally, range and safety can be improved using new
498
transmitter technology (OSRC, 2014) whereby any VLOS restriction can be overcome by placing multiple
499
operators within a catchment, each with their own transmitter. This allows the UAV to be used as a cloud server
500
switching between multiple transmitters to increase flight distance. As an example, if two operators were working
501
within a catchment and spaced 500 m apart, the UAV could cover twice the area in a single flight without the
502
need to land, move and take off again (e.g. ~2 km2 compared with ~1 km2 with a single operator). This could also
503
be particularly useful technology in the context of pipeline or riparian zone surveys where a series of operators
504
could be positioned along-track to allow a long flight line to be captured in a single acquisition. Looking forward,
505
UAV ‘swarms’ (where multiple co-operating vehicles can move in an organized way to survey a landscape) look
506
set to revolutionize UAV surveying and mapping applications (Ryan et al., 2004). By using multiple vehicles, a
507
larger area can be covered more rapidly. Abdelkader et al. (2014) for example, demonstrate a fast numerical
508
scheme for optimizing control of swarming UAVs with a view to their use in post-flood disaster mapping. Several
509
issues are still to be overcome in making this an operational tool for WRM (e.g. energy management (Leonard et
510
al., 2014) and vehicle-to-vehicle communications (Dac-Tu et al., 2012)) but in the next decade it seems likely that
511
swarming UAV models for efficient landscape mapping will be a reality.
512
513
4.2 Weather conditions may hamper flight opportunities
514
As in many remote sensing applications, weather plays a fundamental role in determining the success or
515
otherwise of UAV deployments. A particular barrier to UAV operations is high wind speed (optimal operational
516
wind speeds for most aircraft are below a threshold of 24 - 32 km hr-1). Additionally, rain or atmospheric moisture
517
will severely affect on-board electronic devices if they are not sufficiently waterproof, and will also impact on the
518
reproducibility of data by affecting radiometric image quality. Adverse weather at altitude, where low cloud
519
interferes with image data obtained from satellite and light aircraft (Biggin, 1996) could also provide an
520
opportunity for UAVs, as they could be deployed in storm conditions where cloud cover is typically dense and
521
other remote sensing systems suffer from surface occlusion. The low flying capability of UAVs means that low
522
cloud could be avoided, however, this could be at the cost of optical interference and reduced signal-to-noise
523
ratios in the resultant data (Slater, 1985). This would be a key consideration if one was concerned about
524
reproducibility in the at-sensor radiance values, but less so if the user was just concerned with collection of data
525
for basic geometric mapping of surface water (for example).
526
527
4.3 Innovations in lightweight components
528
Innovations in various supporting technologies could also improve the capability of UAVs to support WRM.
529
Table 3 summarizes several areas where developments in the past decade have been particularly rapid. In all of
530
these cases, these developments would be helpful for improving airborne surveys from lightweight UAV
531
platforms – benefiting WRM along with many other application areas.
532
533
{Table 3 goes here}
534
535
4.4 Cost effectiveness compared to other remote survey methods
536
537
The argument is often made that UAVs offer a more cost-effective method for remote sensing survey than their
538
satellite or piloted aircraft counterparts. However there has been very little economic analysis to support this
539
viewpoint. There are some facts that can be used to support the argument that UAV systems are generally cheaper
540
to buy than other remote sensing platforms – for example, most lightweight airframes (with payload capacities of
541
up to 1 kg) can be bought ready to fly for less than £1000. Once equipped with a good quality consumer grade
542
camera and fitted with an automatic trigger connected to the autopilot (at extra cost, around £500), the water
543
resource manager could have an airborne photography system in the air for less than £4000 with spares and
544
adequate computing facilities included. Such a system would be useful for regular repeat surveys of a catchment
545
of around 20-50 ha, where flying times (with a fixed wing UAV) would be of the order of 20-30 minutes. From
546
the author’s experience, data processing times for ortho-photo generation for catchments of this size then take a
547
further 1 day of processing time on a desktop computer (using Agisoft Photoscan version 1.0.4). For this reason,
548
over small catchments of this size, cost-effectiveness could be achieved if a well-organised and robust post-
549
processing sequence were developed (e.g. to generate an ortho-mosaic from the individual images acquired
550
during the flight and assimilate the mosaic into a GIS). Assuming the UAV did not suffer a crash or require
551
expensive repairs, several surveys could be collected for a lower cost than that of a piloted aircraft survey. The
552
same cannot necessarily be said for a larger catchment (e.g. > 100 ha) where several flights would be needed to
553
cover the extent of the catchment, and where the data processing demands would be considerably higher. So, it
554
must be borne in mind that when assessing the cost-effectiveness, it is not simply the hardware that must be
555
costed. There is a high level of certainty that lightweight UAVs will have a lower baseline cost, but with
556
autonomous operations, the pilot buys into a whole processing chain and this has a high level of associated costs.
