Detecting and Monitoring Salt-affected Land

A report from the LWRRDC project

Detecting and Monitoring Changes in Land Condition Through Time using Remotely Sensed Data

September 1995

S.L. Furby, J.F. Wallace, P. Caccetta and G.A. Wheaton

CSIRO Mathematics, Informatics and Statistics

Agriculture Western Australia

Summary

This report summarises the work and findings of the salinity component of the LWRRDC-funded project, 'Detecting and Monitoring Changes in Land Condition Through Time Using Remotely Sensed Data' (project CDM1). Salinity mapping and monitoring methodology has been developed and evaluated in representative areas of the Western Australian wheatbelt.

The aims of the salinity study are:

• to develop methods for detecting, measuring and monitoring change through time in land condition using remotely sensed data integrated with landform maps and other spatial data;
• to demonstrate the application of these methods to provide maps of the spatial extent of salinity in representative areas of the Western Australian wheatbelt: the Moora and Kalannie Goodlands regions in the northern wheatbelt, the Esperance region along the south coast, and the Narrogin region in the Upper Great Southern; and
• to demonstrate the application of these methods to determine the change in area affected by salinity in the northern wheatbelt and near Esperance.

The outputs from the project are:

• image maps, with accuracy assessments, of salt-affected land for each of the study areas derived by combining the image data from two seasons with a landform map;
• image maps showing the change in area affected by salinity from 1987 to 1993 in the Moora - Kalannie Goodlands and Esperance study areas -- these maps indicate areas of increasing vegetation cover as well as degrading areas;
• images showing the long-term change in area affected by salinity, from 1981 to 1993, in the Moora - Kalannie Goodlands study area -- clearing maps over the Moora - Kalannie Goodlands and Esperance study areas have also been produced;
• images showing trends in condition in the Moora - Kalannie Goodlands study area;
• case studies of salt-affected areas that have emerged or increased in size / expanded over the period from 1987 to 1993 and spectral indices that highlight these changes;
• a report listing the best and adequate image dates within the growing season for mapping salinity in the Moora - Kalannie Goodlands region;
• reports on the analyses of the spectral separability of the salt-affected and non salt- affected cover classes within and between stratification zones for the Moora - Kalannie Goodlands study area;
• reports on the spectral variability of the identified cover classes through the period from 1987 to 1993, in the Moora - Kalannie Goodlands study area;
• reports on the spectral variability of the identified cover classes between the Moora - Kalannie Goodlands and Esperance study areas; and
• a database of rectified calibrated imagery for each study area.

The results show that good regional maps of salt-affected land can be produced by choosing appropriate image dates from within the growing season. Using August and October data, salt- affected land has been mapped with 99.5% accuracy near Moora and with 75% accuracy (85% bare salt, 70% marginal salt-affected classes) in the lower-rainfall Xantippe catchment north- east of Moora. In both cases, 20% of poor crop and pasture sites were incorrectly mapped as salt-affected (errors of commission). By combining data from two seasons with landform maps to reduce the confounding of seasonal and management effects, the errors of commission have been reduced to about 2%, at the expense of a decrease in the ability to accurately detect salt- affected land. These image maps can be compared through time to show where and by how much the salt-affected regions are changing. Examples are given in section 4 of this report.

On a more local scale, a series of calibrated images can be used to show the sequence of changes that occur as known salt-affected regions expand. These can be used to develop spectral indices to highlight the changes and suggest potential problem areas. Examples are given in section 6 of this report.

The results of the mapping and monitoring studies have been presented to the farming communities at workshops and clinics held in the study areas and to the remote sensing community through workshops and conference presentations. A list of these activities is given in section 10 of this report.

0. Background

It is widely acknowledged that dryland salinity is a major threat to the resource base of many rural industries around Australia. Its impact is felt not only on the farm; salinity generates effects that affect a wide cross section of the community. Currently more than 1.2 million ha of productive land are affected by salinity across Australia, with a further 1.6 million ha at risk. It is estimated that $243 million a year is lost in agricultural production (National Dryland Salinity R, D & E Program, 1994). In Western Australia, the Australian Bureau of Statistics (ABS) conducted a farmer survey in 1989 which reported that 443000 ha (2.8%) of the land cleared for farming in WA was now saline. This represented an annual increase of about 18000 ha per year since the previous survey in 1979. Some shires reported that up to 9% of the previously arable land had been lost, while individual property owners reported that up to 40% of their property was salt-affected (George, 1990).

Farmer-based surveys such as these are likely to underestimate the problem. New property owners do not always know when areas on their recently-acquired properties became saline. Many emerging saline areas support a cover of salt-tolerant species such as barley grass (Hordeum geniculatum) and saltbush (Halosarcia spp.). These make adequate pasture if fresh water is available, and some farmers do not include these areas in their estimates of salt-affected land.

These surveys provide statistical summaries of the amount of salt-affected land on a shire basis. They don't produce maps of where the salinity is, where it is spreading, or how fast.

Air photo interpretation has been used to map salt-affected land in some areas (Nulsen, 1981). Work by CSIRO in 1982-83 showed that multi-temporal Landsat MSS data could map some degrees of salinity (Campbell and Modawski, unpublished report). Hick and Russell (1990) used high-resolution field and multi-spectral airborne scanner data to show that near-infrared and shortwave infrared wavelengths were important for mapping saline areas.

More recently, salt-affected land has been mapped in four test areas typical of different parts of the WA wheatbelt using a single Landsat TM image (Wheaton et al, 1990 and 1992). Severely salt-affected land was mapped accurately in all four regions. Marginally saline land and salt-affected land with a good cover of salt-tolerant species could only be mapped in two of the areas, where the accuracy was 80%. These areas are spectrally confused with areas in poor condition due to other factors, such as poor or heavily grazed pasture or areas subject to wind erosion.

In 1993, a workshop on 'Remote Sensing Methods for Identification of Discharge Areas' was held as part of the National Dryland Salinity Research, Development and Extension Program. The emphasis in the workshop was on the identification of methods / approaches which can be applied now. In this context, the workshop concluded that the use of multi-temporal Landsat Thematic Mapper (TM) data was considered the most appropriate method for mapping dryland salinity at the regional (~ 500 000 ha area) and catchment (~ 30 000 ha area) scales. At this stage, Landsat TM imagery was seen as the only cost-effective data source for monitoring land condition.

1. Introduction

The aim of this study is to develop and evaluate methods of using multi-temporal Landsat TM imagery to detect and monitor changes in land condition in test areas in the Western Australian wheatbelt. Sequences of images from 1987 to 1994 are analysed to develop procedures, and to identify optimal image combinations, for discriminating between land in persistently poor condition due to salinity and land exhibiting a short-term decrease in productivity caused by factors other than salinity. These procedures are used to produce more accurate salinity maps and maps of change in land condition.

Earlier studies have indicated that paddocks with poor volunteer pasture growth, that have been over-grazed or that have been left fallow, cannot be spectrally discriminated from marginally salt-affected sites supporting salt-tolerant indicator species using a single date of imagery. By using multi-temporal satellite images, the different management and seasonal trends over these cover types can be used to help determine the correct condition label.

Salt-affected land usually remains so from year-to-year, perhaps dependent on the ground water level, whereas crops are rotated and grazing patterns change. If the low productivity is a temporary condition caused by management effects, the paddock will be cropped in a subsequent season and have good green vegetation cover. At that time, the paddock is likely to be easily separated from salt-affected sites. Images from consecutive seasons are used in section 4.1.3 to investigate the temporal trends of such sites to develop methods for improving the salinity mapping.

Temporal trends of sites with low productivity during a growing season are investigated in section 4.1.1, using a sequence of images to determine the optimal image dates for mapping and monitoring land condition.

Knowledge of the position of a site within the landscape also contributes to our understanding of its condition. Poor vegetation cover at sites on hilltops or slopes is more likely to be caused by grazing or erosion than salinity. Associations between salt-affected sites and landform units are investigated in section 4.1.3 and used to improve the accuracy of the salinity maps produced.

