Collecting Ground Truth Data for Salinity Mapping and Monitoring

 

 

Suzanne Furby, Fiona Evans, Jeremy Wallace, Ruhi Ferdowsian, John Simons

 

 

CSIRO Mathematical and Information Sciences

Agriculture Western Australia

 

 

Introduction

1. An Introduction to Remote Sensing

What is Remote Sensing?

How Satellites Acquire Images

Interpreting Image Displays

Digital Elevation Models And Derived Landform Classes

2. Creating Salinity Maps from Satellite Images

Identifying the Salinity Categories in the Scene

Stratifying the Scene

The Role of Training Sites of Each Cover Type

Combining Information from Several Years to Produce Salinity Maps

Assessing the Salinity Maps

3. Ground Data Collection

Ground Data Requirements For Each Scene

Stratification

Located Ground Sites

Existing Datasets and Knowledge

Locations of Areas of Recent Change

4. Salinity Prediction

Ground Data Requirements

Making Predictions

Appendix: Categories of Salt-Affected Land

 

Introduction

The Land Monitor project aims to provide information about land condition, specifically salinity and the status of remnant vegetation, for the whole of the south-west agricultural region. It is a collaborative project involving Agriculture WA, CSIRO, CALM, DOLA, Water and Rivers Commission, Environmental Protection and Main Roads.

Satellite images, digital elevation maps and ground data will be used to produce maps of land condition, such as the current extent of salinity, the changes since 1990 and predictions of future salinity risk. These products, and the data they are formed from, can be used by land managers and advisers as part of the process of managing that land.

Ground data are vital to extracting land condition information from satellite images. Samples of land in good, poor and salt-affected condition are needed in order to learn how to understand the variation in the satellite images. It is also necessary to understand the relationships between salinity and salinity risk and any other datasets that are available for a region, for example ‘in region X, salinity never occurs on soil type Y’. Extra ground data are needed to validate the maps that are produced and to understand what types of land condition can and cannot be mapped using satellite images. The general process for producing salinity maps is illustrated in the diagram on the next page.

The aims of this manual are to:

o provide a brief introduction to remotely sensed data (satellite images); o provide an introduction to how satellite images are used to map and monitor land

condition; and o provide some guidelines and examples for providing ground data for the salinity

mapping and monitoring aspects of the Land Monitor project.

This manual is divided into four sections. The first section gives an introduction to satellite images, how they are acquired and how they are interpreted. The second section gives an overview of how salinity maps are formed and the role of ground data in that process. The third section gives a detailed description of the ground data required to support the salinity mapping and monitoring aspects of the Land Monitor project. The fourth section describes the ground data needed for the salinity prediction aspects of the Land Monitor project.

The appendix to this manual begins a description of the different categories of salt-affected land found in the south-west agricultural area that are relevant to mapping and monitoring salinity from satellite images. It is anticipated that these descriptions will be expanded during the first few months of the project and the updated version will be distributed as a stand-alone document.

 

 

 

1. An Introduction to Remote Sensing

 

 

A satellite image of the Perth region is displayed above. The spatial patterns of the ocean and the Swan River will be familiar to most Western Australians. Lake Monger is near the top of the image and Garden Island is in the bottom left hand corner. The CBD and Fremantle appear in shades of light blue.

The information that follows in this section will help you understand how images like this are acquired, how the displays are created and can be interpreted and why they make such a useful dataset for land condition monitoring.

 

 

What is Remote Sensing?

Remote sensing is one of a suite of tools available to land managers that provides up-to-date, detailed information about land condition. Remote sensing uses instruments mounted on satellites or in planes to produce images or 'scenes' of the Earth's surface.

Remotely sensed images can be used in many applications, for example for mineral exploration, monitoring ocean currents, land use planning, and monitoring the condition of forest and agricultural areas. The uniqueness of satellite remote sensing lies in its ability to show large land areas and to detect features at electromagnetic wavelengths which are not visible to the human eye. Data from satellite images can show larger areas than aerial survey data and, as a satellite regularly passes over the same plot of land capturing new data each time, changes in the land use and condition can be routinely monitored.

In the Land Monitor project, satellite images are being used to provide information on land condition and the changes in that condition through time, specifically salinity and the status of remnant vegetation, to help farmers, environmental managers and planners better manage the land. One of the outcomes of the Land Monitor project will be an archive of satellite images of the south-west agricultural region. To get additional information about land condition, the satellite images are combined with other data such as air photos, digital elevation maps (DEMs) and ground data.

Farmers, landcare workers and field officers, with their detailed knowledge of the vegetation and soils in their own paddocks or regions, can extract information on productivity from simple displays of the satellite images.

 

 

Satellite images

show very large areas of land detect features at wavelengths not visible to the human eye are regularly and routinely acquired and archived are the most cost-effective dataset for monitoring change over large areas

 

 

 

How Satellites Acquire Images

Satellite sensors record the intensity of electromagnetic radiation (sunlight) reflected from the earth at different wavelengths. Energy that is not reflected by an object is absorbed. Each object has its own unique 'spectrum', some of which are shown in the diagram below.

Remote sensing relies on the fact that particular features of the landscape such as bush, crop, salt-affected land and water reflect light differently in different wavelengths. Grass looks green, for example, because it reflects green light and absorbs other visible wavelengths. This can be seen as a peak in the green band in the reflectance spectrum for green grass above. The spectrum also shows that grass reflects even more strongly in the infrared part of the spectrum. While this can't be detected by the human eye, it can be detected by an infrared sensor.

