copy the linklink copied!Chapter 8. Case Study 3. Gully erosion monitoring in Australia’s Great Barrier Reef catchments

This case study provides a practical example of how remote sensing technologies and data or analytical products generated using these technologies can improve the effectiveness and efficiency of gully erosion and sediment control programmes.

    

copy the linklink copied!Context: Tackling water quality impacts of sediment transport in Great Barrier Reef catchments

Australia’s Great Barrier Reef (GBR) is an international icon of great value and is listed as a World Heritage Area. However, the health of the reef has been in decline for many years now, due to a variety of environmental pressures. One important pressure is the transport of nutrients (nitrogen and phosphorous) and sediment downstream from GBR catchments into the GBR lagoon (Jacobs, 2014[1]).

Recent studies have identified that “gully erosion is a dominant contributor of sediment, particularly in the Burdekin and Fitzroy catchments” of the GBR. In addition, gully erosion is also a problem for livestock graziers, as it degrades the condition of the land, reducing productivity (Tindall, 2014[2]).

In recognition of the significant negative impacts caused by gullies, the Reef Trust Gully Erosion Control Programme was established in 2016, through which the Australian Government allocated AUD 7.5 million (exclusive of GST) towards “projects across the four targeted natural resource management regions in Queensland, to fund community groups and organisations to work with private landholders to remediate high risk gullied areas”.1 Also, in order to be able to track how erosion and sediment management initiatives are impacting transport of sediment to the GBR, the Paddock to Reef Integrated Modelling, Monitoring and Reporting Program (P2R) was established, with funding jointly supplied by the Australian and Queensland Governments.2 As explained by Darr and Pringle (2017, p. 1921[3]), “the catchment loads modelling component of this programme estimates average annual loads of key pollutants for catchments draining to the GBR, and assesses changes against baseline levels due to improvements in land management practices. As well as reporting progress against water quality targets, the models are used to guide investment priorities.”

copy the linklink copied!Use of digital technologies to improve gully erosion mapping

The problems

While substantial resources have been allocated to gully erosion prevention and control initiatives (as described above), these funds are finite and must be used as cost-effectively as possible. Information on where gullies are located, and how they (and sediment transport downstream) are changing over time, is fundamental to being able to target prevention and control efforts to where they will be most cost-effective. The modelling component of the P2R initiative (used to track overall progress towards sediment-related goals for the GBR) similarly relies on having accurate information on a range of complex physical processes, including sediment erosion and transport. However, until recently, this information has been scarce and costly to obtain. According to Tindall (2014[2]):

There has been limited work undertaken to comprehensively map gully locations, and to quantify and monitor gully erosion processes in GBR catchments at scales or resolutions appropriate for land management decision-making. Where mapping studies have been conducted, the information has been of limited use due to low accuracy, scale limitations or the maps being of limited geographic extent.

Darr & Pringle (2017, p. 1920[3]) similarly note that:

“Previous attempts to map gully density within the GBR catchments have been conducted by either intensively mapping gully erosion for relatively small isolated areas where gullies are prominent, or by defining the extent of gully erosion at a number of sample sites and then using predictive models to estimate gully density across much larger areas. Due to scale limitations, low accuracy or limited geographic extent, both these approaches have produced maps with limited usefulness for modelling water quality improvements. Consequently there is a need for a methodology that can improve the confidence in gully density maps over broad areas, in a timely fashion, and at a spatial scale that enables the modelling of water quality improvements due to on-ground investments, and allows prioritising of remediation strategies in the GBR.”

These problems are not unique to sediment erosion in GBR catchments. They relate to fundamental challenges caused by information gaps, and the high costs involved in gathering the required information using traditional data sources and collection methods, particularly over very large spatial scales (see conceptual framework in Figure 2.1 in main report).