557
Any future studies where UAVs are used preferentially over other remote sensing systems would do well to report
558
these costs and benefits so that as a community, it is possible to trace where the biggest investments in time and
559
money go.
560
561
5. Examples where UAV capability will support sustainable WRM in the future
562
In the context of future climate change, and recorded trends of amplified intensity of both precipitation and
563
drought events within the Northern Hemisphere (Goswami et al., 2006; Meehl et al., 2000; Trenberth et al., 2003;
564
Zhai et al., 2005), UAV technology looks set to be in high demand for supporting WRM decision-making
565
globally. We believe that there are considerable market opportunities for UAV platforms that can provide on-
566
demand, fine resolution spatio-temporal data in a wide range of WRM areas. The preceding sections have already
567
pointed to the considerable opportunities that UAV science can deliver. To place these developments in the
568
context of future WRM using UAVs, table 4 has been compiled using existing studies and with a strategic
569
forward-look. It summarizes the broad suite of applications that could be serviced at catchment-scales from
570
UAVs. For example, recent developments in UAV thermal imaging capabilities will allow water resource
571
managers to monitor changes in the biological and chemical oxygen demand (BOD, COD) of reservoirs regularly,
572
and sensors used in UAVs could be easily taken out to be used as hand-held systems for monitoring biological
573
activity in sewage treatment work filters. Combining different sensor types offers even greater opportunities – for
574
example, diffuse pollution hotspots or critical source areas within catchments could be detected using a
575
combination of multispectral or hyperspectral and thermal imaging. The ‘renaissance’ of photogrammetry through
576
the earlier described SfM approaches provides a means of improving current understanding of flood planning and
577
prevention because data can be more easily acquired over fine time-steps and at fine-spatial resolution. In terms
578
of upscaling, there is a huge and as yet unexplored potential for UAV data to support the validation of larger scale
579
data sets obtained from satellites (Lu et al., 2012). There are two key steps that need to be taken to achieve the
580
latter:
581
(a) There needs to be more work undertaken with UAVs that progresses from the current focus on collecting basic
582
photographic images towards more robust radiometric data capture akin to that delivered by radiometric remote
583
sensing instruments. Some work in this domain is developing (Burkhart et al., 2014) and we argue that if there is
584
a move towards collection of high quality radiometric data from UAV platforms, then science will be in a good
585
position to deliver local-scale UAV data for validating global-scale hydrological models.
586
(b) Currently the radiometric quality of data delivered by standard optical camera systems deployed on UAVs is
587
not adequate for robust automated land cover classification (because many user-grade cameras adjust the
588
brightness of images to achieve an appealing visual scene). Users need to improve their understanding of the
589
capabilities and limitations of those camera systems and focus on delivering the best science from the best
590
sensors. A key point to be recognized is that low grade camera systems are put to best use in SfM modeling but
591
data should be handled carefully in the spectral domain.
