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Remote Sensing for Monitoring the Land

Productivity of Deep Drains

F i n a l R e p o r t

Prepared for: Jason Lette, Department of Water

Prepared by: Dr Halina T. Kobryn, Professor Richard Bell and Ross Lantzke

Date: 12 May 2011

Table of Contents

Abstract ...................................................................................................... 10

Introduction ................................................................................................ 10

Objectives ............................................................................................ 12

Study areas ................................................................................................. 12

Morawa ................................................................................................ 13

Pithara ................................................................................................. 13

Beacon ................................................................................................. 15

Narembeen .......................................................................................... 15

Dumbleyung ......................................................................................... 15

Methods ..................................................................................................... 16

Data sets ................................................................................................. 17

Data analysis ........................................................................................... 17

Pre-processing ........................................................................................ 18

Drain layouts ........................................................................................ 18

Buffers and masking of image data ...................................................... 18

Reference sites ..................................................................................... 19

Vegetation index data .......................................................................... 20

Processing of NDVI images ...................................................................... 21

Extracting vegetation index data .......................................................... 21

Data manipulation and plotting in Excel ............................................... 21

NDVI values and distance from the drain – Slope of the fitted line. ....... 22

Image differences- pairwise comparisons for selected spring images ... 23

Results ....................................................................................................... 24

Long-term trends for reference sites ....................................................... 24

Individual drain sites ............................................................................... 30

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Morawa ................................................................................................ 30

Pithara ................................................................................................. 37

Beacon ................................................................................................. 46

Narembeen .......................................................................................... 53

Dumbleyung ......................................................................................... 62

Additional data analyses .......................................................................... 69

NDVI values and rainfall ....................................................................... 69

Discussion .................................................................................................. 71

Overview of trends ............................................................................... 71

Change in NDVI values along the transects around the drains .............. 74

Conclusions ................................................................................................ 76

Recommendations ................................................................................... 77

References .................................................................................................. 78

Appendix 1: Sources of images ............................................................... 81

Appendix 2. Image Processing: ................................................................ 86

List of Figures

Figure 1. Location of deep drain sites at Morawa, Pithara, Beacon, Narembeen and

Dumbleyung) and reference sites Dryandra Forest, Lake Magenta Reserve and Stirling

Ranges National Park) in the south west of Western Australia. (Background Landsat TM

mosaic: GeoScience Australia). .......................................................................................... 14

Figure 2. Conceptual flowchart of the methods used to extract vegetation index data from

the long-term vegetation index satellite data series. ......................................................... 16

Figure 3. Overview of the data processing steps. ............................................................... 17

Figure 4. Illustration of the process of combining all the masks within the 500m buffer zone

(white areas) of the deep drain and creating systematic point sampling scheme within

unmasked (green areas) (top left insert) which were 25m apart, at the centre of each Landsat

pixel, to extract NDVI data values (this example is based on Narembeen site). .................. 19

Figure 5. Illustration of three possible scenarios of the relative greenness within and outside

the drain buffer and the resulting plots of fitted lines. ....................................................... 22

Figure 6. Using slope of the average NDVI values as an indicator of the effectiveness of the

deep drain. ........................................................................................................................ 23

5

Figure 7. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake

Magenta Nature Reserve and the area surrounding Morawa site. Blue rectangles correspond

to the native vegetation patches at Morawa extracted from the Landsat TM data series. ..... 25

Figure 8. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake

Magenta Nature Reserve and the area surrounding Pithara site. Blue rectangles correspond

to the native vegetation patches at Pithara extracted from the Landsat TM data series. ...... 26

Figure 9. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve,

Lake Magenta Nature Reserve and the area surrounding Beacon site. Blue rectangles

correspond to the native vegetation patches at Beacon extracted from the Landsat TM data

series. ............................................................................................................................... 27


Lake Magenta Nature Reserve and the area surrounding Narembeen site. Blue rectangles

correspond to the native vegetation patches at Narembeen extracted from the Landsat TM

data series. AgImages used to determine the NDVI for 1995, 1997, 2000-04, 2006-2007,

2009, Land Monitor images were used for 1988, 1993, 1996. ........................................... 28


Lake Magenta Nature Reserve and the area surrounding Dumbleyung site. Blue rectangles

correspond to the native vegetation patches at Dumbleyung extracted from the Landsat TM

data series. ....................................................................................................................... 29

Figure 12. Comparison of summer NDVI values (Land Monitor) at four sites using reference

patches with native vegetation in the vicinity of the drains (most were at least 500m from

the drain). ......................................................................................................................... 30

Figure 13. Annual rainfall (mm) at Morawa 1998-2009, with long-term average of 277mm

indicated by the dotted line, (BOM, 2010). ......................................................................... 31

Figure 14. Site map for Morawa. ........................................................................................ 32

Figure 15. NDVI spring images for Morawa, values have been stretched to the range 0.0-0.7

and displayed using the NDVI colour palette, where the greener the image, the higher the

NDVI values are. The dates of the images are indicated in the titles. The drain was

constructed in January 2005. The top images (red box) correspond to the three different

years before the drain was constructed and bottom images (green box) show spring data

and vegetation response after the drain was completed. .................................................... 33

Figure 16. Average NDVI values from spring data subset at Morawa with all masks applied

plotted against the distance from the drain. For clarity, the NDVI values for each image have

been grouped and averaged into distance bins with 50 m interval. .................................... 34

Figure 17. Average NDVI values at Morawa from spring data subset plotted against the

distance from the drain. Areas which were not cropped are shown in the plot. The masked

pixels include perennial vegetation, roads, rocky outcrops and salt pans and the drain. For

clarity, the NDVI values for each image have been grouped and averaged into distance bins

with 50 m interval. ............................................................................................................ 34

Figure 18. Average NDVI values at Morawa for pre- and post-drain periods versus distance

from drain (50-500m) with all masks applied. ................................................................... 35


from drain (50-500m) with all masks applied. ................................................................... 35

6

Figure 20. Average NDVI values at Morawa for pre- and post-drain periods plotted against

the distance from drain based on the ‗Native Vegetation and Roads Mask‘. ........................ 36


from drain based on the ‗Native Vegetation and Roads‘ mask but including areas which have

not been cropped. ............................................................................................................. 36

Figure 22. Annual rainfall for Dalwallinu (15km north of Pithara), with the long-term average

of 356mm indicated by the dotted line (BOM, 2010). ......................................................... 37

Figure 23. Site map for Pithara. ......................................................................................... 38

Figure 24. NDVI spring images for Pithara, values have been stretched to the range 0.0-0.7

and displayed using the NDVI colour palette. The greener the image, the higher the NDVI

values. The dates of the images are indicated in the titles. Deep drain was installed in early

2004, so the top images represent spring vegetation response before- (enclosed in the red

box) and the bottom images - after the drain has been constructed (green box). .............. 39

Figure 25. Difference in spring (2009-2004) NDVI values for Pithara as standardised

difference image (left) and class intervals (right)(sd=standard deviation). Mean value for

NDVI 2004 was 0.400, and for 2009 = 0.325. For the whole series of NDVI spring images

refer back to Figure 24. ..................................................................................................... 40

Figure 26. Average NDVI values from spring data subset at Pithara plotted against distance

from the drain with all masks applied, based on the extensive drain, including the NE

extension and a short section in the SE (Figure 23). For clarity, the NDVI values for each

image have been grouped and averaged into distance bins with 50 m interval. .................. 41

Figure 27. Average NDVI values from spring data subset at Pithara plotted against distance

from the drain with the native vegetation and roads masked but including areas apparently

not cropped, based on the extensive drain, including the NE extension and a short section in

the SE (Figure 23). For clarity, the NDVI values for each image have been grouped and

averaged into distance bins with 50 m interval. ................................................................. 41

Figure 28. NDVI vs. distance from the drain of the deeper drains at Pithara in the SE zone

using all masks across the distance range 0-500m from the drain. Data for pre-drain NDVI

values are shown in blue and for the post- drain, in red. Linear curves have been fitted to

each dataset. ..................................................................................................................... 42

Figure 29. NDVI vs. distance from the drain for the deeper drains at Pithara in the SE zone,

using all masks across the distance range 0-150m from the drain. ................................... 42

Figure 30. NDVI vs. distance from the drain for the shallower drain at Pithara in the NE zone

using all masks across the distance range 0-500m from the drain. ................................... 43

Figure 31. Average NDVI values for Pithara for pre- and post-drain versus distance up to 0-

150m from the shallow drain at Pithara in the NE zone using all masks. ............................ 43

Figure 32. Average NDVI values for Pithara for pre- and post-drain versus distance from

drain based on the ‗No Cropping‘ Mask for total drain excluding the NE very shallow zone.

0-500m ............................................................................................................................ 44

Figure 33 Average NDVI values at Pithara for pre- and post-drain versus distance from drain

based on the ‗No Cropping‘ Mask for total drain excluding the NE very shallow zone. 0-

150m ................................................................................................................................ 44

7

Figure 34. Average NDVI values at Pithara for the distances between 0-400m from the drain

with no masks, except for NE shallow zone. ...................................................................... 45

Figure 35 Average NDVI values at Pithara for the distances between 0-150m with no masks

except for NE shallow zone. .............................................................................................. 45

Figure 36. Annual rainfall for Beacon, with the long-term average of 332mm indicated by

the dotted line (BOM, 2010). .............................................................................................. 46

Figure 37. Beacon site map, Note: For the Beacon site the ‗No Cropping‘ mask and the

‗Permanent Vegetation and Roads‘ mask are the same. ...................................................... 47

Figure 38. NDVI spring images for Beacon, values have been stretched to the range 0.0-0.7

and displayed using the NDVI colour palette, where the greener the image the higher the

NDVI values are. The dates of the images are indicated in the titles. Deep drain was

operating by November 2005. The last three bottom –right images represent vegetation

response after the drain has been installed (green box); images enclosed in the red box

correspond to the vegetation response before the deep drain was installed. ...................... 48

Figure 39. Standardized image difference for Beacon for two spring images: August 2004

and September 2009. The mean NDVI in August 2004 was 0.395 and for 25 September

2009 it was 0.404. (sd=standard deviation). Blue lines indicate the position of the drain and

the 500m buffer. ............................................................................................................... 49

Figure 40. Average NDVI values from spring data subset for Beacon with all masks applied

plotted against the distance from the drain. For clarity, the NDVI values for each image have


Figure 41. Average NDVI values for pre-drain (blue series) and post-drain (red series) over

450m from the drain for Beacon. ....................................................................................... 50

Figure 42. Average NDVI values for pre-drain (blue series) and post-drain (red series) over

the first 150m from the drain for Beacon with all masks applied. ....................................... 51

Figure 43. Plot of average NDVI values for pre-drain (blue series) and post-drain (red series)

over the 400m without any masking for Beacon. Only 400m possible as Excel will only plot

up to 30,000 points. ......................................................................................................... 52