Knowledge of the soil type at a site is also expected to contribute to our understanding of its condition. Although consistent soil maps are not generally available at a regional scale, relationships between soil type and land condition are investigated in section 4.2.2 for the Esperance region and in section 8 for a small catchment in the Moora study area.

Associations of salinity, or potential salinity, with other ancillary data sets are being investigated as part of the LWRRDC-funded project, 'Integrating Remotely Sensed Data with Other Spatial Data Sets to Predict Areas at Risk form Salinity', being carried out in the Kent River Catchment, in the south-west of Western Australia.

The study areas are described in the next section. The individual steps in the salinity mapping and monitoring procedure are described in section 3, and the results are discussed in section 4. The methodology has been developed based on analyses of data from the Moora - Kalannie Goodlands study area and has then been applied, evaluated and refined in the other study areas.

Discriminant analyses have been performed to investigate temporal trends in the data and to choose optimal image dates. The results are reported with the salinity maps in section 4. Discriminant analysis procedures have also been applied to look for differences and similarities within cover types between different years and between the study areas. The results and their implications for simplifying the training steps in the classification are discussed in section 5.

Sequences of images over known expanding salt-affected sites have been examined. These results are presented in section 6. Longer-term changes, from 1981 to 1993, have been investigated using MSS images. This shows the clearing history as well as changes in the most severely salt-affected regions. These results are presented in section 7.

The results of incorporating additional datasets, such as soil types, into the salinity mapping procedure are presented in section 4 for the Esperance region and in section 8 for the Xantippe catchment in the Moora-Kalannie Goodlands study area.

The analyses were directed towards producing salinity maps and maps showing areas of changing condition by addressing the following questions:

• How accurately can salinity be mapped at a regional scale in each of the study areas?
• How accurately can changes in salt-affected land be monitored at a regional scale in the Moora-Kalannie Goodlands and Esperance study areas?
• What are the relationships between known salt-affected sites and other spatial data sets, such as soil, geology, landscape variables and rainfall? How can these relationships be used to better map and monitor salinity? Which of these data sets are needed, and at what scale are they needed to be useful?
• Can the history of a pixel, or the history of the whole paddock, be used to better identify current condition?
• Are there regional trends in the major cover types -- crop, pasture, salt-affected land and remnant vegetation -- within the study areas or between the study areas? Can these trends, or their absence, be allowed for or used to simplify the processing?
• Can productivity or condition indices be developed to discriminate between salt-affected land, land in poor condition for reasons other than salinity, and land in good condition? Can these indices be applied regionally to a catchment or shire, or only locally to individual properties?
• What is the temporal response of barley grass, saltbush, pasture (in particular paddocks in poor condition or heavily grazed), and remnant vegetation within a growing season and can it be used to more accurately map marginal salt-affected areas?
• What image dates, image bands or linear combinations of image bands best discriminate between the above cover types?

2. The Data

2.1 The Study Areas

The salinity monitoring work has been conducted in two study areas in the south-west agricultural region of Western Australia:

• the Moora region and the adjacent Kalannie Goodlands catchment in the northern wheatbelt, approximately 200km north east of Perth (Landsat scene 112-081); and
• the Esperance area along the south coast, approximately 600km south east of Perth (Landsat scene 108-083).

The salinity mapping aspect of this study has been carried out in these two regions as well as in:

• the Narrogin region in the Upper Great Southern, approximately 100km south east of Perth (Landsat scenes 111-083 and 111-084).

The locations of these study areas are shown in figure 1.

Figure 1: Location map showing the study areas

The Moora-Kalannie Goodlands study area, shown in the image in figure 2, is 125km by 115km (approximately 1,437,500ha). The south-western quadrant, the Moora region, has been the subject of previous mapping projects. The region has several different geologies which are typical of the northern wheatbelt. The rainfall varies from 480mm at Moora to 340mm in the Kalannie Goodlands catchment in the north east of the study area. This catchment has been cleared more recently than the Moora region, and is just beginning to show an emerging salinity problem.

Figure 2:

Figure 3 shows the monthly rainfall for the Moora region and the Kalannie Goodlands catchment. A green vegetation cover index, derived from NOAA AVHRR satellite data, is also shown. This shows the cycle of crop growth in the region.

Figure 3:

The Esperance study area, shown in the image in figure 4, is 180km by 78km (approximately 1,400,000ha). It is typical of southern sandplain areas experiencing salinity. The amount of salt- affected land in this area has increased after an unusually wet year in 1989. The typical annual rainfall in the region is approximately 500mm. Figure 5 shows the monthly rainfall and vegetation index data.

Figure 4:

Figure 5:

The original Cuballing - Narrogin study area was expanded to include the entire area of the Blackwood Catchment which is covered by Landsat scenes 111-083 and 111-084. This was done in response to the interest of local catchment groups in obtaining baseline salinity maps. These groups are participating in the evaluation and refinement of the maps. The area covered is 150km by 140km, centred on Dumbleyung. Rainfall in the area ranges from around 600mm in the west to 350mm in the east.

2.2 Image, Ancillary and Ground Data

2.2.1 The Moora-Kalannie Goodlands region

A sequence of Landsat TM images over this region has been assembled and analysed. Landsat data are received every 16 days. An archive of TM data from 1988 is held at ACRES. Some images are available from 1986 and 1987. Images have been obtained for each year from 1987 to 1993 to investigate changes in the area affected by salinity and from each month in the 1990 growing season to investigate the optimal image dates for salinity mapping and monitoring. Earlier MSS scenes have been used to look at longer-term changes in land condition.

The dates of imagery used are:

Some cloud patches are present in the September 1989, August 1990 and September 1992 images.

Digital height data were acquired as contours (10 and 20 metres) from the Department of Land Administration (DOLA). These data were gridded to form a digital terrain model over the study area, from which landform units, such as hilltops and broad valleys, were derived using water accumulation models. These procedures are described in Wheaton et al (1994).

Roads and cadastral boundaries have been obtained digitally from DOLA. Image pixels along these boundaries are spectrally mixed and have been labelled as saline in previous mapping exercises. Roads can also act as barriers to natural water flow, leading to outbreaks of surface salinity.

Farm plans, cropping histories and locations of salt-affected and changing sites have been supplied by farmers for twenty-three properties across the study area. Additional saltbush and barley grass sites were identified and located using GPS coordinates as part of the previous salinity mapping work. Field officers from the Department of Agriculture (DAWA) have supplied additional ground information and have assisted in the validation of the image maps produced. The local landcare groups have also assisted with this validation process.

Other digital data acquired as part of the project were only available for a few individual properties or for small catchment or sub-catchment regions. These data sets include soil maps and maps of salt-affected and potentially salt-affected areas. These data have been used as part of the validation process.

2.2.2 The Esperance Region

The sequence of Landsat TM data acquired over this region has been limited by extensive cloud cover on the days of the satellite overpasses. Instead of acquiring images from optimal dates during the growing season, we have acquired the only images available. All of the images used in the study of this area have cloud over some part of the study area. It has not been possible to acquire a sequence of cloud-free images during a growing season.

The image dates that have been used are:

Digital height data were acquired as contours (20 metres) from DOLA for a part of the study area. Contour data for the rest of the study area are not available in digital form. The data were gridded to form a digital terrain model over the study areas, from which landform units were derived using water accumulation models.

A soil landscape map was provided by the Department of Agriculture. It covers a large part of the Esperance study area.

Farm plans, cropping histories and locations of salt-affected and changing sites have been provided by twenty-four farmers in the area. Officers from the Department of Agriculture have also provided valuable advice based on their knowledge of the area.

2.2.3 Narrogin Area

Salinity maps for this area were produced using Landsat TM data from:

Digital terrain data were supplied by DOLA and processed as described above.

Ground information was available in the Narrogin area from an earlier salinity survey conducted by the Cuballing LCDC group. Further information was supplied by farmers through the Blackwood Catchment Coordinating Committee.