Instruments mounted on satellites detect and record the energy that has been reflected. The detectors are sensitive to particular ranges of wavelengths, called 'bands'. The satellite systems are characterised by the bands at which they measure the reflected energy. The Landsat TM satellite, which provides the data used in this project, has bands at the blue, green and red wavelengths in the visible part of the spectrum and at three bands in the near and mid infrared part of the spectrum and one band in the thermal infrared part of the spectrum. The satellite detectors measure the intensity of the reflected energy and record it as a number between 0 and 255.

Another feature that characterises each satellite system is its footprint or pixel size. This is the smallest area on the ground for which it can record the reflected energy. For every 30m by 30m plot of land, the Landsat TM scanner records a number for each of the seven bands, which is the average intensity of the reflected energy for the features in that plot of land.

The final feature that characterises a satellite system is the frequency with which it revisits a particular location. The Landsat TM satellite revisits each location every 16 days. The data for Australia are relayed to a receiving station at Alice Springs run by the Australian Centre for Remote Sensing (ACRES). Each image is routinely archived. Theoretically, a site could be viewed every 16 days to detect changes in land use or condition. In practice, some of these images are unusable because the satellite sensors cannot see through cloud. In general, for the Land Monitor project, one image is purchased each growing season. Spring images are used to map and monitor the agricultural lands and summer images are used to map and monitor the remnant vegetation.

The goal of image processing is to detect features, and changes in those features over time, and to be sure that what is seen is related to the ground cover rather than to interference caused by the atmosphere. To do this, sequences of images are aligned to each other and to standard map grids (registration and rectification) and are calibrated to remove the effects of atmospheric differences.

 

Satellite provide information on land cover and condition because features of the landscape such as bush, crop, salt-affected land and water reflect light differently in different wavelengths

Satellites are characterised by the

wavelength 'bands' at which reflected energy is measured the size of the footprint or pixel for which they measure reflected energy the frequency with which they revisit a particular location

The Land Monitor Project uses Landsat TM images. These images:

have 6 wavelength bands that are routinely used (3 visible, 3 infrared) have 30m pixels are acquired every 16 days (provided conditions are cloud-free)

 

 

 

Interpreting Image Displays

The satellite images, as recorded by ACRES, consist of numbers which are measurements of the amount of energy that has been reflected from the earth's surface in different wavelength bands. Some of these bands, such as the infrared bands which contain so much information about vegetation growth and condition, can't be seen with the human eye. So, how do we make pictures which show changes in reflected energy which the human eye can’t see? The answer is that the data are represented on a computer screen, or on a hardcopy print, using colours that we can see. The numbers recorded for the different satellite bands are displayed in red, green and blue colour guns on a computer screen.

When the red, green and blue bands of an image are assigned to the same colours on the computer screen, a true-colour image is formed. These images look like aerial photographs, since they indicate the true colours of objects – green trees and grass and brown soil. When mixtures of the visible and infrared bands are assigned to the red, green and blue colours on the computer, false-colour images are formed. In these images, the different colours on the screen represent different intensities in the wavelength bands that are assigned to each screen colour. Studies have shown that the human eye distinguishes changes in red better than in blue or green, so the band mostly strongly related to the feature of interest is usually assigned to the red colour on the screen.

As well as deciding which image band to assign to which screen colour, choices can be made about how to relate the range of numbers recorded by the satellite to the 256 levels of each colour on the computer screen. Although the satellite can record intensities between 0 and 255, typically the actual intensities associated with the ground covers present in agricultural images occupy a much smaller range of values. The way the range of digital numbers in the image is related to the computer colour levels is called 'image enhancement'.

Different image enhancements can be used to highlight different detail in an image. For example, the minimum image intensity could be set to colour level 0 and the maximum image intensity set to colour level 255. This would maximise the number of colours on the computer screen and show some information over the whole image. Alternatively, the range of image intensities corresponding to just remnant vegetation could be assigned to the 256 colour levels, highlighting the detail in the image about remnant vegetation at the expense of other cover types in the image.

The images below show different band and enhancement combinations. Different ground cover types and features are highlighted in each image. The images show the local variation in ground cover within a paddock and between the paddocks on a property.

 

This is a true colour image using bands 1 (blue), 2 (green) and 3 (red) from the Landsat TM satellite. The crop and pasture paddocks look green and the bush looks dark and woody. In this image, it is difficult to distinguish between crop and pasture paddocks.

 

This false-colour image shows Landsat TM bands 4 (near infrared), 5 (mid infrared) and 3 (red) in the red, green and blue colours respectively. TM band 4 responds most strongly to green vegetation cover and has been assigned to red here. Areas with the most green vegetation cover (crop) appear as bright red. Areas where soil is mixed with the green vegetation when viewed from above appear in duller shades of red. Grazed pasture paddocks show up as various shades of orange and green. Salt-affected areas, with little or no vegetation cover, appear in shades of blue.

 

This false-colour image displays Landsat TM bands 4 (near infrared) , 5 (mid infrared) , and 2 (green) in red, green and blue respectively. The colours have been adjusted to show the range of colours in the remnant vegetation. Here the different shades of red and green colours in the bush areas show the different vegetation types and differences in condition within the species type. The cleared agricultural areas appear as bright yellow or white colours in this enhancement.

 

As well as showing differences in vegetation cover within a paddock or between different paddocks within a property, satellite images can also show broad regional trends in vegetation cover. The image below shows an area of about 125km by 115km north-east of Moora including the Kalannie-Goodlands catchment. The redder the image appears, the greater the green vegetation cover on the ground. The shift in colour from the south-west (bottom-left) to the north-east (top-right) is due to the decreasing rainfall (480mm to 340mm per annum) and the change from predominantly clay-based soils to largely sandy soils. The amount of green vegetation cover associated with a very good crop in the Kalannie-Goodlands catchment would only be considered to be average or poor in the catchments further to the south-west. The yellow line indicates the boundary between two 'stratification zones'. These zones were treated separately during the salinity mapping process for this area.