Figure 44. Plot of average NDVI values for pre-drain (blue series) and post-drain (red series)

up to 150m without any masking for Beacon. .................................................................... 52

Figure 45. Annual rainfall 1997-2009 for Narembeen, with the long-term average of

335mm indicated by the dotted line (BOM, 2010). ............................................................. 53

Figure 46. Narembeen site map. ........................................................................................ 55

Figure 47. NDVI spring images for Narembeen, values have been stretched to the range 0.0-

0.7 and displayed using the NDVI colour palette, where the greener the image the higher the

NDVI values are. The dates of the images are indicated in the titles. Deep drain was

operational by September 2001. Red box encloses data before- and green box, after- the

drain was installed. ........................................................................................................... 56

Figure 48. Difference in spring NDVI data (1997 and 2007) for Narembeen, as standardised

difference image (left) and class intervals (right). Mean NDVI value in 1997 was 0.516 in

2007 = 0.506. .................................................................................................................. 57

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Figure 49. Average NDVI values for Narembeen from spring data subset plotted against

distance from the drain using all masks. For clarity, the NDVI values for each image have


Figure 50. Average NDVI values for Narembeen from spring data subset plotted against

distance from the drain, showing areas not cropped but masks for native vegetation and

roads have been applied. For clarity, the NDVI values for each image have been grouped and

averaged into distance bins with 50 m interval. ................................................................. 58

Figure 51. Spring NDVI values for pre- and post-drain versus distance from drain at

Narembeen with all masks applied including areas not cropped up to 500 from the drain. . 59


Narembeen with all masks applied including areas not cropped to 150m from the drain. ... 59


Narembeen with all masks applied including areas not cropped to 200m from the drain. ... 60


Narembeen with no masking applied up to 500m from the drain. ...................................... 60


Narembeen with no masking applied up to 150m from the drain. ...................................... 61

Figure 56. Annual rainfall data for 1997-2009 in Dumbleyung, with the long-term average

of 434mm indicated by the dotted line (BOM, 2010). ......................................................... 62

Figure 57. Site map for Dumbleyung deep drain Note: The top right insert shows the

location of the native vegetation plots, some of which were located over a kilometre from

the buffer. ......................................................................................................................... 63

Figure 58. NDVI spring images for Dumbleyung, values have been stretched to the range

0.0-0.7 and displayed using the NDVI colour palette, where the greener the image the

higher the NDVI values are. The dates of the images are indicated in the titles. Deep drain

was installed in December 2002, so the top images (red box) represent data before the drain

and bottom images show vegetation response after the drain was installed (green box). .. 64

Figure 59. Dumbleyung image difference from 2003-2007 expressed in standardized Z

scores 2003 average NDVI = 0.495 and 2007 average NDVI=0.502. .................................. 65

Figure 60. Average NDVI values from spring data subset for Dumbleyung with all masks

applied plotted against the distance from the drain. For clarity, the NDVI values for each

image have been grouped and averaged into distance bins with 50 m interval. .................. 66

Figure 61. Plot of average NDVI values in Dumbleyung for pre-drain (blue series) and post-

drain (red series) for the area up to 500m from the drain with all masks applied. .............. 67

Figure 62. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-

drain (red series) for the area up to 150m from the drain with all masks applied. .............. 67


drain (red series) within 500m of drain without any masks. ............................................... 68


drain (red series) within 150m of drain without any masks. ............................................... 68

Figure 65. Summary of NDVI values versus distance from the drain data for all sites using

before- (blue) and after- (red) for the five deep drain sites. Lines of best fit were plotted

9

based on each data subset (before and after the drain construction) with all masks applied

to the data. ....................................................................................................................... 72

Figure 66. Spring NDVI transects from transect line 1 at Beacon. On the x-axis, 0 represents

the location of the deep drains that were installed in 2005 (from van Dongen, 2005)......... 74

Figure 67. Illustration of spring NDVI values plotted as a function of distance from the start

of the drain to the end, 50m west of the drain, at Beacon site using spring data with no

masking applied. ............................................................................................................... 75

List of tables

Table 1. Summary of details for deep drains constructed at the study sites. ------------ 16

Table 2. Summary of areas of 500m and 36m buffers and total area of ―No Cropping‖ and

―Native Vegetation and Roads‘ mask for the deep drains. --------------------------- 30

Table 3. Sample of the extracted data used to calculate the correlation between NDVI and

rainfall for Beacon. ---------------------------------------------------------- 69

Table 4. Site based correlations between rainfall and average spring NDVI for the period

1987 and 2009. ------------------------------------------------------------- 70

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Abstract

The installation of deep drains is an engineering approach to remediation of land affected

by dryland salinity. It is costly and its economic viability is, in part, dependent on the drains‘

area of influence. The zones of drain influence may be determined by assessing biological

productivity response of adjacent vegetation over time. The aim of this project was to use

multi-temporal satellite remote sensing to analyse temporal and spatial changes in

vegetation condition surrounding a deep drainage sites at five locations in Western

Australian wheatbelt, Morawa, Narembeen, Dumbleyung, Pithara and Beacon. There was no

strong evidence for broad scale changes in perennial vegetation in the region between 1982

and 2009. Analysis at the site scale, within 500m buffer from the drains, showed the need

to mask areas not used for agricultural production before studying the effects of drains.

Spring NDVI images showed that three sites have improved as a result of deep drainage

(Beacon, Dumbleyung and Narembeen), while at Morawa and Pithara there was little or no

improvement. The method applied here demonstrated utility of spring NDVI for rapid and

relatively simple assessment of the site condition after implementation of drainage,

compared to the pre-drainage NDVI within the 500m buffer zones of the drains.

Introduction

Dryland salinity in south-west Australia is caused by accelerated recharge of water into the

semi-confined aquifer bringing water tables close to the surface (George, 2004). As

groundwater reaches within 2 m of the soil surface, capillary rise of salts causes salinisation

of root zones and generally a decline in plant productivity (Nulsen 1981a). In the south-west

of Western Australia almost 1 million hectares of land were mapped as saline in 1996 and a

further 5.4 million hectares are at risk of future salinisation ( McFarlane et al., 1992a;

McFarlane et al., 2004; EPA, 2007). While the regional scale assessment of salinity is useful

for natural resource managers and planners, mapping the areal extent of the salt-affected

landscape and its change over time would be useful at a farm or paddock scale.

Some techniques to lower the watertable and alleviate the effects of dryland salinity include

revegetation, the use of high water use crops and the installation of deep drains (State

Salinity Council, 2000). The installation of deep drains, an engineering approach to salt-land

remediation, was initiated in the northern wheatbelt of Western Australia in the late 1970s

and is increasingly seen by farmers and catchment groups as a viable option to manage

salinity (Ruprecht et al., 2004).

Deep drains (2 – 3 m deep) cause the watertable to drop by increasing groundwater

discharge (National Dryland Salinity Program, 2001). Salts can then be leached from the soil

(Dogramaci and Degens, 2003). This allows cropping in areas threatened by rising water

tables and salinity and also allows for waterlogged/saline areas to be reclaimed (Cox and

Tetlow, 2004). However, installation and maintenance of deep drains is costly, and economic

viability is in part dependent on the drains‘ area of influence.

The area of influence of deep drains is a measure of the drain‘s efficiency. It is generally

expressed as the lateral extent of the drain‘s influence on the watertable. This is dependent

11

on soil physical parameters such as hydraulic conductivity (Fitzpatrick et al., 2001). From

studies at a deep drainage site at Narembeen, Ali et al. (2004a, 2004b) reported that one

year after the drain was installed, groundwater levels dropped to below 1.5 m for a distance

of 200-300 m from the drain. Root zone salinity also decreased for a distance up to 100 m

from the drain over the following years.

Lowering the watertable and decreasing root zone salinity may not translate to improved

crop growth. The removal of salts from some soils may cause them to become dispersive

(Cox and Tetlow, 2004). The recovery of soil structure and soil organic matter levels will

also have a significant bearing on soil productivity after draining (Bell and Mann, 2004).

Clearly there is a need to understand the efficiency of drainage (Deep Drainage Taskforce

Report 2000). The State Salinity Council (2000) recommended that monitoring and

evaluation of deep drainage should be carried out at the property, catchment and regional

scales. Monitoring drain impacts would help to develop guidelines to ensure the appropriate

and most effective application of deep drains.

To determine whether the drains are effective, an accurate measure of pre-drainage land

productivity must be acquired and used as a benchmark for gauging changes in land

productivity following drainage. Monitoring is required to assess the efficacy of the drains

and any benefits in improved agricultural productivity. Some early surveys in Western

Australia used stereo aerial photography combined with extensive fieldwork (Nulsen,

1981b). Assessing soil conditions such as dryland salinity over large areas over time is a

very costly and demanding task and a number of approaches have been developed over the

last 25 years. Satellite remote sensing has been used to gather and analyse multispectral

data on soils and vegetation response over time (Mougenot et al. 1993; Verma, 1994;

Metternicht and Zinck, 1996; Gao and Liu, 2008). GIS and other spatial modelling tools have

been used to map current extent and predict risk and future extent of saline areas (Caccetta

and Dunn, 2010). Airborne hyperspectral and field spectroscopy methods have been shown

to improve discrimination of salt-affected areas (Dutkiewicz and Lewis, 2009; Farifteh et al.,

2007).

Apart from detailed field assessments, remote sensing techniques have been used to

describe spectral properties of saline soils (Rao et al., 1995), map, assess and model spatial

extent and severity of soil salinity (Verma et al.,1994; Furby et al., 2010). Remote sensing

has been used to assess crop biomass and yield on a large spatial scale (Smith et al., 1995;

Sharma et al., 2000; Metternicht and Zinck, 2003). Salinity in the landscape can be detected

and mapped as either direct signal from salt crystals or crust or as an indirect signal

expressed through the types and density of the vegetation cover (Mougenot et al. 1993).

Spectral responses of vegetation to salinity, whether positive or negative, can act as an

indicator of the impact of the drains. The major limitation however is if the salt-affected (or

naturally saline land) is covered with salt tolerant plants (Dutkiewicz et al., 2009;

Metternicht, 1996). The spatial and temporal characteristics of salt-affected land can also be

used to distinguish it from other areas. This approach was adopted by the Land Monitor

Project to map salinity in the south-west region of Western Australia (Caccetta et al., 2000).

