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Desktop surveillance of farm damsAreas covered by Parkes and Braidwood 1:100,000 scale maps

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NSW Department of Primary Industries, Office of Water

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The NSW Office of Water manages the policy and regulatory frameworks for the state’s surface water and groundwater resources, to provide a secure and sustainable water supply for all users.

It also supports water utilities in the provision of water and sewerage services throughout New South Wales. The Office of Water is a division of the NSW Department of Primary Industries.

Desktop surveillance of farm dams – areas covered by Parkes and Braidwood 1:100,000 scale maps

December 2011

ISBN 978 0 7313 3975 4

This publication may be cited as:

Shaikh, M., Whyte, J. and Pobre, L. (2011), Desktop surveillance of farm dams – areas covered by Parkes and Braidwood 1:100,000 scale maps, NSW Office of Water, Sydney

© State of New South Wales through the Department of Trade and Investment, Regional Infrastructure and Services, 2011

This material may be reproduced in whole or in part for educational and non-commercial use, providing the meaning is unchanged and its source, publisher and authorship are clearly and correctly acknowledged.

Disclaimer: While every reasonable effort has been made to ensure that this document is correct at the time of publication, the State of New South Wales, its agents and employees, disclaim any and all liability to any person in respect of anything or the consequences of anything done or omitted to be done in reliance upon the whole or any part of this document.

NOW 11_331


Contents

Executive summary ............................................................................................................................... 1

1. Introduction........................................................................................................................................ 2

Drivers of this project..................................................................................................................... 3

Commonwealth ................................................................................................................... 3

NSW Office of Water........................................................................................................... 3

NSW legislative framework ........................................................................................................... 3

Dams on third order or greater watercourses ..................................................................... 3

Dams not on third order or greater watercourses ............................................................... 3

Study areas ................................................................................................................................... 4

Parkes area......................................................................................................................... 4

Braidwood area................................................................................................................... 4

Scope of study............................................................................................................................... 5

In scope............................................................................................................................... 5

Not in scope ........................................................................................................................ 5

2. Remote sensing of farm dams .......................................................................................................... 6

Imagery used in project ................................................................................................................. 6

Landsat and SPOT5 Imagery ............................................................................................. 6

Digital aerial imagery (ADS40)............................................................................................ 6

3. Approaches to classification of remote sensing data ........................................................................ 7

Pixel-based classification .............................................................................................................. 7

Object-based classification............................................................................................................ 8

On-screen digitisation (visual interpretation)................................................................................. 9

4. Results of remote sensing............................................................................................................... 10

Pixel-based classification ............................................................................................................ 10

Object-based classification.......................................................................................................... 10

5. Discussion of remote sensing ......................................................................................................... 16

6. Recommendations for remote sensing............................................................................................ 19

7. References ...................................................................................................................................... 20

Appendix 1. Coverage of digital aerial imagery (ADS40) over NSW .................................................. 21

i | NSW Office of Water, December 2011


ii | NSW Office of Water, December 2011

Figures

Figure 1: Location of the Parkes and Braidwood study areas ......................................................... 5

Figure 2: Farm dams appear as dark spots on a false colour composite of Braidwood

SPOT5 imagery .............................................................................................................. 10

Figure 3: SPOT5 image classification of the same location shows farm dams in yellow, but omits dams at points A and B ................................................................................... 10

Figure 4: A natural colour composite of the Braidwood digital aerial imagery (ADS40) ................ 11

Figure 5: Results of image segmentation of digital aerial imagery (ADS40) ................................. 11

Figure 6: Farm dams showing as yellow in digital aerial imagery.................................................. 12

Figure 7: The distribution of farm dams shown as yellow in the Braidwood area.......................... 13

Figure 8: Results of farm dams in the Parkes area........................................................................ 15

Figure 9: Results of farm dams in the Braidwood area.................................................................. 15

Figure 10: Boundaries of farm dams mapped by Geoscience Australia (red) and our study (yellow)............................................................................................................................ 17

Figure 11: Traced outline (red) shows inaccuracies in Geoscience Australia mapping. The

yellow outline mapped in our study shows the boundary of the dams ........................... 18


Executive summary

This document describes pilot studies to assess the use of remote sensing and geographic

information systems (GIS) to map farm dams. The GIS analysis is reported in a separate document and not published, to protect individual privacy.

The term ‘farm dam’ is widely used across Australia. For the purpose of this study, farm dams are

considered to be man-made water storages used to:

intercept watercourses and capture runoff

or

hold water pumped into them.

NSW is committed to promoting and facilitating the implementation of the National Water Initiative,

which has listed interception of water by farm dams as having significant impacts on catchments.

Accurate baseline mapping of farm dams could provide data for determining their level of compliance with any relevant legislation. It could also provide useful information for water planning, water balance

modelling and climate change impact—all of which could improve the management of water resources.