592
593
594
{Table 4 goes here}
595
A real opportunity for UAV survey data within WRM lies in the promise of this technology to deliver lower-cost,
596
high density, fine-resolution topographic and micro-topographic spatial data models (Ouedraogo et al. 2014)
597
which could revolutionise scientific understanding of hydrological behavior in human-modified agricultural
598
systems. Here there is a lack of topographic data at a sufficiently fine resolution to tackle questions about
599
hydrological flows and impacts on other variables such as soil surface depression storage, infiltration and
600
connectivity (Ouedraogo et al. 2014). Incorporation of UAVs into farm toolkits for real-time intelligent survey
601
could improve the targeting of water resources or management of soils, in a more sustainable way. Furthermore,
602
UAV topographic surveys from UAV-based LiDAR or SfM approaches, will undoubtedly provide new data
603
describing surface roughness (e.g. for spatial description of Manning’s roughness coefficients) that could be used
604
to improve spatial understanding and model parameterization of factors such as hydrological channel routing and
605
thus lead to better prediction of downstream supply (Sharma and Tiwari, 2014). For these benefits to WRM to be
606
realized improvements in spatial accuracy assessment of UAV-derived topographic models must be found
607
(Ouedraogo et al. 2014).
608
609
In undertaking this horizon scanning review, it is also obvious that there are a range of potential uses for UAVs as
610
platforms for agencies and non-governmental organizations working in developing countries where budgetary
611
constraints are common but where timely spatial information on WRM would result in critical improvements in
612
understanding. In remote regions, the portability of the UAV system is attractive. We have identified a few areas
613
where enhanced water scarcity is likely under climate change and an easy to use portable remote sensing system
614
such as a UAV would be very helpful in monitoring landscape hydrology and/or water resource dynamics:
615
616
617

Monitoring of cryospheric stores of water in glaciers is already being explored with the help of UAV
618
technology (Ryan et al., 2014) where photogrammetric image analysis tools have been used to quantify
619
glaciological processes and understand glacial dynamics. In the Andes and in other dry mountain regions of
620
the world, the cryosphere holds hidden water in features such as rock glaciers (Rangecroft et al., 2013;
621
Brenning, 2005), and little is known about these. Fine scale data collected on-demand from UAVs could help
622
local non-governmental organisations to better understand these sources. Globally there are many similar
623
issues in locations such as Kazakhstan, which have potentially large stores of water hidden in cryospheric
624
reserves.
625

In China, there are areas experiencing lower intensity rainfall events leading to extended periods of water
626
scarcity (Zou, 2005) and the ability to use UAVs to improve monitoring of water resources or regulate usage
627
(e.g. regular surveys of reservoir resources, soil moisture assessments using thermal imaging or in precision
628
agriculture to target consumption more effectively) could strengthen existing knowledge.
629

In arid areas of the world (e.g. in Sub-Saharan Africa) the ability to detect the fine scale patterning of soil
630
moisture reserves would be hugely advantageous to non-governmental organisations and other local groups
631
trying to understand aquifer recharge rates for provision of potable water for local communities. In addition,
632
spatial understanding of soil moisture would permit targeted irrigation, only in areas where soil moisture
633
deficit was present, ensuring that costly irrigation of agricultural land was minimized (Kim et al., 2008).
634

In disaster zones, where large numbers of displaced people aggregate, the ability to map and quantify the soil
635
water resource rapidly would be helpful in assisting humanitarian aid efforts because it would allow near-
636
surface potable water resources to be identified and appropriate water supplies to be established. There is
637
already a community of humanitarian ‘UAViators’ (Pronounced “way-viators” http://uaviators.org/) who are
638
‘a global volunteer network of UAV pilots; professional, civilian or responsible hobbyists, who facilitate
639
information sharing, coordination and operational safety in support of a broad range of humanitarian efforts’.
640
Future work in this domain could provide useful insights for WRM in other settings.