Previous study by van Dongen (2005) which included four out of five of the current study

sites, examined the relationship between field soil conductivity and satellite measured

12

vegetation index. The spatial and temporal changes in the area of saline land were assessed

using Normalised Difference Vegetation Index (NDVI) derived from Landsat TM data

acquired between 1987 and 2004. NDVI and soil electrical conductivity (ECah) measured with

an EM38 instrument were analysed through regression. A strong relationship between NDVI

and ECah was found at three of the four sites (R2 = 0.5 to 0.7) (van Dongen, 2005). At

Dumbleyung, Beacon and Pithara the salinity maps showed that, from 1988 to

2003/4,during the period preceding the installation of deep drains, the area of saline land

increased. At Narembeen, between 1996 and 2003, spanning the period before and after

the deep drain was installed, the mapped area of saline land declined by 11.2 %. The 2003/4

salinity maps explained 87 to 93 % of variation in field ECah data and were comparable to

salinity maps produced in 2000 by the Land Monitor Project (van Dongen, 2005).

In this study, vegetation indices were used to summarise multispectral data for the multi-

temporal data set. Vegetation indices can be used to provide a quantitative assessment of

vegetation condition, in the form of density and vigour (Dwivedi and Sreenivas, 2002;

Eastman, 2003). Many studies identified the red and near-infrared (NIR) wavelengths as the

best two-band combination for identifying saline agricultural land (see review by

Metternicht and Zinck, 2003). The simple ratio of NIR/red can be correlated with the

photosynthetic activity of plants but is affected by changing illumination conditions such as

surface slope and aspect. Due to this, the Normalised Difference Vegetation Index (NDVI)

has been used for the past 25 years as one of the standard vegetation indices for

application to crop canopies (Hatfield et al., 2004).

NDVI images of the south-west region of Western Australia are easily accessible. Processed

images originally derived from Landsat TM, have been archived by the West Australian

Department of Land Information (DLI) and are available at property scale, via an online web

delivery service. Data from other spatially coarser satellites with daily coverage are available

from various free data archives.

Objectives

This project was to:

(a) use remote sensing data to map areas of land surrounding deep drains;

(b) provide an analysis of the changes vegetation cover and health, attributed to

salinisation, over time; and

(c) Estimate the area of influence of deep drainage using remote sensing.

Study areas

The five study sites were located in the south-west region of Western Australia (Figure 1).

They have mediterranean climates with hot, dry summers and cool, wet winters.

Presentation of these sites is in geographical order from north to south and also along an

increasing rainfall gradient. Morawa is located in the northern region, Dumbleyung is

13

located in the south central, medium rainfall agroclimatic zone, Narembeen in on the border

of the central, medium and low rainfall agroclimatic zones and Beacon and Pithara are in the

north central, low rainfall agroclimatic zone (Moore, 2004).

Morawa

This site is the most northern of all study areas, located over 370km north of Perth, on the

eastern edge of the wheatbelt. The deep drain, nearly 7km long flowed from the NE to the

SE, following natural drainage. The drain was only installed in 2005. Two small tributaries

were added to the starting section of the drain, adding another 600m to its length. Annual

rainfall is approximately 277mm (BOM, 2010).

Pithara

The study site was 23 km east of Pithara (approximately 200 km north/north-east of Perth)

along Pithara East Rd. The deep drain flowed in a north-westerly direction and was 21.5 km

long. The drain was installed in August, 2004. Several tributaries added to a central drain as

it progressed down the catchment.

Soil associations present within the study area include mainly saline soils, with loamy

duplex, sandy earth and alkaline, red, shallow and deep loamy duplex (Department of

Agriculture, pers. comm.). Pithara has an approximate annual average rainfall of 356 mm

(BOM, 2010) (data only available for Dalwallinu).

14

Figure 1. Location of deep drain sites at Morawa, Pithara, Beacon, Narembeen and Dumbleyung) and

reference sites Dryandra Forest, Lake Magenta Reserve and Stirling Ranges National Park) in the south

west of Western Australia. (Background Landsat TM mosaic: GeoScience Australia).

15

Beacon

Beacon is located approximately 250 km north-east of Perth. The average annual rainfall is

332 mm, 70 % of which falls between May and October (Grealish and Wagnon, 1995). The

soil profile consists of sandy loam topsoil, which at a depth of greater than 80 cm, grades to

red clay subsoil (Grealish and Wagon, 1995). Alkalinity is moderate at the surface and it is

often slightly salt-affected. Large portions of the land became saline after the watertable

rose due to above-average rainfall in 1999 (G. Kirby, pers. comm.).

Narembeen

The study site was 40 km east of Narembeen (approximately 280 km east of Perth).

Narembeen‘s long term annual rainfall average is 335 mm (BOM, 2005). Soils within the

drainage area were described by Ali et al. (2004a) as duplex with loamy sand underlain by

sandy clay. Permeability is high in the top sandy layer, and low in the underlying clay. A

ferricrete layer is 2.0 m below the surface.

This study focused on a small section of the arterial Narembeen drain. This section was

located in the upper portion of the Wakeman sub-catchment. In this section the drain to the

east of Hyden Mt Walker Rd was installed in July, 1999 to a depth of 2.5 m and de-silted in

August, 2001. The drain to the west of Hyden Mt Walker Rd was installed in September,

2001 to a depth of 1 to 1.5 m.

Dumbleyung

This was the most southern of all sites and was located 11 km north-east of Dumbleyung

(225 km south east of Perth). Cereal crops are the current dominant land-use, however,

saltbush and tree planting have been undertaken in recent years. Dumbleyung has an

approximate long term annual rainfall of 434 mm (BOM, 2010).

The soil profile of the Dumbleyung site consists of a thin layer of dark grey sandy topsoil

with an abrupt boundary to a clay subsoil which becomes heavier with depth (Percy, 2000).

Bedrock of weathered granite is located at a depth of 4 to 6 m (Cox, 2002). Groundwater

levels at the Dumbleyung site prior to installation of the deep drain fluctuated between 0.70

and 1.1 m from the surface (Cox, 2002).

The drain was installed in December, 2002. It was 4354 m long and ranged from 3 to 1.62

m deep. It consisted of a collector drain, running approximately north-west, and four lateral

drains branching to the west. The drain discharged into Dorodine Creek, a tributary of Lake

Dumbleyung.

Deep drains were constructed at different times at each site, between 1999- late 2005

(Table 1).

16

Table 1. Summary of details for deep drains constructed at the study sites.

Site Comments

Morawa 13 January 2005, plus 2 short subsidiary drains added later

Pithara Construction early July 2004, The Pithara main drain has a large network of subsidiary drains. Following input from the Department of Water the SE portion of the drain was classified as the deep drain zone. Extra analysis was also carried out on an extended NE zone that consisted only of shallow drains

Beacon Only 2 short subsidiary drains, intermittent flow was blocked for adjustments after construction, free flowing 1 November 2005

Narembeen Relative to Hyden Mt Walker Rd: Eastern section completed in July 1999; western part in September 2001, extends much further west than other drains with headwaters short distance to the south, several subsidiary drains that linked to the main drain in this area were also incorporated

Dumbleyung Constructed December 2002, main drain flows into a natural creek and has four substantial subsidiary drains

Methods

Satellite remote sensing data with only very simple data extraction and processing approach

were chosen for this study as they were the best means of gaining a synoptic view of the

deep drainage sites and their surroundings. Techniques used here did not aim to create

maps of saline land, rather, through analysis of greenness of the landscape to assess if

drainage was making any difference in the vegetation response by the lowering of the

groundwater.

Methods used in this study were largely the same as in van Dongen (2005), except temporal

comparisons were undertaken on all data points within the deep drain buffer zones instead

of transect approach used by van Dongen (2005) which only sampled a small subset of

available data and key locations surveyed in the field (Figure 2). In addition, pairs of NDVI

images of before- and after- the drain construction were analysed for spatial patterns

within and outside the drain 500m buffer.

In this study extracted multi-temporal satellite vegetation index data within 500m buffers of

deep drains were compared to the distance from the drain over time.

Figure 2. Conceptual flowchart of the methods used to extract vegetation index data from the long-

term vegetation index satellite data series.

Extract vegetation index vs. time vs.

distance from the drain

Historical vegetation index data Area of interest around the drains

Veg

inde

x

Time

Veg

inde

x

Distance from the drain

17

Data sets

Three main data sets were used in this project: satellite imagery (AVHRR, MODIS and

Landsat TM), vector GIS data for drains, roads and town locations and aerial photography.

Details of satellite imagery are provided in Appendices 1 and 2.

Data analysis All image analysis was undertaken using IDRISI software (v16.05) (Eastman, 2010). Data

processing followed a six step process (Figure 3).

Figure 3. Overview of the data processing steps.

Landsat

data

•Check georeferencing

•Create spatial subsets

•Rescale data to 0-1 NDVI range

Vector

layers of

drains

•Import, Check validity

•Digitise missing sections

Buffers and

masks

•Create 500m buffers along the drains

•Mask around the drain and roads, perennial vegetation and non-cropped areas

•Create point vector data, where each pixel= 1 vector point with unique ID

NDVI Time

Profiles

•Create time series per site

•Extract mean NDVI data across time series using point data

NDVI

pairwise

differences

•Select before- and after- the drain spring season images

•Calculate the mean, create difference images and standardised class difference

images per site

NDVI data

within

buffers

•Extract NDVI values to show distance from drain vs. NDVI as pre- and post- drain

•Create scatter plot in Excel of distance vs. NDVI

•Fit trendline, calculate slope

•Calculate average values for all spring data

18

Pre-processing

Drain layouts

As all of satellite data were sourced from the Land Monitor and AgImage projects (Landgate,

2010), data calibration including atmospheric and geometric corrections were already

applied by the agency as part of their routine data processing.

As comparison between sites had to be made over time, the most recent drain locations

were used. For Dumbleyung, Morawa, Narembeen, and Pithara, the Google Earth images

were used to digitise the drain layout into vector files (DNRGarmin software was used to

export from Google Earth

(http://www.dnr.state.mn.us/mis/gis/tools/arcview/extensions/DNRGarmin/DNRGarmin.ht

ml)). The Google Earth image for Beacon was flown prior to the drain construction, so a

2007 georeferenced aerial photograph supplied by the Department of Water was used

instead (Beacon_2007_50cm_z50.ecw).

In Idrisi, the vector files of the drains were converted into raster format.

Buffers and masking of image data

Raster files for drains were used to create 500m buffers for extraction of NDVI data. The

500m buffer was used as previous work established that the impact of the drains (zones of

influence) was very unlikely to extend beyond 500m (Ali et al., 2004; van Dongen, 2005). By

coincidence, a 500m buffer meant that the number of sampling points used in the analysis

approached the 30,000 point upper limit that Excel (2007 version) can handle.

Since the analysis focused on the landscape greenness due to annual crops, many areas not

available for cropping were masked. These included rocky outcrops, wetlands, native

vegetation, roads and areas not recently cropped. All masks were defined using existing

data such as roads and further refined by visual interpretation of high resolution aerial

photography and Google Earth images.