Recent advances in object-based algorithms and availability of high resolution imagery prompted

these studies to investigate their applications. Remote sensing techniques have several advantages for mapping farm dams, especially over a large area, as they provide multi-spectral, multi-temporal and synoptic coverage. Also archival images can be analysed to determine when a dam was

constructed. High resolution digital aerial imagery (ADS40) and SPOT5 were used in this study.

Pilot projects were conducted on farm dams in the Parkes and Braidwood areas. Different remote sensing techniques were assessed to determine their suitability. Pixel-based classification and on-

screen digitisation were applied in the Parkes area. Object-based classification was used in the Braidwood area.

Pixel-based classification produced an inaccurate outline of farm dams. On-screen digitisation of

digital aerial imagery (ADS40) was labour-intensive; however, it generated a highly accurate outline of dams. Object-based classification generated a highly accurate outline of farm dams, using less resources.

As a result of these studies, we recommend adopting a semi-automatic method based on object-based classification using high resolution digital aerial imagery (ADS40), complemented by SPOT5 imagery.

Although farm dams were the subject of these studies, the results and recommendations could be applied to investigation of other types of dams, in other locations where there is a need for surveillance for legal compliance or environmental purposes.

1 | NSW Office of Water, December 2011



1. Introduction

Farm dams are the lifeline of most farming businesses as they provide a supply of water essential for

irrigation and other agricultural purposes. However, farm dams intercept surface flows, reducing the availability of water to downstream water users and the environment. Although some water storages (such as turkey’s nest dams) are not on watercourses, water held in them is part of the catchment’s

water budget.

In NSW, farm dams can be constructed legally if statutory requirements are complied with to assist equitable sharing of water. This project investigated the use of high resolution imagery to assist

desktop surveillance of farm dams, including turkey’s nest type dams.

Pilot projects were conducted on farm dams in the Parkes and Braidwood areas (1:100,000 scale map). The area around Parkes was selected on the basis of some preliminary work by the Office of

Water which identified a large number of farm dams and potentially a high level of non-compliance. The Braidwood area was chosen as some remote sensing work had already been done in this area for another project.

Different remote sensing techniques were assessed to determine their suitability for mapping farm dams. An automated GIS analysis was developed which uses the acquired imagery to identify all dams on individual properties, identify the order1 of the stream the dams intercept, provide data on licensed

dams, estimate each dam’s capacity, and determine their level of compliance with NSW legislation.

An increased use of remote sensing techniques and GIS analysis will enable the Office of Water to more accurately assess illegal water capture, have a better understanding of the level of compliance

of farm dams, and effectively target its resources to areas of illegal activity that have the greatest impact on water availability. Information provided to compliance officers will enable them to target identified properties for on-ground investigations of farm dams. It will also enable education

campaigns to be appropriately targeted to improve the level of understanding of water users.

The CSIRO (2008) has made projections based on historical data for farm dam growth and current policy controls. They suggest a possible 10% increase in the total capacity of farm dams by 2030, and

a reduction in stream flow in the Murray–Darling Basin (MDB) by 170 GL a year by 2030, or 0.7%. The projected farm dam development would reduce average annual runoff in eastern NSW by 1.0 to 1.5%. At the local (sub-catchment) scale, particularly areas close to major rural centres, the increase in the

number of farm dams may have a significant impact on runoff.

Geoscience Australia (2008) and State partners completed the mapping of farm dams in 15 of the eastern Murray–Darling Basin (MDB) catchments. This study indicates the highest density of dams is

located within peri-urban and rural residential areas around the major population centres. Around 41%, 34% and 25% of the dams occurred in the upland, slopes and lowlands respectively. A total of 519,931 man-made water storages were mapped for a nominal year of 2005. The overall increase in

the number of dams in the study area is estimated to be 6% between 1994 and 2005. Most of this increase is in the point (smaller) dams, therefore volumetric increase is likely to be less. Beecham and Stazic (2011) estimated the increase in volume of the farm dams was less than 3% between 1994 and

2005. The degree of change varies considerably between and within catchments. There are significant regions of very high development of farm dams, particularly within commuting distance of the major regional centres. The NSW regions with the highest levels of development during the period include

Wagga Wagga, Albury, Tumut, Bathurst, Armidale and Tamworth (Murray–Darling Basin Commission. 2008).

The recent advances in object-based algorithms and availability of high resolution imagery prompted

this study to investigate high resolution imagery’s usefulness in mapping farm dams.

1 The method for determining the stream order of a watercourse (based on the Strahler system)


Drivers of this project

Commonwealth

NSW is committed to promoting and facilitating the implementation of the National Water Initiative, which has listed interception of water by farm dams as having significant impacts on catchments. The

output of this study will be useful in identifying and managing interception activities such as the use of farm dams.

The NSW Implementation Plan for the National Framework for Compliance and Enforcement Systems

in Water Resource Management Project describes the activities NSW will undertake in relation to the National Framework (National Water Commission 2008). Project activities include evaluating uses of remote sensing to enable remote monitoring of up to 25% of water resource entitlement in high risk

areas. This will build on work being undertaken by the Office of Water. Commonwealth funding will allow the Office of Water to expand its proactive desk top surveillance program for monitoring farm dams, increasing its capacity to detect breaches and improve compliance.