Digital solutions

Advances in remote sensing technologies offer the opportunity to improve information on gully erosion, at lower cost than existing methods. While remote sensing—particularly aerial images—has long been used to supplement in-field measurement, there is a range of newer remote sensing technologies that, until recently, had not been deployed to map gully erosion, in GBR catchments or elsewhere. The Queensland and Australian governments have funded several projects that aim to assess the suitability of a range of remote sensing technologies in this context. Key projects are:

Gully mapping and drivers in the grazing lands of the Burdekin catchment (Project RP66G)

Funded by the Department of Environment and Heritage Protection's Reef Water Quality (RWQ) Science Program and led by the Queensland Department of Science, Information Technology, Innovation and the Arts’ (now Department of Environment and Science) Remote Sensing Centre (RSC), this project mapped and quantified gully extent and rates of change at a range of scales in the Burdekin catchment using airborne and terrestrial LiDAR3 data.

Airborne LiDAR survey (ALS) typically ranges in cost from around AUD 60-100 per km2, depending on providers, sensor and flying specifications, area acquired and post-processing requirements. This makes airborne LiDAR a relatively expensive option, however, with appropriate industry standards and effective survey control both within and between multi-date acquisitions, and appropriate sampling design, it remains an effective and accurate approach for detailed characterisation of gully morphology and relative changes over time. The RP66G project captured a number of locations in a sampling strategy aimed a multi-date, detailed gully change monitoring approach. Some issues were encountered in the project with data quality and post-processing, highlighting the need for the establishment of industry standards and potentially the development of guidelines for the capture of ALS specifically intended for gully change monitoring. The RSC has addressed some of these issues by developing an end-to-end ALS processing system which standardises ALS data acquired from multiple providers and a range of specifications, improving the ability to make change estimates over time. However, deriving gully extent information from ALS remains a challenge and automated classification approaches should aim to quantify uncertainty in any estimates of gully change, particularly when evaluating the effectiveness of remediation efforts. Importantly, the RP66G project progressed research into quantification of uncertainty in change estimates derived from airborne and ground-based or terrestrial LiDAR (TLS). The work has culminated in a recent publication by Goodwin et al. (2017[4]) which compared survey control data, ground based LiDAR and airborne LiDAR to quantify and report uncertainty in change estimates derived from these technologies. The authors concluded that:

“ALS can detect large scale erosional changes with head cutting of gully branches migrating…” while “TLS captured smaller scale intra-annual erosional patterns largely undetectable by the ALS dataset…” and therefore “suggests TLS and ALS surveys are complementary technologies and when used together can provide a more detailed understanding of gully processes at different temporal and spatial scales, provided the inherent errors are taken into account”.

This project “[p]rovide[d] spatially-comprehensive mapping and monitoring of gully erosion in the Burdekin catchment to improve knowledge of where gullies occur and to attempt to better understand the processes and drivers of gully erosion, particularly in the grazing lands of the catchment” (Tindall, 2014, p. i[2]). The improved mapping, produced at 5km and 1km resolutions, was achieved by “visual [i.e. manual] observation of satellite and aerial imagery and predictive modelling”.4 A mapping guideline (Darr, Tindall and Ross, 2014[5]) was also developed to support ongoing application of this approach in other parts of the GBR grazing lands and potentially other locations facing similar challenges. The project also published a number of data outputs (e.g. gully presence maps and digital elevation models) on departmental websites under a Creative Commons licence.5 These outputs serve multiple needs, including:

  • providing improved information for targeting erosion prevention and remediation efforts;

  • supporting grazing extension programmes aimed at improving grazing land management to improve sustainability of the grazing industry in GBR catchments;

  • helping to improve water quality models (e.g. the P2R models) which are used to assess progress in achieving environmental objectives for the GBR.

Building on the work of the RP66G project, Darr and Pringle (2017[3]) applied the project’s techniques to build grid-based presence maps6 (GBPM) of gully erosion at 1 ha spatial resolution. They then linked these maps with “a range of landscape attributes such as slope, distance-to-stream and soil erodibility to produce a predictive model that has the ability to generate gully density maps for all GBR catchments” (p. 1920[3]).