Drains and the areas of spoil from the drains were also masked as they have an impact on

the reflectance and hence the vegetation index computed from the satellite images. The

Landsat images had spatial resolution of 25x25m. The drains did not neatly fit into the

centre of each pixel, but cut across pixel corners; with the spoil also cutting into adjacent

pixels. Through trial and error, a buffer of 36m, based on the centreline of the drain, was

used. This process created a mask of 3 pixels wide (one for the drain alignment and one

either side) along the length of the drain and eliminated any reflectance values directly

attributable to the drain. Vector file which assigned one point per pixel (centre) within the

buffer area was created for data extraction of vegetation index (Figure 4). Distance from

drain measure was also calculated and assigned to each extracted data point.

19

Figure 4. Illustration of the process of combining all the masks within the 500m buffer zone (white

areas) of the deep drain and creating systematic point sampling scheme within unmasked (green

areas) (top left insert) which were 25m apart, at the centre of each Landsat pixel, to extract NDVI data

values (this example is based on Narembeen site).

Reference sites

In any long-term comparisons of greenness across the landscape there is always a

possibility that factors other than those studied (lowering of the groundwater table due to

deep drainage) may be contributing to the signal measured by the satellite. These factors

maybe due to climate change, large-scale groundwater level changes and differences due to

satellite sensors. To ensure that any long-term climatic or groundwater variability affecting

the entire region was captured, a number of reference sites of native vegetation patches

were selected.

Two types of native vegetation areas were used: relatively small patches in the vicinity of

each deep drain site and larger patches of uncleared native vegetation in reserves. The site-

based patches were near the drains, sometimes outside the 500m buffers. As conservation

reserves were quite sparse near the study areas, most available reference sites were quite

small. Areas with rocky outcrops were excluded. All sites were selected using high

resolution aerial images and checked for homogenous type of vegetation cover. These

polygons were later converted to raster and used to extract historical vegetation index data

and create temporal profiles. Due to lack of consistent data set, Narembeen native

vegetation patches for reference plot comparisons were plotted using the following

combination of data:

AgImages used to determine the NDVI for 1995, 1997, 2000-04, 2006-2007, 2009

Land Monitor images used for 1988, 1993, 1996.

20

In addition, very large nature reserves and national park in the region were included,

namely, sections of Stirling Ranges National Park, Dryandra Forest and Lake Magenta Nature

Reserve (Figure 1). The size of these reference sites was important due to the fact that

AVHRR and MODIS pixels were quite large (64km2 and ~27km2, respectively), These large

reference sites were only used for the long-term, multi-sensor comparison for the period

1982-2009 of the vegetation index data with the whole study area and native vegetation

patches within each study area. Once again, areas selected were quite homogenous and only

included vegetation cover.

To obtain the output for AVHRR images, at least one pixel (8x8 km) was required, but if the

site did not fit within one pixel, additional pixels were used so that the site was fully

covered. Likewise, for MODIS data, at least one pixel (0.05 degrees, approximately 5.57km

east by 4.88km north) was required, but if the site did not fit within one pixel, additional

pixels were used so that the site was fully covered.

Vegetation index data

There were two sources for the images used to create NDVI images for drain sites:

Land Monitor Project images. These came in the form of Landsat 6 band images. The

NDVI values were calculated in Idrisi directly using the formula: (NIR-RED)/(NIR+RED).

For Landsat images that translates to: (band4-band3)/(band4+band3).

AgImage data for spring images from 2004/2005 to 2009 were used.

Two additional data sets were used to examine longer-term trends at the reference sites

and regional scale:

Monthly average NDVI from daily observations by AVHRR instrument from 1982-1998

and,

Monthly average NDVI from daily observations by MODIS from 2000-2009.

The above instruments collect daily observations at coarser spatial resolution than Landsat

25m pixels (MODIS= 250m pixel and NOAA AVHRR= 1km pixels). Daily coverage of AVHRR

and MODIS ensures availability of mean monthly NDVI data products with no cloud cover

interference. Data extracted from the AVHRR images had to be converted back to NDVI

values. The following formula was used:

NDVI value = (DN*0.0028)-0.05, where DN is the extracted value (conversion from 0-255

scale to 0-1 scale).

The data extracted from the MODIS images had to be converted back to NDVI values range.

The following formula was used:

NDVI value = DN/10,000, where DN is the extracted value.

Although for MODIS data other vegetation indices are also available (Enhanced Vegetation

Index or EVI) and have been shown to be very effective in mapping soil degradation (Lobell,

et al. 2010), but in order to be consistent with AVHRR and Landsat TM data, only NDVI was

used in this study.

21

Processing of NDVI images

Even though all satellite images were already geocoded, some additional georeferencing was

performed on the Landsat TM images for the individual sites so that within-site positional

accuracy was at least ± ¼ pixel. Images were spatially subset to cover the same geographic

area, resampled to 25m by 25m pixels and saved into raster group files (multi-temporal

data cubes). As the scale values in supplied NDVI AgImages was from 0 to 100 these were

converted back to NDVI range by dividing by 100 (range 0 to 1).

Extracting vegetation index data

IDRISI module PROFILE was used to extract NDVI values for reference sites and deep drain

sites from the multi-temporal data series. Data were extracted based on the centre point of

each pixel in the data series. Each point had a unique identifier, distance from the drain and

NDVI value for each date in the NDVI data series. These extracts were saved as text files and

exported to Excel for further analysis and plotting.

Data manipulation and plotting in Excel

Standard data plotting tools were used. Multi-sensor data set which covered the period

1982-2009 was collated from three data subsets: NOAA AVHRR, MODIS and Landsat NDVI

for each of the sites including reference sites to allow for visual analysis of any long-term

changes in the study sites and surrounding catchments.

Two types of plots were generated: vegetation index data over time and distance from the

drain vs. vegetation index value using spring NDVI images (Figure 2). Spring images

(August-September) have previously been shown to best capture greenness in the cropped

areas (van Dongen, 2005). Linear trend lines of best fit were added to the plots (Figure 5).

The average NDVI values for pre- and post-drain construction were calculated and plotted

against distance for each site. As Excel can only plot up to 30,000 points using scatter plot,

for some sites such as Beacon and Pithara where the sample points was greater than

30,000, the maximum distance was limited to 400 to 450 metres for some plots. For all

other sites 500m was used.

To visually compare the change of NDVI values between spring NDVI images, the data were

averaged into 10 groups (up to and including 50m, >50m-100m, >100-150m etc. up to

500m). These average values were plotted for each spring image in the data series. Spring

images prior to the drain construction were plotted with dotted lines and post-drain

construction values were plotted with solid lines. The standard deviations of the data were

also calculated but only in the case of Morawa were two of these incorporated as error bars

in the plot (as an example).

Initially, for a more detailed analysis of the slope of the NDVI vs. distance, the pre- and

post-NDVI values across all images were averaged and plotted against distances (36-50m,

>50-100m and >100-150m) as well as plotting the raw data for the points 0-150m from

the drain. Linear trend lines for each plot were inserted over each plot to investigate if the

slope had changed over time. For visual clarity the trend lines were extended forward and

backward one unit. The equations for the trend lines were incorporated in the charts.

22

NDVI values and distance from the drain – Slope of the fitted line.

A positive impact of the deep drain, associated with the water table drawdown was expected

closer to the drain and could be assessed through increase in NDVI values over time.

One way to test the impact was through examination of the slope of the fitted line of NDVI

vs. distance from the drain (Figure 5 and 6). If the slope declines then this would suggest

that the drain was having a positive impact as the greenness within the buffer would be very

similar to that outside the buffer. If the slope remains the same, there is no change and if

the slope increases, there is deterioration. The more likely impact (change) was expected in

the first, say; 150m (van Dongen, 2005). This was explored visually through the charts of

NDVI data plotted for the 0-150m for pre- and post-drain constructed with all masks

applied (except for Morawa where ―no cropping‖ mask was not applied for the one of the

subsets) (Figure 6).

Figure 5. Illustration of three possible scenarios of the relative greenness within and outside the drain

buffer and the resulting plots of fitted lines. Three green boxes illustrate aerial view around the drain

(drain buffer) with the area close to the drain being marked as a separate region. On the right and top

left are plots illustrating the possible patterns of lines when plotting distance from the drain (X axis)

against the greenness values (Y-axis). Change in slope of these lines can be used as a surrogate for

improvement, decline or no change in greenness as a function of the distance from the drain.

23

Figure 6. Using slope of the average NDVI values as an indicator of the effectiveness of the deep drain.

Image differences- pairwise comparisons for selected spring images

Idrisi's Image difference module (IMAGEDIFF) was used to compare the changes between two

years of similar (in rainfall and range in NDVI) spring NDVI images for pre- and post drain

construction. Dates used in this pairwise comparison were chosen by visually inspecting the

whole data series and selecting quite similar images, thereby avoiding comparisons between

extremely different conditions in the ―before‖ and ―after‖ images. Image differencing

produced images that showed the standardized difference image and standardized class

image (mean ± 3-4 standard deviations) for spring in the following years:

Pithara: difference 2004 to 2009

Beacon: difference 2004 to 2009

Narembeen: difference 1997 to 2007

Dumbleyung: difference 2003 to 2007

24

Results

Long-term trends for reference sites

Analysis of long-term (1982-2009), multi-sensor NDVI data showed that while there were

noticeable differences in the range of NDVI data between the sensors (especially AVHRR and

MODIS over the decadal scales), there was no obvious change in greenness trend within

native vegetation patches for each of the sensors. Overall, AVHRR measurements were in the

lower range while the more recent MODIS instrument for the same areas showed slightly

higher minima and maxima (for example Stirling Ranges National Park in Figure 7). Results

for Lake Magenta and Dryandra Reserves were very similar, with MODIS NDVI values slightly

higher than those measured by NDVI. As expected, range of NDVI values for areas used for

agricultural production had lower minima (bare or nearly bare soils) and very similar maxima

as those of native vegetation in the reserves. Each study site followed typical seasonal

trends of vegetation response to the rainfall and clearly showed years which were

significantly above or below the long term average rainfall. Comparisons to reference sites

at nature reserves showed similar trends. Individual, small native vegetation patches near

the deep drains followed similar seasonal trends. Data for each site showed much higher

seasonal variations compared to areas with permanent native vegetation cover (Figs 5-9).

Compared to relatively infrequent availability of Landsat TM data (at best every 16 days),

MODIS and AVHRR instrument have a definite advantage of daily observations which allow

us to build up very detailed picture of the greening in the catchment. The long term series

of AVHRR images did not exhibit any sensor drift although there was a shift to higher NDVI

values between AVHRR and MODIS data series. This is due to different sensors on each

satellite.

25

Figure 7. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake Magenta Nature Reserve and the area surrounding Morawa site.

Blue rectangles correspond to the native vegetation patches at Morawa extracted from the Landsat TM data series.