NSW Office of Water

The Office of Water’s Compliance Branch is responsible for implementing a range of compliance activities, including monitoring compliance with the statutory framework. Monitoring includes desktop surveillance and on-ground activities.

Previously desktop surveillance was able to focus only on small areas of NSW due to the limitations of previous remote sensing techniques and the availability of resources. However, improved remote sensing techniques and an automated GIS analysis have the potential to map farm dams in large

areas of the State, or focus on targeted areas, and identify potential breaches with a minimal amount of resources.

Increased desktop surveillance of farm dams will enable resources to be focused more effectively on

high-risk areas where dams may not be complying.

NSW legislative framework

The management of farm dams in NSW is administered through the Water Act 1912 (Water Act) and the Water Management Act 2000 (WM Act). In general, the licensing and approval provisions of the WM Act apply where a gazetted water sharing plan for the water source has commenced. All other

water sources continue to be administered through the Water Act.

Legislative requirements differ for dams depending on whether they are located on third order or greater watercourses or on first and second order watercourses.

Dams on third order or greater watercourses

Unless exempt, dams on third order or greater watercourses must be licensed, regardless of whether the dam is used for stock, domestic, irrigation or commercial purposes.

Dams with a capacity of less than 7 ML, constructed on a third order or greater watercourse prior to 1

January 2001, and used for stock and domestic purposes only, do not require a licence.

Dams not on third order or greater watercourses

The Harvestable Rights Order for the Eastern and Central Division of NSW allows landholders to capture up to10% of the average regional rain water run-off in a dam or dams on first or second order

watercourses on their property. The harvestable right is intended to satisfy essential farm needs such as domestic consumption and stock watering but may be used for any purpose.



First or second order watercourses are ‘minor’ streams. Licences are not required for dams on first or second order watercourses if the total capacity of these dams on a landholding is less than the Maximum Harvestable Right Dam Capacity (MHRDC). The MHRDC can be calculated by multiplying

the area (hectares) of the landholding by the multiplier corresponding to the location of the land shown on the MHRDC map of the Office of Water (NSW Office of Water, n.d.).

Dams constructed after 1 January 1999 and exceeding the MHRDC should be licensed.

The following dams on first and second order watercourses are included when determining whether a landholder has complied with their MHRDC:

dams licensed after 1 January 1999

unlicensed dams.

The following classes of dams are not to be included when determining whether a landholder has

complied with their MHRDC:

dams licensed under Part 2 of the Water Act which were initially licensed prior to 1 January

1999

dams solely for the control or prevention of soil erosion

dams solely for flood detention and mitigation

dams solely for the capture, containment and recirculation of drainage and/or effluent

dams approved for specific environmental management purposes

dams without a catchment, such as turkey’s nest dams and ring tanks.

The MHRDC does not apply to dams on lots in subdivisions where the subdivision was approved by a

local council before 1 January 1999. In this situation, where these lots would have a right to capture rainwater run-off of less than 1 ML, the dams are allowed to have a total capacity of 1 ML.

Study areas

Each of the study areas for the Parkes and Braidwood pilot projects cover an approximate area of 50 km x 50 km, a total of 2,500 km2. This area is equivalent to a 1:100,000 topographic map. There are a total of 347 1:100,000 topographic maps in NSW.

Parkes area

The Parkes area is in the Lachlan region in the Murray–Darling Basin. Parkes town is located in the northern part of the 1:100,000 topographical map. Parkes is the main settlement in Parkes Shire in the central west of New South Wales. It is on the western edge of the Great Dividing Range and has a rich

variety of farming in the surrounding region. Irrigation occurs for pastures, hay and cereal grain production. At the 2006 census Parkes had a population of 9,826.

Braidwood area

The Braidwood area, covered by the 1:100,000 topographical map, is in the upper Shoalhaven

catchment, situated in the Southern Tablelands of NSW in Palerang Shire. It is on the eastern side of the Great Dividing Range, about 200 km south west of Sydney and about 60 km inland from the coast. Braidwood is a service town for the surrounding region which is based on sheep farming, beef cattle

and forestry operations. The area supports an increasing range of hobby and part-time orchardists and farmers raising livestock such as goats and alpacas, and growing crops such as olives and grapes. At the 2006 census, Braidwood had a population of 1,108.



Figure 1: Location of the Parkes and Braidwood study areas

Scope of study

In scope Any water storage identifiable as a dam in digital aerial imagery (ADS40) for Parkes

(1:100,000 scale map).

Any water storage identifiable as a dam on digital aerial imagery (ADS40) for Braidwood

(1:100,000 scale map).

All licensed and unlicensed dams in the Parkes and Braidwood areas.

Not in scope Any water body not identifiable as a dam on digital aerial imagery (ADS40).

Dams in areas outside the defined boundaries around Parkes and Braidwood.