Monitoring Gullying Processes in the Great Barrier Reef Catchments (Photogrammetry project)

Funded by the Australian Department of Agriculture and Water Resources and led by CSIRO, this project assessed the suitability of “digital photogrammetry7 applied to aerial images routinely collected by state land survey agencies [for addressing] the challenges posed by gully erosion and sedimentation” (Poulton et al., 2018, p. i[6]). The outputs of the project are:

  • An assessment of the suitability of digital aerial photogrammetry for mapping and monitoring of gully erosion processes in the GBR Natural Resource Management (NRM) regions.

  • High resolution ortho-rectified images, digital surface models (DSMs) and associated ground elevation model (GEM) and water flow maps to help landholders, NRM groups and researchers identify locations of high erosion risk requiring evaluation, monitoring or intervention.

  • Documentation of specifications required for future air photo capture to enable DSM generation at appropriate resolutions for gully mapping and monitoring at other locations across Australia.

Poulton et al. (2018, p. ii[6]) provide an overview of the technical process to produce the DSM and GEM:

[H]igh performance computing and digital photogrammetry was employed to generate radiometrically calibrated image mosaics and to create a digital surface model (DSM) capturing landscape and watershed features including gullies. Aerial data was acquired at a native image resolution of 0.1 m for two case study regions covering 520 km2 in the Upper Burdekin and Bowen-Bogie catchments in Queensland. Surface infrastructure and vegetation was removed from the DSM using automated computer algorithms to generate a ground elevation model (GEM). This GEM is applied to the generation of a flow path prediction model that simulates water flow across a landscape surface. These GEMs were compared with high resolution (± 2.5 cm accuracy in elevation) survey points distributed within both study areas and correlated with aerial laser scanning (ALS) and terrestrial laser scanning (TLS) surveys within the confines of selected gully sites. Analysis of the GEM for the surveyed sites found that 48% of the photogrammetric elevations in the Upper Burdekin site, achieved < 0.1 m vertical error in detecting the ground surface, with 81% of locations within 0.3 m of the surveyed measurements. Both study areas exhibited 14% of sites with > 0.5 m vertical error, a product of filtering and interpolation error due to shadowing by standing vegetation.

This description makes clear that a number of different digital technologies are combined to produce the final project outputs, including:

  • Digital photography to acquire aerial images.

  • High performance computing and digital photogrammetry to process aerial images to produce elevation measurements.

  • Algorithms to remove surface infrastructure and vegetation, as described above, and also to interpolate ground surfaces for areas below dense tree canopy.

  • Digital flow path modelling.

This study concluded that “[t]he technology is cost effective and capable of capturing high resolution (sub metre) data for large regional areas with acquisition and processing at AUD 35-70 per km2 for resolutions of 0.5-0.2 m and is compared with current acquisition of [aerial laser scanning] ALS at AUD 50-100 per km2(Poulton et al., 2018, p. ii[6]). The cost structure displays economies of scale, as fixed costs of deploying an aircraft to the region of interest account for a large portion of the cost (Poulton et al., 2018, p. 39[6]). Further, since aerial photographs are routinely taken by government agencies for a range of purposes, the cost of acquiring imagery for a specific purpose (in this case, gully erosion mapping) could be at least partially shared across different users. Finally, use of satellite data, the costs of which are declining rapidly, is promising for the future.

However, photogrammetry does have certain drawbacks, including that the cost of acquiring imagery is weather-dependent (as photographs cannot be taken through clouds), and that it is not an accurate method in areas of higher vegetation cover and is still unproven in detailed gullied environments. Further, case study participants noted that the ability to take advantage of routine acquisition of data by government depends on data collection protocols providing sufficiently high quality data,8 currently, government acquisitions do not capture the data with appropriate specifications for deriving accurate high-resolution DEMs and therefore are not readily applicable. Additionally, many government captures do not provide overlapping photography at all due to new sensors and cost reductions (pers. comm. Dan Tindall, Queensland Department of Environment and Science & Joint Remote Sensing Research Program Remote Sensing Centre, August 2018).