641
6. Conclusion
642
The last few decades have seen both a rapid growth in human population and change in climate, both of which
643
present challenges for 21st century life especially with regards to landscape ecohydrological function and
644
subsequent water supplies. This period has also seen rapid advancement in remote sensing technologies alongside
645
scientific development and uptake of UAV-based technologies. UAVs, lightweight sensors and supporting
646
technologies (e.g. novel data processing techniques) are now at a point where there is a nexus of opportunity for
647
the practical water resource manager. With their unparalleled ability to deliver on-demand, fine spatial and
648
temporal resolution data, UAVs can offer a range of products at relatively low-cost to WRM. There are
649
challenges that lie ahead – particularly in the further miniaturization of sensors such as LiDARs, in the legal
650
framework governing safe deployment and in issues surrounding societal ‘trust’ of UAVs and in delivery of high
651
accuracy spatial data products from UAVs. However, this paper has shown that there remains a wealth of
652
opportunities for exploring UAV use in improving scientific and social understanding of a broad spectrum of
653
WRM issues.
654
655
656
7. References
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658
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661
Abdelkader, M., Shaqura, M., Ghommem, M., Collier, N., Calo, V., and Claudel C. (2014), Optimal multi-agent
path planning for fast inverse modeling in UAV-based flood sensing applications, 2014 International
Conference on Unmanned Aircraft Systems (ICUAS). pp. 64-71. (available from:
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Agisoft (2006), Agisoft software, Available from: http://www.agisoft.ru/ [date accessed: 28 April 2014].
Alexakis, D.D., Hadjimitsis, D.D., and Agapiou, A. (2013), Integrated use of remote sensing, GIS and
precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus,
Atmospheric Res., 131:108–124, doi:10.1016/j.atmosres.2013.02.013.
Anderson, C.A., Vivoni, E.R., Pierini, N., Robles-Morua, A., Rango, A., Laliberte, A., and Saripalli, A. (2012),
Characterization of Shrubland-Atmosphere Interactions through Use of the Eddy Covariance Method,
Distributed Footprint Sampling and Imagery from Unmanned Aerial Vehicles, Poster presentation,
American Geophysical Union Fall Meeting, San Francisco, California, USA. 3-7 December 2012.
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726
Figure 1: Web of Knowledge keyword search statistics showing a) the number of published items and b) the
727
number of citations in each year relating to key words “unmanned aerial vehicle”. Date of search: 14 July 2015.
728
729
730
Figure 2 example data demonstrating the potential for UAV technology to support WRM (a) Aerial photograph
731
captured from a QuestUAV fixed wing platform showing pilots (top left) and launch line (centre). The visible
732
white line is the UAV launch line with a diameter of approximately 1 cm. (b) Aerial photograph captured from a
733
QuestUAV fixed wing platform showing tussocky Molinea caerulea grasses within a culm grassland and adjacent
734
to an improved managed grassland, where tussocks are typically up to 30 cm diameter. (c) Aerial photograph
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captured from a QuestUAV fixed wing platform at an eroded upland site within the Forest of Bowland showing
736
hydrological erosion features where water flow has incised into peat, causing gullying. The gauge of steel fencing
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visible in the imaging data is less than 1 cm and tree branching structures are clearly noticeable.
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740
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Figure 3: A visualization of data post-processing showing a typical workflow with UAV data from a Sony NEX7
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camera flown on a QuestUAV fixed wing system at 100 m altitude over a degraded, eroded catchment in
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Northern England. (a) A 2 cm spatial resolution orthomosaic generated in Agisoft Photoscan from 25 images
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collected at a headwater location within the uplands of the Forest of Bowland, Lancashire; (b) Agisoft Photoscan
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point cloud exported to CloudCompare where the data was subsampled to a 5cm spatial resolution and an area of
747
interest - highlighted by the yellow box – was cropped and exported as an .xyz ascii file for further analysis; (c)
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2D topographical model produced by gridding the CloudCompare ascii file within Matlab at one-third resolution;
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(d) applying light enhancement to the 2D Matlab grid, a 3D digital surface model (DSM) can be produced with
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definition of channel and slope; (e) two example cross-sections taken that show both slope and also channels.