 

 

Satellite image displays are created by

choosing image bands to assign to the red, green and blue colours on the computer screen

choosing intensity ranges from the image values to assign to the colour levels on the computer screen

Satellite image displays can show

local variation in vegetation cover within a paddock local variations in vegetation cover between paddocks broad regional variations in vegetation cover

 

 

 

Digital Elevation Models And Derived Landform Classes

 

A digital elevation model (DEM) represents the height of the earth's surface in digital form. It consists of a grid of elevation values, where each value in the grid represents the elevation at that position. The image below shows a representation of a DEM for the Upper Kent catchment. The lower areas in the landscape are coloured yellow and green, while the highest elevations are shown in red.

 

DEMs can be used to derive other maps describing the terrain of an area, such as landform (hilltop, slope or valley), slope, aspect, drainage patterns and water accumulation. These help us understand the flow of water across the environment and the rate and direction of surface flow. In addition, if ground water flow is related to surface flow, assumptions can be made about the movement of ground water within a landscape.

The image below shows a water accumulation map for the Upper Kent catchment. It is derived by simulating a rainfall event and measuring the flow of water across the landscape. Areas of lowest water accumulation (yellow) are on hill tops and areas of highest water accumulation (red and pink) are in valleys and drainage lines. Stratification of the water accumulation map gives discrete landform classes.

 

Soils, land cover, land use, geology and hydrological structures are all related to variations in the terrain. Knowledge of the terrain can be used to understand and model areas affected by and at risk of salinity and to define management strategies for improving productivity in these areas.

DEMs can be derived directly from stereo air photos using photogrammetric techniques, or they can be obtained from spatial interpolation of contour data. The former approach will be used as part of the Land Monitor Project to create DEMs accurate to 1-2m over the south-west agricultural area. This type of accuracy is required for salinity prediction work.

DEMs for some regions have already been formed using spatial interpolation to fill in the space between contours. The contour data have been obtained from DOLA in intervals varying from 5m to 20m. The accuracy of the DEM depends on the contour interval and the type of terrain being modelled. For example, 20m contour intervals in a landscape of very broad, flat valley systems do not provide an adequate representation of the landscape.

 

 

Digital elevation models (DEMs):

are derived from stereo air photos or interpolation of contour data represent the height of the earth's surface in digital form can be used to derive other maps describing the terrain of an area, such as landform,

slope and water accumulation

Knowledge of the terrain can be used to understand and model areas affected by and at risk from salinity

 

 

 

 

2. Creating Salinity Maps from Satellite Images

 

 

This section of the manual gives an overview of how salinity maps are formed from satellite images, digital elevation models and ground data. The emphasis is on how the ground data fit into the mapping and monitoring process. Details of how to acquire and present the ground data are contained in the next section of this manual.

The flowchart on the following page shows the steps in the salinity mapping and monitoring process. Each of these steps will be discussed in more detail in the following pages. The discussion of predicting areas at risk from salinity is left to the fourth section of this manual.

Spring images (late August through to September) are used to map salinity; this is the time of maximum green vegetation cover on the ground. Variations in the amount of cover within and between paddocks can most easily be related to productivity problems or longer-term land degradation problems.

In the image display above, some pixels in the satellite image have been coloured in according to their vegetation cover and condition. Regions coloured

o green are remnant vegetation.

o red are salt-affected land supporting little or no vegetation cover

o yellow are marginally salt-affected land supporting salt-tolerant vegetation cover, such as barley grass.

The areas in shades of grey do not belong to any of these three cover classes.

The image is a display of the salinity map formed for this region. Highlighting the remnant vegetation and overlaying the map on a greyscale version of the satellite image helps the users of the map locate the salt-affected areas.

 

 

Identifying the Salinity Categories in the Scene

 

The first step in producing a salinity map is to identify the types and expressions of salinity in the region covered by the satellite image. Different types of salt-affected land might appear as different ‘colours’ in the satellite images. Some types of salt-affected land, such as bare salt pans, may appear very different from all other cover types in the image and some types, such as areas of barley grass, may appear very similar to other non-saline cover types. Certain expressions of salinity, such as hillside seeps, may also be associated only with particular parts of the terrain. It is also important to identify the areas with lower productivity that are not caused by salinity. These areas are the cover types most likely to be confused with salt-affected land in the satellite image.

 

Examples of different categories of salt-affected land

 

Understanding the types of salt-affected land to be found in a region will help to determine appropriate strategies for mapping and monitoring, and may suggest additional datasets, such as DEMs, geology maps and soil maps, to support the process.

Consistent descriptions for saline cover types and problem non-saline cover types across all the scenes, and agreed criteria for identification of these cover types on the ground, will ensure that everyone involved in the process ‘is talking the same language’. They will allow successful mapping strategies to be transferred to similar regions. They will also help everyone understand

the accuracy and limitations of the salinity maps produced in terms of the ground expressions of salinity in each region.

 

Different types of salt-affected land may appear as different colours in the images

Understanding the types of salt-affected land to be found in a region will help to determine appropriate strategies and supporting datasets for the mapping process

 

 

Stratifying the Scene

Each Landsat TM satellite image covers an area approximately 200km by 200km. Within that area, there may be different rainfall zones, different farming practices, different soils and geology and different landform patterns. Some or all of these may contribute to the expression of salinity and the processes driving it.