copy the linklink copied!Lessons learned for the application of remote sensing and predictive modelling technologies for erosion mapping in agricultural lands

Lesson 1. Use of advanced remote sensing techniques to map erosion processes over large spatial scales is technically feasible and yields improved results but is still relatively costly and challenging to undertake. Large knowledge gaps remain, and a combination of tools may be necessary to enable cost-effective mapping techniques and erosion management strategies

In the synthesis report for the RP66G project, Tindall (2014, p. 78[7]) concluded:

Gully mapping across large areas using remotely sensed imagery is challenging. It relies on having a consistent, repeatable and mappable definition of gullies which can be applied at multiple scales and across multiple image capture platforms. Simple, pragmatic and efficient methods are required to ensure consistency in the application of any mapping approach. Outputs must balance available resources for mapping against end-user requirements. A key outcome of this project has been the development of a guideline for catchment-scale gully mapping in Queensland. The guideline provides clear definition, guiding principles and efficient methods for manual and semi-automated mapping of gullies.

Similarly, for the photogrammetry project, Poulton et al. (2018, pp. 41-42[6]) concluded that:

While aerial photogrammetry cannot provide the level of surface detail of ground based RTK [real time kinetic] GPS, it is currently an economical method for delivering a high resolution GEM and associated surface flow path prediction model at a regional scale when compared with alternative technologies.

DOMs [digital ortho mosaics] and RGB images and in particular the flow path model overlay are powerful communication tools for use in discussion with researchers, agricultural and natural resource managers and wider community groups. Integrating photogrammetry techniques for generating a DSM and GEM with routine aerial acquisition by state and commercial agencies will provide additional layers of contextual information to existing aerial photographic images. Application of aerial photogrammetry in deriving a ground elevation model for evaluating changes at a coarser resolution and for larger regional catchments will help inform landscape managers and enable better targeting of resources for prevention or remediation in areas subject to erosion processes.

Nevertheless, a number of challenges remain. The key challenge for all aerial techniques studied is how to improve interpretation of ground surfaces, especially in areas with high vegetative cover. Therefore, a mix-methods approach appears to be the most cost-effective: use of photogrammetry techniques for large areas with low vegetation cover, supplemented by (more expensive) ALS or TLS techniques where detailed gully profiles are required or in areas where dense vegetative cover predominates.

Part of the challenge in tracking changes in erosion levels over time is that historical data (e.g. photographs) may be difficult to locate and are often of poor quality.9 This highlights that the usefulness of initiatives to track erosion (and other physical processes which occur over similar timeframes) is dependent on having a sufficiently high quality time series data. Therefore, even those initiatives which are now acquiring high quality data may take some time to yield precise results.

As noted above, while the RP66G project made use of new technology in the form of LiDAR data and new predictive modelling, it still relied on visual (i.e. manual) inspection of satellite and aerial photography to identify and classify gullies. The project did investigate the possibility of automating processing of LiDAR data to accurately map and quantify gully extent and volume; however, Tindall (2014, p. 73[7]) commented that [t]he automated method used to classify gully extents for individual dates was not be robust enough to reliably compare and map change in gully extents between dates and over time.” The authors noted (p.81) that machine learning approaches suggested by others may warrant further inquiry, but that this was beyond the scope of the current project.

Darr and Pringle reported that their project (based on the RP66G methodology), as of 2017, used approximately 1.4 full-time staff equivalents to map and quality check on average 4 200 km2 per month, achieving 87% accuracy when checked against field observations. At this rate, the authors estimated approximately five years would be required to fully map the remaining 300 000 km2 of the GBR catchments. Thus, while more efficient than other manual techniques, this is “not a quick process” for basins as large as those in the GBR catchment (Darr and Pringle, 2017, p. 1925[3]). These authors also identified that a further step would be to automate the mapping process using machine learning techniques, but, as with the RP66G project, this has yet to occur.