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752
753
754
755
756
757
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Table 1: A summary of operational constraints and considerations when choosing different lightweight, sub 7kg
UAVs for WRM. 7 kg was chosen as a threshold weight as this is widely accepted as the lowest weight division
and subsequently has the least restriction to deployment and associated cost.
a) note that the cost of these platforms is indicative of their payload capacity combined with the level of ‘pre-build’
b) Figures obtained from πr2 where operator is centroid and radius=visual line of sight (VLOS) at both 500 and 1000m
c) Diminishing or unreliable helium supply poses the biggest barrier to wide-scale balloon/blimp deployment (Nuttall et al., 2012)
759
Example
platformsa
Typical
endurance
Payload
Capability
Stability
Ability to fly
in wind
Expertise
required
Spatial
coverage in
single flight
Take
off/landing
capability
Velocity/thru
st failure
Cost of ready
to fly system
Fixed Wing
Multi Rotor
Tethered balloons
Blimp
or kites
Top end: MAVinci1,
Penguin-B2
Mid-range: QuestUAV
(see photo)3, Gatewing4
Low-cost: Arduplane5,
flightriot6
Top-end: Aibot X67
Mid-range: DroidworxAD-8-HL (see photo)8
Low-cost: 3d Robotics
Iris9 or Arducopter10
Public Lab11
Survey Copter12
25-75mins
6-25mins
Hours
Hours
1-2kgs
1-2.5kgs
>2.5kgs
>2.5kgs
Medium
High (in low winds)
Medium
High (in low winds)
Medium/high
Medium
Medium/high
Low
Training is required to ensure safe operations and
compliance to local aviation rules. Pilots should
keep flight logs. Training should include simulator
training, lightweight trainer aircraft manipulation
and operational field deployment.
80-320hab
Launch line/catapult or
hand launch – open area
for landing / Parachute
Glide
capability
/
controlled
crash
/
parachute
Top end: $30K+
Mid-range: $5-30K
Low-cost: $1-5K
c20-40ha
Vertical take-off
landing (VTOL)
Balloons, blimps and kites are easily deployed and
safely monitored from the ground, so require
minimal training and low skillsets.
Sensor footprint only
and
Crash
VTOL for balloons.
Kites depend on
wind.
Wind failure = crash
for kites. No issue
for balloons.
From $100
variable
VTOL
No issue, equivalent would
be rupture in balloon
equaling a crashc
$1K-$10K depending on
size
Platform
image
QuestUAV in flight (Author’s
own photo)
Droidworx AD-8-HL (Author’s
own photo)
760
761
762
1
http://www.mavinci.de/
http://www.uavfactory.com/product/46
3
www.questuav.co.uk
4
http://uas.trimble.com/
5
http://diydrones.com/profiles/blogs/ardupilot-mega-home-page
6
http://flightriot.com/
7
http://www.aibotix.com/aibot-x6.html
8
http://aeronavics.com
9
http://store.3drobotics.com/products/IRIS
10
http://www.arducopter.co.uk/
11
http://store.publiclab.org/products/balloon-mapping-kit
12
http://www.survey-copter.com/english/uavs-blimp/
2
Public lab balloon with
camera attachment11
Survey Copter Blimp
camera survey set-up12
with
763
764
Table 2: UAV industry and legislation for the major economic regions of the world. Data collated in January
765
2015.
Australia Canada China Europea India Mexico Russia UK USA
Brazil
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Nb
Yc
N
N
N
Y
Y
Y
Commercial
UAV
Y
flights permitted?
Y
Yb
Y
N
Y
N
Y
N
Y
Hobbyist
UAV
Y
flights permitted?
Y
Y
Y
?