There are three reasons for identifying the different regions within the Landsat TM scene boundary and processing these regions separately. They are:

o variations in vegetation cover between the regions o variations in the amount or type of salt-affected land observed in the regions o variations in the salinity risk factors and hydrological zones.

In the example below (reproduced from the first section), differences can be seen in vegetation cover across the Bencubbin scene caused by soil types and rainfall. Good-condition areas have the same amount of green vegetation cover in the lower rainfall region as poor-condition areas in the higher rainfall region, requiring the two regions to be treated separately during the salinity mapping and monitoring process. The yellow line shows the boundary between the two zones that was identified from the average of a five-year sequence of images.

 

Other factors that might cause differences in apparent vegetation cover are changes in land use, for example from predominantly pasture to mixed pasture and cropping, geology and soil types. Often these differences are obvious visually in an image display and appropriate zone boundaries can be drawn onto an image print.

Another factor that might distinguish between regions is the expression of salinity. For example, hillside seeps might be common in one part of the scene and very rare in other parts, or there might be anecdotal evidence that 'the salinity problem is much better/worse east of X'.

The types of land forms may differ across the scene. The broad flat areas of the Mills Lake region around Ongerup give way to well-dissected hills and valleys further east towards Jerramungup. The terrain will influence the expression and location of salt-affected land differently in these two areas. These distinctions may not be so obvious in simple image displays and require the knowledge of ground experts.

Just as the expressions of salt-affected land or other problem areas may be different in the different parts of the scene, our strategies for mapping may also need to be different. In the first example from the Bencubbin scene, pixels with a particular level of vegetation cover were called 'good' if they were located in the low rainfall region and called 'poor' if in the higher rainfall region. In a region with no hillside seeps, we can have a rule 'if poor vegetation cover on a hilltop or slope, then not salt-affected', but we can't have such a rule where hillside seeps are common. We might decide that a geology map is needed in regions where geology is closely related to the type or location of salt-affected land, but we might decide not to bother where the relationship is much weaker.

Further stratification of the scene may be necessary for the salinity prediction process, based on the hydrological systems and hydrological zones in the region. These regions may have different salinity risk factors or the processes driving the salinity may differ. A hydrological system is a combination of landform patterns within a rainfall zone that have similar attributes, such as geology, weathering depth, slope, relief and hydraulic gradient, that give them similar hydrological properties. Combinations of these hydrological systems form hydrological zones which have dissection, stream frequency and channel development patterns that are almost the same across their area. Within hydrological zones, the same "rules" can be used to predict areas at risk from salinity. These "rules" may differ between hydrological zones. The green boundaries on the Bencubbin image displayed on the next page correspond to hydrological zones within the previous identified units that will be used in the salinity prediction process for that scene.

The stratification of the scene, or division into regions, is performed by a combination of talking to people who know the area, looking at displays of the image data and considering other datasets such as soil and geology.

 

We only need to split a scene up into separate regions when there is reason to believe that there are different salinity expressions or risk factors between the regions. For example, if a scene contains two broad geological systems, but both are equally susceptible to salinity and the expressions of salinity are the same, there is no need to consider the two regions separately.

If a scene is divided into more than one region, the subsequent steps in the salinity mapping, monitoring and predicting process are then performed separately (in parallel) for each region. That is, ground data must be provided for each region, the image data are processed separately

for each region and the salinity maps that are produced must be assessed separately for each region. The individual maps are then combined to provide an overview of the whole scene.

 

A scene needs to be stratified when:

there are different amounts or expressions of salinity or salinity risk in some parts of teh scene; or

there is variation in the amount of vegetation cover.

Factors to consider for stratification are:

anecdotal evidence about the amount or type of salinity in an area rainfall, soil and geological zones land use and vegetation cover variations different expressions (cover type or location) of salt-affected land

Different strategies may be required to produce the best salinity maps for each region identified

 

 

 

The Role of Training Sites of Each Cover Type

 

The next step in the process of forming salinity maps is to learn to recognise how each of the cover types on the ground appears in the image data and what the spectral differences are between good and poor vegetation cover within each stratification zone. This information is used to attach a label describing the ground cover and condition to each pixel in the image. These are the stages of numerical analysis of the image data and are performed by the image processing team. Ground data are essential for these analyses.

To learn how the different ground cover types and conditions appear in the images, the image processing team needs locations of typical areas of each of the saline and non-saline cover types. Some ground cover types, such as water, bush and very good green vegetation cover, are easy to pick out just by looking at image displays, such as in the examples in the first section of the manual. Other ground cover types, such as marginally salt-affected land, grazed pasture and waterlogged crop, cannot be picked out just by looking at the images. The image processing

team needs to be told where to find examples of such cover types. The location, boundary and a description of the cover type and condition of typical sites of each cover type are required for each stratification zone.

The spectral data for the sites in each zone are extracted and compared to the data for the other cover types in the zone. From this sample or training data, representative ‘signatures’ for each of the cover types are developed. These signatures characterise what each cover type or vegetation condition class looks like in the images. By comparing the spectral response of unknown samples to the signatures of each ground cover class, it can be established to which ground cover class the sample is most likely to belong.

The signatures are analysed to establish which signatures are unique and which groups of signatures are similar. When signatures are unique, new samples can be unambiguously assigned to the correct class. When the spectral signatures for two different ground cover types overlap, it may not be possible to tell the difference between the two cover types in the image. An example of cover classes that overlap in most images are heavily grazed pasture and salt-affected areas with a good cover of barley grass. In this case, the best that can be done is to label the area as having poor condition vegetation cover.