2. Remote sensing of farm dams Remote sensing involves gathering information about the Earth’s surface by using sensors and

cameras in aircraft, spacecraft or satellites.

There are two main platforms for collecting images, airborne and satellite. The airborne ADS40 digital sensor captures high resolution data. Satellite sensors (Landsat, SPOT5) are cost effective and can

cover larger areas in a short duration, but with coarse pixels (CSIRO Environmental Remote Sensing Group, 2003).

Various forms of imagery datasets, such as Landsat, SPOT5 and digital aerial imagery (ADS40), are

held in the archives of the NSW Office of Water. These datasets were assessed to determine whether they are suitable for mapping of farm dams.

Imagery used in project Landsat and SPOT5 Imagery

For more than 30 years the Landsat satellite has provided imagery, a useful source of information for managing natural resources. The Landsat sensor provides imagery at a ground resolution of 30 m in

six multi-spectral bands covering the visible and near-infrared parts of the electromagnetic spectrum.

However, due to the imagery’s coarse resolution when using multi-temporal images, it is difficult to interpret features which are the size of a single pixel (30 m). Therefore, a minimum mapping unit

which could be reliably mapped in Landsat images is about two pixels (3,600 m2).

Small farm dams are difficult to detect by Landsat imagery. This is supported by Johnston and Barson (1993) who evaluated the usefulness of Landsat imagery for mapping the extent of wetlands,

particularly open water in Victoria. They found that Landsat imagery successfully detected open lake/pond wetland areas, achieving a classification accuracy of 95%, but failed to map riverine wetlands adequately. The failure of the technique with riverine wetlands was attributed to the narrow width of

oxbow lakes and the fact that the wetlands were empty if the images were acquired during a drought.

Previous work by the Murray–Darling Basin Authority (Good and McMurray, 1997; Neal et al. 2002) demonstrated that Landsat imagery is not suitable for mapping small water bodies. Therefore, due to

its coarse resolution, Landsat imagery was precluded from the study of farm dams around Parkes and Braidwood.

The SPOT5 satellite captures three multi-spectral bands at a resolution of 10 m, and captures the

short-wave infrared band at a resolution of 20 m. The short-wave infrared band is resampled to 10 m and supplied with the three multi-spectral bands to provide a four-band multi-spectral image in the visible and near-infrared parts of the electromagnetic spectrum. SPOT5 images have a higher ground

resolution but lack the spectral information of Landsat images.

Digital aerial imagery (ADS40)

Digital aerial imagery for Parkes and Braidwood (1:100,000 scale map) were captured by Land and Property Management Authority (LPMA) using a Leica ADS40 sensor. The capture date for Parkes is

2 January 2008 and for Braidwood is 28 January 2009. Although LPMA acquires imagery in multi-spectral bands similar to SPOT5 the imagery is routinely supplied in compressed format. The imagery compressed format is ERMapper ECW. The ECW is a proprietary compression algorithm that reduces

image size. The compressed imagery is easy to manage for viewing as the file size can be reduced to 5% of the original. However, pixel-based classification performs poorly on the compressed image due to the lack of spectral information in the pixel.

Parkes and Braidwood ADS40 imagery was provided by LPMA at a ground resolution of 50 cm in the compressed ECW format. As a result of its higher resolution ADS40 imagery provides a valuable source of information for detecting and monitoring smaller water bodies. The digital aerial imagery is

available for a large portion of the central west and coastal areas of the state (Appendix 1).



3. Approaches to classification of remote sensing data

Remote sensing techniques have several advantages for mapping farm dams, especially over a large

area, as they provide multi-spectral, multi-temporal and synoptic coverage. Also archival images can be analysed to determine when a dam was constructed, providing useful information when considering whether a dam complies with legislative requirements.

Pixel-based classification

An image is made up of thousands of pixels stitched together. Conventional image classification

methods, such as unsupervised and supervised classification, rely on the spectral characteristics of pixels. However, pixel-based classification does not take into account other information present such as tone, texture, shape, context and neighbouring features. In image processing domain, each pixel is

an individual unit and classification commonly works on individual spectral characteristics to classify that pixel.

Unsupervised classification is the most commonly applied method in mapping open water bodies

(Shaikh et al., 2001, 1998; Frazier and Page, 2000). Many other studies have used both supervised and unsupervised multi-spectral classification of optical remote sensing data to delineate water boundaries (Kingsford et al., 1997; Lee and Lunetta, 1995). In unsupervised classification, an

individual pixel is compared to each discrete cluster to see which one it is closest to (Richards and Jia, 1999). All the pixels in the image are classified to a user-defined number of classes. This must then be interpreted by the user to determine what classes the surface feature belongs to.

In supervised classification, data for sites known to the user (these sites are referred to as training sites) are fed into the computer. The classification program then proceeds by statistically comparing every pixel with pixels from the known site and classifying them.

The challenge for pixel-based classification in mapping water storages is to be able to eliminate spectral confusion between empty water storages and various surface features including wetlands and grazing areas.

