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
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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
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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
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. ........................................... 28
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. ....................................................................................................................... 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
Figure 19. Average NDVI values at Morawa for pre- and post-drain periods versus distance
from drain (50-500m) with all masks applied. ................................................................... 35
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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
Figure 21. Average NDVI values at Morawa for pre- and post-drain periods versus distance
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
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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
been grouped and averaged into distance bins with 50 m interval. .................................... 50
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
been grouped and averaged into distance bins with 50 m interval. .................................... 58
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
Figure 52. Spring NDVI values for pre- and post-drain versus distance from drain at
Narembeen with all masks applied including areas not cropped to 150m from the drain. ... 59
Figure 53. Spring NDVI values for pre- and post-drain versus distance from drain at
Narembeen with all masks applied including areas not cropped to 200m from the drain. ... 60
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. ...................................... 60
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. ...................................... 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
Figure 63. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-
drain (red series) within 500m of drain without any masks. ............................................... 68
Figure 64. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-
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
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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
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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
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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).
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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.
Figure 19. Average NDVI values at Morawa for pre- and post-drain periods versus distance from drain
(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‘.
Figure 21. Average NDVI values at Morawa for pre- and post-drain periods versus distance from drain
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.
Deep drain digitised in Google Earth
500m buffer from drain
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
500m buffer from drain
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
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
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.
Figure 52. Spring NDVI values for pre- and post-drain versus distance from drain at Narembeen with all masks
applied including areas not cropped to 150m from the drain.
60
Figure 53. Spring NDVI values for pre- and post-drain versus distance from drain at Narembeen with all masks
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.
Deep drain digitised in Google Earth
500m buffer from drain
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
Figure 63. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-drain
(red series) within 500m of drain without any masks.
Figure 64. Dumbleyung: Plot of average NDVI values for pre-drain (blue series) and post-drain
(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
1 May to date of image 0.145 0.149
Pithara 1 Jun to date of image -0.067 -0.066 Poor correlation
1 May to date of image 0.014 0.015
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
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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
10 Aug 2004 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
22 Sep 1993 Land Monitor Project Previous study
22 Aug 1999 Land Monitor Project Previous study
2 Sep 2003 Land Monitor Project Previous study
21 Jul 2005 AgImage New
12 Aug 2007 AgImage New
1 Aug 2009 AgImage New
Morawa Aug 1993 Land monitor Previous study
Aug 2003 Land Monitor Project Previous study
17 Aug 2004 AgImage New
20 Aug 2005 AgImage New
11 Sep 2007 AgImage New
31 Aug 2009 AgImage New
Narembeen 23 Aug 1988 Land Monitor Project Previous study
23 Sep 1993 Land Monitor Project Previous study
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4 Aug 1995 AgImage Previous study
29 Aug 1996 Land Monitor Project Previous study
25 Aug 1997 AgImage Previous study
10 Sep 2000 AgImage Previous study
28 Aug 2001 AgImage Previous study
16 Sep 2002 AgImage Previous study
26 Aug 2003 AgImage (used in
preference to following
image, similar)
Previous study
2 Sep 2003 Land Monitor Project Previous study
Aug 2004 AgImage Previous study
6 Aug 2006 AgImage New
7 Aug 2007 AgImage New
1 Aug 2009 AgImage New
Pithara 29 Sep 1987 Land Monitor Project Previous study
26 Sep 1992 Land Monitor Project Previous study
26 Aug 1998 Land Monitor Project Previous study
10 Aug 2004 Source unknown Previous study
15 Jul 2005 AgImage New
3 Aug 2007 AgImage New
25 Sep 2009 AgImage New
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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
26 Jan 2008 Land Monitor Project New
27 Dec 2008 Land Monitor Project New
31 Jan 2010 Land Monitor Project New
Dumbleyung 23 Dec 2003 Land Monitor Project New
10 Jan 2005 Land Monitor Project New
2 Mar 2006 Land Monitor Project New
16 Jan 2007 Land Monitor Project New
11 Jan 2008 Land Monitor Project New Landsat 7 - striped
27 Jan 2008 Land Monitor Project New Landsat 7 - striped
5 Jan 2009 Land Monitor Project New
24 Jan 2010 Land Monitor Project New
Morawa 9 Feb 2005 Land Monitor Project New
27 Jan 2006 Land Monitor Project New
14 Jan 2007 Land Monitor Project New
18 Dec 2008 Land Monitor Project New
2 Feb 2008 Land Monitor Project New
22 Jan 2010 Land Monitor Project New
Narembeen 25 Dec 2004 Land Monitor Project New
16 Jan 2007 Land Monitor Project New
11 Jan 2008 Land Monitor Project New Landsat 7 - striped
27 Jan 2008 Land Monitor Project New Landsat 7 - striped
5 Jan 2009 Land Monitor Project New
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24 Jan 2010 Land Monitor Project New
Pithara 2 Feb 2005 Land Monitor Project New
7 Jan 2007 Land Monitor Project New
26 Jan 2008 Land Monitor Project New
27 Dec 2008 Land Monitor Project New
31 Jan 2010 Land Monitor Project New
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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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