Once sufficient ground samples are available to characterise each of the cover classes in the image and the spectral variation between these cover classes is understood, the image can be ‘classified’. This is the process where the spectral response of each pixel is compared to the signatures of each cover class and the pixel is assigned the label of the cover class that it best matches. Typically this label is one of water, bush, bare salt-affected land, marginally salt-affected land, bare, poor vegetation cover (not saline), average vegetation cover or good vegetation cover.

 

It is important to note that the satellite measures variations in the amount and type of vegetation cover, from which a measure of productivity or condition can be inferred. The satellite does not measure a signal directly associated with soil salinity.

This process is repeated with images from as many growing seasons (years) as are available. Typically, about 2-3 images from around 1990 are used, together with 2-3 images from as recently as possible to look at how the extent of salinity has changed in the last 5 to 10 years.

 

Training samples of each cover type and vegetation condition in the image are needed to:

characterise the spectral 'signature' of each cover class understand which cover classes are distinct in the image understand which cover classes appear similar in the image

The information required is:

the location and extent of typical sites of each cover type a description of the cover type and condition for the site

The satellite is measuring variations in the type and amount of vegetation cover, not soil salinity -- productivity and land condition are inferred fro the signal

The spectral response of each pixel is compared to the signatures of each cover class and the pixel is assigned the label of the cover class that it best matches

 

 

Combining Information from Several Years to Produce Salinity Maps

 

This step in the production of the salinity maps turns the series of images of condition class labels into a 'current' salinity map and a map of the change in the salt-affected land from the time of the earliest available image. This is done by considering the pattern of cover class labels through time for each pixel, the position in the landscape of the pixel and any other supporting information that is relevant, such as soil type or geological unit.

The previous section described how training samples of each cover type and condition are used to assign a label to each pixel in the image. Typically this label is one of water, bush, bare salt-affected land, marginally salt-affected land, bare, poor vegetation cover (not saline), average vegetation cover or good vegetation cover. Some of these labels, such as water, bush and good vegetation cover are unambiguous. Others, such as marginally salt-affected land and poor vegetation cover or bare salt-affected land and bare, tend to overlap.

In most images, heavily-grazed pasture appears the same as regions of barley grass. Typically, both would be labelled as ‘marginally salt-affected land’. If the site was barley grass, this label is correct, but if the site was heavily-grazed pasture, the label is incorrect. If only a single satellite image is considered, the correct label is unknown.

However, more than a single satellite image is available to us. Satellite images from other years are available. A paddock that has poor vegetation cover because it is heavily grazed is a management effect that the farmer can easily correct. In that case, the paddock is likely to have good vegetation cover in the following season. A barley grass region, however, will still be salt-affected in the following year. Terrain information is also available. If the region is on a hill or slope, it is less likely to be salt-affected than a similar area in a local valley. Other rules or relationships that have been suggested by those who know the area can also be used, provided that the relevant supporting datasets are available in digital form.

The salinity maps are formed by combining the cover class labels from each of the years for which image data are available with landform images, using rules based on the pattern of condition labels through time and the assessment of salinity risk based on position in the landscape or other supporting datasets. Two maps are formed, one showing the salt-affected land based on the most recently available images and the other showing the changes based on the earliest available images.

 

The images above show a typical current salinity map (right) and an image of the corresponding area (left). Areas mapped as salt-affected (red and yellow) have been mapped as being in poor condition for more than one growing season. Areas in poor condition for only one season, such as the wind-eroded areas that appear in white in the top right hand corner of the image, have not been mapped as salt-affected. Some errors may remain, such as dry dams being mapped as salt-affected, because they persistently have no vegetation cover.

 

Salinity maps are formed by combining the cover class labels from each year with landform images, using rules based on the pattern of condition labels through time and the assessment of salinity risk based on position in the landscape or other supporting datasets

The rules are derived from expert knowledge of the area and the relationships between salinity risk and the available datasets

 

 

 

Assessing the Salinity Maps

The accuracy of the salinity maps needs to be field-checked and reported. The maps have been created by extrapolating data from a relatively small sample of ground sites to very large areas, up to 200km by 200km. We need to know how accurate this process has been.

All maps have, or should have, an accuracy statement included. For example, 1:100 000 map sheets state that features are accurate to +/- 25m in the horizontal plane and heights are accurate to +/- 5m in the vertical plane.

Quoting accuracies for salinity maps is not quite so simple, but the amount of salt-affected land that has been correctly identified can be estimated as well as how much land that is not salt-affected that has been labelled as salt-affected. For example, in the Moora region we were able to say that

o 96% of the bare salt-affected land was detected; o 81% of the marginally salt-affected land was detected; and o 2% of the good condition land was mapped as salt-affected.

As well as knowing how many errors are made, it is also important to understand what sort of errors are made. What do the 19% of marginally salt-affected sites have in common? Perhaps they have less than 20% barley grass cover mixed with poor and average crop. Perhaps they are in gullies not adequately mapped by the terrain model. In the case of the 2% of non-saline areas mapped as salt-affected, most were sites that had been wind-eroded in previous seasons that had not yet returned to full productivity. Some of the errors may be able to be corrected, but others may have to be accepted as the limitations of what can be mapped from satellite images.

The accuracy figures quoted above are very good. It is not often that maps that good are produced from a first attempt at the mapping process. Typically, a first-pass salinity map will be produced, it will be assessed to identify the sorts of errors that are being made, and strategies will be developed to correct them. The mapping process will be repeated, incorporating the new strategies and potentially new datasets, and the updated product will be assessed. This process continues until an adequate salinity map is produced.