As water has a unique spectral signature, optical imagery allows water bodies to be easily detected by image classification techniques. However, there are problems with automatic classification in delineating the boundary of a farm dam if the image is acquired when the dam is empty. Empty farm

dams are not easy to detect as they do not have a unique spectral response. The spectral signature of empty farm dams is confused with other surface features such as bare paddocks which have a similar spectral response.

In such cases the image must be visually interpreted to identify farm dams. This is also noted in a Geoscience Australia report (2008) which states that when the imagery was captured most of the areas were in severe drought, which precluded using digital classification techniques. Instead, labour-

intensive manual digitising was undertaken.

The study of farm dams in the Parkes and Braidwood area applied unsupervised pixel-based classification on the SPOT5 imagery. In unsupervised classification, image processing software

classifies an image based on natural groupings of the spectral properties of the image pixels. An analyst defines parameters such as the number of classes, iterations and which bands to use, otherwise the software may generate any number of classes based on natural groupings of pixels in

the image. An initial 128 classes were specified with 200 iterations at 0.95 confidence level using Leica ERMapper software.

Unsupervised classification yields an output image in which a number of classes are identified and

each pixel is assigned to a class. These classes may or may not correspond well to land cover and the user needs to assign meaningful labels to each class. The process requires an analyst to overlay



classification results on a backdrop of imagery, identify farm dams, and label classification results to the farm dam category based on visual interpretation.

The advantages of unsupervised classification are that it is a quick method which is highly successful

in finding spectral clusters inherent in the image. It doesn’t require prior knowledge of the classes. However, it does not always generate classes based on themes that correspond to types of land cover.

In the Parkes and Braidwood area unsupervised classification did not generate reliable results, especially when the farm dams were empty. The classification did not perform well in separating empty farm dams from grazing area and bare paddocks.

Object-based classification

The main difference between pixel-based and object-based image classifications is that in object-based classification the basic processing units are objects or segments in an image and not single

pixels. Object-based classification works on a group of pixels in the image. It segments an image into a number of objects based on a user-specified scale or resolution based on spatial, spectral (brightness and colour) and texture characteristics.

The results from object-based classification mimic, to a certain extent, traditional aerial photo interpretation (API). In addition to spectral information, object-based classification also considers information relating to the texture, size, shape and context of neighbouring pixels (Hurd et al., 2006).

The spectral and spatial characteristics of each object are utilised to segment the image into various classes. This process is somewhat similar to the human visual cognitive process.

Object-based classification was not attempted in the Parkes area as the object-based classification

program was not available within the Office of Water at that time.

The study of farm dams in the Braidwood area applied an object-based classifier to digital aerial imagery (ADS40) using Definiens® imaging software. Definiens is spatial pattern recognition software

that has advanced classification techniques including fuzzy logic. Fuzzy logic is a process whereby the assignment of an object to a final class is done via assessing the probabilities of the object belonging to numerous classes (Zhang & Maxwell, 2006). While the aim is to accurately delineate and classify

objects, the limitless range of segmentation scales and classification possibilities can complicate the process.

Different image segmentation algorithms are available in the Definiens software. A multi-resolution

segmentation was applied on the Braidwood ADS40 imagery. The basis of multi-resolution segmentation is conceptually quite simple and is described as a ‘local optimisation procedure’. The process starts with a ‘seed’ pixel that iteratively tests itself against neighbouring pixels for similarity or

homogeneity. If they are similar, within user-defined spectral-spatial thresholds, then they are merged (Zhang & Maxwell, 2006). This process repeats itself with pixels creating an object until the threshold is reached. Once the threshold is reached the object is finalised and the process starts again until the

whole image is segmented into a number of objects.

Object-based classification is a semi-automatic process. After the image has been segmented by the software program an output is generated and exported as an Esri® shape file containing a large

number of polygons. The user then has to visually interpret the Esri shape file, on a backdrop of high resolution imagery. The user needs to select the individual polygons corresponding to farm dams and attribute each polygon to a ‘farm dam’ class. This process was less labour intensive compared to on-

screen digitisation as the polygon outline is already present in the Esri shape file.



On-screen digitisation (visual interpretation)

SPOT5 unsupervised classification failed to classify empty farm dams accurately in the Parkes area. Therefore, on-screen digitisation was undertaken using digital aerial imagery (ADS40) to map farm

dams. On-screen digitisation is a highly labour-intensive, manual, on-screen process in which the user pans through the image and captures farm dams by digitising on-screen and attributing mapped polygons to farm dams. Visual interpretation relied on a thorough understanding of the image patterns

which were based on tone, shape, size, pattern, texture and context of the neighbouring features. This process was able to map all the farm dams irrespective of whether they were empty or full of water.



4. Results of remote sensing

Due to the poor resolution of Landsat imagery it was precluded from our study of farm dams around

Parkes and Braidwood. High resolution digital aerial imagery (ADS40) and SPOT5 were used to map farm dams in this study.