26

Figure 8. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake Magenta Nature Reserve and the area surrounding Pithara site.

Blue rectangles correspond to the native vegetation patches at Pithara extracted from the Landsat TM data series.

27

Figure 9. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake Magenta Nature Reserve and the area surrounding Beacon site.

Blue rectangles correspond to the native vegetation patches at Beacon extracted from the Landsat TM data series.

28

Figure 10. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake Magenta Nature Reserve and the area surrounding Narembeen

site. Blue rectangles correspond to the native vegetation patches at Narembeen extracted from the Landsat TM data series. AgImages used to determine the

NDVI for 1995, 1997, 2000-04, 2006-2007, 2009, Land Monitor images were used for 1988, 1993, 1996.

29

Figure 11. Long-term NDVI data for reference sites in Stirling Ranges, Dryandra Reserve, Lake Magenta Nature Reserve and the area surrounding Dumbleyung

site. Blue rectangles correspond to the native vegetation patches at Dumbleyung extracted from the Landsat TM data series.

No apparent trend was observed in the comparisons of the NDVI data from Landsat TM in the native vegetation patches using spring or

summer data sets (Figure 12). The lower values corresponded to the summer event and higher values to the spring greening following the rain.

30

Figure 12. Comparison of summer NDVI values (Land Monitor) at four sites using reference patches

with native vegetation in the vicinity of the drains (most were at least 500m from the drain).

Individual drain sites Results for each deep drainage site are presented in a geographic sequence, from north to

south, not in the order of significance of results.

Morawa

Deep drain at this site had only two small extensions in the NE section with comparatively

small area within the 500m buffer cropped in recent years (Figure 14 and Table 2). Only six

spring images were available for this site (Figure 15). As this was a very small selection and

two of the images corresponded to some of the driest periods, no pairwise differences for

NDVI images were generated. The drain with the 500m buffer covered most of the areas of

lowest NDVI values. Large inter-annual variability in spring data (usually the peak in NDVI)

can be seen both before and after the drain was constructed (Figure 15 and Figure 16).

Table 2. Summary of areas of 500m and 36m buffers and total area of ―No Cropping‖ and ―Native

Vegetation and Roads‘ mask for the deep drains.

Site

Area (ha) 500m Buffer

Area (ha) 36m buffer

Area (ha) ‘No Cropping’

Mask

Area (ha) ‘Native Veg and Roads’

Mask

Length (km) of drain (trunk)

Morawa 831 73 586 51 6.63

Pithara 2839 293 562 401 13.36

Beacon 2358 203 557 557 20.8

Narembeen 1097 161 173 138 8.68

Dumbleyung 328 32 75 43 1.92

31

Figure 13. Annual rainfall (mm) at Morawa 1998-2009, with long-term average of 277mm indicated

by the dotted line, (BOM, 2010).

NDVI values gradually increased as a function of distance from the drain (Figure 16-18). As

the Morawa site had a very large zone which was not cropped (compared to the native

vegetation and roads areas), plots for both situations (with and without the ‗No Cropping‘

mask) were created. NDVI values before the drain was installed (2005) were generally lower

compared to after the drain was commissioned, except for 2007, one of the driest years on

record (Figure 13). Use of native vegetation and roads masks but inclusion of areas

apparently not cropped resulted in much stronger relationship of NDVI increase with the

distance from the drain, with the highest values measured in 2009 (Figure 17). Comparison

of data in Figure 16 and Figure 17 clearly demonstrates the importance of spatial sub-

setting based on land cover type and land use. There was a noticeable increase in the mean

NDVI values from about 100 m from the drain (Figure 17).

Plots of all data points for the 1993-2004 period (before the drain construction) and 2005-

2009 (after the drain) showed firstly generally higher NDVI values in the post- drain images

(Figure 18- Figure 21). Secondly, linear fitted curves became flatter after the drains were

commissioned suggesting slight improvement in the land productivity. That trend was much

clearer on the plots showing only data points up to 160m from the drain (Figure 21).

Overall, there was small positive improvement after the construction of the deep drainage.

0

50

100

150

200

250

300

350

400

rain

fall (m

m)

32

Legend:

Native vegetation patches – used to assess if NDVI values have changed over time.

Deep drain digitised in Google Earth

500m buffer from drain

Permanent vegetation, Road/Tracks and long-term unproductive crop areas.

Note: Areas outside the 500m buffer were not used in the analysis.

Additional mask to the permanent vegetation roads and tracks including areas that

have not been recently cropped. Note: Areas outside the 500m buffer were not used

in the analysis.

Figure 14. Site map for Morawa.

33

Figure 15. NDVI spring images for Morawa, values have been stretched to the range 0.0-0.7 and displayed using the NDVI colour palette, where the greener

the image, the higher the NDVI values are. The dates of the images are indicated in the titles. The drain was constructed in January 2005. The top images (red

box) correspond to the three different years before the drain was constructed and bottom images (green box) show spring data and vegetation response after

the drain was completed.

34

Figure 16. Average NDVI values from spring data subset at Morawa with all masks applied plotted

against the distance from the drain. For clarity, the NDVI values for each image have been grouped

and averaged into distance bins with 50 m interval.

Figure 17. Average NDVI values at Morawa from spring data subset plotted against the distance from

the drain. Areas which were not cropped are shown in the plot. The masked pixels include perennial

vegetation, roads, rocky outcrops and salt pans and the drain. For clarity, the NDVI values for each

image have been grouped and averaged into distance bins with 50 m interval.

35

Figure 18. Average NDVI values at Morawa for pre- and post-drain periods versus distance from drain

(50-500m) with all masks applied.


(50-500m) with all masks applied.

36

Figure 20. Average NDVI values at Morawa for pre- and post-drain periods plotted against the

distance from drain based on the ‗Native Vegetation and Roads Mask‘.


based on the ‗Native Vegetation and Roads‘ mask but including areas which have not been cropped.

37

Pithara

Pithara site had the largest surface area within the 500m buffer of all sites and the second

longest drain (Table 2). Annual rainfall pattern was similar to Morawa, with the 1997, 2002

and 2007 having the lowest rainfall during the study period (Figure 22). Highest rainfall was

measured in 2000 and 2009. This site was divided into the northern region with relatively

shallow drains and the southern region with deeper drains (Nick Cox, Department of Water,

pers. comm.) (Figure 23).

Large seasonal and annual variability in rainfall as well as local conditions including

cropping regime and impacts of salinity resulted in highly variable NDVI data over the series

of spring data sets (Figure 24). Some of the lowest NDVI values were measured in 1987,

2003 and 2007, while 1998 and 2004 NDVI values were relatively high across the study

area. Data for 2004 and 2009 were used to calculate image differences including

standardised difference (Figure 25). This series of images (1997-2009) illustrates how

variable NDVI values can be even for the same season in different years and how much of

that variability is confined to areas near the drains.

Figure 22. Annual rainfall for Dalwallinu (15km north of Pithara), with the long-term average of

356mm indicated by the dotted line (BOM, 2010).

Pattern of change in NDVI over time highlighted the general trend of NDVI increase with the

distance away from the drain. Generally, years with lower rainfall had lower NDVI values and

the fitted lines of distance vs. NDVI were flatter (Figure 26 and Figure 27). In 2004, NDVI

values were the highest in the series examined here. The NDVI plotted for the year with the

highest rainfall (2009) did not show the highest NDVI values.

Plots of the whole NDVI data series against the distance from the drain showed higher

values for the post drain NDVI (2006-2009). The linear fit curve was flatter for that subset

suggesting some improvement in the vegetation response (Figure 28- Figure 35).

Comparisons between areas with shallower (north-east) and deeper (south) drains at Pithara

showed that deeper drains has slightly more effect than shallower drains (Figure 28 and

Figure 30).

0

50

100

150

200

250

300

350

400

rain

fall (m

m)

38

Comparison of two spring images before and after the drain (2004 and 2009) through

image difference showed large spatial variability within the 500m buffer as well as outside.

Most of the areas which were much greener after- compared to before the drain was built (1

-2 standard deviations for the mean) were located in the upper reaches of the drains (Figure

25). This example illustrates high spatial variability of the vegetation response to lower

groundwater table.

Legend:

Native vegetation patches – used to assess if NDVI values have changed over time.



Permanent vegetation, road/tracks and long-term unproductive crop areas.

Areas not been recently cropped

Deep drain zone (It may not be possible to distinguish between deep drains and

shallower ones)

Shallow drain zone

Figure 23. Site map for Pithara.

39

Figure 24. NDVI spring images for Pithara, values have been stretched to the range 0.0-0.7 and displayed using the NDVI colour palette. The greener the

image, the higher the NDVI values. The dates of the images are indicated in the titles. Deep drain was installed in early 2004, so the top images represent

spring vegetation response before- (enclosed in the red box) and the bottom images - after the drain has been constructed (green box).

40

Figure 25. Difference in spring (2009-2004) NDVI values for Pithara as standardised difference image (left) and class intervals (right)(sd=standard deviation).

Mean value for NDVI 2004 was 0.400, and for 2009 = 0.325. For the whole series of NDVI spring images refer back to Figure 24.

Standardized

Difference

Difference in

Standardized Classes

41

Figure 26. Average NDVI values from spring data subset at Pithara plotted against distance from the

drain with all masks applied, based on the extensive drain, including the NE extension and a short

section in the SE (Figure 23). For clarity, the NDVI values for each image have been grouped and

averaged into distance bins with 50 m interval.

Figure 27. Average NDVI values from spring data subset at Pithara plotted against distance from the

drain with the native vegetation and roads masked but including areas apparently not cropped, based

on the extensive drain, including the NE extension and a short section in the SE (Figure 23). For

clarity, the NDVI values for each image have been grouped and averaged into distance bins with 50 m

interval.

42

Figure 28. NDVI vs. distance from the drain of the deeper drains at Pithara in the SE zone using all

masks across the distance range 0-500m from the drain. Data for pre-drain NDVI values are shown in

blue and for the post- drain, in red. Linear curves have been fitted to each dataset.

Figure 29. NDVI vs. distance from the drain for the deeper drains at Pithara in the SE zone, using all

masks across the distance range 0-150m from the drain.

43

Figure 30. NDVI vs. distance from the drain for the shallower drain at Pithara in the NE zone using all

masks across the distance range 0-500m from the drain.

Figure 31. Average NDVI values for Pithara for pre- and post-drain versus distance up to 0-150m

from the shallow drain at Pithara in the NE zone using all masks.

44

Figure 32. Average NDVI values for Pithara for pre- and post-drain versus distance from drain based

on the ‗No Cropping‘ Mask for total drain excluding the NE very shallow zone. 0-500m

Figure 33 Average NDVI values at Pithara for pre- and post-drain versus distance from drain based on

the ‗No Cropping‘ Mask for total drain excluding the NE very shallow zone. 0-150m

45

Figure 34. Average NDVI values at Pithara for the distances between 0-400m from the drain with no

masks, except for NE shallow zone.