The assessment of the salinity maps is based on extra ground sites and expert knowledge of the region. It may also involve extra field visits. The ground sites used to assess the final maps must be separate from the training data used to produce the maps. If sufficient ground sites are supplied for the mapping process, some are set aside, or reserved, for the assessment phase. Preliminary maps are also supplied to people who know the area and who supplied the original training samples. They can tell us the areas that are correctly mapped and, more importantly, the areas that are wrongly mapped and why. These areas become training samples to improve the salinity maps.

 

The salinity maps are assessed to:

understand how well the categories of salt-affected land are mapped understand how the salinity maps can be improved

Assessment data come from:

reserved training samples expert knowledge extra field visits

 

 

 

3. Ground Data Collection

The previous section of the manual described how salinity maps are formed from satellite images, terrain maps and ground data. This section of the manual describes the ground data requirements and the processes for providing it.

The first requirement is for consistent descriptions for saline cover types and problem non-saline cover types, and agreed criteria for identification of these cover types on the ground. Such descriptions will ensure that everyone involved in the process is ‘talking the same language’. They will also help everyone understand the accuracy and limitations of the salinity maps produced in terms of the ground expressions of salinity.

The categories need to make sense both from the perspective of the ground expert and the image processing team and in terms of their expectation of being identifiable from satellite images. The categories will be defined as consistently as possible across the whole of the south-west agricultural area, although the categories actually present in any particular region will vary.

Particular categories that have been found to be helpful include:

o bare salt-affected land, < 10% vegetation cover o salt-affected land, dead trees and chenopods, < 60% vegetation cover o salt-affected land, dead trees and chenopods, > 60% cover o salt-affected land, salt-tolerant grasses, < 60% cover o salt-affected land, salt-tolerant grasses, > 60% cover o salt-affected remnant vegetation, dying trees o bare / poor condition land – water erosion o bare / poor condition land – wind erosion o waterlogged crop/pasture

o other poor condition crop / pasture – note reasons

Appendix 1 contains more detail of some of these categories, including photographs of typical areas. It is expected that a complete database of the categories (description and photographs) will be built up and made available to all participants during the first few months of this project.

All ground data to be provided should be described in terms of the agreed categories whenever possible.

 

Ground Data Requirements For Each Scene

 

The ground information required to support the salinity mapping and monitoring process for each Landsat TM scene is:

 

1. Stratification of each scene area into zones based on physical factors (such as rainfall and/or geology), land use, image changes and expressions or risks of salinity.

2. The locations of sufficient sites of saline and non-saline cover types for each zone, described and spatially located on aerial photographs (or photocopies) or large-scale image prints.

3. Summaries of any existing salinity maps and an assessment of their quality and accuracy; summaries of other existing data sets relevant to risks or occurrence of various types of salinity; and summaries of any ‘rules’ or relationships based on these datasets.

4. Locations of any known areas of recent change (since 1987) together with a description of the change and approximate timing.

 

For each Landsat TM scene, a team consisting of the ground expert (Agriculture WA hydrologist) and an experienced member of the image processing group will be set up to make sure each of the tasks identified in the requirements is performed. They will be responsible for setting realistic deadlines and, together with the project management team, ensuring that they are met. This team need not provide all the labour for these tasks, but should call on the expertise of other groups.

The map on the following page shows the Landsat TM scene boundaries and the scene names. These are shown in relationship to the focus and recovery catchments.

A 'dot point' report summarising the ground information for each scene will be produced and presented or circulated to a critical audience (e.g. Richard George, Don McFarlane) to ensure that all data sources have been considered. The report will identify the stratification zones, list the existing datasets and indicate for which areas of each stratification zone detailed ground sites will be supplied. The Land Monitor Project Coordinator may assist with the acquisition of supporting datasets, particularly those held by agencies other than Agriculture WA.

The first three tasks listed above are essential for creating the salinity maps. Without adequate ground data and an understanding of the expressions of salinity in the scene, no amount of image manipulation will produce a useful salinity map. The final task, identifying areas of recent change, is less time critical. Such sites will allow the salinity change products to be assessed and will also aid the salinity prediction process, but typically they can be provided after the mapping process has commenced.

 

 

 

 

Stratification

The aim of this task is to identify the different regions within the Landsat TM scene boundary and to divide the scene into zones based on general vegetation cover and / or the processes causing salinity or the expressions of salt-affected land. It is the responsibility of the whole ground data team.

Factors to consider to define the regions include:

o image displays o distribution of existing salinity o rainfall zones o soil and geology variations o land use variations (cropping versus pasture) o expressions of salinity (hillside seeps versus valley systems) o terrain types o salinity risk factors

It is only necessary to split a scene up into separate regions when there is reason to believe that there are different salinity risk factors between the regions. For example, if a scene contains two broad geological systems, but both are equally susceptible to salinity and the expressions of salinity within each are the same, there is no need to consider the two regions separately.

Sources of expertise within other groups in Agriculture WA and in other agencies should be consulted.

 

Located Ground Sites

The aim of this task is to produce the training sites necessary to process the image data into salinity maps. Representative areas of saline and non-saline cover types for each zone, described and spatially located on aerial photographs (or photocopies) or large-scale image prints, are required. These are to be used for training the mapping process and for assessment of accuracy of the salinity maps produced.

This task is primarily the responsibility of the ground expert. The emphasis is on locating representative sites of salt-affected land and other low-productivity areas that may look spectrally similar to salt-affected land in the images. Areas of good crop and pasture, remnant vegetation and water can be interpreted from the image.