Y
?d
Y
Y
Y
Commercial
use currently
under
revision by
the FAA. No
fly
zones
implemented
in
certain
regions.
Currently a
complete
ban
on
commercial
UAV use
Country
UAV
Industry
Regulated?
Other notes
Related
2kg defines
‘small’ UAV
(including
payload);
license
required.
UAV
flights
permissible
under
license.
Variation in
definitions
across
different EU
member
states
see
(Silverburn,
2013)
Liberal
attitudes
to UAV
use
(Garcia,
2013)
766
767
a) Europe has been included as a single entity due to its unified trade and legislation.
768
b) China appears to allow operation by authorisation at a provincial/town planning level but has nothing in place at a national level.
769
c) Whilst there are overarching EU guidelines, it is also stated that each member state is responsible for applying their own law relating to UAV use.
770
d) There are competitions involving UAVs and plenty of hobbyists flying, but regulation relating to hobby pursuits has been difficult to find.
771
772
773
774
775
Table 3: Overview of UAV related technological developments that could have a beneficial impact on WRM.
Advancement
Example
technologies
Why
Benefit to WRM
Microcomputing
Raspberry pi13 Real-time data
or Arduino14 processing/co
minimmunication
computer
processors
References if
applicable
Lightweight mini-PCs provide an ‘all in one’
system for rapid data collection and processing onboard the UAV. Emergency scenarios such as a
flooding event would hugely benefit from dataready systems like this.
Lightweight
SwiftNav
differential
PIKSI15
(real-time
kinematic) GPS
with sub-10 cm
accuracy
Improved
positional
accuracy
On-board lightweight GPS have not until recently (Jones and Gross,
offered a spatial accuracy aligned with the 2014)
resolution of data delivered by UAVs. Piksi is the
first differential GPS on the market – at greatly
reduced cost, weight and size in comparison to
existing ground-based unit. Provides a WR
manager with centimeter precision for mapping.
Improved
aerodynamics
for fixed-wing
UAVs
Improvements
in
stability,
lift
and
endurance
Having a UAV that through better design and (Kroo,
2005;
without getting heavier can lift a heavier payload, Mueller
and
fly longer or manage in-flight turbulence DeLaurier, 2003)
better will increase sensor options for deployment,
further increasing existing or proposed capabilities
for WRM. So more research is required on wing
and fuselage design, the use of new materials and
improved energy sources.
Aerodynamic
design (wings,
fuselage) and
materials
innovation
Improvements 3DRobotics16 Ease
in multi-rotor
use/cost
capability
of Ready to fly (RTF) systems that are inexpensive, (Colomina
lightweight whilst remaining sensor-capable allow Molina 2014)
minimal training, much wider scale deployment
and operational readiness.
Lighter, greater Li-s or Li-Air Increase
in Neither is available commercially yet, but with (Van
endurance
energy density both systems having a much higher energy per kg 2014)
batteries
(storage)
storage, batteries for UAVs could become lighter
with the same power, or greater power for the
same weight. Overall implications for UAV size,
payload capacity or endurance.
Lightweight
Titan
solar
aerospace17
photovoltaics
for improving
endurance
of
fixed-wing
UAVs
13
Increase
flight
endurance
in Scaling of this technology could allow UAS to be
deployed on long time-series missions. Would
allow real-time fine-scale data collection over a
catchment without any need to pause in temporal
scales. There are multiple considerations relating
to weight and endurance (Fazelpour and
Vafaeipour, 2013).
http://www.raspberrypi.org/
http://www.arduino.cc/
15
http://swift-nav.com/piksi.html
16
http://3drobotics.com/
17
http://titanaerospace.com/
18
http://saildrone.com
19
http://www.nasa.gov/centers/dryden/news/ResearchUpdate/Helios/
14
and
Noorden,
Examples exist in
other
disciplines,
e.g.
the
solarpowered Saildrone18
and NASA have
tested with large
UAVs
(e.g.
Helios19).