The RP66G project and photogrammetry project authors also identified that a range of other emerging remote sensing technologies could be useful to improve mapping efforts, and recommended these be explored in further research.10

Further, knowledge of where and when gullies occur is not the only information gap needing to be filled. Other crucial areas of inquiry are to understand the “fate and timing of sediment delivered from gullies” and to develop “the most appropriate technologies and approaches for managing and monitoring gullied areas” (Tindall, 2014[2]). The RP66G project concluded that:

Emerging technologies such as ground-based laser scanning, imagery and LiDAR capture from Unmanned Aerial Vehicles (UAVs), sediment tracing and digital soils mapping all present opportunities to help improve our understanding of gully processes to enable effective management strategies for improving land condition and water quality in the grazing lands of the GBR. (Tindall, 2014[2])

Thus, it is important to place the use of technology for a specific purpose (monitoring gullies) in the broader context of the overall policy objective (reducing negative impacts of erosion on the GBR), and ensure that there is a balanced approach to investigating different questions.

Lesson 2. Improved understanding of physical processes must be balanced by economic considerations

The techniques described here have the ability to significantly reduce information gaps about where and when gully erosion is occurring. This knowledge is fundamental to efforts to address the negative impacts of erosion, both for the Great Barrier Reef and more broadly for livestock producers and rural communities who rely on the productivity of land at risk from gullying.

However, there is still “very limited information about the cost-benefit of gully prevention and remediation approaches” (Tindall, 2014, p. 14[2]). A holistic assessment of costs should include both the actual implementation costs of different approaches as well as the transactions costs of programmes which aim to increase uptake of management actions by land managers. Targeting remediation and prevention efforts based only on the information provided by gully mapping ignores spatial differences in management costs and transactions costs, which may be substantial.11 Information on both the benefits and costs of alternative erosion management activities is needed to ensure efforts are targeted cost-effectively. Tindall et al. (2014, p. 82[7]) recommend that “where possible, science and monitoring efforts be combined with on-ground efforts and economic modelling to improve knowledge of where and when to expend resources for gully management.”

Work to evaluate the relative costs of different erosion management activities and programme-related transactions costs is ongoing.12 However, in a recent audit of measures taken to address issues affecting the GBR, the Queensland Audit Office found that, as of June 2018, the Queensland government “cannot measure the degree of practice change or assess the value achieved from its investment of public funds. The Office of the Great Barrier Reef is currently negotiating with industry groups to gain access to the data the departments need and should have access to” (Queensland Audit Office, 2018, p. 9[8]).

Lesson 3. Benefits and challenges of collaboration across organisations and across projects

Both the RP66G and photogrammetry projects were highly collaborative and brought together researchers from a range of state and national government agencies, including CSIRO, Department of Agriculture and Water Resources, Queensland Department of Department of Science, Information Technology, Queensland Department of Natural Resources and Mines, the National Environmental Science Programme and Innovation and North Queensland Dry Tropics Regional NRM group. These projects are part of a broader portfolio of research activities that are continuing to contribute to identifying, defining, characterising, measuring and modelling change in gully systems in key Great Barrier Reef catchments. This research utilises a range of data collection methodologies and techniques (e.g. airborne and terrestrial laser scanners and ground based and aerial photogrammetry) each with unique strengths, weaknesses and costs associated with collecting and data processing.

Increasing costs associated with this type of research and the rapid on-going technological development in the collection of ground based, remoted sensed and large spatial data requires adaptation, innovation and successful collaboration of the research community. For the photogrammetry project, having access to a wider research network currently undertaking project activities within the GBR region enabled transfer of localised knowledge which helped identify suitable case study areas. Selected sites were aligned with existing ground measurements undertaken by research collaborators in the region. In this case, collaboration facilitated opportunities to access data sources from past and current projects (e.g. aerial and ground based LiDAR, gully location mapping, aerial and ground photography, satellite imagery) as well as experience gained by those researcher’s familiar with use of the different technologies. Collaborative exchange delivered cost savings in data collection for individual projects as well as useful calibration and validation data made available between different project groups. Spatial data collected and generated by the photogrammetry project is to be made available to ongoing projects within the GBR study region.