The preferred means of providing the information is in the form of annotated aerial photographs, or hardcopy images that have been provided by the image processing team at a suitable scale. It is important to have an estimate of the boundaries of all the salt-affected land within a designated area in each photo and the locations of other 'problem' areas within the photo area. The image processing team needs to be able to assume that any areas within the designated area in a photo or annotated area of an image that are not marked are not salt-affected, whatever the productivity level that those areas may display in the image. The boundaries of the salt-affected land may be approximate, derived from air photo interpretation or changes in the vegetation cover observed in the field. Detailed sampling is not required.

Examples of ground data provided by John Simons for the salinity monitoring work in the Fitzgerald Biosphere region are shown on the next few pages. The areas have been marked on

prints of scanned 1:25 000 aerial photographs and identify all areas of salt-affected land within the mapped areas. A total of six photos were identified within the West River catchment, representing the major cover types and soil types to be found in the catchment. Each photo covers about half a dozen paddocks and it is clear which areas within the photos have been mapped.

 

 

Several samples of each of the salt-affected and other low productivity cover types need to be located within each of the stratification zones identified in the scene. Ruhi Ferdowsian interpreted approximately 5% of the area of Upper Kent catchment in this way. In general, a much smaller proportion of area may be required, but it is the responsibility of the ground data team to decide on this on a scene-by-scene basis. Quite often it is the quality of the ground data that counts, not the quantity.

The hydrologist need not provide all the labour for this task, but it is the responsibility of the hydrologist to ensure that the interpreted sites are consistently labelled and provide, to the best of his/her knowledge, a reasonable representation of the mapped area. The image processing expert will participate as the data are collected to check that the data are suitable and can be matched to the images, helping to determine when enough data are available to begin the processing. Sources of expertise within other groups in Agriculture WA and in other agencies should be consulted.

 

 

Existing Datasets and Knowledge

 

The aim of this task is to identify and obtain any other data that might contribute to the salinity mapping and monitoring process and/or to the understanding of the processes causing the salinity problem, drawing together all the information available about the area. Sources outside Agriculture WA should be considered. This task is the responsibility of the whole ground data team.

Examples of datasets that might be available are:

o existing salinity maps for any part of the region that may have been produced for other reasons (e.g. by landcare groups)

o any soil, geology, vegetation or ground water maps that exist over a large area o any relationships between salinity and the datasets that have been identified, for

example, 'salinity never or rarely occurs on soil type A'.

Existing salinity maps can be in either hardcopy or digital form, provided that there is sufficient detail to locate areas within the image data. 'Spot' information on salt-affected land, from roadside surveys or anecdotal information from field officers and/or farmer contacts, can also be considered here. Such areas can be mapped on images, photos or rough mud maps. The difference between these data and the data produced as part of task 2 is that the boundaries of regions may not be as exact and the whole photo or region of a photo may not have been

mapped. The ground expert should have an understanding of the accuracy and limitations of any maps and how they relate to the salinity categories identified.

Other supporting maps, such as soils and geology, need to be available digitally or be able to be easily digitised.

The relationships or 'rules' should summarise the ground expert's knowledge of the area and the processes contributing to salinity. Remember, it is just as important to know where salinity will not occur as it is to know where it might occur.

 

 

Locations of Areas of Recent Change

 

The aim of this task is to identify sites that have changed since 1987. This is the year when the Landsat TM images were first routinely acquired and archived. The task is the responsibility of the ground expert and sources outside Agriculture WA should be considered. The output will be areas marked on photos, images or locatable 'mud maps'.

Such sites help to check salinity change maps produced using historical satellite data. We cannot go back to 1990 to field check our historical salinity maps, so anecdotal evidence is important to calibrate these products. Historical air photos can also be used.

Examples of the types of changes to note are:

o recently dead or dying trees in valleys o recently 'lost' productive land o areas that have been recently fenced where the vegetation cover has improved o rehabilitation sites that are regularly monitored.

These sites will include anecdotal locations from farmers and project officers, and qualitative information on some areas.

 

 

 

4. Salinity Prediction

 

 

 

In the image display above, the salinity change map has been combined with a map of areas predicted to be at risk from salinity in the future. Green areas were mapped as saline in 1977, light blue areas became saline by 1988, dark blue areas were saline by 1994 and the magenta-coloured areas are predicted to be at risk from salinity during the next ten years.

This section of the manual briefly describes the process used to make predictions about which areas will be affected by salinity in the future. Ground data are essential inputs to the process of salinity prediction. The salinity prediction is only as good as the hydrologist and his/her data and experience. Details about how to prepare and provide ground data for salinity prediction are discussed in this section.

The ground data are used to determine local rules which define the relationships between current and historical land condition, landform and salinity. Automated computer processes are used to derive the rules from the ground data. Once these rules are established, they are applied to generate broad-scale maps of areas which are predicted to be at risk of salinity.

Data sets used to produce salinity prediction maps include maps showing areas currently affected by salinity and how they have changed through time and landform data derived from digital elevation models.

The flowchart on the following page shows the steps in the salinity prediction process. It builds on the mapping and monitoring process discussed in the earlier sections of the manual. Inputs from the mapping and monitoring process are indicated in red.

 

 

 

 

 

 

Ground Data Requirements

Ground data for salinity prediction are the responsibility of the hydrologist. It is important to remember that prediction is the process of making educated inferences about what is going to happen in the future. The hydrologist will need to collate all the available information that can help him / her decide which areas are at risk from salinity, and then provide his/her best guess about what will happen in the future.

Ground data should be provided in the form of annotated aerial photographs. Areas currently affected by salinity should be marked, as well as areas which are at risk from future salinity. Areas mapped should include all of the categories of salinity identified as part of the process of selecting ground data sites for salinity mapping, and should be chosen to cover each of the stratification zones within the region. In addition, data should be provided for a representative sample of landform types. For example, salinity risk areas might include broad valley floors, local valleys or streamlines, or isolated seeps in break of slope positions on hillsides.