3. Salinity Mapping and Monitoring Methodology

The steps used to produce the salinity maps and salinity change maps produced during this study are:

1. Co-register the satellite images to a common map base (here, AMG coordinates at 30m pixel size). This allows ground sites to be traced through time and the satellite data to be compared with ancillary map-based data sets.
2. Calibrate the image data from different dates to 'like-value' so that digital numbers from different dates can be compared (Campbell, Furby & Fergusson, 1994).
3. Locate sample sites of all the major cover types in the image data. In this study, these ground data come from farm plans and cropping histories. If possible, half of the sites of each cover type (selected at random) are reserved for validating the results.
4. Stratify the study area into zones within which there are no marked regional variations in rainfall, crop type or rotations, geology, predominant soil types or visible patterns in the image. If there are strong spectral differences between the zones, the zones should be analysed separately.
5. Apply neighbourhood-modified maximum likelihood classification techniques (Campbell and Wallace, 1989) to the best combination of image dates in each growing season. This produces probabilities of belonging to each of the major cover classes in each season for each pixel in the image.
6. Combine the cover class probabilities from two or three consecutive seasons with position in the landscape -- hilltop, slope, local valley or broad valley -- or soil type to calculate the probability of each image pixel being salt-affected. A Bayesian network reasoning model has been used for the calculations.
7. Combine the probabilities calculated from multiple seasons over two or more time intervals. Again, a Bayesian network reasoning model has been used. The cover classes for which probabilities are calculated are:
   • consistently salt-affected land
   • land in consistently good condition
   • land initially in good condition that becomes salt-affected over the time intervals
   • land initially in very poor condition (labelled as salt-affected) which has good vegetation cover at subsequent time intervals
   • inconsistent condition labels (eg: good, salt-affected, good)

The final class -- inconsistent condition labels -- does not correspond to changes that actually occur on the ground. Errors in the salinity mapping at each multi-season interval mean that this combination of image labels might be obtained for a small proportion of the region. By treating these errors as a separate class, the affected areas are highlighted and can be investigated further.

Salinity maps are produced from the probabilities in step 6. Maps of change in condition are produced from the probabilities in step 7. Other map-based ancillary datasets can be included in the network used in step 6 when these are available.

The method used to combine the land condition data from two seasons, landform and, where available, soil types is a Bayesian network reasoning model. In its simplest form, this can be thought of as a series of rules governing the interpretation of the intersection of cover and landform labels. For example, one rule might be 'if cover in year 1 is salt and cover in year 2 is salt and landform is local or broad valley, then condition at time 1 is salt-affected'. This very simple formulation, however, ignores some important sources of information.

One source of information is the probability of class membership. The classification procedure applied to the image data in each year calculates probabilities of belonging to a number of cover classes. The label assigned to each site is the cover class for which it has the highest probability. Consider two sites, both labelled as salt-affected by the classification, but the first has a probability of being salt-affected of 0.95 and a probability of being in poor condition of 0.05 and the second has a probability of being salt-affected of 0.6, a probability of being in poor condition of 0.3 and a probability of being in good condition of 0.1. We might have more confidence that the first site is salt-affected than the second and want to incorporate this into our rules. Our confidence in the label is incorporated into the network model by using the probabilities instead of the label.

A second source of information that is ignored is the accuracy of the classification. Ground reference sites can give an indication of the accuracy of the individual classifications. If it is found that the salt class correctly identifies salt-affected sites 80% of the time and misses 15% of all salt-affected sites, we want to modify our rules to reflect this. This is done by expressing the rules themselves as probabilities. For example, the new rule might be 'if label equals salt, then the cover is salt with probability 0.8'.

The final two sources of information that are ignored in the simple example above relate to terrain information. The first relates to the likely occurrence of salt on hilltops or slopes. Although poor condition land in these parts of the landscape are more likely to be caused by erosion, hill side seeps are an indication that while the probability of salinity is low, it is not zero. The rules are expressed in terms of the probabilities of the occurrence of salt-affected land on each landform unit. The landform units themselves are also subject to uncertainty. The units are derived from a digital elevation model calculated by gridding contour data. If the contours are widely spaced in the horizontal direction, the edges of valleys cannot be accurately defined. A site labelled as in a valley may actually be on the slope at the edge of the valley. Instead of using a landform label, a landform probability is used.

The Bayesian network reasoning model provides the framework for combining these probabilities. The same model is used to combine salinity probabilities from two-season time intervals. The final output is a series of probabilities for the classes of consistently good condition land, consistently salt-affected land, emerging salinity and improving vegetation cover. The labels derived from these probabilities form the salinity change maps.

4. Regional Results

4.1 Moora - Kalannie Goodlands region

This study area provided the most complete set of image and ancillary data. The south-western and central portions of this study area are typical of a large portion of the Western Australian wheatbelt. The data and techniques were developed and evaluated over this study area and then applied in the remaining regions.

4.1.1 Optimal Image Dates

A sequence of images is available during the 1990 growing season. Figure 6 shows the image dates superimposed over the vegetation cover index from figure 3. The images cover each stage in the crop growth from seeding, through anthesis and curing, to a sequence of post- harvest images. Previous salinity mapping work (Wheaton et al, 1992), using the September 1990 image, concluded that salt-affected areas covered in barley grass were not spectrally separable from non-saline pasture areas that had been heavily grazed.

Figure 6:

A sequence of pasture training sites, obtained from farmer information, have been compared to barley grass and saltbush sites identified in the field using GPS coordinates obtained during the previous salinity mapping work. Figure 7 illustrates the spectral overlap of these sites in the September image.

Figure 7:

Typical spectral curves for barley grass, saltbush and the spectrally similar pasture sites were compared at different dates; examples are shown in figure 8. Although the spectral curves are quite similar in September, these particular pasture sites show a different temporal progression from the barley grass and saltbush sites. In August, there is much greater, but not quite complete, separation of the spectral curves for pasture and barley grass. In October, the two sets of spectral curves have different shapes. The spectral curves for pasture show a steady increase in reflectance values from band 3 to band 5. The spectral curves for barley grass show an increase in reflectance values from band 3 to band 4, followed by only a slight increase or no change from band 4 to band 5. Although the actual reflectance values are very similar, the different shapes of the spectral curves provide the potential for the two cover types to be separated.

Figure 8:

Further comparison of the spectral curves showed that other pasture sites were spectrally similar to the salt-affected sites in October, but that the combination of August and October images should separate the salt-affected and heavily grazed sites. These qualitative observations of the spectral curves were confirmed quantitatively using discriminant analysis techniques. Figure 9(a) illustrates the increased separation of the pasture and salt-affected sites in the August data. Figure 9(b) shows the separation using both the August and October images.

Figure 9:

Multivariate discriminant analyses were performed on all of the single images and all two- and three-date image combinations. The image combinations were ranked on their ability to discriminate between the salt-affected and pasture sites. These image combinations were then further assessed to determine which are adequate for discrimination. The adequate date combinations are listed in table 1. The optimal image dates are August with October or August with a December or February image. The best single image is the August image. These image dates can be related to the stage of the growing season using the vegetation cover index. Thus, the optimal images are from anthesis (August), curing (October) and post-harvest (December or February).

Cloud-free August images are available in this study area in only three out of the seven years of data studied. Further south, the proportion of cloud-free spring images decreases. Since the Spot satellite can be directed to look sideways as well as straight down, providing a greater number of potentially cloud-free repeat views of a scene, the analyses were repeated using only TM bands 2, 3 and 4 to simulate Spot image data. These analyses showed that an increased number of Spot scenes was required to maintain the discrimination between the salt- affected and pasture sites and that any image combination must include an August (anthesis) image.

Further details of these analyses can be found in Furby (1994).

Table 1 : Adequate Image Combinations for Discriminating Between Barley Grass, Saltbush and Heavily Grazed Pasture

4.1.2 Stratification of the Study Area

Variations in colour can be seen in the image of the study area in figure 2. While different colours indicate different cover types in the image, there is a general shift from very bright reds (good green vegetation cover) in the Moora region in the south-west to duller reds and more blues (less complete green vegetation cover and more bare soil) in the Kalannie Goodlands region to the north-east. A below-average wheat crop in the Moora region, perhaps caused by salinity, might be considered as average vegetation cover, not salt-affected, in the Kalannie Goodlands catchment. These variations in the amount of vegetation cover across the study area are related to factors such as rainfall, soil type and geology.