While there was a willingness for collaboration between projects, in reality researchers share their time between a number of competing activities. Opportunities for the wider research community, particularly different organisations, to meet face to face regularly are infrequent. Within a twelve-month period one successful workshop was held which brought together a larger group of researchers with wide-ranging experience in technologies and methodologies for quantifying gully systems. Focused discussion and a sharing of experience targets not only a knowledge exchange between researchers but helps quantify information on the appropriate technology for a particular application and helps to inform the wider research and government agencies.

References

[3] Darr, S. and M. Pringle (2017), Improving gully density maps for modelling water quality within Great Barrier Reef Catchments, https://www.mssanz.org.au/modsim2017/L22/darr.pdf (accessed on 8 August 2018).

[5] Darr, S., D. Tindall and J. Ross (2014), Guidelines for catchment scale gully erosion mapping in Queensland: Principles, procedures and definitions (Unpublished report).

[4] Goodwin, N. et al. (2017), “Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland”, Geomorphology, Vol. 282/282, pp. 195-208, http://dx.doi.org/10.1016/j.geomorph.2017.01.001.

[1] Jacobs (2014), Independent review of the Institutional and Legal Mechanisms that provide Coordinated Planning, Protection and Management of the Great Barrier Reef World Heritage Area. Report to the Department of the Environment and Energy, http://www.environment.gov.au/marine/gbr/publications/independent-review (accessed on 8 August 2018).

[6] Poulton, P. et al. (2018), Evaluating the utility of photogrammetry to identify and map regions at risk from gully erosion. Report to Department of Agriculture and Water Resources., CSIRO Agriculture and Food, https://publications.csiro.au/rpr/pub?list=SEL&pid=csiro:EP18475&sb=RECENT&expert=false&n=1&rpp=550&page=1&tr=1&q=PID%3A%22csiro%3AEP18475%22&dr=all (accessed on 9 August 2018).

[8] Queensland Audit Office (2018), Follow-up of Managing water quality in Great Barrier Reef catchments (Report 16: 2017–18), http://www.parliament.qld.gov.au/documents/tableOffice/TabledPapers/2018/5618T931.pdf (accessed on 8 August 2018).

[2] Tindall, D. (2014), Gully mapping and drivers in the grazing lands of the Burdekin catchment: RP66G Summary Report, Queensland Department of Science, Information Technology, Innovation and the Arts Remote Sensing Centre, https://publications.qld.gov.au/dataset/gully-mapping-burdekin/resource/40a7bb78-8092-4056-a7bf-93deb3ef5af3 (accessed on 8 August 2018).

[7] Tindall, D. (2014), Gully mapping and drivers in the grazing lands of the Burdekin catchment: RP66G Synthesis Report, Queensland Department of Science, Information Technology, Innovation and the Arts Remote Sensing Centre, https://publications.qld.gov.au/dataset/gully-mapping-burdekin/resource/ce81fc22-63f9-4627-be1e-f57ae177f6aa (accessed on 8 August 2018).

[9] Wilkinson, S. et al. (2015), Managing gully erosion as an efficient approach to improving water quality in the Great Barrier Reef lagoon. Report to the Department of the Environment, http://ttps://publications.csiro.au/rpr/pub?pid=csiro:EP1410201 (accessed on 8 August 2018).

Notes

← 1. Source: http://www.environment.gov.au/marine/gbr/reef-trust/gully-erosion-control, accessed June 2018. Queensland is the Australian state in which GBR catchments are located.

← 2. Source: https://www.reefplan.qld.gov.au/measuring-success/paddock-to-reef/, accessed August 2018.

← 3. LiDAR (Light Detection and Ranging) is an active remote sensing sampling tool which uses the length of time a laser beam takes to return to the sensor to calculate distance. It is a key technology to obtain data used to construct Digital Elevation Models (DEMs) and derive metrics of vegetation height, structure and cover. For a simple explanation, see https://gisgeography.com/lidar-light-detection-and-ranging/, accessed August 2018.