Pixel-based classification

Figure 2 shows farm dams in a dark tone on a false colour composite of a Braidwood SPOT5 image. Pixel-based image classification of SPOT5 imagery (Figure 3) shows farm dams in yellow. The results

of the classification of farm dams (Figure 3) show that some farm dams were not mapped by the pixel-based classifier as shown at points A and B in Figure 3. The outlines of the farm dams appear pixelated due to the relatively coarse resolution of the image compared to the size of the farm dams.

Figure 2: Farm dams appear as dark spots on a false colour composite of Braidwood SPOT5 imagery

Figure 3: SPOT5 image classification of the same location shows farm dams in yellow, but omits dams at points A and B

A

B

Object-based classification

A natural colour composite image of digital aerial imagery (ADS40) in the Braidwood area is shown in Figure 4. Farm dams are clearly visible in a dark tone with a geometrical shape.

A multi-resolution segmentation of digital aerial imagery (ADS40) resulted in a large number of

polygons (Figure 5). Large homogeneous areas in the image were delineated into larger segments compared to other parts of the image that were segmented into smaller size polygons.



Figure 4: A natural colour composite of the Braidwood digital aerial imagery (ADS40)

Figure 5: Results of image segmentation of digital aerial imagery (ADS40)

As displayed in Figure 6, the farm dam boundaries are accurate in the digital aerial imagery compared

to the pixel-based classification of SPOT5 imagery (Figure 3). The outline of the farm dams appears to be continuous and accurately follows dam boundaries. In digital aerial imagery (ADS40) object-based classification mapping is based on the object rather than the pixel. Typical pixel-based analysis

produces irregular boundaries which are not clearly defined.



Figure 6: Farm dams showing as yellow in digital aerial imagery

The overall distribution of farm dams in the Braidwood area is illustrated in Figure 7.



Figure 7: The distribution of farm dams shown as yellow in the Braidwood area

Table 1 shows the total number and surface area of farm dams in the Parkes and Braidwood areas. In the Parkes area there were 5,473 dams, with a total surface area of 680 ha. In the Braidwood area there were 7,225 dams, with a total surface area of 436 ha. Table 1 also shows the minimum,

maximum and mean surface areas of the farm dams.

Braidwood dams, whether full or empty, were smaller than dams in the Parkes area. The minimum surface area of farm dams detected in the Parkes area using ADS40 was 28 m2. The minimum

surface area of farm dams in the Braidwood area using ADS40 was 18 m2. This indicates that high resolution digital aerial imagery (ADS40) is useful for detecting small farm dams.



Our results showed the mean surface areas of farm dams in the Parkes and Braidwood areas in 2008–09 were 1,242 m2 and 605 m2, respectively. Whereas, Geoscience Australia (2008) reported an average dam size of 2,131 m2 for the Parkes area in 2004–05.

Table 1. Surface area of farm dams in the Parkes and Braidwood areas

Farm dams (ADS40)

Total number Minimum surface area (m2 )

Maximum surface area (m2 )

Mean surface area (m2 )

Total surface area (ha)

Parkes 5473 28 479,603 1242 680

Braidwood 7225 18 16,450 605 437

The surface area of the dams, along with an estimation of their depth, can be applied to a standard equation to estimate their volume. The surface area of a farm dam can be translated into its volume by applying the following equation used by the NSW Office of Water:

Volume (m3) = 0.4 x surface area (m2) x depth (m)

(0.4 is a conversion factor that takes into account the slope of the sides of dams.)

For our results, a depth of 3 m was applied in the above equation. The depths of farm dams vary, depending on their location, and cannot be determined from a desktop analysis. Based on local knowledge, it was assumed that farm dams in the Parkes area have an average depth of 3–5 m and

farm dams in the Braidwood area have an average depth of 2–4 m. Applying a depth of 3 m may mean that the volume of some dams in the Parkes area is underestimated and the volume of some dams in the Braidwood area overestimated.

In Table 2, farm dams in the Parkes and Braidwood areas have been split into two groups based on their surface area. Farm dams with a surface area less than 3,600 m2 (4.3 ML) would not be detected by the Landsat satellite. Digital aerial imagery (ADS40) was able to detect small farm dams with a

surface area of 18 m2 (0.02 ML).

Table 2. Volume of farm dams in the Parkes and Braidwood areas

Parkes Braidwood Surface area of farm dams (m2)

Total number of farm dams


Total volume (ML)

Total number of farm dams


Total volume (ML)

18–3,600 5295 (96.7%)

412.79 (60.7%)

4,953.5 (60.7%)

7170 (99.2%)

402.14 (92.0%)

4,825.7 (92.0%)

>3601 178 (3.3%)

267.32 (39.3%)

3,207.8 (39.3%)

55 (0.8%)

34.75 (8.0%)

417.0 (8.0%)

Total 5473 680.1 8,161.3 7225 436.89 5,242.7

Table 2 shows that almost 97% of farm dams in the Parkes area, and over 99% of farm dams in the

Braidwood area, are small dams with a volume between 0.02 ML and 4.3 ML (for a surface area between 18 and 3,600 m2). These dams would not be detected by the Landsat satellite as they are too small. As well, these very small dams account for nearly 61% (4,953.5 ML) of the total volume of all

the dams in the Parkes area and 92% (4,825.7 ML) of the total volume of all the dams in the Braidwood area (Figure 8 and 9).