The study area was stratified into two zones. The zone boundary is shown in yellow in figure 2. Within each zone, there are no marked regional variations in the spectral response of sites within each cover type.

The Moora zone, in the south-west of the study area, has a higher average rainfall (~480mm annually) than the Kalannie zone (~340mm annually). The vegetation index in figure 3 shows a higher average green vegetation cover in the Moora zone compared to the Kalannie zone. This green vegetation cover difference can also be seen in the average annual wheat yield in these regions. The Moora zone, in the Moora shire, has an average annual wheat yield of 1.77 t/ha and the Kalannie zone, contained in the Dalwallinu shire, has an average annual wheat yield of 1.54 t/ha (Cooperative Bulk Handling (Ltd) of WA). Although the zone boundary shown here was digitised from inspection of an image formed by averaging a spring image from each year from 1987 to 1993, it shows a close association with rainfall gradients and soil and geological maps of the region.

Multivariate discriminant analyses show that some of the major cover types vary spectrally between the zones. Figure 10 illustrates the spectral separation. Red and green symbols are used to show sites from the two major zones. The remnant vegetation and salt-affected sites are similar in both zones, but there is a shift in the well-vegetated sites -- pasture, wheat and lupins. Since the aim of this study is to map salinity, the differences in the cropped sites could be ignored since they are both well-separated from the salt-affected sites.

Figure 10:

Focussing once again on the salt-affected and pasture sites, discriminant analyses show that the pasture sites show much more spectral overlap with the salt-affected sites in the Kalannie Goodlands catchment than in the higher-rainfall Moora region. This is illustrated in figure 11. For this reason, the two zones are treated separately in the classification process.

Figure 11:

4.1.3 Salinity Maps

Figure 12 is an example of a salinity map produced using maximum likelihood classification procedures applied to data from August and October 1990. The training data for this process were obtained from farm plans and cropping histories. Approximately half of the sites obtained were reserved for validating the salinity maps. The red area through the centre of the image corresponds to a saline watercourse. Salinity is spreading into nearby paddocks. At the top and bottom right of the image, the red areas, labelled salt-affected, are regions of poor vegetation cover in pasture paddocks that are not caused by salinity.

Figure 12:

Table 2 (a) shows the results of the validation over a 30km by 30km region in the centre of the study area using the reserved validation sites. They show that the amount of salt-affected land is greatly over-estimated, with 19% of the poor pasture and crop sites being labelled as salt-affected. Some salt-affected sites are also labelled as poor crop or poor pasture. These correspond to sites with a good cover of barley grass, or sites where the salt may be just emerging; while there is clear evidence that the crop is not as good as in the surrounding paddock, the vegetation cover is still relatively good. Overall, 75% of the salt-affected sites are correctly labelled, with 85% of the bare salt-affected sites and 70% of the marginal salt-affected sites correctly labelled.

Table 2(a): Accuracy Assessment of the 1990 Salinity Map in the Xantippe Catchment

Table 2(b) shows the results of the validation over a 30km by 30km area in the Moora region. This region has a higher rainfall than the Xantippe catchment. Salt-affected sites are mapped more accurately in this region; 100% of bare salt-affected sites and 91% of marginal sites are correctly labelled. Again 20% of the poor pasture and crop sites are labelled as salt-affected.

Table 2(b): Accuracy Assessment of the 1990 Salinity Map in the Moora Region

Further iterations of the classification process could be performed to refine the labels assigned to the poor crop and pasture pixels, for example by including a bare, or fallow, cover class, making several incremental improvements to the mapping accuracy.

An alternative approach that has been adopted here is to use the data from another season to update the labels. Poor vegetation cover can be caused by a number of factors other than salinity, such as heavy grazing, below-average rainfall, or wind erosion events early in the season. Causes such as salinity will persist over many seasons, whereas grazing rates, cropping rotations and rainfall vary from year to year. Paddocks with poor vegetation cover due to such causes will return to good cover as these conditions alter. Thus a paddock labelled as salt-affected in two consecutive years is more likely to be salt-affected than a paddock labelled as salt-affected in one year and as good crop in the other year.

Associations between land condition and position in the landscape were also investigated. Landform units were derived by stratifying a water-accumulation map derived from the digital elevation model. This processing is described in Wheaton et al (1994). The sites used in the comparison are the validation sites for which the cover -- salt-affected, barley grass or non salt-affected -- is known. The probabilities listed in table 3 were calculated by counting the coincidence of the landform and cover labels.

Table 3 : Probability of Being Salt-Affected for Each Landform Unit

Salinity is less prevalent on hilltops or slopes than in the local or broad valley systems. Hilltops and slopes are more susceptible to erosion problems. Thus a pixel on a hilltop or slope has a lower probability of salinity than one in a valley. The probabilities for landform classes that are not prone to salinity are much more predictive than for those classes that are prone to salinity. For example, there is strong prior evidence that a hilltop will not be saline, while that for a broad valley being saline is equivocal. The relatively poor predictive power of the broad valley class may result from the digital elevation model data not accurately mapping valley floor edges because of the contouring interval. This class may contain not only the broad valley, but also a proportion of good crop-growing slopes.

Figure 13 shows an example of the salinity map produced by combining the probability of salinity from the 1990 and 1991 seasons with the probability of salinity based on the landform unit of each pixel. Only the areas in red are labelled as salt-affected by this process. These correspond to regions with very poor or no vegetation cover in both seasons. Most of the regions that were labelled as salt-affected in 1990 in paddocks at the top and bottom right of figure 12 correspond to regions of poor vegetation cover in pasture paddocks. In 1991 most of these regions produced a good crop and were not labelled as salt-affected. The data combination process has correctly relabelled these regions as not salt-affected. Tables 4(a) and 4(b) show the results of the validation of the combined salinity map. The regions are the same as for the validation of the single-year salinity map in tables 2(a) and (b). The data in the tables show a large reduction in errors of commission (good condition sites labelled as salt-affected), but with an increase in the errors of omission (salt-affected sites labelled as good condition).

Figure 13:

Table 4(a) : Accuracy Assessment of the Combined 1990, 1991 and Landform Unit Salinity Map in the Xantippe Catchment

Table 4(b) : Accuracy Assessment of the Combined 1990, 1991 and Landform Unit Salinity Map in the Moora Region

4.1.4 Changes in Salt-Affected Land Through Time

The salinity maps produced from the pairs of seasons can be combined through time to produce maps of change in salt-affected areas. Figure 14 is an example of the combination of salinity maps from 1987/88, 1990/91 and 1992/93. The regions in red, including the salt lake chains, have been salt-affected for the whole of the study. Areas in dark and light blue have become saline during the time period of this study.

Green regions have improved over the time of the study. They include regions which have been fenced off and consequently the cover of saltbush or other salt-tolerant species has greatly increased, regions that have been consistently wind-blown early in the study and are now improving, as well as marginally saline areas where changing water levels have lead to improved crop growth.

Yellow indicates regions where the salinity mapping has been inconsistent. Such regions have been labelled as good, salt-affected, then good or vice versa. The majority of these areas are not salt-affected but are on deep sands, or have been wind-blown on several occasions with a slow recovery time. These regions are land in poor condition, but the cause is less likely to be salinity. A small proportion of such sites may be emerging salt-affected areas that will still support a reasonable crop in drier years.

Figure 14:

Tables 5, 6 and 7 show the validation results on the salinity change maps for three sub-regions of the study area. The data in tables 5 and 6 are for the same regions for which validation results are given in tables 2 and 4.

The final accuracy assessment, table 7, corresponds to a region in the Kalannie Goodlands catchment between the two salt lake chains in the top right hand corner of figure 2. The classification accuracy here is quite low, particularly for salt-affected sites covered in barley grass. The reasons for this are two-fold. The rainfall in this region is quite low, sandy soils predominate, and it is one of the more marginal areas along the northern edge of the wheatbelt. The vegetation cover is much less here, even in a good year. Cropping tends to occur one in every three or four years, rather than one in every two or three years in the other regions. Combining data from two seasons is less likely to include a good crop. The terrain data are also less accurate in this region. East of Lake Goorly, only 20m contours are available digitally. In this relatively flat terrain, accurately defining the edges of valleys and local rises is impossible. Cloud cover has also caused problems in this region.