Figure 35 Average NDVI values at Pithara for the distances between 0-150m with no masks except for

NE shallow zone.

46

Beacon

This site recorded consistently declining annual rainfall between 1997 and 2009. Similarly to

Pithara, the highest rainfall was measured in 1999, while the lowest was in 2007 and 2002

(Figure 36). Compared to the long-term average rainfall of 332mm (BoM, 2010), only three

years in the period studied here exceeded that value (1999, 2000 and 2006). Of all the

study sites, Beacon deep drain site had the longest drain (trunk) of 20.8km, second largest

area within the 500m buffer and the largest area covered by native vegetation and roads

mask (Figure 37 anTable 2).

Figure 36. Annual rainfall for Beacon, with the long-term average of 332mm indicated by the dotted

line (BOM, 2010).

The deep drain at this site consisted of a single trunk drain flowing from north to south. Of

the five spring NDVI images for the period before the drain. Two (1987 and 1992) had very

low NDVI values, while the other two (1998 and 2003) were relatively high. Areas along the

drain had consistently low NDVI values before 2005 when drainage was constructed (Figure

38). Of the three post- drainage NDVI images obtained between July and September, the

2009 data set showed the highest greenness values across the landscape including areas

close to the drain (Figure 38).

Average spring NDVI values plotted against distance from the drain corresponded very

closely to the spatial patterns, that is, 1987 data with the lowest rainfall had the lowest NDVI

values, and the curve showed in fact slightly downward trend with the increasing distance

from the drain (Figure 40). The fitted line for 2009 had the highest values despite that year

being well below the average rainfall.

0

100

200

300

400

500

600

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

rain

fall (m

m)

47

Legend

Native vegetation patches –

used to assess if NDVI values

have changed over time.

Deep drain digitised in Google

Earth


Permanent vegetation,

road/tracks and long-term

unproductive crop areas.

Areas that have not been

recently cropped

Figure 37. Beacon site map, Note: For the Beacon site the ‗No Cropping‘ mask and the ‗Permanent

Vegetation and Roads‘ mask are the same.

48

Figure 38. NDVI spring

images for Beacon,

values have been

stretched to the range

0.0-0.7 and displayed

using the NDVI colour

palette, where the

greener the image the

higher the NDVI values

are. The dates of the

images are indicated in

the titles. Deep drain

was operating by

November 2005. The

last three bottom –right

images represent

vegetation response

after the drain has been

installed (green box);

images enclosed in the

red box correspond to

the vegetation response

before the deep drain

was installed.

49

Figure 39. Standardized image difference for Beacon for two spring images: August 2004 and September 2009. The mean NDVI in August 2004 was 0.395 and

for 25 September 2009 it was 0.404. (sd=standard deviation). Blue lines indicate the position of the drain and the 500m buffer.

Difference in Standardized

Classes

50

Figure 40. Average NDVI values from spring data subset for Beacon with all masks applied plotted

against the distance from the drain. For clarity, the NDVI values for each image have been grouped

and averaged into distance bins with 50 m interval.

Figure 41. Average NDVI values for pre-drain (blue series) and post-drain (red series) over 450m from

the drain for Beacon.

51

Figure 42. Average NDVI values for pre-drain (blue series) and post-drain (red series) over the first

150m from the drain for Beacon with all masks applied.

Comparison between plots extracted from masked and unmasked images (Figure 41 and

Figure 43) showed that in the case of masked images, the post-drain data fitted curve

flattens out (red line) with increasing distance from the drain, whereas if unmasked data are

used, line of best fit after the drain was essentially parallel to the ‗before the drain‖ line.

This suggest that in order to clearly see the effects of deep drains on non-native plants, all

masks, including ―no cropping‖ mask must be applied. This trend is even more pronounced

within the distance of up to 160m from the drain (Figure 42 to Figure 44). The degree of

flattening in the post drain fitted curve indicates substantial improvement in the condition

of this area after drains have been installed.

Difference between August 2004 and September 2009 spring NDVI images showed that

while there was more improvement measured through increase in NDVI in the southern

most area, most of the pattern of change was paddock-scale. Some of the differences were

up to 2 standard deviations from the mean for the whole image area, suggesting it is

possible to achieve relatively high greenness measures in areas which previously had quite

low productivity (Figure 39).

52

Figure 43. Plot of average NDVI values for pre-drain (blue series) and post-drain (red series) over the

400m without any masking for Beacon. Only 400m possible as Excel will only plot up to 30,000

points.

Figure 44. Plot of average NDVI values for pre-drain (blue series) and post-drain (red series) up to

150m without any masking for Beacon.

53

Narembeen

Annual rainfall at the site was quite variable over the period of 1997 to 2009 but slightly

higher compared to Beacon for example. Highest rainfall was measured in 2008 and 1999

while the lowest in 2002 and 2007, similar to other sites but still mostly well below the

long-term average of 335mm (Figure 45).

Figure 45. Annual rainfall 1997-2009 for Narembeen, with the long-term average of 335mm indicated

by the dotted line (BOM, 2010).

Compared to other sites, deep drains at Narembeen were quite complex in their spatial

layout, with several smaller drains joining the main trunk. The general flow direction was

from southeast to northwest (Figure 46), with the total length of the trunk drain of

approximately 8.7km. Total area within the 500m buffer was the third largest compared to

the other sites and with relatively low proportion of native vegetation or areas not cropped

(Figure 46Table 2). In fact, there were no significantly large patches of native vegetation

within the 500m buffer so for the long-term comparison, areas outside the buffer were

selected (Figure 46).

As the deep drainage was implemented at this site quite early, in 2001, there were seven

images captured before- and seven, after the drain was installed (Figure 47). Once again,

there was a considerable spatial variability before- and after- the drain construction,

essentially reflecting rainfall conditions as well as cropping regime. Data for 1997 and 2007

were selected for the pairwise comparisons of spring NDVI values. The choice of years was

based on the image data not rainfall, as 2007 was a year of the lowest rainfall (Figure 45)

and yet greenness captured in early August was comparable to 2003 which had much higher

annual rainfall (Figure 47). Standardised image difference showed that over 50% of the

500m buffer area had noticeable improvement in the greenness values. Most areas which

showed significant increase in NDVI were to the west and to the south of the main trunk of

the drain (Figure 48).

Average NDVI values over time were highest for data sets during years of higher rainfall and

mostly post 2001 when the drain was constructed. There was general increase in NDVI

0

50

100

150

200

250

300

350

400

450

rain

fall (m

m)

54

values with the distance from the drain, except for September 2002 which was only one year

after drain construction and a fairly average rainfall and AgImage data for spring 2004

(Figure 49 andFigure 50). August 2009 data also showed very low average NDVI values.

Highest NDVI values were for August 2003 (above average rainfall) and August 2007 (below

average rainfall). In this particular study, site average NDVI values extracted were very

similar regardless of whether the ‗non cropped areas‘ mask was applied. Levelling off of the

curves with distance from the drain was noted at about the 150-200m mark.

Plots of all data points within the 500m buffers showed higher NDVI values (by

approximately 0.1) after the drain construction (2003-2009) compared to before (1988-

1997). Fitted lines showed flattening effect for the post-drain data (Figure 51 and Figure

52).

This is the only site where 0-150m and 0-200m buffer slopes have been compared (Figure

52and Figure 53). The slope for 0-150 was steeper for both pre- and post-drain

construction, however the trend was similar. Both plots indicate that the post-drain slope

was flattening over time indicating little differences in greenness as a function of distance

from the drain.

Comparison of plots of these data without any masking does not show the flattening in the

post-drain data set fitted curve, clearly demonstrating the need to mask areas not used for

agricultural production (Figure 54 and Figure 55).

55

Legend:

Native vegetation patches – used to assess if NDVI values have changed over time



Permanent vegetation, Road/Tracks and long-term unproductive crop areas.

Note: Areas outside the 500m buffer were not used in the analysis

Areas not cropped recently

Figure 46. Narembeen site map.

56

Figure 47. NDVI

spring images

for Narembeen,

values have

been stretched

to the range

0.0-0.7 and

displayed using

the NDVI colour

palette, where

the greener the

image the

higher the

NDVI values

are. The dates

of the images

are indicated in

the titles. Deep

drain was

operational by

September

2001. Red box

encloses data

before- and

green box,

after- the drain

was installed.

57

Figure 48. Difference in spring NDVI data (1997 and 2007) for Narembeen, as standardised difference image (left) and class intervals (right). Mean NDVI value

in 1997 was 0.516 in 2007 = 0.506.

Difference in Standardized Classes Standardized Difference

58

Figure 49. Average NDVI values for Narembeen from spring data subset plotted against distance from the drain

using all masks. For clarity, the NDVI values for each image have been grouped and averaged into distance bins

with 50 m interval.

Figure 50. Average NDVI values for Narembeen from spring data subset plotted against distance from the drain,

showing areas not cropped but masks for native vegetation and roads have been applied. For clarity, the NDVI

values for each image have been grouped and averaged into distance bins with 50 m interval.

59

Figure 51. Spring NDVI values for pre- and post-drain versus distance from drain at Narembeen with all masks

applied including areas not cropped up to 500 from the drain.


applied including areas not cropped to 150m from the drain.

60


applied including areas not cropped to 200m from the drain.

Figure 54. Spring NDVI values for pre- and post-drain versus distance from drain at Narembeen with no masking

applied up to 500m from the drain.

61

Figure 55. Spring NDVI values for pre- and post-drain versus distance from drain at Narembeen with no masking

applied up to 150m from the drain.

62

Dumbleyung

Dumbleyung, the most southern of all sites, had much higher annual rainfall compared to the

others. Lowest annual rainfall was measured in 2002 and 2007 while the highest values were in

1998 and 2008 (Figure 56).

Total area covered by deep drainage within 500m of the drain was only 328ha, the smallest of

all sites, the drain was also the shortest (<2km) (Table 2). Design of the drain was very simple,

with the main trunk flowing from north to south and the four short side branches directing the

flow from west to east (Figure 57).

Figure 56. Annual rainfall data for 1997-2009 in Dumbleyung, with the long-term average of 434mm

indicated by the dotted line (BOM, 2010).

There was not much difference in NDVI image sequence over time between years with average

rainfall (1999) and the year below the average rain (2007), after the drain was constructed,

however NDVI values were very low in 2009, when the rainfall was well below the long term

average (Figure 57). Pairwise comparison of spring images for NDVI between 2003 and 2007

showed significant increase in the NDVI, mostly in the upper and middle part of the drain and a

marked decrease in the NW part (Figure 59).

