5.3.1 Conclusions
5.3.1.1 The Application of 2.4 m Multi-spectral Imagery
The 2.4 m multi-spectral imagery on its own was insufficient to identify potential weed infestations. However, in combination with the textural information provided by the panchromatic imagery, the vegetation signal inherent in this data was critical to the successful differentiation of crop from weed and subsequent identification of potential weed infestations.
5.3.1.2 The Application of Textural Analyses
Textural analysis proved to be an essential component in producing successful classification and change detection results in the context of this study. Of the textural analysis techniques tested the edge enhancement technique was the most successful in delineating crop rows. This delineation of crop rows was the key to distinguishing crop from weed, but it was still necessary to combine the crop row data with the multi-spectral data set to achieve a successful classification. These results agree with other studies (e.g. Ouma et al., 2006; Tso and Mather, 2001;
Coppin, 1991; Fung and Le Drew, 1987) that reported improved classification results due to the inclusion of textural analyses in the classification process.
5.3.1 .3The Effect of Stand Age
Stand age played a major role in the classification success, as those stands (or compartments) less than three months old or older than fourteen months were not classified as successfully as stands between these ages. The optimal period within which to identify potential weed infestation in wattle stands is three to fourteen months. However, identification of potential weed problems in stands younger than three months can still be done with reasonable success. Stands older than fourteen
months tend to be close to canopy closure, when it is no longer necessary to monitor weed infestations. Hence, knowledge of stand age is probably a prerequisite.
5.3.1.4The Effect of the Classification Matrix
Despite some level of cross-classification, particularly in the "early crop" stage, the four-class matrix provided a good basis for deriving the weed infestation levels.
5.3.1.5 Quantification of Weed Infestation Levels
In addition to effectively identifying weed infestations spatially, it was possible to quantify the level of infestation and report it on a hectare basis, as well as the percentage of the compartment affected. The success in identifying and quantifying weed infestation achieved in this study was higher than reported in other studies.
(Gray et al.,2004; Nilson et al., 2001). However, this approach would most probably only yield meaningful results in plantation forestry, where regularly Iineated forest stands is generally a feature.
5.3.1.6 The Success of the Change Detection Process
The change detection process was able to identify, and quantify, areas of weed increase between consecutive images. While only bi-temporal change detection was undertaken in this study, in terms of the actual change detection procedures, visual comparison of the results gave one an indication of the temporal trends that occurred in weed infestation.
5.3.1.7 Comparison of Classified Imagery with Operational Database
It was not possible to draw conclusive results when comparing operations recorded in the operational management database to the classified image results. This was mainly due to the imagery not being obtained sufficiently close enough to when the operations occurred, such that any weed or crop growth masked operations previously carried out. The only exceptions to this were the second or third spacing operations, where a reduction in crop cover was recorded by the imagery. Based on the image results, one could draw conclusions as to crop and weed status at the time of image acquisition, but not infer any conclusions regarding previous operations to the stands.
5.3.1.8 Application of Theoretical Ground Cover Model
While conclusive threshold values were not obtained, there was sufficient evidence to show that this concept could work, and that where required it was possible to rescale the values derived from field measurements such that threshold values could be derived for use with data derived from imagery. The net result would be thresholds that could be used to alert managers to potential weed infestation levels within forest stands.
5.3.1.9 Weed Detection in Eucalyptus Coppice Stands
There was insufficient evidence to draw any conclusions regarding the identification of weed status inEucalyptus coppice stands as only one compartment was available within the study area. However, it was apparent that the wattle model developed in this study would not be suitable for application in Eucalyptus coppice stands. This appeared to be mainly due to the faster rate at which coppice stands attained canopy closure, compared to wattle stands.
5.3.1.10 Detection of Coppice Reduction Operations
Although there was only one sample site, there were strong indications that coppice reduction operations could be monitored using high resolution imagery.
5.3.1.11 Weed Detection in Pine Stands
No conclusions could be drawn regarding the application of these techniques in pine stands, as no suitable pine stands were available within the study site.
In summary, this study has shown that the application of change detection and textural analysis techniques to high resolution imagery can be used to quantitatively assess weed status in plantation forest stands less than 24 months old.
5.3.2 Recommendations
5.3.2.1 Identification of Weed Infestations
Techniques involving the combination of multi-spectral and edge enhanced panchromatic high resolution imagery should be used to identify and quantify potential weed infestations as a management tool to improve the monitoring of weed status within plantation forest stands. It will, however, be required to know stand
ages when interpreting the classified data, due to the effect stand age has on this process.
5.3.2.2Automation of Analysis Procedures
Due to the number of processes required to run these analyses, techniques should be developed to automate as much of the processing as possible. This will allow results to be provided more rapidly, which is a critical factor due to the time-sensitive nature of monitoring weed infestations.
5.3.2.3 Application inEucalyptus and Pine Stands
The application of the techniques described in this study should be tested in both Eucalyptuscoppice stands andEucalyptus planted stands, as well as in Pine stands, as each of these types have some unique characteristics that differ from wattle stands, as well as from each other.
5.3.2.4 Further Development of Textural and Frequency Domain Techniques
Further investigation should be undertaken to determine whether the Variance function, in the textural domain, can be used to identify landscape fragments, or clumps, that might indicate weed concentrations. Similarly, further investigation of the Fourier Transform could reveal its potential in delineating crop rows.
5.3.2.5 Use of Imagery Results to Audit Operational Databases
It is not recommended to utilise image classification results to audit operational records in a management database for operations such as weed control, particularly where chemical control is applied, as these effects are only evident several weeks after application. Some assessment may be possible in cases where manual weed control operations are undertaken, and image acquisition coincides with these operations. It may also be possible to use such image results to check whether the later (second or later) spacing operations have been done, especially where the image acquisition is coincident with the actual operation. However, this would best serve a supplemental source of this information, rather than being a primary source.
5.3.2.6 Use of Theoretical Ground Cover Model
More rigorous testing should be done to produce conclusive threshold values in wattle stands. The possibility of better results being obtained from seedling crops, particularly gum or pine stands should also be tested.