Table 5 : Accuracy Assessment of the Salinity Change Map for the Xantippe Catchment

Table 6 : Accuracy Assessment of the Salinity Change Map for the Moora Region

Table 7 : Accuracy Assessment of the Salinity Change Map for the Kalannie Region

Table 8 shows the amount of salt-affected land for the region in figure 14, part of the Xantippe catchment, and for a region in the Moora district. Note that these figures include the salt lake chains.

Table 8 : Percentage of Moora and Xantippe Regions Labelled as Salt-Affected

Combining the condition labels from the individual-year classifications allows the production of a land condition map such as that in figure 15. The cover type labels in each year have been divided into good condition land, poor condition land, salt-affected land and remnant vegetation. The occurrence of these labels over the seven years from 1987 to 1993 has been counted for each pixel. Bright green areas consistently have good green vegetation cover. Blue areas have consistently poor cover but are not recognised as saline. Red areas are consistently labelled as salt-affected. Yellow and pink indicate regions which are sometimes labelled as salt-affected and sometimes good or poor condition respectively. Areas covered by barley grass tend to fall into the latter category, and isolated grazing or erosion effects fall into the former category. The lighter blue (cyan) regions indicate mixed good and poor condition labels. These condition regions are coherent across paddock and road boundaries, suggesting a strong relationship with soil and seasonal conditions, independent of specific crop or pasture cover types.

Figure 15:

The condition image has been compared to the labels of the 1993 validation sites. The results are displayed as a series of histograms in figure 16. The histograms in each column show the distributions of the number of years in which sites of each cover type were labelled as being in good condition, poor condition and salt-affected over six years. The colours correspond to the three test sub-regions.

Figure 16:

The histograms show that crop and pasture sites are very rarely labelled as salt-affected in more than two out of the six years. In the Moora region, the majority of the salt-affected validation sites are labelled as salt-affected in all six years. In the Kalannie Goodlands and Xantippe regions, the number of times these sites are labelled as salt-affected is more evenly distributed, particularly for sites supporting some salt-tolerant vegetation cover. This suggests that better accuracy could be obtained by combining data from more than two seasons.

Inspection of the histograms of counts of poor condition labels suggests that the majority of sites that are labelled as poor condition in four or more years are salt-affected. Relabelling pixels based on the number of poor condition labels should also increase the accuracy of the salinity maps, particularly in the Kalannie Goodlands region.

The data combination process was extended to include data from three seasons for the Kalannie Goodlands region. Occurrences of three poor condition labels in consecutive seasons were assumed to have a high probability of corresponding to salt-affected land. Only a small incremental improvement was obtained in the accuracy of the salinity maps produced.

The most obvious difference between the condition image and the way the data combination network operates is that the former is based on counts of condition labels whereas the latter operates by combining condition probabilities. A salinity map was derived from the condition labels image as follows:

• Not salt-affected:
  • Labelled as good condition at least once

• Consistently poor:
  • Never labelled as good condition
  • Labelled as salt-affected at most once
  • Labelled as poor condition at least three times

• Poor / Salt-affected:
  • Never labelled as good condition
  • Labelled as salt-affected at least two times
  • Labelled as poor condition at least three times

• Salt-affected:
  • Never labelled as good condition
  • Labelled as salt-affected at least two times
  • Labelled as poor condition at most two times

The validation results in table 9 show that if the latter three condition classes are assumed to correspond to salt-affected land, almost all of the barley grass covered sites are detected as salt-affected. The accuracy of the salt-affected class with uncertain cover has also improved.

Table 9 : Accuracy Assessment of the Salinity Map from Condition Labels for the Kalannie Region

The accuracy of the bare salt-affected class as measured against the 1993 reference sites has decreased. Some of the 1993 salt-affected reference sites became saline during the interval from 1987 to 1993 and supported a reasonable vegetation cover in 1987 or 1988. Classification based on the labels from all years assigns these sites to the not salt-affected class because they receive at least one good condition label. Comparison of the salinity map to the 1987 reference sites shows that all of the bare salt-affected reference sites are correctly labelled as salt-affected.

Combining the condition labels from all years in this way has increased the accuracy of the salinity map produced. However, because data from so many seasons are required to accurately assign a condition label, the time interval of data required to separate actual change in condition from varying vegetation response caused by management and seasonal effects also increases. For example, a sequence of condition labels of good condition, good condition, poor condition, salt-affected and salt-affected may indicate a site becoming salt-affected, as for some of the bare salt-affected reference sites in 1993. It may also indicate a site cropped early in the time interval and left as pasture in the later, perhaps poorer, seasons, as for a small proportion of the pasture reference sites in 1993. If the condition labels continue to be poor and / or salt-affected, the site would be considered to have become saline. If a good condition label occurs in the next season or two, it is more likely that the site is in good condition, but a combination of seasonal and management effects has temporarily caused poor vegetation growth.

In the higher rainfall Moora and Xantippe regions, condition change can be separated from management and seasonal effects after two to three years. In these regions crop rotation patterns are typically three years. Data from four years are sufficient to produce maps of condition change. In the Kalannie region, data from at least four and up to six seasons is required to accurately determine land condition. In this region, crop rotations are typically four years. The lower rainfall and sandier soils also contribute to poorer vegetation cover. Data from more than six years are required to map changes in land condition in this region.

4.2 Esperance

This study area was chosen to validate and refine the salinity change detection methodology. A very wet year in 1989 (674mm) caused large salinity problems to emerge.

Frequent cloud cover over this region has severely limited the amount of image data available. The images used have been the only partially cloud-free scenes available from each year and analyses to determine optimal image dates could not be performed.

4.2.1 Stratifying the Study Area

Regional differences are visible across the study area in figure 4. The study area was stratified based on visual inspection of an image formed by averaging the cloud-free spring images; close association with soil and landform zones was seen. Separate classifications were not performed for each zone as insufficient ground data were available in the northern, or transition, zone. It was observed in the classification images produced that the actual ground cover corresponding to some of the spectral classes varied between the zones. For example, a particular spectral class might contain very poor pasture and marginally salt-affected land in the higher-rainfall sandplain zone and average pasture (non salt-affected) in the transition zone.

4.2.2 Salinity Maps

Salinity maps for individual years have been produced in the same manner as for the Moora - Kalannie Goodlands study area. Extra refinements were made to the individual classifications, as cloud cover makes the combination of data over consecutive seasons more difficult.

Figure 17 shows the salinity map produced by this process for a typical region in the southern or sandplain zone for 1993. As in the Moora and Kalannie Goodlands regions, several paddocks with poor pasture cover have been labelled as salt-affected. Table 10 shows the accuracy of the map, assessed using reserved validation sites. Only 72% of the salt-affected sites are correctly labelled and 15% of the crop and pasture sites are incorrectly labelled as salt-affected.

Figure 17:

Table 10 : Accuracy Assessment of the 1993 Salinity Map in the Sandplain Zone

Associations between the validation sites and their position in the landscape have been investigated. In the Esperance region, only 20 metre contours are available digitally. This resolution is too coarse for accurate derivation of local and broad valleys, but there is a weak association between hilltops and land not affected by salinity. Table 11 summarises these associations. The probabilities are derived from counts of the coincidence of the landform units and cover types of the validation sites.

Table 11 : Associations of Salinity with Landform at Esperance

Table 12 shows the accuracy of the salinity map produced by combining data from two seasons with some weak landform association rules for the area shown in figure 17. The data show that the errors of commission have been greatly reduced, although at the expense of reduced ability to detect salt-affected sites.

Table 12 : Accuracy Assessment of the Combined 1991 & 1993 Salinity Map in the Sandplain Zone

Tables 13 and 14 show the accuracy assessment for single-date and combined-seasons salinity maps for a region in the transition zone to the north. This region shows less green vegetation cover than in the sandplain zone. Again, 76% of the salt-affected reference sites are correctly labelled in the single-year map, with 15% of the crop and pasture sites labelled as salt- affected. Combining data from two seasons reduces the errors of commission to almost zero, without significantly changing the percentage of salt-affected reference sites correctly labelled.

