Warwick Grace, Business Development Manager   

Airborne Research Australia,  Parafield Airport SA


PO Box 335 Salisbury South SA 5106  ph 08 8182 4000


A cost effective means of rapid assessment and monitoring of vegetation at the paddock or larger scale.


Agricultural and environmental managers need up-to-date information on the state of health and vigour of different vegetation components such as crops, pastures, native forest, planted forest, remnant and riparian vegetation.  Remote sensing techniques provide important spatial information for this purpose. Airborne remote sensing is a non-invasive, rapid and efficient method of generating large-scale geo-referenced maps that can help identify areas within a field that need different treatments in order to improve yields.


Airborne remote sensing can provide spatial detail finer than current satellite technology can provide (with resolution less than one metre compared to 20-30 metres from satellites) and it is possible to usefully distinguish variations of vegetation or soil surface conditions at the paddock scale.  Additionally, the flexibility in the frequency and time of data acquisition means that imagery can be tied closely to the phenomena being assessed. Although conventional aerial photographs can be used for mapping, they have some drawbacks in terms of timeliness because scanning of the film is required to enable data processing and analysis in a digital environment. This means a longer time lag before the data is available.  More importantly, there is a lack of spectral resolution and also an inability to obviate local shadows or cloud shadow. This means that the use of computer techniques to truly intercompare imagery of the same site obtained at different times is usually more practical with scanner image data.


ARA uses a variety of remote sensing systems. The system briefly described here is the ARA-AWI trispectral scanner – a ‘push-broom’ three channel line scanner developed jointly by ARA and the Alfred Wegener Institute of Germany (AWI).  The term push-broom means that the scanner measures the reflectance radiometric signal simultaneously along a line across the flight path from the ground immediately below the aircraft. That data is ingested and the aircraft moves forward and a few milliseconds later another line is registered, and so on. The lines combine to form a continuous scroll-type of digital image.


The scanner is flown in an ARA aircraft at a height of between 1,000 and 10,000 feet above ground level, depending on the spatial resolution required (images with a resolution ranging from less than half a metre to five metres can be produced depending on a combination of altitude, scanner field of view and aircraft speed).  Images are registered as continuous 'scrolling' images composed of transverse lines with 2048 pixels.  A two–kilometre swath therefore has a cross-flight resolution of one metre.  The along-flight resolution is matched to the cross-flight resolution by adjusting the aircraft's forward speed.  The 2048 pixel/line registration is several times that of other systems and means that there are fewer, if any, 'split' images to be combined and less wastage on margin overlap, and a greater area can be covered in the same flight time.  Each pixel from the raw image is geometrically and radiometrically corrected to eliminate distortions due to angle of view and to variations in aircraft speed and pitch, roll and yaw.


Just as the colour of each pixel on a TV or computer screen is created by combining three colours (red, green and blue – hence RGB screens) to give all the hues that we perceive, the trispectral scanner in RGB mode measures three components of the natural colour in sunlight and from that data we can re-create closely the original colour.  Our scanner can operate in Red, Green and Blue mode but it turns out to be more useful to use the three channels of Green, Red and Near Infrared.  Near Infrared is invisible to humans.


Figure 1 shows the ARA-AWI scanner bands in relation to two typical spectral signatures.  Firstly, note the general difference in spectral signatures of dry grass and of vigorously growing rye grass.  A remarkable feature is the large jump in response for the rye grass at about 750 nanometres in the radiation that is invisible to us.  This turns out to be an inherent feature of healthy vegetation.  Secondly, the rye grass has a minor peak in the green band (which is why it appears green to us). Thirdly, the Near infrared band is in the high reflectance region but the Red band, even though it is fairly close, is not. Taking advantage of this feature, the satellite sensing community has concentrated much research on the difference between the Red and Near Infrared signals.  So much so that a useful parameter, the NDVI, has become commonly used.  The NDVI is the normalised differential vegetation index and is defined as the difference between the Near infrared and Red band reflectances divided by their sum.  NDVI values vary with absorption of red light by plant chlorophyll and the reflection of infrared radiation by water-filled leaf cells. In most cases NDVI is correlated with photosynthesis. Because photosynthesis occurs in the green parts of plant material the NDVI is normally used to estimate green vegetation. By way of  example, reference to Figure 1 indicates that the NDVI of dry grass is about 0.15 and for growing rye grass about 0.75.


