Know Your Instrument.

As noted in Chapter 4, a given sensor has an optimum set of operating parameters. The result is that there are tasks and conditions for which a given sensor is totally unsuited (Phinn, 1998, Lu and Weng, 2007). As noted in the previous chapter, this was one of the criteria applied when the various sensors for use in the research were selected. The principle also extends to an operator understanding his or her own field instrument’s capabilities and limits. According to Salisbury (1998) and Clark (1999), the operator needs to know how to set up the instrument for a field campaign prior to leaving the laboratory, and how to maintain it once in the field. While in the field, the operator then needs to monitor the environmental conditions so that those conditions, which limit the instrument’s safe operation and handling, are not exceeded. The acquired readings need to be monitored for tell-tale abnormalities to ensure the reliability of the readings (Salisbury, 1998, Clark, 1999). Failure of the operator to address this knowledge-need adequately will result in substandard field data.

This issue for the Woomera Field data collection was addressed by specialist technicians accompanying the ASDs, one each from the Defence Science and Technology Organisation (DSTO) and CSIRO. Each was responsible for the final checks of the quality and integrity of the data, according to the data collection protocols for their institutions. The technician accompanying the CSIRO instrument reported that the CSIRO protocols for which he was responsible include:

a) safe and secure transport and packaging of the instrument;

b) daily calibration checks of the sensor using a white and black mat;

c) monitoring the changing environmental conditions so that they did not exceed those appropriate for the particular instrument;

d) nightly checks on the data for artefacts due to individual sensor drift (as evidenced by steps in the curve); inappropriate collection angle (detected by the wide spread of multiple curves about an average for the one target); checking the targets for mixtures (on-site checks of target for possible leakage from adjacent areas and background material);

e) appropriate warm-up times for the sensor (also in Salisbury (1998)); and f) battery maintenance in the field.

(pers comm. (Byrne, 2002)) 5.2.2.2 Data Collection

Data collection is integral to the overall success of any campaign or project (Phinn, 1998). One of the principle areas of concern is the accurate calibration of the field instruments. Analytical Spectral Devices Inc User Guide (2002) states that the hand-held field sensor should be well calibrated prior to leaving the laboratory and well maintained with a regular full service regime. Poor calibration will likely result in absorption bands being shifted with respect to their true wavelengths (Salisbury, 1998). A spectral library collected in the field will be a failure if the sensor is poorly calibrated. Clark, (1999) points out that poorly calibrated spectrometers collecting library spectra is the most common cause for poor library data.

5.2.2.2.1 Limiting Collection of Mixed Spectra

Care must also be taken in obtaining pure spectra. Mixed spectra are common when collecting vegetation spectra in the field, due to background contamination from scattered EMR or from open canopies (Lewis, 2001a). Cochrane (2000) and Lacar et al (2001) cut vegetation samples from the plants and collected spectral samples using controlled conditions thus limiting spectral contamination from external sources. The vegetation sampling occurred within an hour of the samples being cut from the plant to limit the introduction of spectral artefacts due to stress. Other researchers, (Lewis et al., 2000b) covered the aperture of the field instrument with leaves that had been removed from the plant less than two hours previously.

Woomera is at least eight hours drive from the nearest laboratory, and the field site was over an hour’s drive from the base station. The spectral sampling regime used by the previous researchers was not an appropriate option on this occasion. To limit artefacts and mixed pixels, spectra were acquired from large dense clumps of vegetation. The findings of other researchers (eg Lewis 2001) strongly advise against using only one plant to obtain reference spectra because of intra-species variability. However, the problems of identification of Atriplex species noted in Flora of Australia (Wilson, 1984), indicated that by following this protocol, there was a high possibility that spectra from more than one

plants that could be visually discriminated were collected, and only one plant per identifiable species was sampled. Table 7 lists the species used as end members. Appendix 3 provides detailed descriptions and identification of these end members.

Once the fruiting bracteoles of the field samples were studied under the microscope, it was revealed that five Atriplex species were sampled at the site. Many of these were incorrectly identified in the field, supporting the wisdom of using only one bush per class. The spectral sampling was an average of 20 readings per end member plant.

Table 7 List of Field Sample numbers and identification of targets used as classes for classification of imagery

Sample

Number Classification Name Identification

23 CanegrassA Eragrostis australasica

35 CanegrassB Eragrostis australasica

21 SaltbushA Atriplex macropterocarpa

50 SaltbushB Atriplex vesicaria

54 SaltbushC Atriplex lindleyi

24 Spikeycottonballs Dissocarpus paradoxus aka Bassia paradoxa 71 Loosesoil Aeolian sand/clay pellets

65 Swampsoil Gleyed clays

01 Gibbers Ironstone coated pebbles

5.2.2.2.2 Calibration Check of Imaging Spectrometer

The imaging spectrometer also needs to be well calibrated. As with the field spectrometer, well-characterised spectral features inherent in the collected data may be used to determine if an imaging spectrometer, collecting in the 400 nm – 2500 nm range of the spectrum, is correctly calibrated. For the purposes of this campaign, the position of the oxygen absorption feature at 760 nm in the radiance data of the HyMap® data provided a guide as to the calibration accuracy of the instrument. This very narrow feature is quite distinctive and Salisbury, (1998) states that it is a good indicator of the calibration accuracy of a given sensor.