Table 13 : Accuracy Assessment of the 1993 Salinity Map in the Transition Zone

Table 14 : Assessment of the Accuracy of the Combined 1991 & 1993 Salinity Map in the Transition Zone

The landform information derived from the contour data contributes little to the accuracy of the combined salinity maps. The soil landscape map gives an indication of flat and low-lying areas as well as indicating areas that are more or less susceptible to salinity. The soil types were amalgamated from seventy individual units into four categories. The associations of these soil categories with salinity were derived by comparison with the 1993 reference sites and are listed in table 15.

Table 15 : Associations of Soil Categories with Salinity at Esperance

Combining image data from three seasons rather than two to improve the accuracy of the salinity maps was also investigated. The optimal salinity mapping strategy for the Esperance region is to combine image data from three seasons with the soil category data. Using three seasons of satellite data alone was almost as accurate.

Figure 18 shows the salinity map produced by combining data from 1990, 1991 and 1993 with the soil data. Assessment against the reference sites, shown in tables 16 and 17, shows that salt-affected sites are detected with 90% accuracy, with little adverse effect on the errors of commission.

Figure 18:

Table 16 : Assessment of the Accuracy of the Combined 1990, 1991 & 1993 Salinity Map in the Sandplain Zone

Table 17 : Assessment of the Accuracy of the Combined 1990, 1991 & 1993 Salinity Map in the Transition Zone

4.2.3 Change in Salt-Affected Land Through Time

The salinity maps produced from three seasons have been combined through time to produce maps of change in salt-affected areas. Figure 19 is an example of the combination of salinity maps from 1987/88/90 and 1990/91/93. The regions in red have been mapped as salt-affected for the whole of the study interval. Areas in light blue have been mapped as becoming salt- affected during the time period of this study.

Figure 19:

Areas in green have been mapped as improving over the time of the study. Whilst the vegetation cover in some of these areas may be improving, in other areas it corresponds to errors of commission in the 1987/88/90 salinity map. In 1987 and 1988, the only spring data available are from late in October. There is much less spectral discrimination between salt-affected and other poor condition sites this late in the growing season, and the errors of commission are much greater than when August or September images are used. Combining data including two such seasons does not completely correct such errors of commission.

Table 18 shows the 'Mahalanobis distances' between the cover classes used in the classification for August 1993 and October 1988. In particular, the spectral class for poor crop and pasture is much closer to the partially vegetated salt-affected spectral classes.

Table 18 : Mahalanobis Distances Between Spectral Classes in the Esperance Study Area

(a) October 1988

(b) August 1993

Table 19 shows the assessment of the accuracy of the salinity change map using the 1993 reference sites.

Table 19 : Accuracy Assessment of the Esperance Salinity Change Maps (a) Sandplain Zone

(b) Transition Zone

Table 20 shows the amount of land mapped as salt-affected in each of the test regions.

Table 20 : Percentage of the Esperance Regions Labelled as Salt-Affected

4.3 Narrogin

Initial salinity maps have been produced using the 1993 and 1994 TM data, and the methodology for combining results over years which is described above. Landform summaries have been used to remove some errors of commission. Large-scale hard copy maps have been produced for five study areas of 30km by 24km. These maps are being evaluated by farmers and local catchment groups. Communication with these groups will continue. Information from this evaluation will be used to refine the present mapping and to produce accuracy and error estimates. The results for this region will be reported separately.

5. Using Data from Other Years and/or Regions to Simplify Processing

Supervised classification techniques are labour-intensive in the training and iteration stages. A potential benefit of sequences of calibrated images is that training signatures ought to be able to be applied to a sequence of images of a region or to different regions with similar rainfall and soil conditions.

Data from the Moora - Kalannie Goodlands study area have been used to examine whether training data can be transferred from year to year. Figure 20 shows a sequence of plots illustrating the spectral overlap between data from different dates within the Moora stratification zone. The first plot shows August 1990 and 1993 data. The only temporal trend observed is a group of crop sites in 1993 that do not seem to have spectral counterparts in the 1990 training data. 1993 was a better season than 1990 and these represent very good crops, better than were obtained anywhere in 1990. These variations do not affect the accuracy of the salinity mapping process, and so August training data can be used interchangeably between years.

Figure 20:

The second plot, 20(b), shows a comparison of August and September data. Temporal trends are evident in most cover classes. Since these differences contribute to the separability of the salt-affected and good condition sites, data cannot be transferred between years at different stages during the growing season.

Data from both the Moora - Kalannie Goodlands and Esperance study areas were used to investigate whether training data can be transferred between regions. Images of these regions can be calibrated to each other via a sequence of intermediate, overlapping scenes, although the calibrations are not as repeatable as between different images of the same region.

Figure 21 illustrates the comparison of the training data between these regions in September 1990. Comparison of the green vegetation cover curves in figures 3 and 5 suggests that the timing of the growing season is similar in the two regions. Figure 21(a) shows the comparison of data from the Kalannie zone with data from Esperance. Trends are evident in all cover types. Figure 21(b) compares data from the higher-rainfall Moora region to the data from Esperance. Again differences are evident for all cover types.

Figure 21:

It is not clear how much of this difference in the spectral response of each cover type is due to an imperfect calibration of the Esperance image to the Moora image and how much is due to the effects of different soil types, geology and rainfall. Further investigation of the calibration procedures and comparisons of the ancillary data sets are required to determine whether training data from Moora can be used in the classification of the Esperance scene.

6. Using Image Sequences to Monitor Changing Sites

Sequences of calibrated images can be used to monitor sites where changes are known to be occurring. A site from the Xantippe catchment where salt is spreading into a paddock is used here to illustrate some of the possible products.

Figure 22 shows a sequence of images over the site. A salt lake chain passes through the property just above the north-west corner (top left) of the highlighted paddock. That corner of the paddock is salt-affected; since 1987 this region has been extending towards the centre of the paddock. Shades of red in these images indicate crop and pasture. The brighter the red, the more complete the green vegetation cover when viewed from above. Lupins or lush pasture correspond to the brightest red. Bare areas appear as white or light blue.

Figure 22:

Comparing the 1988 and 1994 images, left of figure 22, shows that the 'tongue' of salt has extended further into the paddock and that a new bare area has emerged in the bottom left of the paddock. This newly bare area supported some of the best vegetation cover in the paddock in 1988. The salinity change map in the bottom right of figure 22 shows the time at which the salinity monitoring procedure detected the changes in productivity.

The 1989 image at the top right of figure 22 shows a 'red halo' along the edge of the salt- affected region. 1989 was an unusually dry year and many slightly salt-affected regions supported good vegetation cover. This flushing of good vegetation around known salt-affected sites has been observed in images from drier years and from late October. It appears to indicate areas with access to good groundwater that persist well after rainfall events. As the groundwater level rises, such areas are next in line to become salt-affected. Several farmers have reported very high yields just before an area becomes salt-affected.

Samples of good condition, poor condition and salt-affected land from this paddock were used to derive a condition index, displayed as an image for 1988 and 1993 in figure 23. Bright red areas correspond to little or no green vegetation cover and bright green areas have very good vegetation cover.

Figure 23:

Figure 24 shows this condition index plotted over time for good condition sites, salt-affected sites and a changing site within the paddock. The lines in green correspond to good condition areas in the paddock. The lower red line corresponds to an area in the salt lake chain and the upper red line to the salt-affected area in the top left corner of the paddock. The blue line corresponds to the emerging bare area. From 1987 to 1989, it is very similar to the good condition sites. From 1991 onwards, it is clearly in poorer condition than the rest of the paddock and by 1994 is very similar to the salt-affected sites.