Plots of the average NDVI values over time showed less clear trends with increasing distance

from the drain. Most lines representing the average NDVI were either quite flat (2005, 1989 and

1993) or decreased in the first 150m from the drain (2007 and 2003) (Figure 60).

0

50

100

150

200

250

300

350

400

450

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

rain

fall (m

m)

63

Figure 57. Site map for Dumbleyung deep drain Note: The top right insert shows the location of the native vegetation plots, some of which were located over a

kilometre from the buffer.

Legend

Native vegetation patches – used to assess if NDVI values

have changed over time.



Permanent vegetation, Road/Tracks and obvious long-term

unproductive crop areas.

Areas that have not been recently cropped.

SE corner

masked out

as the drain

does not

impact on this

zone.

SE corner masked

out as the drain

does not impact on this zone.

Figure 58. NDVI spring images for Dumbleyung, values have been stretched to the range 0.0-0.7 and displayed using the NDVI colour palette,

where the greener the image the higher the NDVI values are. The dates of the images are indicated in the titles. Deep drain was installed in

December 2002, so the top images (red box) represent data before the drain and bottom images show vegetation response after the drain was

installed (green box).

65

Figure 59. Dumbleyung image difference from 2003-2007 expressed in standardized Z scores 2003 average NDVI = 0.495 and 2007 average

NDVI=0.502.

Difference in Standardized Classes Standardized Difference

Figure 60. Average NDVI values from spring data subset for Dumbleyung with all masks applied

plotted against the distance from the drain. For clarity, the NDVI values for each image have been

grouped and averaged into distance bins with 50 m interval.

Data for all pixels extracted from the 500m buffer from the drain showed that after the

drain was installed the NDVI were higher, (0.1 to 0.2 of NDVI units) (Figure 61 - Figure

64). The slope of the fitted line was quite flat for data sets which had all masks applied

(Figure 61) compared to a slight slope when no masks were used (Figure 63). The slope

of the line in the first 150m increased for the post drain construction data when no

masking was used, whereas when masking is used it markedly decreased (Figure 62 and

Figure 64).

67

Figure 61. Plot of average NDVI values in Dumbleyung for pre-drain (blue series) and post-drain

(red series) for the area up to 500m from the drain with all masks applied.

Figure 62. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-drain

(red series) for the area up to 150m from the drain with all masks applied.

68


(red series) within 500m of drain without any masks.


(red series) within 150m of drain without any masks.

69

Additional data analyses

In the course of image data processing and analysis of the results it became apparent

that it would be useful to look into some aspects of correlations between NDVI and

rainfall for example. In the following section we briefly present results from some

additional investigations which were carried out and which may add to the

understanding of the analysis steps followed in the main part of the report.

NDVI values and rainfall

The variability in NDVI between the different sites appeared to be related to the rainfall.

To explore this, correlation coefficients for the average NDVI values for time series were

calculated and compared to firstly, the rainfall from the 1st of June up to the date of the

image acquisition and secondly, the rainfall from the 1st of May up to the date of the

images acquisition. Using Beacon site as an example here, average NDVI values for each

50m buffer within the 500m buffer around the drain were extracted and tabulated with

the rainfall data (Table 3).

Table 3. Sample of the extracted data used to calculate the correlation between NDVI and rainfall

for Beacon.

Image date / average NDVI values

Distance from Drain (m) 29/09/1987 26/09/1992 26/08/1998 1/07/2003 10/08/2004 15/07/2006 3/08/2007 25/09/2009

>36-50 0.035 0.251 0.408 0.273 0.377 0.210 0.302 0.429

>50-100 0.026 0.229 0.400 0.295 0.393 0.212 0.299 0.426

>100-150 0.027 0.222 0.401 0.333 0.418 0.210 0.306 0.433

>150-200 0.026 0.220 0.405 0.366 0.440 0.209 0.309 0.437

>200-250 0.023 0.224 0.405 0.386 0.442 0.208 0.303 0.442

>250-300 0.023 0.230 0.403 0.392 0.446 0.203 0.299 0.447

>300-350 0.018 0.232 0.403 0.408 0.441 0.202 0.300 0.452

>350-400 0.017 0.240 0.404 0.421 0.442 0.200 0.299 0.457

>400-450 0.015 0.241 0.403 0.433 0.438 0.202 0.300 0.464

>450-500 0.014 0.238 0.399 0.431 0.433 0.205 0.299 0.468

Average NDVI across the buffer 0.022 0.233 0.403 0.374 0.427 0.206 0.302 0.445

Rainfall (mm):June->Date of image 105.13 222.5 170.2 100.2 118.6 12.5 50.3 133.1

Rainfall (mm):May->Date of image 142.13 231 226.2 150.6 162.8 28.5 58.3 163.9

70

Overall, correlation coefficients between mean NDVI and rainfall were as high as ~0.8

but only when very low values for 1987 data set were removed from the calculations

(Table 4).

Table 4. Site based correlations between rainfall and average spring NDVI for the period 1987 and

2009.

Correlation Coefficient (Excel)

Site ‘No Cropping’ Mask

Native Vegetation and

Roads Mask

Comment

Beacon 1 Jun to date of image 0.244 0.244 Ignored very low 1987 value. Modest +ve correlation

1 May to date of image 0.419 0.419

Dumbleyung 1 Jun to date of image -0.261 -0.306 Weak negative Correlation 1 May to date of image -0.251 -0.288

Morawa 1 Jun to date of image 0.836 0.774 Strong positive Correlation 1 May to date of image 0.813 0.632

Narembeen 1 Jun to date of image 0.075 0.079 Poor correlation


Pithara 1 Jun to date of image -0.067 -0.066 Poor correlation


This brief analysis suggests that total rainfall, defined as the period from the 1st of May

or 1st of June to the date of the image acquisition, was not highly or consistently

correlated with NDVI at all sites. Other factors must be playing an important role, such

as soil type and condition, degree of salinisation and waterlogging, terrain slope, type of

crops and their growth stage at the time of satellite data acquisition.

71

Discussion

Long-term analysis of native vegetation patches within the study sites and at three

reference sites in the region provided useful check on the decadal trends that may have

been otherwise unobserved at the paddock scale. Despite sensor differences, both

spatial and temporal, there were no major changes in the native vegetation communities

which would have indicated additional factors were at play in the measured NDVI signal

in this region.

While direct comparisons between the sensors were not necessary in this study, for any

such work to be useful some sort of scaling would need to be implemented. Also,

additional information on perennial vegetation condition, including fire regimes would

have been helpful.

Five sites selected for this study were (except for Morawa) previously investigated by van

Dongen (2005). Some of the data collected by van Dongen was too early post drain to

see clear results. This study analysed data from a much longer time period before and

after the drains were constructed. The benefit of additional data sets and expansion of

the sampling framework over all data points within the 500m buffer from the drain and

addition of Morawa site allowed for clearer patterns to emerge.

Some sites showed clear improvements (Dumbleyung), two sites had small

improvements (Narembeen and Beacon) while Morawa and Pithara sites showed no

significant effect of the drainage (Figure 65).

Overview of trends

Declining slope of the fitted line based on before- and after- NDVI vs. drain distance

was observed at only three sites (Beacon, Dumbleyung and Narembeen), indicating an

improvement in land productivity closer to the drain compared to areas further away

over time (Figure 65). The other two sites, Morawa and Pithara showed little or no

improvement. If the slope has decreased after the drain was constructed this indicates

that the drain may be having a beneficial impact by increasing vegetation cover and

productivity in the zone of influence.

72

Morawa (all masks applied)-, small positive effect

Morawa (perennial vegetation and roads/track mask), small positive effect

Pithara- small negative effect

Beacon- small positive effect

Narembeen small positive effect

Dumbleyung- large positive effect

Figure 65. Summary of NDVI values versus distance from the drain data for all sites using before-

(blue) and after- (red) for the five deep drain sites. Lines of best fit were plotted based on each

data subset (before and after the drain construction) with all masks applied to the data.

Vertical displacement (shift) in the fitted lines would reflect soil moisture condition,

plant species and their growth densities. Data exploration allowed determination of the

73

importance of masking. For example, the slope of fitted line for Morawa was calculated

for both the NDVI values for the ‗‗No Cropping‘ Mask‘ and the ‗Native Vegetation and

Road Mask‘, mainly because of significant distance from the drain for most cropped

areas. There was a wide buffer that was not cropped along a large proportion of the

drain. The average NDVI was markedly different between the two masked areas; much

higher (0.39-0.44) where cropping only occurs, compared to around 0.24 for areas

excluding native vegetation and roads.

Unlike traditional field-based assessment, the remote sensing approach used in this

study provided a snapshot of the whole study region and at each site over time. While,

like aerial photography, satellite images can be interpreted visually, using expert

designed interpretation keys, digital image processing yields much more objective,

repetitive as well as quantitative results. Long-term data series analysis can be

particularly valuable for assessment and monitoring at the sub catchment or even

paddock scale as inter-annual variability and even same season from year to year, can

be naturally very high in these areas of relatively low rainfall. These multi-year measures

of vegetation condition are especially important in light of global climate change and,

specifically in the south west of Western Australia, declining rainfall.

As in many previous studies, this work was based on free data archives from the NOAA

AVHRR, MODIS and Landsat TM satellites, with varying ground resolution from 1km,

through to 250m, to 25m. The vegetation greenness indicator in the form of NDVI

provided robust comparisons between the years and across the sensors, and that is

despite sensor differences. NDVI can be correlated to indicators of land productivity,

such as leaf area index, crop biomass and crop yield (Smith et al., 1995; Hodgson et al.,

2004). Changes in NDVI values along the space away from the drain can be related to

degradation or remediation. The approach for analysis in this study was to keep the

method as simple as possible, in order to be able to implement and repeat it in the

future by simple extraction of the NDVI values from the areas of interest, adding them

to the existing plots for each site and evaluating the trend.

This study did not aim to produce maps of areas which are salt-affected. Such work has

already been undertaken in the region, for example (Furby et al. 2010 and Caccetta et

al. 2010). They have provided very comprehensive analysis approaches, created salinity

probability maps, however in addition to the Land Monitor products, they required data

sets such as DEM and derived terrain variables, extensive field assessment and a multi-

stage classification for the combined data sets. In such studies, any improvements in the

quality of the outputs have to be carefully weighed against greater processing times and

operational costs, including fieldwork.

Results from this study, while following the general trends of previous work by van

Dongen (2005), being purely a desktop study, should be taken cautiously as they have

not been validated in the field.

74

Change in NDVI values along the transects around the drains

While the bulk of the data extraction in this study was pixel by pixel, so that the entire

data set of NDVI values within the 500m buffer was sampled, this can be time

consuming. Sampling of NDVI using transects (perpendicular or parallel to the drain)

may be another simpler and faster approach. Previous study by van Dongen (2005)

analysed the time series of NDVI using a single line (transect) across the drain, the line

being the spatial unit to extract NDVI data from the time series. The NDVI plot after

drainage (2004) was implemented showed marked increase on both sides of the drain,

compared to the data before the drain (1992) (Figure 66). This approach also allows for

tracking conditions for individual paddocks, especially if information on land use

changes is available (for example shift fro m samphire communities to grazing plants).