Strictly speaking, the data from the scanner are radiometric signals, not reflectances.  To derive reflectance typically entails the use of calibration targets in the field and atmospheric corrections.  A partial solution is to fly at a low altitude and to use ratios of signals.  However, flying too low means that the edge portions of the swath have large look-down angles if full use is made of the swath width.  Nevertheless, a series of single band images from the signal data may be inspected and many interesting features relating to vegetation and soil drawn out:  some bands and computer enhancements highlight particular features well. Exploratory manipulations of this type are not possible with aerial photography.


There are many vegetation and soil conditions that can be detected at an early stage using remotely sensed data and which are not easily detected visually. Several products, ranging from false colour composites to more sophisticated  indices or digital image classification, can be used to this end.  A significant advantage of using indices is that they 'normalise' the data, reducing the effect of terrain variations and different sun angles or shading within a scene or particular day.


The main index used is the NDVI, however other indices such as the photosynthetic vigour ratio and the plant cell ratio, as well as false colour imagery are available to agricultural users.  The advantage of the composite false colour imagery is that it has texture, shadowing and shade effects which provide depth and contextual cues and is more immediately 'absorbed' by the human.  Imagery derived from indices is, at least in theory, unaffected by shadow and texturing but is much more useful for computer manipulation.


The photosynthetic vigour ratio (Green / Red) is high for leaves with strong absorption of red light by chlorophyll.  This ratio may be useful in detecting, earlier than would otherwise be the case, the yellowing-off of plants due to nutrient deficiency, disease, fungus or insect attack.



An example of the use of the NDVI is shown at Figure 2.  Here the vines show up mostly as green (high NDVI) but there is a spatial anomaly (arrowed) which was not readily apparent visually either at ground level or from the air.  From ground inspection by an agricultural consultant, it was apparent that the affected vines were in a section where too much of the original fertile top soil had been removed during construction.  As well, similar imagery has allowed the early detection of differences in irrigation.


Additionally, different spectral and/or structural image classification techniques can be applied.  With GIS (Geographic Information Systems), computerised maps of   parameters such as local yield, soil type and slope can be overlain on the NDVI  or other indices.


In other applications not directly related to viticulture, we have demonstrated that galvanised iron  roofing, paving, roads, grass, trees can be differentiated in the imagery and quantified and compared over time – all by computer with little or no operator intervention. Mathematically, each pixel has three values associated with it. There are mathematical techniques to analyse such data and “pull out” all those pixels that have a signature close to some target signature which could be weeds such as olive trees, illicit vegetation, water-logged areas, burnt areas, salt-tolerant pasture grasses and different soil types as well as man-made structures.  For example, Figure 3 is an image from near Parafield Airport which was analysed and then, without any operator intervention, specialist software was able to differentiate galvanised roofing, paved and tiled areas, bushes, and freshly watered lawn and recently mown grass and other grass.  Such information could be used by engineers in modelling rainfall runoff and flash flooding risks.  For certain applications it may be worthwhile having intensive operator intervention. Multi-temporal images can be geo-referenced and co-registered and then further analysed, to detect change and trend.  For example, one-metre resolution data being gathered monthly by ARA is being analysed to produce a product showing variations in sand and silt areas with time at the Murray Mouth  (see Figure 4).


The important caveat remains:  changes in the remote sensing imagery effectively show variations in vegetation and surface conditions, but do not tell the reason for those variations. It is still necessary to go into the field to determine the reasons for the observed variation.


The ultimate goal of detecting and managing field variability is to save costs and/or to produce better yield quality or consistency.  Some researchers have reported a potential increase of net profits of more than $50 per hectare, as well as improved administration and environmental benefits.  Depending on the area flown, the cost of processed digital imagery at one metre spatial resolution is around a dollar or two per hectare. Therefore imagery derived from the ARA-AWI trispectral scanner clearly has the potential to be widely used as a cost-effective tool for rapid and flexible mapping of variability in vineyards and other fields. 


ARA provides the platform for all the State Government’s aerial photography.  As well as the trispectral scanner ARA, operates other scanners and conventional cameras and video in our own right or for other parties. 








Further information:  contact Warwick Grace at Airborne Research Australia.