Figure 24:

The different values of the condition index in each year for the good condition site are due to variations in the type of vegetation in the paddock - wheat crops in 1988, 1992 and 1993 and volunteer pasture in the other years. The maximum vegetation cover expected in ideal conditions differs for these vegetation types. Unless these differences can be adjusted for on a paddock-by-paddock basis, condition index images cannot be interpreted consistently on a regional basis. They are, however, useful for investigating specific sites of interest.

7. Longer-Term Changes in Land Use and Condition

Landsat MSS data are available in August 1980 for the Esperance study area and August 1981 for the Moora - Kalannie Goodlands study area. These data have been compared to the Landsat TM data from the 1990s to look for changes in land condition over the ten-year interval. Landsat MSS data have a spatial resolution, or pixel size, of 60 metres by 80 metres. Only changes in area by at least 60 - 100 metres will be detected in the comparison.

7.1 Clearing Maps

Figure 25 shows clearing maps for the Moora and Xantippe regions. Areas in green are remnant vegetation in 1993. Areas in orange were covered by remnant vegetation in 1981; the vegetation has been lost due to clearing or salinity.

Figure 25:

The left hand image in figure 25 is typical of the Moora region. Very little clearing of the remaining reserves has occurred; however, most of the bush left along the major watercourses has been lost. These watercourses are now salt-affected and lined by dead trees. 3% of the area displayed is mapped as remnant vegetation and a further 2.7% of the area is mapped as losing remnant vegetation cover since 1981.

The right hand image in figure 25 is typical of the Xantippe and Kalannie Goodlands catchments. Large-scale clearing of the area for agriculture occurred about thirty years later than in the Moora region. Several small bush blocks have been cleared in the past twelve years. 4.2% of the Xantippe catchment is remnant vegetation, with 0.8% cleared since 1981. In the Kalannie Goodlands catchment, 6.5% of the area remains as remnant vegetation, with 2.8% of the region cleared since 1981.

The final clearing map, figure 26, shows the eastern portion of the Esperance study area. It shows quite large tracts of land that have only recently been cleared for agriculture. 23.2% of the Esperance area remains bush, while 18.1% has been cleared since 1980.

Figure 26:

7.2 Change in the Extent of Salt-Affected Land

Image displays created by combining image bands from the MSS and TM images were used to detect changes in the extent of salt-affected areas in the Moora - Kalannie Goodlands study area. In the very south-west corner of the study area, the Moora region, most of the changes which are apparent correspond to loss of vegetation cover along the water courses. Further north-east, towards the centre of the study area, numerous areas of expanding and newly- emerging salinity can be found. The image sequence in figure 27 shows one such example. Further north-east, in the Xantippe and Kalannie regions, no significant changes in salt-affected areas can be observed. This area is only just beginning to show an emerging salinity problem.

Figure 27:

8. Using Other Ancillary Data to Map Salinity

One of the most common errors in the salinity and salinity change maps is the labelling of areas that are consistently in poor condition as salt-affected. Examples of this are the yellow areas in figures 14 and 19. These typically correspond to areas that are frequently windblown or areas of deep sands, which rarely support good vegetation cover. This latter error might be corrected by incorporating data on soil type into the processing. The results of such a procedure are described here. For more details of the processing, see Caccetta et al (1995).

Detailed soil maps have been obtained for the Xantippe catchment, just north-east of the centre of the image in figure 2. These detailed soil classifications have been combined into three major types - deep sands, clay flats and 'other'. Associations between these soil types and salinity have been investigated using a salinity map produced by the catchment advisor from extensive field work. These comparisons showed that deep sands have a high probability of poor vegetation cover, but a low probability of being salt-affected. Clay flats, however, are the predominant soil type in the valleys and have a higher probability of being saline.

Another ancillary data set that has been derived from the digital terrain model is the drainage network, from which points at which water flows converge can be derived. Expert advice suggests that these areas also have a higher probability of salinity.

Figure 28 shows an example of the salinity map produced using the satellite data from 1990, 1991 and 1993 in the form of class label images, landform units, soil maps and drainage networks. Also shown are the salinity map produced using just the class label images from 1990 and 1991, reproduced from figure 13, and the 1993 salinity map produced by the catchment advisor. Including the extra soil and drainage information has relabelled many of the 'salt- affected' areas mapped by using only the satellite and landform data. Comparison with the ground-truth salinity map suggests that the new salinity map is more accurate. Using the soil and drainage information has also relabelled some of the 'good condition' areas adjacent to areas mapped as salt-affected. Again, comparison with the ground-truth map suggests that these new labels are correct.

Figure 28:

Tables 21(a) and (b) show the accuracy assessment of these salinity maps against the ground- truth map. These data confirm the improved mapping of both non salt-affected and salt-affected areas.

Table 21 : Accuracy Assessment of Salinity Maps Against Ground Mapping (a) 1990, 1991 and Landform Units

(b) 1990, 1991, 1993, Landform Units, Soil Types and Drainage Networks

This example illustrates that including soil type and drainage networks increases the discrimination between areas in poor condition due to salinity and areas in poor condition due to other factors. Unfortunately, consistent soil maps are not available digitally over the whole of this study area. Higher-resolution digital elevation data are also required to derive drainage networks in low-relief regions such as in the Esperance study area.

9. Conclusions

This study has shown that more accurate salinity maps can be produced by incorporating multi- temporal satellite images and landform and soil type data into the mapping process. It has also demonstrated the ability to detect and display short-term (2 - 4 years) and medium-term (~ 10 years) changes in land condition.

Earlier salinity mapping studies concluded that land in poor condition due to causes other than salinity could not be spectrally separated from salt-affected land supporting a cover of salt- tolerant grasses, such as barley grass, using satellite image. This study has shown that in the Moora region, such sites show different temporal trends during the growing season and that the combination of August (anthesis) and October (curing) images allows better separation of these sites. Data from both the Moora-Kalannie Goodlands and Esperance study areas show that even when only a single image is used, the choice of image date can greatly affect the accuracy of the mapping.

Using images from more than one season greatly improves the accuracy of the salinity maps produced. Salt-affected land remains so from year to year, perhaps dependent on the ground water level, whereas crops are rotated and grazing patterns, management strategies and rainfall patterns change from year to year. If the low productivity is a temporary condition, the paddock is likely to be cropped or to support good pasture cover in a subsequent season. At that time, the paddock is more easily separated from the salt-affected sites. Data from the Moora region suggests that two seasons are adequate to correctly label all but 1% of the sites in poor condition that are not salt-affected. Three seasons are needed in the Kalannie Goodlands and Esperance regions.

Landform information, where it can be obtained with sufficient accuracy, has contributed to the production of better salinity maps. Poor condition land on hilltops and slopes has been shown to be unlikely to be caused by salinity in the Moora region. Although similar associations are expected in other regions, the coarseness of the original contour data in the Kalannie Goodlands and Esperance regions meant less accurate terrain models could be derived. Better resolution is required in these relatively flat terrain types.

Soil type information has also been shown to be important to the accuracy of the salinity maps produced. Areas of ‘deep sands’ were shown to have consistently less vegetation cover than surrounding areas, but had a low probability of being saline in both the Esperance region and the Xantippe catchment in the Kalannie Goodlands region. ‘Clay flats’, the predominant soil type in the valley floors in the Xantippe catchment, were found to have a high probability of salinity. Including this information was shown to not only help correct the labels assigned to sites in poor condition not caused by salinity, but also to correctly label salt-affected areas wrongly labelled using just satellite and landform data. Such soil maps are not available digitally over the whole of the study areas. Even when the soil maps were available, expert assistance was required to categorise the soil types into classes relevant to salinity mapping.

Combining the labels from the single-year classifications to produce a condition image in the Moora - Kalannie Goodlands study area identifies alike regions that cross paddock and road boundaries. This suggests that the sequence of labels is identifying underlying land condition, independent of particular cover types in particular years.

Salinity change maps can be produced at a regional scale, although only small examples are shown here. Assessment of these maps against validation sites and evaluation by DAWA field officers and catchment advisors suggest that the changes in condition being detected are accurate but that omissions exist. Not all of the changes identified are caused by salinity. Many of the areas mapped as having increasing vegetation cover were in poor condition at the beginning of the study, due to management or persistent wind erosion, and the recovery cycle is 3 - 4 years to reach their current good-condition status. A small proportion of these areas were, and still are, salt-affected but were fenced off early in the study interval and now support a very good cover of salt-tolerant species.