Figure 8: Results of farm dams in the Parkes area

Parkes

5295

178412.79

267.32

4,953.50

3,207.80

0

1000

2000

3000

4000

5000

6000

7000

8000

18-3,600 >3601

Surface area of farm dams (m2)

Total number of dams


Total volume (ML)

Figure 9: Results of farm dams in the Braidwood area

Braidwood

7170

55

402.14

34.75

4,825.70

417

0

1000

2000

3000

4000

5000

6000

7000

8000

18-3,600 >3601

Surface area of farm dams (m2)

Total number of dams


Total volume (ML)



5. Discussion of remote sensing

The results of this study on remote sensing of farm dams provide valuable information for choosing

the most appropriate imagery and reliable methods for mapping farm dams. An indication is also given of the resources required.

There is a large archive of Landsat imagery dating back several decades which is relatively cheap or

free to acquire. However, due to the imagery’s coarse resolution (30 m) a minimum mapping unit is about two pixels (3,600 m2). Therefore, Landsat imagery is not able to detect farm dams with a surface area less than 3,600 m2. Also, it is difficult to accurately stack pixels on top of each other when using

multi-temporal images for change detection.

As the Landsat satellite is in the last stages of its lifecycle it would be prudent not to plan a long-term project based on Landsat data. However, Landsat imagery may prove useful in some inland areas for

monitoring large dams. It also provides continuous multi-temporal data which may be useful for monitoring the increase in the number of larger farm dams

As high resolution digital aerial imagery (ADS40) is able to map farm dams as small as 18 m2, it is the

most suitable imagery for mapping farm dams throughout NSW. It can map farm dams in upper catchments and coastal areas where typical dams are smaller than dams further west. The coverage of digital aerial imagery (ADS40) over New South Wales (Appendix 1) shows a large portion of the

coast and the central parts of NSW have been acquired.

Three different methods were used to map farm dams. Pixel-based classification and on-screen digitisation were applied in the Parkes area. Object-based classification was employed in the

Braidwood area.

Pixel-based classification produced an inaccurate outline of farm dams. The outline appears pixelated due to the relatively coarse resolution of the image pixels when compared to the size of the farm

dams. The results of classification of farm dams (Figure 3) shows that some farm dams were not mapped by the pixel-based classifier. Pixel-based classification performs well when water storages are full. However, it performs poorly on empty farm dams where the dams are incorrectly classified as

grazing land or bare paddock.

On-screen digitisation of digital aerial imagery (ADS40) is a tedious and labour-intensive process for mapping dams; however, it generates a highly accurate outline of the dams. It requires 4–5 weeks for

one person to complete a single 1:100,000 map (50 km x 50 km), depending on the density of farm dams.

Object-based classification is a semi-automatic process. After the image has been segmented into a

large number of polygons by the software program, the user has to identify farm dams and attribute them to a ‘farm dam’ category. This process generated a highly accurate outline of the farm dams (Figure 6), and took about two weeks to complete a single 1:100,000 map. Object-based classification

of farm dams in the Braidwood area captured water storages regardless of whether they were full or empty. Therefore, object-based classification of digital aerial imagery (ADS40) should be adopted for all future mapping of farm dams. This would generate accurate baseline information on farm dams in

NSW.

Geoscience Australia has mapped farm dams in the Murray–Darling Basin. Results from Geoscience Australia and our study were compared for the Parkes area. There were discrepancies in the surface

area of farm dams mapped by Geoscience Australia (2008) and dams mapped in this study (Figures 8 and 9).

Farm dams in the Parkes area mapped by Geoscience Australia using 2004—05 SPOT5 data were

compared with farm dams mapped by the Office of Water using 2008–09 ADS40 data in this study. This was undertaken by selecting farm dams which were common to both Geoscience Australia and



Office of Water studies. This comparison shows that the total surface area mapped by Geoscience Australia is about 36% higher than the total surface area mapped by the Office of Water. This means that Geoscience Australia claims an additional volume of 3,543 ML of water (Table 3). The

discrepancy in the surface area is possibly due to inaccuracies in the mapping of dam boundaries by Geoscience Australia. See the red outline in Figures 8 and 9.

Mapping of farm dam boundaries by the Office of Water using object-based classification is highly

accurate and should be adopted for all future mapping of farm dams. See the yellow outline around the dams in Figures 8 and 9.