Figure 66. Spring NDVI transects from transect line 1 at Beacon. On the x-axis, 0 represents the

location of the deep drains that were installed in 2005 (from van Dongen, 2005).

With the large variation in the soil types and crops grown along and across the drain, it

may at times be difficult to average out the results and comment on the overall value of

the drain. By including more transects, one could evaluate at the paddock scale a lot

more confidently.

This transect approach could therefore be extended to add parallel transects at equal

intervals (for example 50m) from the drain. As well as being more appropriate at the

paddock scale, this method also allows the calculation of more meaningful statistics.

Depending on the spatial layout of the drain, the initial setting out of transects in GIS

could be time consuming, but once generated, new data could be extracted from the

same lines. IDRISI software allows for rapid extraction of NDVI values along the line and

export to Excel for plotting.

If data on productivity/yield for individual paddocks were available, these figures could

be related to the NDVI data over time at that paddock scale. Such yield data or even

information on types of crops was not available for this study.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

55

0

50

0

45

0

40

0

35

0

30

0

25

0

20

0

15

0

10

0

50 0

50

10

0

15

0

20

0

25

0

30

0

Distance in metres from deep drain

ND

VI

1992

1998

2004

75

Another approach would be to plot the NDVI data for an upstream to downstream

section, the assumption being that certain regions along the drain may be improving

faster after the drainage is implemented than others. The Beacon site was used as an

example of such process. No masking was applied to the data set before extraction and

data were extracted 50m west of the drain line. The lowest values corresponded to the

1987 data set, the year with very low rainfall (240 mm). The post drainage data (2004)

showed higher NDVI values in the upper reaches from the drain compared to the

downstream section (Figure 67). Similar trend could be noted for the pre-drainage

profile for 1998. This data plot is provided here only as an illustration of alternative

spatial sampling to either all data points within the 500m buffer or a series of transects

across the drain. Depending on the site characteristics, either of these approaches could

be used as a monitoring tool. Advantage of the plots along the drain might be to

demonstrate how far downstream from the drain do these measured improvements

occur.

The results for the crop productivity trials at Beynan Road, Beacon and Wallach Creek

drains suggest that it takes 3-5 years following the drainage before crop yields are

restored to levels that area comparable to non-saline land and profitable. Hence, there

may be merit in splitting post-drain images into those covering the recovery period of

3-5 years after drainage from those taken more than 5 years after drainage.

Figure 67. Illustration of spring NDVI values plotted as a function of distance from the start of the

drain to the end, 50m west of the drain, at Beacon site using spring data with no masking applied.

R² = 0.0013

R² = 0.0861

R² = 0.2263

R² = 0.1713

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 5,000 10,000 15,000 20,000

ND

VI

Distance (m)

Aug 1987 Sep 1992 Aug 1998 Aug 2004

76

The NDVI of pre-2004 values based on the Land Monitor images yielded much lower

NDVI values for the native vegetation patches near drain sites. They were about 0.2 NDVI

units lower compared to AgImage data. However, when the NDVI for cropped areas were

calculated this difference was not apparent. Refer to the yearly plots of the NDVI value

versus distance away from the drain. Several of these plots showed the pre 2004 NDVI

values based on the Land Monitor images are higher than the post 2004 NDVI images

obtained from the AgImages. The pre-drain construction years on the graphs are dotted

lines; the post-drain construction years on the graphs are illustrated with full lines.

This suggest that the standardising protocol used for the Land Monitor images,

especially for the late 1980's to mid 90's was different than used in standardising the

AgImages. We were not able to get any clarification on this matter.

Several issues need to be considered when using spring NDVI images derived from

Landsat for the individual sites. The amount of rainfall and temperature regime would

vary from your to year, thereby affecting growth rates of plants. In this study we have

found strong correlations between rainfall up to the 1st of June and NDVI in Morawa,

whereas at Dumbleyung that correlation was weak and also negative. Soil and crops

types will vary within sites and at times it was not possible to determine if the area was

fallow or cropped and therefore included or excluded in the data extraction. Extent of

waterlogging was not known but it is likely to play an important role in plan growth

(McFarlane et al., 1992b).

While no cost-benefit trials were attempted to analyse the recovery after drainage, it

should be possible to undertake it with the data assembled for this project. For

example, on a waterlogging-prone site in years with prolonged waterlogging, crop

growth may well be poorer with greater distance from the drain. Any future studies

could also incorporate DEM and their derivatives in either constructing the data masks

or being incorporated into the analysis itself.

Conclusions

There was no strong evidence of change over the years in the NDVI of surrounding

perennial vegetation. Therefore, a decreasing NDVI slope in the NDVI vs. distance from

the drained areas is likely to be due to factors such as the declining impact of salinity

and/or water logging, indicating that the deep drain is having a positive impact.

Using the slope of the lines of best fit of the mean of the pre- and post-drain NDVI

values, especially from 50 to 150m, appears to be a useful indicator of the effectiveness

of the deep drains. A declining slope indicates an improvement in NDVI suggesting

improved vegetation growth and cover.

One NDVI image per spring season, if available, is not adequate to track the absolute

health of crops using the NDVI values as there are potentially too many variables.

77

Greenness index relative to previous season is a much more robust measure of the

vegetation response to changing groundwater levels.

Masking out non-annual crop zones was necessary to obtain useful data on relative

gains/losses of NDVI with distance from drain.

While it was not the aim of this project that the efficacy of the deep drains be assessed,

four sites benefitted from the implementation of the drains, (only one in a substantial

manner, Dumbleyung site) and one site at Pithara showed no improvement at all.

One of the main constrains in the study was obtaining good, cloud free spring images

for each year especially post-drainage. It is also important to note that no other spatial

data were used in the analysis. It would be very useful to include detail soil information

as well as DEM, including slope.

Recommendations

If this work was to be continued into the future, we would recommend the following:

Careful selection of image data to ensure continuity in the data set.

Masking of cover which is not used for agricultural production: native vegetation,

roads, drain and the banks, rocky outcrops improves the analysis.

Pairwise comparisons can be run against a selected ―standard image‖ which can

be either one selected for the year which is considered ―typical‖ or ―good‖ or a

series of images used to create a ―mean‖ or median‖ image to act as a benchmark.

78

References

Bell R., Mann S., 2004, Amelioration of salt and waterlogging-affected soils: implications

for deep drainage. In 'National Salinity Engineering Conference' (Eds S Dogramaci and A

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81

Appendix 1: Sources of images

AVHRR monthly average NDVI July 1981 to December 2000 with the

exception of some missing data in 2004 with spatial resolution of 0.1

degrees. (http://www.clarklabs.org/products/global-gis-image-

processing-data.cfm)

MODIS monthly NDVI images for 2000-2009 Australia with spatial

resolution of 0.05 degrees. (http://www.clarklabs.org/products/global-

gis-image-processing-data.cfm)

Landsat TM images (source: Landgate): Spring and summer images in

tables 1 and 2.

See the tables 1 and 2 on the following pages for details:

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Table 1. Details for spring images used in the study.

Site Spring imagery Source of Landsat image Previous or New

(previous study= van

Dongen (2005)

Beacon 29 Sep 1987 Land Monitor Project Previous study

26 Sep 1992 Land Monitor Project Previous study

26 Aug 1998 Land Monitor Project Previous study


15 Jul 2006 AgImage New

3 Aug 2007 AgImage New

25 Sep 2009 AgImage New

Dumbleyung 10 Aug 1989 Land Monitor Project Previous study







Morawa Aug 1993 Land monitor Previous study

Aug 2003 Land Monitor Project Previous study





Narembeen 23 Aug 1988 Land Monitor Project Previous study


83

4 Aug 1995 AgImage Previous study



10 Sep 2000 AgImage Previous study


16 Sep 2002 AgImage Previous study

26 Aug 2003 AgImage (used in

preference to following

image, similar)

Previous study


Aug 2004 AgImage Previous study




Pithara 29 Sep 1987 Land Monitor Project Previous study



10 Aug 2004 Source unknown Previous study




84

Table 2. Details for summer images used in the study.

Site Summer imagery Source of Landsat image Comment

Beacon 2 Feb 2005 Land Monitor Project New

7 Jan 2007 Land Monitor Project New


27 Dec 2008 Land Monitor Project New


Dumbleyung 23 Dec 2003 Land Monitor Project New


2 Mar 2006 Land Monitor Project New


11 Jan 2008 Land Monitor Project New Landsat 7 - striped




Morawa 9 Feb 2005 Land Monitor Project New




2 Feb 2008 Land Monitor Project New


Narembeen 25 Dec 2004 Land Monitor Project New





85


Pithara 2 Feb 2005 Land Monitor Project New





86

Appendix 2. Image Processing:

The new Land Monitor summer images were provided in ER Mapper format and

converted into Idrisi raster format (6 bands). Although various GIS techniques were

trialled to create masks for different features it was decided to manually digitise the

areas to be masked using Google Earth images for all sites except Beacon where the

Google Earth image was not recent. The Department of Water provided georeferenced,

2007 aerial photograph of the Beacon area and this was used to digitise the non

cropped areas.

The Google Earth images used for digitising various features were:

Dumbleyung: Date: 24 APR, 2010 01:56:02 UTC. Satellite: SPOT 5. Lat/Long

(center): -32.9234/117.49. Scale: 2.5 m colour

Morawa: Date:04 FEB, 2010 02:13:35 UTC. Satellite: SPOT 5. Lat/Long (center): -

28.9687/115.896. Scale: 2.5 m colour

Narembeen: Date: 10 JAN, 2010 01:54:38 UTC. Satellite: SPOT 5. Lat/Long (center): -

31.9366/118.716. Scale: 5 m panchromatic.

Pithara: Date : 08 APR, 2010 02:02:53 UTC.Satellite :SPOT 5. Lat/Long (center) : -

30.4539/117.027. Scale : 2.5 m colour

In late August 2010 the Department of Water provided georeferenced aerial

photographs of the four other sites. These were used as the background images to

create vector polygons and lines. These vector layers included the location of native

vegetation patches, the drain locations and the masks for the perennial vegetation,

tracks rocky outcrops as well as the mask of the non-cropped areas close to the drain.

The names of the Department of Water images:

Beacon: Beacon_2007_50cm_z50.ecw

Dumbleyung: Dumbleyung_2006_50cm_z50.ecw

Morawa: Mellenbye_2006_50cm_z50.ecw

Narembeen: Hyden_2004_50cm_z50.ecw

Pithara: Dalwallinu_2006_50cm_z50.ecw

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