Feedback on the salinity maps and salinity change maps from DAWA officers and catchment group advisors in the Moora and Kalannie Goodland regions has indicated that even though these maps are not completely accurate, they are still very useful products when combined with a recent image of the area. Salinity maps have been produced by DAWA officers for parts of this study area based on extensive field work over a period of twelve months. To create a salinity change map would require another twelve months of field work. Having an image- derived salinity change map, updated by local knowledge and field visits to particular sites of interest, is perceived as a much more time effective means of updating their information base.

The salinity mapping and monitoring methodology presented in this report was developed using image and ancillary data from the Moora region. Using data from two seasons with landform units, salt-affected areas can be detected with 90% accuracy with 2% of other areas mapped as salt-affected. Data from four years are required to produce maps of condition change. This methodology was then routinely applied and evaluated in the Kalannie Goodlands and Esperance regions. Reduced accuracy was obtained.

The methodolgy was then refined in these regions. In the Kalannie Goodlands region, condition labels from 1987 to 1993 were combined. This increased the salinity detection accuracy to 71% (from ~40%) with less than 1% of other sites labelled as salt-affected. Data from more than six years are required to map changes in land condition in this region. In the Esperance region, combining image data from three seasons with soil data leads to salt-affected land being detected with 90% accuracy (from ~75%) with 4% (sandplain zone) and 8% (transition zone) of other areas mapped as salt-affected. Data from six years are required to map changes in land condition in this region.

10. Workshops / Clinics / Conference Presentations

The results of the project have been actively disseminated at workshops and clinics for agricultural advisors and LCDCs, at agricultural shows and at conferences. (Some of the workshops have been run jointly with an NLP-funded project on 'Remote Sensing Training for Resources Officers and Farmers'.)

November 1992:

• workshop on Remote Sensing and Salinity Mapping for farmers at Esperance - presented by Norm Campbell, Buddy Wheaton and Mark Palmer - a clinic was run on the day after the workshop, at which local farmers were invited to view displays of their properties.

April 1993:

• workshop on Remote Sensing and Salinity Mapping for farmers at Dalwallinu - presented by Suzanne Furby and Buddy Wheaton in conjunction with the Moora District Office of DAWA - a clinic was run following the workshop, at which farmers from the surrounding districts were invited to view displays of their properties.

July 1993:

• workshop on Remote Sensing for the Moora District Office of DAWA (jointly with the NLP project) - presented by Norm Campbell (CSIRO), Andrew Sanders (DAWA) and Richard Stovold (RSAC).

August 1993:

• ABC Television Landline segment on Remote Sensing - appearances by Norm Campbell and Buddy Wheaton.

August 1993:

• workshop on Remote Sensing for the Lake Grace District Office of DAWA and local LCDCs (jointly with the NLP project) - presented by Norm Campbell, Andrew Sanders and Richard Stovold - a clinic was run on the morning after the workshop, at which local farmers were invited to attend to view displays of their properties from images stored on CD- ROM.

August 1993:

• LWRRDC workshop on Remote Sensing Methods for Identification of Discharge Areas - presentations by Norm Campbell and Buddy Wheaton.

September 1993:

• Leeuwin Center opening - workshop on Agricultural Remote Sensing - presentation by Buddy Wheaton.

September 1993:

• DAWA Catchment Hydrology Group workshop at Esperance - presentation by Buddy Wheaton.

October 1993:

• WA Royal Agricultural Show - the group had an area within the DAWA stand - farmers were invited to view displays of their properties from images stored on CD-ROM - hard-copy prints were produced subsequently for several farmers.

October 1993:

• Landcare Conference, Perth - presentation by Buddy Wheaton.

October 1993/94:

• Katanning & Districts Agricultural Show - farmers were invited to view displays of their properties from images stored on CD-ROM.

February 1994:

• Remote Sensing / GIS Seminar for Albany LCDC - presentation by Norm

Campbell. March 1994/95:

• Wagin 'Woolarama' Show - farmers were invited to view displays of their properties form images stored on CD-ROM.

April 1994:

• workshop on Remote Sensing for the Beverley LCDC (jointly with the NLP project) - presented by Norm Campbell, Andrew Sanders, Richard Stovold and Buddy Wheaton - a 'clinic' was run following the workshop.

May 1994:

• Bremer Bay - meeting of Project Officers from DAWA and Land Conservation Officers from the Southern Agricultural Region - presentations by Buddy Wheaton and Mark Palmer.

May 1994:

• workshop on Remote Sensing for the Bunbury office of DAWA and local LCDCs - presented by Norm Campbell, Andrew Sanders and Richard Stovold - the workshop was followed by a day of hands-on display of images.

September 1994:

• Resource Technology ‘94 Conference - presentation by Jeremy Wallace.

October 1994:

• Jerramungup Agricultural Expo - farmers were invited to view displays of their properties from images stored on CD-ROM - two 'clinics' were run for local catchment groups on the following day.

November 1994:

• an interview was recorded for the ABC radio Country Hour show - Norm Campbell, Suzanne Furby and Buddy Wheaton - the interview has not yet gone to air.

Workshops will be held to present the results of the salinity mapping and monitoring work to farmers, land care advisors and DAWA officers in the Moora and Kalannie Goodlands region (at Dalwallinu), Esperance and the Dumbleyung / Narrogin regions.

Project results will also be presented at the 8th Australasian Remote Sensing Conference in Canberra in March 1996 and at the 4th Australian National Workshop on Rehabilitation and Productive Use of Saline Land at Albany, Western Australia in March 1996.

11. References

Caccetta, P., Campbell, N. A., West, G., Kiiveri, H. T. and Gahegan, M. (1995). Aspects of reasoning with uncertainty in an agricultural GIS environment. Workshop on Knowledge Based Systems in Resource Management. AI ‘94. Armidale, Australia, November 1994.

Campbell, N. A., Furby, S. L. and Fergusson, B. (1994). Calibrating Images from Different Dates, Interim Report to LWRRDC from the project, Detecting and Monitoring Changes in Land Condition Through Time Using Remotely Sensed Data (CDM1).

Campbell, N. A. and Wallace, J. W. (1989). Statistical methods for cover class mapping using remotely sensed data. Proc. Int. Geosci. Remote Sensing Symp: 493-496.

Furby S. L. (1994). Discriminating Between Pasture and Barley Grass and Saltbush Using Multi- temporal Imagery. CSIRO Division of Mathematics and Statistics Technical Report.

George, R (1990). The 1989 saltland survey. J. Agric. West. Aust., 31, 159-166.

Hick, P. T. and Russell, W. A. R. (1990). Some spectral considerations for remote sensing of soil salinity. Aust. J. Soil. Res, 28, 417-31.

Nulsen, R. A. (1981). Salt-affected land in the Shire of Wongan - Ballidu, Western Australia, Aust. J. of Soil Res., 19, 87-91.

Wheaton, G. A. and Wallace J. F. (1990). Mapping Dryland Salinity. Interim Report to the National Soil Conservation Programme, Project number 7405 Mapping Land Degradation Using Remote Sensing.

Wheaton, G. A., Wallace, J. F., McFarlane, D. J. and Campbell, N. A. (1992). Mapping salt- affected land in Western Australia. Proceedings of the 6th Australasian Remote Sensing Conference, Wellington, New Zealand 1992, Volume 2, 369 - 377

Wheaton, G. A., Wallace, J. F., McFarlane, D. J., Furby, S. L., Campbell, N. A. and Caccetta, P. (1994). Mapping and monitoring salt-affected land in Western Australia. Proceedings of Resource Technology ‘94 Conference, Melbourne, Australia 1994, 531-543.

Appendix A: Supporting Technical Reports

For More Information

For more information on salinity mapping and monitoring contact Suzanne Furby.

A long version of this document is available that contains the figures in the main body of the document for easier printing.

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last updated 04/11/09
Xiaoliang.Wu@csiro.au