Accurate baseline mapping of farm dams provides data for determining the level of compliance of farm

dams. It also provides useful information for water planning, water balance modelling and climate change impact, which will improve the management of water resources. Establishing baseline information collected from high resolution digital aerial imagery (ADS40) will improve the Office of

Water’s capacity to analyse historical time-series of medium resolution imagery such as SPOT5. It will provide a better understanding of the status of a dam at specific dates.

Figure 10: Boundaries of farm dams mapped by Geoscience Australia (red) and our study (yellow)

Table 3. Comparison of farm dams mapped by Geoscience Australia (2008) in the Parkes area

Mapping by Total number of farm dams


Total volume (ML)

Geoscience Australia 3641 808.29 9,699.52

Office of Water 3679 512.98 6,155.76



Figure 11: Traced outline (red) shows inaccuracies in Geoscience Australia mapping. The yellow outline mapped in our study shows the boundary of the dams



6. Recommendations for remote sensing

This study recommends the following:

1. High resolution digital aerial imagery (ADS40) should be used to map farm dams. Accurate baseline information will be useful for assessing historical time-series using SPOT5.

2. The semi-automatic method based on object-based classification should be adopted for

highly accurate results.

3. Farm dams of interest will be assessed for historical time-series using SPOT5 and aerial

imagery to determine their date of construction (pre- or post-1999).




7. References Beecham, R and Stazic, D 2011, ‘Hillside farm dam growth in NSW MDB: 1994/5 – 2004/5’, NSW

Office of Water, unpublished report.

CSIRO 2008, Murray–Darling Basin Sustainable Yields Project.

CSIRO Environmental Remote Sensing Group 2003, Determination of SRA habitat indicators by remote sensing: Technical scoping document, CSIRO Land and Water.

Geoscience Australia 2008, Mapping the growth, location, surface area and age of farm dams in the Murray–Darling Basin, Murray–Darling Basin Commission / Geoscience Australia (Agreement No. MD949) March 2008, 29 pp.

Good, M and McMurray, D 1997, ‘The management of farm dams and their environmental impact in the Mount Lofty Ranges’ in ANCOLD seminar: Dams and the environment (updated), Water Resources Group, South Australian Department of Environment and Natural Resources.

Hurd, JD, Civco, DL, Gilmore, MS, Prisloe, S and Wilson, EH 2006, Tidal wetland classification from Landsat imagery using an integrated pixel-based and object-based classification approach, ASPRS Annual Conference, Nevada, 2006.

Kingsford, RT, Thomas, RF, Wong, PS and Knowles, E 1997, GIS database for wetlands of the Murray–Darling Basin: Final report to the Murray–Darling Basin Commission, National Parks and Wildlife Service, Sydney, Australia, 85 pp.

Lee, KH, and Lunetta, RS 1995, ‘Wetlands detection methods’, in Wetlands and environmental applications of GIS, edited by JG Lyon and J McCarthy, USA: CRC Press. Inc, pp. 249–283.

National Water Commission 2008, Approaches to, and challenges, of managing interception.

Neal, BP, Nathan, RJ, Schreider, SY and Jakeman, AJ 2002, ‘Identifying the separate impact of farm dams and land use changes on catchment yield’, Australian Journal of Water Resources, vol. 5, no. 2, pp.165–175.

NSW Office of Water 2010, ‘Maximum harvestable right calculator’, Harvestable right dams, viewed 27 September 2011, http://www.water.nsw.gov.au/Water-licensing/Basic-water-rights/Harvestable-right-dams/Harvesting-runoff/default.aspx.

Price, J, Lewis, B & Rutherford, I 2003, ‘Water quality in small farm dams’, Proc. 28th International Hydrology and Water Resources Symposium, IEAust, Wollongong, NSW, 10–14 Nov. 2003.

Richards, JA and Jia, X 1999, Remote sensing digital image analysis, Springer-Verlag Berlin Heidelberg New York.

Shaikh, M, Brady, A, and Sharma, P 1998, ‘Applications of remote sensing to assess wetland inundation and vegetation response in relation to hydrology in the Great Cumbung Swamp, NSW, Australia’, in Wetlands for the future, AJ McComb and JA Davis (eds), Gleneagles Publishing, Adelaide, pp. 595–607.

Shaikh, M, Green, D and Cross, H 2001, ‘A remote sensing approach to determine environmental flows for wetlands of the Lower Darling River, New South Wales, Australia’, International Journal of Remote Sensing, vol. 22, no. 9, pp. 1737–1751.

Zhang, Y and Maxwell, T 2006, ‘A fuzzy logic approach to supervised segmentation for object-oriented classification’, Proceedings of the American Society of Photogrammetry and Remote Sensing (ASPRS) Annual Conference, Reno, Nevada.

http://www.water.nsw.gov.au/Water-licensing/Basic-water-rights/Harvestable-right-dams/Harvesting-runoff/default.aspx

http://www.water.nsw.gov.au/Water-licensing/Basic-water-rights/Harvestable-right-dams/Harvesting-runoff/default.aspx


Appendix 1. Coverage of digital aerial imagery (ADS40) over NSW


Documents

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