← 4. “The 5km resolution mapping combined high resolution mapping, a predictive model of gully presence and visual observations of gully prevalence across the entire catchment. Gully presence was mapped in 7 classes relating to the amount of gullying present, where gullying was observed. The 1km resolution mapping was achieved entirely through visual interpretation of a 1km grid, each grid divided into one hundred, 100m x 100m cells to provide a count or percentage of gullying evident in each 1km grid cell. Mapping was targeted at key areas identified in the 5km map as having high gully presence.” (Tindall, 2014, p. i[2])

← 5. See Tindall et al (2014[7]), Table 21.

← 6. Grid-based presence mapping is a technique which a process which “allows an operator to map the presence or absence of gully erosion within a grid cell, using custom-built geographic information system (GIS) tools, aerial photography and uniform grids.” (Darr and Pringle, 2017, p. 1920[3])

← 7. As explained by Poulton et al. (2018, p. 16[6]): “Digital photogrammetry is the science of making, among other things, geometric measurements from images. Digital aerial photogrammetry attempts to reconstruct three-dimensional surfaces from overlapped (stereo) aerial images. The process of digital aerial photogrammetry is as follows: acquire aerial image data, triangulate images, generate three-dimensional surface models and orthoimages. The main processing tasks are performed by means of a digital photogrammetric system. Additional process steps are applied to further analyse the outcomes and create specific derivative products.”

← 8. Poulton et al. (2018, p. 10[6]) note that “[p]rovided sufficient digital data for panchromatic – nadir (forward and backward view) and 4-band multispectral nadir (backward views) are retained and the collection method follows standardised and rigorous protocols detailed in Section 2.1.3 and Appendix 3 [of this paper], future aerial acquisition may provide a source of low cost data needed to produce and periodically update fine scale digital surface models (DSMs) aiding erosion risk management.”

← 9. Tindall (2014, p. 79[7]) observe that “[m]apping changes in gully extents using historical imagery is challenging and resource intensive, particularly for large areas. Locating historical imagery for a particular location requires extensive investigation of air photo archives to find suitable imagery that can be geo-located accurately to be able to reliably compare change over time. Identifying gullies in older imagery, and also in some new imagery, can be extremely difficult, resulting in a large degree of subjectivity in mapping outputs.”

← 10. “New technologies are emerging such as Unmanned Aerial Vehicles (UAVs) and space-borne stereo imagery. DAFF has previously demonstrated the application of UAVs for capturing imagery and generating digital surface models over a gully remediation trial on Spyglass Research Station in the Burdekin. Outputs still require testing and validation but the results did show some promise. It is suggested that further investigation of UAV technology for mapping and monitoring gullied areas be considered. With regards to space-borne stereo imagery, RSC has an agreement with the Chinese Satellite Applications Centre for Surveying and Mapping (SASMAC) who operate the ZY-3 satellite. This satellite has high resolution stereo-imagery capable of producing 4m digital surface models” (Tindall, 2014, p. 81[7]). See also section 5.1 “Future Opportunities” in Poulton et al. (Poulton et al., 2018, pp. 43-44[6]).

← 11. For example, Wilkinson et al. (2015[9]) reports that the direct management cost (i.e. excluding any reports that the direct management cost (i.e. excluding any programme-related transactions costs) of the recommended combination of management techniques for GBR grazing lands (consisting of fencing around gullies, gully channel revegetation with native perennial tussock grasses, use of check dams or other sediment traps to prevent gullying, and managing grazing pressure to avoid vegetation clearing and restore perennial pastures) varies between AUD 4 500 and AUD 9 000 per km of gully. Variation in cost-effectiveness per tonne of reduction in mean-annual sediment load is largely dependent on the efficiency of fencing.

← 12. See, for example, Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands, http://nesptropical.edu.au/index.php/round-2-projects/project-2-1-4/, accessed August 2018.

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Chapter 8. Case Study 3. Gully erosion monitoring in Australia’s Great Barrier Reef catchments