776
Table 4: Future uses of UAV platforms and sensors in the water resources sector
Potential
deployment for
Landscape
assessment
of
environmentally
designated areas or
agricultural
practice
within
catchments
Identification
of
water
resource
issues related to
pollution, erosion,
invasive species or
failure of water
resources
infrastructure
Required
platform and UAV Product
sensors
Relates to WRM
Example
studies
proof-of-concept
Duties
to
relevant
national
Orthophoto and
Fixed wing +
legislation/asset management. Ability
land
cover
optical camera
to monitor fine-scale change over
classification
time in sensitive areas.
(Bortels
et
al,
2011)
successfully applied this
using satellite data from
ASTER in wetlands
Ability to detect poor agricultural
practice which may impact water
Orthophoto, land
supply such as non contour
cover
ploughing. Highlight areas with
classification,
erosion using SfM or precision
NDVI map and
agriculture techniques using NIR,
SfM point cloud
thermal or multispectral to reduce use
of water, herbicides and pesticides.
(Haboudane et al, 2002;
Thenkabail et al, 2012) have
developed
narrow-band
techniques to identify gross
primary production in crops.
Orthophoto, point
cloud, video or
individual image
Fixed wing +
analysis. SfM to Identify pollution hotspots, illegal
optical camera
produce
DTM discharge or to model topography for
and thermal
models or flow- areas of pollution risk.
video capture
path models for
tracking pollution
transport.
Wang et al. (2004) used
Landsat data to describe
spatial variability in reservoir
BOD.
Fixed wing +
optical
camera, NDVI
sensor,
multispectral
or
thermal
sensors
To better understand soil/sediment (McShane et al, 2014)
Multi-rotor +
loss and erosion events after rainfall.
Orthophoto, SfM
optical camera
To characterize landscape change
and LiDAR point
and/or UAV
after flooding. deposition fans,
cloud
LiDAR
Topographic models for calculating
connectivity, or volume of erosion.
Fixed wing +
optical
and
NDVI
cameras
Use to survey catchments for signs of (Thenkabail et al, 2012) The
Orthophoto fineinvasive species that may have an use of hyperspectral remote
Scale vegetation
impact upon catchment quality, sensing in plant studies.
mapping, NDVI
response or that have a legal
maps.
requirement to control.
Fixed wing or
Thermal
video
multi-rotor +
analysis
to
thermal
identify hotspots
videography
Thermal imaging of water
Saturated soil has a different
‘hotspots’ during cold spells
emissivity to dry soil –one can
or vegetation anomalies after
potentially target investigative site
dry periods from optical data
visits. Early warning system.
(APEM, 2014; Levi, 2005).
Orthophoto
+
point cloud to
generate
land
cover
classification and
SfM topographic
model
Repeat surveys over restored
areas
(e.g.
Sphagnum
reseeding (Moors for the
future, 2014), or grip
blocking (Exmoor mires
project,
2014)).
Spatial
evaluation of soil water
stores (Luscombe et al.,
2014b)
and
riparian
vegetation zones (Dunford et
al, 2009, Fonstad et al, 2011).
Fixed wing+
Monitoring
optical
and
restoration,
thermal
pollution or soil
imaging
water storage
sensors
To accurately assess effectiveness of
restoration efforts with regards to
impact on soil wetness, impact to
runoff/erosion, e.g. in peatlands over
multiple seasons on a long-time-series
Combining thermal and
optical data for detecting
Multi-rotor +
To assist in detecting and reducing diffuse pollution hotspots
Orthophoto
or
optical
and
marine pollution from freshwater (Lega and Napoli, 2010).
video analysis.
Thermal
nutrient sources.
Orthophoto and
(Verhoeven et al, 2011)
Multi-rotor + point
cloud Ecological and archaeological surveys Using aerial photography and
Surveys for impact
optical and/or analysis
required for screening, scoping and SfM for archaeology
assessment
UAV LiDAR including
monitoring.
hillshade models
777