The boundaries of the current extent of salinity in the mapped areas are also required, so it is sensible to use the same regions as for the mapping and monitoring ground data. There are two possible scenarios for the timing of providing the salinity prediction ground data. The predictions can be made at the same time as the assessment of the current extent of salinity, or, since the future risk areas aren't required to train the mapping and monitoring phase of the processing, the salinity predictions can be made at the same time as the preliminary salinity maps are validated.

If salinity predictions are made during the assessment phase of the mapping and monitoring process, the boundaries of current salinity can be drawn on the preliminary salinity maps, providing a field validation of that product and an opportunity to consider areas not visited as part of the original ground data process. By the time of the assessment of the current salinity maps, many of the additional datasets identified as being important to salinity risk, such as water accumulation maps, will be available in map form to contribute to the process of identifying areas at risk.

An example of ground data provided by Ruhi Ferdowsian for the Pallinup (North Stirlings) region is shown below. Currently saline areas are marked in blue, and predicted risk areas are marked in red. This is not the ideal way to present the current extent of salt-affected land, since the ground cover categories have not been identified; however, it is adequate for presenting salinity prediction ground data.

 

 

Making Predictions

 

Collection of ground data for salinity prediction involves the following steps.

1. Select areas which are representative of the different types of salinity and hydrological zones which may occur (to cover approximately 5% of the scene).

2. Examine the areas in the field. 3. Mark the boundaries of areas currently affected by salinity on stereoscopic aerial

photographs. 4. Use hydrologic experience and all the available information to estimate how far up the

hillslopes the salt may spread before stabilising. Useful data would include: any existing data on groundwater levels and the rate at which groundwater

is rising

water balance calculations (e.g. AgET) to estimate the difference between current recharge, discharge from saline areas and base flow - this can be used to estimate how much larger the saline areas will grow before the recharge is balanced

WRC hydrogeology data interpreted hydrogeologic data (shears, faults, dykes, bedrock highs) -

there is high salinity risk where vertical structures cross drainage lines. geology amount and type of remnant vegetation in the catchment electromagnetic, radiometric and magnetic data historical air-photos which might be useful when determining the rate at

which saline areas have been spreading ground water modelling where appropriate

5. Areas at risk from salinity might also include: areas with poor drainage and convergent inflow - these can be identified

using the water accumulation maps or stereoscopic air-photos. concave inflection points (breaks of slope). areas prone to waterlogging non-saline discharge areas dams (as areas at risk or causes)

6. Mark predictions onto the air-photos.

Ground data for salinity prediction can be collected when samples of currently saline sites are located by the ground expert or during the assessment of the mapping and monitoring products

 

 

Appendix: Categories of Salt-Affected Land

This document describes the categories of salt-affected land found in the south-west agricultural area of Western Australia. It provides a photograph and a brief description of each category. The emphasis is on identifying how the categories differ, so a site can be correctly described, rather than on listing a whole series of potential attributes.

The categories identified here are based on the types of salt-affected land that can be identified, and potentially discriminated between, in Landsat TM satellite images. For example, categories based on a ground cover of salt-tolerant grasses have been identified. Particular species of grasses cannot be identified based on the satellite data, so while a field officer may identify sites with different grass species, we are only interested in a classification of grasses versus woody material such as samphire.

This document will grow as the categories of salt-affected land in the first five Landsat TM scene areas are identified, documented and ground data are provided to the image processing team. When it is complete, a full version of this document will be distributed to all people contributing to ground data collection for the Land Monitor project.

 

Categories

Bare Salt-affected Land: Less than 10% Vegetation Cover

Salt-affected Land: Dead Trees and/or Chenopods, < 60% Cover

Salt-affected Land: Dead Trees and/or Chenopods, > 60% Cover

Salt-affected Land: Salt-tolerant Grasses <60% cover

Salt-affected Land: Salt-tolerant Grasses >60% cover

Salt-affected Remnant Vegetation

 

 

Bare Salt-affected Land: Less than 10% Vegetation Cover

 

o Land in this category is predominantly bare o The region may have up to 10% vegetation cover of any type o This category will include the natural salt lake systems as well as salt scalds in

paddocks

 

 

Salt-affected Land: Dead Trees and/or Chenopods, < 60% Cover

 

o Land cover in this category includes woody vegetation, typically mixed with bare areas and other vegetation

o It may have dead or dying trees o It will have chenopod cover o The region will have between 10% and 60% vegetation cover o A related category exists for vegetation cover greater than 60%

 

 

Salt-affected Land: Dead Trees and/or Chenopods, > 60% Cover

 

o Land in this category is covered with predominantly perennial vegetation o It may have dead or dying trees o It will have chenopod cover o The region will have more than 60% vegetation cover o A related category exists for vegetation cover less than 60%

 

 

Salt-affected Land: Salt-tolerant Grasses <60% cover

 

o Land in this category is covered with predominantly salt-tolerant grasses o The region will have less than 60% vegetation cover o A related category exists for vegetation cover greater than 60%

 

Salt-affected Land: Salt-tolerant Grasses >60% cover

 

o Land in this category is covered with predominantly salt-tolerant grasses

o The region will have more than 60% vegetation cover o A related category exists for vegetation cover less than 60%

 

 

Salt-affected Remnant Vegetation

 

o Land in this category is covered with predominantly dead or dying trees o It will have little or no other cover o Some attempt should be made to check that salinity is the cause of the degradation

(as opposed to lerps, for example)

For more information contact Suzanne.Furby@cmis.csiro.au

Last updated: September 1998